Distribution Network Self-Recovery Method Based on Distributed Resources

Abstract

The present invention provides a method for self - recovery of a distribution network based on distributed resources, which constructs a recovery characteristic model for the black - start process for different types of power sources. Secondly, an optimization model for the start - up sequence of distributed resources is constructed with the goal of maximizing the available power generation and the amount of active load recovery. An integrated path evaluation index is constructed based on the node importance and the path recovery time to optimize the recovery path of the units to be started. Further, the start - up sequence optimization model, the recovery path optimization model, and the load recovery optimization model are repeatedly solved according to time steps, so as to obtain the final distribution network recovery plan, thereby inputting the outage load step by step to minimize the outage loss. At the same time, considering the load recovery sequence, the recovery resources are effectively allocated to improve the rapidity and stability of the distribution system recovery process.

Description

Technical Field

[0001] The present invention relates to a method for self - recovery of a distribution network based on distributed resources. Background Art

[0002] With the increasing scale of modern power systems, their structures are becoming more complex. The large - scale use of long - distance transmission lines, the commissioning of large - capacity units, and the construction of high - voltage power grids pose great challenges to the safety and stability of power systems. In recent years, large - scale power outages have occurred successively in some countries, causing irreparable losses to their economies, societies, and people. Therefore, formulating an effective black - start plan is of extremely important significance for quickly restoring power supply to loads, reducing economic losses, and ensuring social stability after a major power outage.

[0003] As the last link in the transmission and supply of electric energy and an important link directly facing users, the power supply reliability of the distribution network is crucial for the operation of the power system. Generally speaking, the black - start process includes three stages: power source black - start, network reconstruction, and load restoration. Most of the existing research focuses on one aspect of the power source start - up sequence, restoration path, and load management, lacking a complete black - start plan for the distribution network after a major power outage. In addition, the current research on the power supply restoration of power systems does not make full use of distributed energy resources (DER), which restricts the value of DER as a black - start power source to a certain extent. Therefore, it is particularly important and urgent to carry out research on the self - recovery plan of the distribution network based on DER after a major power outage. Summary of the Invention

[0004] To solve the above problems and provide a complete method for self - recovery of the distribution network after a fault, the present invention adopts the following technical solutions:

[0005] The present invention provides a self - recovery method for a distribution network based on distributed resources, which is characterized by the following steps: Step S1, obtaining the power system state of the distribution network after a major power outage as the initial working condition and initializing system parameters; Step S2, determining the recovery characteristics of the distributed resources in the distribution network at the current time step; Step S3, determining and solving an optimization model for the start - up sequence based on the recovery characteristics to obtain an optimized start - up sequence plan for the distributed resources; Step S4, determining the node importance based on the optimized start - up sequence plan and solving an optimization model for the recovery path based on the node importance to obtain an optimized recovery path plan for the distributed resources; Step S5, solving a load recovery optimization model based on the optimized start - up sequence plan and the optimized recovery path plan to obtain a complete recovery plan for the current time step; Step S6, taking the next time step as the new current time step and repeating Steps S2 to S5 until the end of the recovery period; Step S7, obtaining and outputting the recovery plan of the distribution network, thereby completing the self - recovery of the distribution network based on the recovery plan.

[0006] The self - recovery method for a distribution network based on distributed resources provided by the present invention may further have the following technical feature: in the optimization model for the start - up sequence, the maximum available power generation of the distributed resources available at the current time step and the maximum power consumption of the active load to be restored are used as recovery objectives, and the corresponding objective function is:

[0007]

[0008] In the formula, maxf1 is the first objective, indicating the maximum available power generation of the distributed resources started within the current time step; maxf2 is the second objective, indicating maximizing the load importance near the distributed power source started within the current time step; N G，k is the number of distributed power sources started at the k - th time step; Δt is the time step length considering the start - up time; α G，i is a 0 - 1 variable, indicating the start - up state of the i - th distributed power source. If the distributed power source is started, it is set to 1, otherwise it is set to 0; P i (t) is the active power output of the i - th distributed power source at time t; ω l is the weight of the l - th level load near the distributed power source. The larger the weight, the more important the load; P i，l is the load power connected to the i - th DG,

[0009] The constraint conditions of the objective function are:

[0010]

[0011] In the formula, and Denote the lower and upper limits of the active power of the i-th DER; and Denote the lower and upper limits of the reactive power of the i-th DER; and are the maximum and minimum values of the node voltage; P xi is the active power flowing through the i-th line; is the maximum allowable active power flowing through the i-th line; N B is the number of nodes; N L is the number of lines.

[0012] The distribution network self-recovery method based on distributed resources provided by the present invention may further have the following technical feature: the node importance is obtained by combining the node criticality reflecting the power system network topology structure and the power transfer coefficient reflecting the power system electrical parameters. The comprehensive importance of node i is:

[0013]

[0014] In the formula: μ is the node importance adjustment coefficient; β i is the criticality of node i after contraction; δ i is the power transfer coefficient of node i; after obtaining the node importance of all nodes, all the node importance is normalized by the maximum normalization method.

[0015] The distribution network self-recovery method based on distributed resources provided by the present invention may further have the following technical feature: the node criticality is evaluated by using the network cohesion degree after node contraction, and the formula is:

[0016]

[0017] In the formula: β i is the criticality of node i after contraction; n i is the total number of nodes in the new network after node i contraction; li is the average shortest path between nodes in the new network after node i contraction; d min,ij is the shortest distance between any two points i and j represented by the number of edges in the new network; in the formula: β i is the criticality of node i after contraction; n i is the total number of nodes in the new network after node i contraction; li is the average shortest path between nodes in the new network after node i contraction; d min,ij is the shortest distance between any two points i and j represented by the number of edges in the new network; V i is the node set in the new network after node contraction.

[0018] The power transfer coefficient in the distribution network self - recovery method based on distributed resources provided by the present invention may also have the following technical feature:

[0019]

[0020] In the formula: P L,max is the maximum injection power of node i; P B is the power reference value.

[0021] The power transfer coefficient in the distribution network self - recovery method based on distributed resources provided by the present invention may also have the following technical feature:

[0022]

[0023] In the formula, is the set of nodes of the k - th recovery path between nodes p and q; is the time required for the recovery path k; Z m is the node importance of node m in; ε is the path adjustment coefficient, used to characterize the weight coefficient of node importance and recovery time in path recovery.

[0024] The power transfer coefficient in the distribution network self - recovery method based on distributed resources provided by the present invention may also have the following technical feature:

[0025]

[0026] In the formula, N L,k is the number of load nodes to be recovered at the k - th time step; α Li is a 0 - 1 variable, indicating whether the i - th load is put into operation, 1 means put into operation, and 0 means not put into operation; ω Li is the corresponding load weight; P Li is the active power of the node load.

[0027] The power transfer coefficient in the distribution network self - recovery method based on distributed resources provided by the present invention may also have the following technical feature:

[0028] Functions and effects of the invention

[0029] According to the distribution network self - recovery method based on distributed resources of the present invention, since for the three stages of power source black - start, network frame reconstruction, and load recovery in the distribution network recovery process, an optimization model for DER start - up order, an optimization model for DER recovery path, and a load recovery optimization model are respectively established, a comprehensive path evaluation index is constructed based on node importance and path recovery time to optimize the recovery path of the units to be started, so as to connect the power - off load in time steps to minimize the power - off loss. At the same time, considering the load recovery order, the recovery resources are effectively allocated to improve the rapidity and stability of the distribution system recovery process. Brief Description of the Drawings

[0030] Figure 1 is the flow chart of the distribution network self - recovery method in the embodiment of the present invention;

[0031] Figure 2 is the schematic diagram of node contraction in the embodiment of the present invention. Detailed Embodiment

[0032] In order to make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the distribution network self - recovery method based on distributed resources of the present invention will be specifically described below in conjunction with the embodiments and the drawings.

[0033] <Embodiment>

[0034] In this embodiment, for a distribution system containing distributed generators (DG), energy storage (ES), electric vehicle (EV) charging and swapping stations, and controllable loads, a complete post - fault distribution system self - recovery scheme is proposed.

[0035] First, by analyzing the output characteristics of distributed energy resources (DER) after a major power outage, a recovery characteristic model of different DERs in the distribution system is established to deeply explore the black - start value and potential of various power sources. Then, for the three stages of power source black - start, network frame reconstruction, and load recovery in the distribution network recovery process, an optimization model for DER start - up order, an optimization model for DER recovery path, and a load recovery optimization model are respectively established. The power source start - up order is determined based on the maximum available power generation and load recovery volume. Furthermore, the path with the largest sum of node importance and fewer lines in the recovery path is selected for network frame reconstruction. At the same time, considering the load recovery order, the stability in the distribution system recovery process is improved.

[0036] Figure 1 is the flow chart of the distribution network self - recovery method in the embodiment of the present invention.

[0037] As Figure 1As shown in the figure, the distribution network self - recovery method based on distributed resources includes the following steps S1 to S7.

[0038] Step S1: Obtain the power system state of the distribution network after a major power outage as the initial working condition, and initialize the system parameters.

[0039] Step S2: Determine the recovery characteristics of the distributed resources of the distribution network at the current time step.

[0040] After an external fault or a major power outage causes the active distribution network to lose external power supply, when there are DERs such as DG, ES, and controllable loads in the distribution system, during the period of a major power outage in the transmission system, the distribution system dispatching agency can call on DERs to establish an island that can continuously supply power and maintain normal power supply to the loads inside the island. Since the available output and recovery characteristics of different types of DERs are different, for an active distribution network containing new - energy DGs such as wind power (WP) and photovoltaic (PV), as well as ES, EV charging stations, and controllable loads, the establishment and continuous operation strategies of its recoverable islands are very different from those of traditional distribution systems. The uncertainty, volatility, limited capacity of DERs with different characteristics, and the cut - off ability of loads have a decisive impact on formulating the recovery strategy of the distribution network. Therefore, it is first necessary to establish models of the main components.

[0041] Regarding the power generation output characteristics of DG:

[0042] The DGs in the distribution system can be divided into intermittent and non - intermittent ones. The power generation characteristics of non - intermittent DGs are relatively stable, such as micro gas turbines (MT) and diesel generators, etc. They also have the ability to control voltage and frequency and can participate in the recovery of the distribution network as the black - start main power source. During the power supply recovery period, mainly consider the constraints of the upper and lower limits of their output power and the ramping ability, as follows:

[0043]

[0044] Where: P M,，i (t), Q M,，i (t) are the active and reactive powers output by the non - intermittent unit at time t respectively; are the upper and lower limits of their active and reactive power outputs respectively; are the maximum downward and upward adjustment values of their active and reactive powers during the t period respectively.

[0045] Distributed WT and PV are both common intermittent DGs in the distribution network, generally only having PQ control capabilities. A single new energy DG cannot be used as the main power source for island restoration. Generally speaking, the power generation outputs of WT and PV fluctuate greatly and are affected by factors such as wind speed, light intensity, and temperature. When the predicted wind and light power values P*W,i(t) for a future period are obtained and the maximum prediction error is set as α i , then the output characteristics of WT and PV can be described by the following formula:

[0046]

[0047] In the formula: P W,i (t), Q W,i (t) are the actual active and reactive power output values of WT or PV unit i at time t respectively; S W，i is the rated apparent power; is the maximum value of its actual output at time t. Equation (7) expresses the error of new energy output prediction such as wind and light in the form of an interval.

[0048] Due to the different characteristics of new energy, different control methods are adopted during the black start process. In the initial stage of black start, in order to provide more power generation for the distribution system, MPPT control is usually adopted for PV and WT; when the power generated by PV and WT varies greatly with the environment, P / Q control is generally used to maintain the consistency of the output active power and reactive power with the reference values.

[0049] Regarding the power generation output characteristics of ES and EV charging and swapping stations:

[0050] The charging and discharging power of ES in the distribution network can achieve stable power output through appropriate control, but the energy storage level of ES during a fault is random. For the problem of distribution system restoration considering a period of time, the energy level stored in ES has a significant impact on the power supply capacity. If the SOC of ES at the moment of external system power outage is greater than a certain level, stable power output can be provided for a period of time, so it can be used as a black start power source within a period of time. When ES is used as a power supply during fault recovery, it is restricted by the maximum charging and discharging power, SOC requirements, etc., and the constraints to be satisfied are expressed as follows:

[0051]

[0052] In the formula: are the charging and discharging powers of ES at time t respectively; are the maximum charging and discharging powers of ES respectively; are the capacities of ES at time t and t - 1 respectively; η C , η D are the charging and discharging efficiencies of ES respectively; They are the upper and lower limits of the ES state of charge respectively; if the ES is equipped with sufficient reactive power compensation devices, the reactive power regulation constraint of the ES is similar to Equation (6).

[0053] As a new type of transportation vehicle, EV has incomparable advantages in alleviating the energy crisis and promoting the harmonious development of humans and the environment. When the available battery capacity in the centralized charging station in the distribution network reaches a certain level, the EV can be regarded as a flexible energy storage unit, which can not only provide electrical energy for the restoration process, but also act as a load to maintain the power balance during the system restoration process. The main difference between the EV charging and swapping station and the ES is reflected in the stronger randomness of the EV charging and swapping station: 1) At the moment t = 0 when the power outage fault occurs, the number N E (0) of parked EVs in the EV charging and swapping station and the total energy stored in these N E (0) EV batteries have great randomness; 2) During the fault occurrence period t ∈ [0, T], the number N E (t) of parked EVs and EV batteries in the charging and swapping station will change randomly, and the total energy of energy storage will also change accordingly. For The EV charging and swapping station located at node i needs to meet the following constraints:

[0054]

[0055] In the formula: They are the charging and discharging powers of a single EV in period t respectively; They are the maximum charging and discharging powers of a single EV respectively; They are the battery capacities of a single EV at moment t and t - 1 respectively; η CV and η DV are the charging and discharging efficiencies of the EV respectively; They are the upper and lower limits of the EV state of charge respectively; P EVC,i (t), P EVD,i (t) are the overall active load and active output of the EV charging and swapping station located at node i. The calculation of the reactive power regulation ability of the EV charging and swapping station is the same as that of the ES. N E,i (t) is the main factor affecting the uncertainty of the output of the EV charging and swapping station. The number N E (0) of EV batteries at each moment during the power outage period can be predicted based on operation experience and N E,i (t); The initial SOC of a single EV will also affect the power generation output of the EV charging and swapping station to a certain extent. When the power outage period is short, the influence of the initial SOC can be ignored.

[0056] General loads are not controlled by the dispatching center and are dominated by power users. Therefore, the actual rated load power input at each load node will fluctuate due to changes in user behavior. The electricity loads of a large number of users have certain statistical laws and can be simulated through historical data and typical energy consumption curves. However, for the active and reactive power fluctuations of general loads at each node, when a power outage fault occurs, the dispatching center can simulate the load during the fault period [0, T] based on the pre-fault node load and historical load curves to obtain the predicted active and reactive power:

[0057] P L,i (i) = P el,i (i) + λ l,i (t)P cl,i #(20)

[0058] Q L,i (t) = Q el,i (t) + λ l,i (t)Q cl,i #(21)

[0059] Where: P L,i (t), Q L,i (t) are the active power and reactive power of node i at time t, respectively; P el,i( t), Q el,i (t) are the estimated values of the active power and reactive power of node i during the fault period, respectively; λ l,i is a 0-1 variable of the controllable load switch state; P cl,i , Q cl,i are the active power and reactive power of the controllable load of node i, respectively.

[0060] Step S3: Determine the startup sequence optimization model according to the restoration characteristics and solve it to obtain the startup sequence optimization plan of the distributed resources.

[0061] Reasonably arranging the startup sequence of DERs is the primary problem to be solved after a distribution network fault. In this embodiment, based on the unit restoration principle of the traditional power grid and combined with the influence degree of different DER characteristics on black start, the following principles are adopted:

[0062] 1) The ultimate goal of DERs participating in the black start of the distribution network is to obtain as much available generating capacity as possible. Therefore, units with large capacities are started first to provide greater startup power, thereby accelerating the speed of restoring power supply.

[0063] 2) Units close to important loads should be started first. After a power outage fault occurs in the distribution network, the loads near the unit can be classified according to their importance. Starting the DERs near important loads first is beneficial to shortening the power supply path and accelerating the restoration time of important loads.

[0064] 3) The length of the DER startup time is directly related to the restoration speed of the distribution network. The shorter the DER startup time, the faster the corresponding distribution network restoration speed. Conversely, DERs with slow startup speeds will have an adverse impact on load restoration and the DER output in the next time step.

[0065] 4) Having the ability to regulate frequency and voltage is a necessary condition for the island operation of the distribution network. After the DER starts up, it needs to have a certain ability to regulate frequency and voltage to operate with load in the island.

[0066] The restoration goal of the distribution network in the initial stage of black start is to maximize the available power generation of the units and lay a foundation for restoring the power outage load as soon as possible in the later stage. Therefore, the available DER power generation maximum and the active load power consumption maximum obtained during the considered time period are used as the restoration goals:

[0067]

[0068] In the formula, maxf1 is the first goal, indicating the maximum available power generation of starting the distributed resources in the current time step; maxf2 is the second goal, indicating maximizing the load importance near the distributed power source started in the current time step; N G，k is the number of distributed power sources started in the k-th time step; Δt is the time step length within the considered startup time; α G，i is a 0-1 variable, indicating the startup state of the i-th distributed power source. If the distributed power source starts up, it is set to 1, otherwise it is set to 0; P i (t) is the active power output of the i-th distributed power source at time t; ω l is the weight of the l-th level load near the distributed power source. The larger the weight, the more important the load; P i，l is the load power connected to the i-th DG,

[0069] The constraint conditions of the objective function are:

[0070]

[0071] In the formula, and represent the lower and upper limits of the active power of the i-th DER; and represent the lower and upper limits of the reactive power of the i-th DER; and are the maximum and minimum values of the node voltage; P xi is the active power flowing through the i-th line; is the maximum allowable active power flowing through the i-th line; N B is the number of nodes; N L is the number of lines.

[0072] Step S4. Determine the node importance according to the startup sequence optimization plan, and solve the restoration path optimization model based on the node importance to obtain the restoration path optimization plan for the distributed resources.

[0073] In a network topology, the degree of a node is defined as the number of edges connected to the node, which reflects the connection ability of the node with other nodes in the network. In a power system network, the degree represents the local electrical connection level of the network topology and also characterizes the reachability of a certain node to the rest of the network; the larger the degree, the stronger the direct connection ability and reachability of the node with other nodes, and the more important the node is in the network. During the network reconstruction process using DER as a black start power source, to reduce power outage losses, it is necessary to restore nodes with strong electrical connections as much as possible to strengthen the connectivity of the system and improve the reachability, so as to prepare for restoring the lost power load as soon as possible.

[0074] This embodiment considers the importance of core nodes and uses the node parameters of the power system to propose a new power system node importance evaluation plan, that is, the node importance is obtained by combining the node criticality reflecting the power system network topology structure and the power transfer coefficient reflecting the power system electrical parameters. Specifically:

[0075] The comprehensive importance of node i is:

[0076]

[0077] In the formula: μ is the node importance adjustment coefficient; β i is the criticality of node i after contraction; δ i is the power transfer coefficient of node i;

[0078] After obtaining the node importance of all nodes, normalize all the node importance through the maximum normalization method.

[0079] In this embodiment, the node criticality is evaluated by using the network cohesion degree after node contraction. Taking Figure 2 Node 12 as an example to illustrate the node contraction process, the contraction of node 12 means short-circuiting nodes 9, 10, and 11 connected to this node with node 12 and replacing them with the new node 9' in Figure (b). Thus, the edges associated with nodes 9, 10, and 11 in Figure (a) are all associated with the core node 9'. It can be seen that node contraction is equivalent to centering on the node to be contracted and condensing the surrounding nodes associated with it into one node, making the entire network better condensed. The node criticality after node contraction is defined as follows:

[0080]

[0081] In the formula: βi is the criticality after the contraction of node i; n i is the total number of nodes in the new network after the contraction of node i; li is the average shortest path between nodes in the new network after the contraction of node i; d min,ij is the shortest distance between any two points i and j represented by the number of edges in the new network; where: β i is the criticality after the contraction of node i; n i is the total number of nodes in the new network after the contraction of node i; li is the average shortest path between nodes in the new network after the contraction of node i; d min,ij is the shortest distance between any two points i and j represented by the number of edges in the new network; V i is the set of nodes in the new network after the node contraction.

[0082] It can be seen from Equation (29) that: 1) The fewer the number of nodes and edges in the network after the contraction of nodes with larger degrees, the greater the criticality of the nodes, and the more important the nodes; 2) The criticality of "core nodes" with high connectivity is relatively high because the shortest paths between many node pairs pass through these nodes. After the contraction of these nodes, the average shortest distance of the network will be reduced, and the network cohesion degree is high.

[0083] When evaluating the importance of network nodes, the electrical parameters of the power system should also be considered, and combined with the node criticality to construct a node importance index. When the power system is operating normally, the maximum injection power of a node reflects the power transfer level of the node. The greater the injection power of a certain node, the stronger the power transmission and carrying capacity of the node. Therefore, to a certain extent, the node injection power also reflects the importance degree of the node. When using DER resources for system restoration, nodes with larger injection powers before the major power outage should be restored as much as possible to improve the power transmission capacity of the system. In this embodiment, the power transfer coefficient of the node is defined as:

[0084]

[0085] where: P L,max is the maximum injection power of node i; P B is the power reference value.

[0086] After obtaining the node importance through the above process, the solution of the restoration path optimization model is completed based on this node importance.

[0087] In the process of reconstructing the distribution network using DER, preferentially restoring the paths with high node importance is conducive to improving the stability level during the power system restoration process. Generally speaking, the greater the node importance of the restored path, the more lines it passes through, and the longer the time required to restore this path, thus the longer the time to restore the load. Therefore, when using DER to select the restoration path, the time required for the restoration path should be taken into account simultaneously, so as to ensure that both the requirement of restoring the load as soon as possible and the stability requirement during the restoration process are met. The evaluation index for the k-th restoration path between nodes p and q is defined as follows:

[0088]

[0089] Where: is the set of nodes of the k-th restoration path between nodes p and q; is the time required for the restoration path k; Z m is the importance of node m in; ε is the path adjustment coefficient, which is used to characterize the weight coefficient of node importance and restoration time in path restoration.

[0090] If the more nodes in, the more lines there will be, then will be smaller, and at the same time may be larger. Minimizing means: selecting the scheme with the largest sum of node importance and fewer lines in the restoration path. Therefore, by flexibly adjusting the path adjustment coefficient ω and selecting the path that minimizes among all paths as the restoration path between nodes p and q, the node importance and restoration time can be taken into account simultaneously, achieving the purpose of flexibly selecting the DER restoration path. Therefore, the optimization goal of the DER restoration path can be defined as:

[0091]

[0092] It should be noted that: the DER restoration path optimization model constructed in this embodiment can reasonably take into account the node importance and path restoration time. The specific restoration time of the lines and load is also related to factors such as the available power generation of the system and the result of power flow verification.

[0093] Step S5, based on the startup order optimization scheme and the restoration path optimization scheme, solve the load restoration optimization model to obtain the complete restoration scheme for the current time step.

[0094] After determining the DER restoration path and finding the optimal restoration path, the DERs in the distribution network already have sufficient power generation capacity at this time. It is necessary to further consider the load restoration sequence during the DER startup process. The core task of load restoration optimization is to comprehensively and quickly restore the power outage load. Based on the DER startup sequence optimization model and restoration path model within the considered time period, the objective function of the load optimization model at the k-th time step is as follows:

[0095]

[0096] In the formula: N L,k is the number of load nodes to be restored at the k-th time step; α Li is a 0-1 variable indicating whether the i-th load is put into operation, where 1 means put into operation and 0 means not put into operation; ω Li is the corresponding load weight; P Li is the active power of the node load.

[0097] In addition, the constraint conditions of the load restoration optimization model include single-step input capacity constraint, network constraint, maximum recoverable load quantity constraint per time step, and maximum charging path weight constraint.

[0098] Single-step input capacity constraint:

[0099] Excessive load quantity input to the load node in a single step will cause the reduction of the system frequency. When exceeding the maximum limit, it will lead to the instability of the entire system frequency. It is stipulated that the maximum load quantity input in a single step at each time step k is:

[0100]

[0101] In the formula: Δf max is the maximum allowable decrease in frequency; N D,k is the number of DERs that have been started and are generating power at the k-th time step; P G,j is the rated active power output value of the j-th DER; df j is the frequency response value of the j-th DG at the load rate at this moment.

[0102] Network constraint:

[0103] Network constraints include equality constraints and inequality constraints. The equality constraints are to satisfy the power flow equations, including active power and reactive power balance equations; the inequality constraints include upper and lower limits of active / reactive power output of DGs, upper and lower limits of node voltages, and line power transmission limit constraints.

[0104] Maximum recoverable load quantity constraint per time step:

[0105] For the maximum recoverable power supply P at the k-th time step within the considered time period loadmax,kshould be less than or equal to the sum of the active power increments of all the activated DERs at this time step:

[0106]

[0107] Maximum charging path weight constraint:

[0108] In the load restoration stage, to reduce the load restoration time, combined with the method in the restoration path, set the path adjustment coefficient ω and line weight μ according to the system restoration time requirement, so as to reduce the overvoltage risk.

[0109] Step S6, take the next time step as the new current time step, and repeat the above steps S2 to S5 until the end of the restoration period.

[0110] In this embodiment, steps S2 to S6 are completed based on the improved genetic algorithm to solve the restoration plan.

[0111] Step S7, obtain and output the restoration plan of the distribution network, so as to complete the self-restoration of the distribution network based on the restoration plan.

[0112] Functions and effects of the embodiment

[0113] According to the distribution network self-restoration method based on distributed resources provided in this embodiment, since for the three stages of power black start, network framework reconstruction, and load restoration in the distribution network restoration process, an optimization model for the DER start order, an optimization model for the DER restoration path, and an optimization model for load restoration are respectively established, and a comprehensive path evaluation index is constructed based on the node importance and path restoration time to optimize the restoration path of the units to be started, so as to connect the power-off load in time steps to minimize the power-off loss, and at the same time consider the load restoration order, effectively allocate restoration resources, and improve the rapidity and stability of the distribution system restoration process.

[0114] In the embodiment, since the restoration path is optimized by the node importance, determining the restoration path of the target network has an important guiding role in accelerating the restoration process, enabling the dispatcher to have a clear idea when implementing the restoration plan and reducing mistakes caused by excessive pressure. The purpose of optimizing the DER restoration path is to determine the optimal target skeleton network, and to ensure the safety and reliability of the restoration process when formulating the distribution network restoration plan.

[0115] The above embodiments are only used to illustrate the specific implementation manners of the present invention, and the present invention is not limited to the description scope of the above embodiments.

Claims

1. A method for self - recovery of a distribution network based on distributed resources, characterized in that, It includes the following steps: Step S1: Obtain the power system state of the distribution network after a major power outage as the initial operating condition and initialize the system parameters; Step S2: Determine the restoration characteristics of the distributed resources in the distribution network at the current time step; Step S3: Determine the startup sequence optimization model according to the restoration characteristics and solve it to obtain the startup sequence optimization plan for the distributed resources; Step S4: Determine the node importance according to the startup sequence optimization plan, and solve the restoration path optimization model based on the node importance to obtain the restoration path optimization plan for the distributed resources; Step S5: Based on the startup sequence optimization plan and the restoration path optimization plan, solve the load restoration optimization model to obtain the complete restoration plan for the current time step; Step S6: Take the next time step as the new current time step, and repeat Steps S2 to S5 until the restoration period ends; Step S7: Obtain and output the restoration plan for the distribution network, so as to complete the self-restoration of the distribution network based on the restoration plan, wherein, the node importance is obtained by combining the node criticality reflecting the network topology structure of the power system and the power transfer coefficient reflecting the electrical parameters of the power system. The comprehensive importance of node i is: where μ is the node importance adjustment coefficient; β i is the criticality after the contraction of node i; δ i is the power transfer coefficient of node i; After obtaining the node importance of all nodes, normalize all the node importance through the maximum normalization method, The power transfer coefficient is: Where: P L,max is the maximum injection power of node i; P B is the power reference value, The objective function of the restoration path optimization model is: In the formula, is the node set of the k-th restoration path between nodes p and q; is the time required for the restoration path k; Z m is the node importance of node m in; ω is the path adjustment coefficient, which is used to characterize the weight coefficients of node importance and restoration time in path restoration.

2. The method for self-restoration of a distribution network based on distributed resources according to claim 1, wherein: Among them, The startup sequence optimization model uses the maximum power generation of the available distributed resources at the current time step and the maximum power consumption of the restored active load as the restoration objectives, and the corresponding objective function is: Wherein, maxf1 is the first objective, indicating the maximum available power generation of starting the distributed resources within the current time step; maxf2 is the second objective, indicating maximizing the load importance near the distributed power source within the current time step; N G，k is the number of distributed power sources started at the k-th time step; Δt is the time step length within the considered starting time; α Gi is a 0-1 variable, indicating the starting state of the i-th distributed power source. If the distributed power source is started, it is set to 1, otherwise it is set to 0; P i (t) is the active power output of the i-th distributed power source at time t; ω l is the weight of the l-th level load near the distributed power source. The larger the weight, the more important the load; P i，l is the load power connected to the i-th DG, The constraint conditions of the objective function are: Wherein, and represent the lower and upper limits of the active power of the i-th DER; and represent the lower and upper limits of the reactive power of the i-th DER; and are the maximum and minimum values of the node voltage; P xi is the active power flowing through the i-th line; is the maximum value of the active power allowed to flow through the i-th line; N B is the number of nodes; N L is the number of lines.

3. The method for self-restoration of a distribution network based on distributed resources according to claim 1, wherein: Among them, The node criticality is evaluated by using the network cohesion degree after node contraction, and the formula is: Where: β i is the criticality after the contraction of node i; n i is the total number of nodes in the new network after the contraction of node i; li is the average value of the shortest paths between nodes in the new network after the contraction of node i; d min,ij is the shortest distance between any two points i and j represented by the number of edges in the new network; Where: β i is the criticality after the contraction of node i; n i is the total number of nodes in the new network after the contraction of node i; li is the average value of the shortest paths between nodes in the new network after the contraction of node i; d min,ij is the shortest distance between any two points i and j represented by the number of edges in the new network; V i is the set of nodes in the new network after the node contraction.

4. The distribution network self - recovery method based on distributed resources according to claim 1, It is characterized in that: wherein, The objective function of the load restoration optimization model is: where N L,k is the number of load nodes to be restored at the k-th time step; α Li is a 0-1 variable indicating whether the i-th load is connected, 1 for connected and 0 for not connected; ω Li is the corresponding load weight; P Li is the active power of the node load.

5. The method for self-restoration of a distribution network based on distributed resources according to claim 4, wherein: Among them, The constraint conditions of the load restoration optimization model include single input capacity constraint, network constraint, maximum recoverable load quantity constraint per time step, and maximum charging path weight constraint.

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