Distribution and micro collaborative recovery method and device for power grid fault

Abstract

The invention provides a power grid fault distribution and micro collaborative recovery method and device, and belongs to the field of power grids. The method comprises the steps of obtaining current operation parameters of a power grid system; considering the influence of the intelligent soft switch, determining constraint conditions based on operation parameters, and establishing a fault recovery model; and solving the fault recovery model by taking the lowest total cost for completing the power support as an objective function to obtain a fault recovery control strategy. According to the power support, the intelligent soft switch is considered, and the fault recovery model is established by considering the influence of the intelligent soft switch, so that the fault recovery control strategy is more reasonable, the power support distribution is more uniform, and the post-disaster power distribution network recovery efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a method and device for coordinated restoration of power grid failures caused by distribution and microelectronics. Background Art

[0002] After encountering extreme disasters, the power system faces a large number of faulty equipment and load losses, and its power supply is insufficient. In the post-disaster recovery phase, the power system needs sufficient power units to participate in power support to ensure post-disaster operation recovery.

[0003] In the prior art, the active distribution network (ADN) is usually dynamically divided into multiple microgrids, and dispatchable units, distributed generation (DG) and battery energy storage system (BESS) and intelligent soft switches (soft open point, SOP) are used to achieve power support in the isolated state, to ensure the rapid recovery of the distribution network after the disaster and meet the high elasticity requirements of the power system. However, SOP is usually not considered in the existing power support means, which makes the power support distribution between the network and the network uneven, affecting the recovery of the distribution network after the disaster. Summary of the invention

[0004] The embodiments of the present invention provide a method and device for coordinated recovery of power grid faults by distribution networks, so as to solve the problem in the prior art that power support during fault recovery does not consider SOP, power support allocation is unreasonable, and fault recovery is affected.

[0005] In a first aspect, an embodiment of the present invention provides a method for coordinated restoration of a power grid failure by a distribution network and a micro-grid, including:

[0006] Obtain the current operating parameters of the power grid system;

[0007] Considering the impact of intelligent soft switches, constraints are determined based on operating parameters and a fault recovery model is established;

[0008] Taking the minimum total cost of completing power support as the objective function, the fault recovery model is solved and the fault recovery control strategy is obtained.

[0009] In a second aspect, an embodiment of the present invention provides a distribution micro-cooperative recovery device for a power grid failure, comprising:

[0010] A parameter acquisition module is used to obtain the current operating parameters of the power grid system;

[0011] A model building module is used to consider the impact of intelligent soft switches, determine constraint functions based on operating parameters, and build a fault recovery model;

[0012] The model solving module is used to solve the fault recovery model with the minimum total cost of completing power support as the objective function to obtain the fault recovery control strategy.

[0013] The embodiment of the present invention provides a method and device for coordinated recovery of power distribution network and micro-distribution network in response to power grid faults. The above-mentioned method for coordinated recovery of power distribution network and micro-distribution network in response to power grid faults includes: obtaining the current operating parameters of the power grid system; considering the influence of intelligent soft switches, determining constraints based on the operating parameters, and establishing a fault recovery model; taking the lowest total cost of completing power support as the objective function, solving the fault recovery model and obtaining a fault recovery control strategy. The intelligent soft switch of the embodiment of the present invention participates in power distribution, and a fault recovery model is established by considering the influence of the intelligent soft switch, thereby making the fault recovery control strategy more reasonable, the power support distribution more reasonable, and improving the efficiency of post-disaster distribution network recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 is a typical structural diagram of a microgrid provided by an embodiment of the present invention;

[0016] Figure 2 It is a flow chart of an implementation method of a distribution micro-cooperative restoration method for a power grid failure provided by an embodiment of the present invention;

[0017] Figure 3 It is a structural schematic diagram of a distribution and micro-cooperative restoration device for power grid failure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0019] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0020] After facing extreme disasters, the distribution network is very likely to be in an island operation state, thus forming multiple microgrids operating together, which lack frequency and voltage support. At this time, frequency and voltage control (fault recovery) is required to participate in the operation of the microgrid and be responsible for the frequency and voltage stability of the microgrid.

[0021] Figure 1 This is a diagram of an application scenario of the distribution-micro-cooperative recovery method for power grid faults provided in an embodiment of the present invention. Figure 1 This is a typical structure diagram of a microgrid. Figure 1 As shown in the figure, the microgrid includes four types of micro-sources: photovoltaic generator (PVG), wind turbine generator (WTG), battery energy storage system (BESS) and gas turbine; all four types of micro-sources are connected to the microgrid public AC bus through inverters. The microgrid adopts hierarchical control, and the microgrid central controller (MGCC) is responsible for micro-source control and load control. It can perform seamless switching of isolated grids and control and dispatch micro-sources to ensure the stable operation of the microgrid. The inverter is the executor of the microgrid control strategy, and its control strategies mainly include PQ control strategy and droop control strategy. When the PQ control strategy is adopted, the inverter provides fixed active power and reactive power according to the set value; when the inverter droop control strategy is adopted, the active power and reactive power output of the inverter are adjusted by simulating the frequency and voltage regulation characteristics of the synchronous generator in the power system. When the MGCC detects a main grid fault, it automatically transitions to the island operation state through seamless switching technology. Since a microgrid operating in an island state requires at least one micro-source with stable output to provide it with voltage and frequency reference, in the early stage of post-disaster recovery, the PVG and WTG inside the microgrid operate under the PQ control mode, and some BESS and intelligent soft switches that do not participate in droop control can provide power support for the microgrid; at this time, the other part of the BESS and gas turbine operate under the droop control mode to provide voltage and frequency support for the power system; when the post-disaster recovery is stable, the micro-source operating in the droop control mode can switch the operation strategy to better provide power support for the entire power system. The switching and control of the above control strategies are all achieved through MGCC. Since communication between micro-sources is not required, droop control is very suitable for the control and power support of island microgrids in the case of post-disaster communication damage, and its output characteristics are:

[0022] P * -P=K P (f * -f)

[0023] Q *-Q=K Q (V * -V)

[0024] Among them, P and Q are respectively the active output and reactive output of the micro-source participating in droop control; P * and Q * are the reference values ​​of active output and reactive output respectively; f and V are the frequency and voltage at the output end respectively; f * and V * are the reference values ​​of the frequency and voltage at the outlet respectively; K P and K Q They are the active power-frequency and reactive power-voltage droop coefficients respectively.

[0025] based on Figure 1 The microgrid shown in Figure 2 is referenced Figure 2 In the embodiment of the present invention, a method for coordinated restoration of power grid failure by distribution and microelectronics is provided, which is applied to Figure 1 The microgrid shown is executed on the microgrid central controller.

[0026] refer to Figure 2 The above-mentioned distribution and micro-coordinated recovery method for power grid faults includes:

[0027] S101: Acquire current operating parameters of the power grid system;

[0028] S102: Considering the influence of the intelligent soft switch, determining the constraint conditions based on the operating parameters, and establishing a fault recovery model;

[0029] S103: Taking the minimum total cost of completing power support as the objective function, solving the fault recovery model to obtain a fault recovery control strategy.

[0030] In an embodiment of the present invention, based on the operating parameters of the current power grid system, the distribution network is dynamically reconstructed into an isolated microgrid, and intelligent soft switches are involved in power support allocation. A fault recovery model is established to obtain a fault recovery control strategy, so that power support allocation is more reasonable and the efficiency of post-disaster distribution network recovery is improved.

[0031] Specifically, the fault recovery control strategy is the power support allocation strategy, which can also be understood as the microgrid voltage and frequency control strategy.

[0032] In order to characterize the economic feasibility of post-disaster recovery, it is most reasonable to take the minimum total cost of active distribution network to complete power support as the objective function.

[0033] Based on this, in a possible implementation, the objective function may be:

[0034] minC=C USE +C INV +C OM

[0035] Among them, C is the total cost of completing power support, C USE is the electricity cost, C INV is the construction cost of intelligent soft switch and energy storage, C OM For operation and maintenance costs.

[0036] Among them, the electricity cost C USE The calculation formula can be expressed as:

[0037]

[0038] Construction cost of intelligent soft switch and energy storage C INV The calculation formula can be expressed as:

[0039]

[0040] Operation and maintenance costs C OM The calculation formula can be expressed as:

[0041]

[0042] Among them, Ω U and Ω T Represent the typical scenario day and the recovery operation time set respectively; D u represents the probability of the u-th scene appearing; Ψ PVG , WTG , SOP and BESS They are PVG node set, WTG node set, intelligent soft switch node set to be built, and energy storage node set to be built; c SOP and c TL are the investment cost per unit capacity of the intelligent soft switch and the investment cost per unit length of the supporting interconnection line respectively; c PVG and c WTG are the unit capacity investment costs of photovoltaic power generation and wind power generation respectively; c BESS,P and c BESS,E are the unit power and capacity investment costs of energy storage respectively; and are the energy storage installation power and capacity at node i respectively; is the number of installed intelligent soft switches of unit capacity on line ij; Whether the supporting contact line at the location where the intelligent soft switch is to be installed on line ij is newly built, 1 means construction, 0 means no construction, which is a decision variable; and are the number of photovoltaic power generation and wind power generation installations, respectively, which are decision variables; L ij is the length of line ij; The active power required by the user side load; and are the active power outputs of photovoltaic power generation and wind power generation at node i at time t in scenario u; c SOP,OM and c ESS,OM are the annual operation and maintenance costs of the smart soft switch and energy storage unit installed capacity respectively; c PVG,OM and c WTG,OM They are the operation and maintenance costs per unit of electricity generated by photovoltaic power generation and wind power generation; The price of electricity.

[0043] In a possible implementation, S103 may include:

[0044] S1031: Perform second-order cone relaxation on the fault recovery model to transform the fault recovery model solution into a convex optimization problem;

[0045] S1032: Solve the convex optimization problem after convex relaxation to obtain a fault recovery control strategy.

[0046] If an optimization problem is a convex optimization problem, the global optimal solution can be solved efficiently and quickly through analytical methods. At the same time, under the condition of accurate convex relaxation, the uniqueness of the solution can be guaranteed, that is, the local optimal solution is the global optimal solution. Therefore, if a certain convex relaxation method can be used to reshape the solution domain of non-convex nonlinear problems and find the optimal solution on the relaxed boundary, efficient solution and global optimal solution can be achieved at the same time.

[0047] Based on this, in an embodiment of the present invention, the fault recovery model solution problem is converted into a convex optimization problem solution, the model is convexly relaxed, and the original mixed-integer non-convex nonlinear problem in the fault recovery model is converted into a mixed-integer second-order cone programming (MISOCP) model.

[0048] Specifically, the second-order cone constraint of the convex optimization problem can be:

[0049]

[0050] Among them, P ij,u,t and Q ij,u,t are the active power and reactive power flowing through branch ij at time t, respectively, ij,u,t is the square of the current amplitude of branch ij at time t, v i,u,t is the voltage amplitude of node i; and are the active power and reactive power of the intelligent soft-switching converter at node i, and are the active power and reactive power of the intelligent soft-switching converter at node j, is the loss coefficient of the SOP converter at node i, is the loss coefficient of the SOP converter at node j; and They are the active power losses of the energy storage converter at node i and node j in scenario u at time t respectively.

[0051] Based on the objective function provided in the above embodiment, the convex optimization problem can be specifically expressed as:

[0052]

[0053] Since the second-order cone constraint is naturally established in the fault recovery model provided by the embodiment of the present invention, the solution to the original problem will not change before and after the model transformation. Under the condition of ensuring the relaxation accuracy, the existing technical means are used to solve the convex optimization problem, which greatly reduces the difficulty of solving the problem and improves the efficiency of solving the problem.

[0054] Specifically, the interior point method can be used to solve the convex optimization problem. The specific solution method is a conventional technical means, including but not limited to the interior point method, which will not be described in detail here.

[0055] In a possible implementation, the constraints may include: active distribution network reconstruction constraints, intelligent soft switch installation constraints, energy storage installation constraints, power support operation constraints, power flow constraints, frequency constraints and safety constraints.

[0056] Disasters can cause the active distribution network to fail and be divided into multiple regional microgrids. Specifically, the active distribution network can be reconstructed through the switches of each tie line to achieve post-disaster power support. Therefore, the active distribution network reconstruction constraints are set.

[0057] In a possible implementation, the active power distribution network reconstruction constraint may include:

[0058] b ij,t =w ij,t +w ji,t

[0059]

[0060] b ij,t =0,i∈Ψ R

[0061] Among them, b ij,t Ψ is the switch state 0 / 1 variable of line ij, 1 means the switch is connected, 0 means the switch is disconnected. B and R They represent the node and the node set connected to the droop control micro-source respectively; w ij,tis the parent-child connection relationship between nodes i and j at time t. ij,t =1 means that node j is the parent node of node j at time t, otherwise w ij,t =0).

[0062] In order to maximize the utilization rate of new energy, photovoltaic power generation and wind power generation usually operate at the maximum power point, that is, photovoltaic power generation and wind power generation usually maintain PQ mode operation in various periods after the disaster, playing a role in post-disaster power support.

[0063] Based on this, in a possible implementation, the power support operation constraints may include: photovoltaic power generation and wind power generation constraints, energy storage constraints participating in microgrid power support, energy storage and gas turbine constraints operating in droop control mode, and intelligent soft switch power support constraints;

[0064] Solar and wind power generation constraints, including:

[0065]

[0066] in, and are the active power outputs of photovoltaic power generation and wind power generation at node i at time t in scenario u; and are the reactive power outputs of photovoltaic power generation and wind power generation at node i at time t in scenario u; and are the maximum active powers of photovoltaic power generation and wind power generation at node i under scenario u; and are the power factors of photovoltaic power generation and wind power generation at node i respectively; Ω PVG and Ω WTG They are respectively the collection of nodes to be constructed for photovoltaic power generation and wind power generation;

[0067] Energy storage constraints involved in microgrid power support can include:

[0068]

[0069]

[0070]

[0071] in, and are the energy storage installation power and capacity at node i, and They are respectively the active output and reactive output of the energy storage at node i at time t in scenario u; and are the lower limit coefficient and upper limit coefficient of the charge state of the energy storage at node i respectively;

[0072] In the power support stage, for the energy storage and gas turbines operating in droop control mode, in order to achieve the voltage and frequency support function, the energy storage and gas turbine constraints in droop control mode include:

[0073] P i,t,Dref -P i,u,t,D =K d,DP (f ref -f i,u,t )

[0074] Q i,t,Dref -Q i,u,t,D =K d,DQ (U d,ref -U i,u,t )

[0075] P i,D min ≤P i,t,Dref ≤P i,D max

[0076] Q i,D min ≤Q i,t,Dref ≤Q i,D max

[0077] P i,D min ≤P i,u,t,D ≤P i,D max

[0078] Q i,D min ≤Q i,u,t,D ≤Q i,D max

[0079] Among them, P i,t,Dref and Q i,t,Dref are respectively the reference values ​​of active output and reactive output given by the droop control element at node i at time t; P i,u,t,D and Q i,u,t,D are the actual active output and actual reactive output of the droop control element at node i at time t in scenario u; f ref is the system rated frequency; U d,ref is the reference voltage at node i; f i,u,t and U i,u,t are the frequency and voltage amplitude of node i in scenario u at time t; K d,DP and K d,DQare the active frequency-frequency droop coefficient and reactive frequency-voltage droop coefficient of the droop control element at node i respectively; P i,D max and P i,D min are the maximum and minimum active outputs of the droop control element at node i, respectively; Q i,D max and Q i,D min are the maximum output reactive power and minimum output reactive power of the droop control element at node i, respectively;

[0080] Smart soft switches can also achieve power transmission and play a role in power support. Smart soft switch power support constraints include:

[0081]

[0082]

[0083] in, and are the active power and reactive power of the intelligent soft-switching converter at node i, and are the active power and reactive power of the intelligent soft-switching converter at node j, is the loss coefficient of the intelligent soft-switching converter at node i, is the loss coefficient of the intelligent soft-switching converter at node j; and are the active power losses of the smart soft-switching converter at node i and node j in scenario u at time t, is the capacity of the intelligent soft switch on branch ij.

[0084] In the embodiment of the present invention, intelligent soft switch installation constraints and energy storage installation constraints are also provided to restrict the installation of the intelligent soft switch and energy storage.

[0085] Specifically, in a possible implementation, the intelligent soft switch installation constraints may include:

[0086]

[0087] in, Indicates whether the supporting contact line at the location where the intelligent soft switch is to be installed on line ij is newly built. 1 indicates construction, and 0 indicates no construction. is the capacity of the intelligent soft switch on branch ij, is the maximum number of installed intelligent soft switches of unit capacity on line ij; is the number of installed intelligent soft switches of unit capacity on line ij, It is the unit installation capacity of the intelligent soft switch; SOP It is a collection of nodes to be built for intelligent soft switches.

[0088] In a possible implementation, the energy storage installation constraints may include:

[0089]

[0090] in, and are the energy storage installation power and capacity at node i respectively; and are the upper limit of energy storage power and the upper limit of capacity installation at node i respectively; β BESS is the energy rate coefficient of the energy storage system; and The upper limit of energy storage power and capacity installation in the distribution network; BESS A collection of energy storage nodes to be built.

[0091] In a possible implementation, the power flow constraint may be a distflow power flow constraint, which may specifically include:

[0092]

[0093] Among them, P ij,u,t and Q ij,u,t are the active power and reactive power flowing through branch ij at time t, P ij,u,t and Q ij,u,t are the active power and reactive power flowing through branch ij at time t respectively; v i,u,t and v j,u,t are the voltage amplitudes at node i and node j respectively; R ij and X ij are the resistance and reactance of branch ij respectively; and are respectively the active power and reactive power injected from node i in scenario u at time t, M is a sufficiently large positive number; l ij,u,t is the square of the current amplitude of branch ij at time t, P i,u,t,D and Q i,u,t,D are the actual active output and actual reactive output of the droop control element at node i at time t in scenario u, respectively; and are the load reactive power and load active power of node i in scenario u at time t, respectively, N is a set of nodes, Ψ L A collection of lines.

[0094] In further embodiments, the frequency constraints may include:

[0095] Since this paper considers frequency, in order to ensure that the frequency in each microgrid formed after the disaster is equal and the frequency between microgrids is independent, the frequency constraints can include:

[0096] M(1-b ij,t )≥f i,u,t -f j,u,t

[0097] -M(1-b ij,t )≤f i,u,t -f j,u,t

[0098] In a possible implementation, during the load recovery process, if the load on a node is restored, the frequency and voltage of the node should be within a safe range, and the transmission line transmission power should also be considered. Therefore, the safety constraints include: voltage and frequency safety constraints and line transmission power safety constraints.

[0099] Specifically, voltage and frequency safety constraints include:

[0100] U i,min ≤U i,u,t ≤U i,max

[0101] f i,min ≤f i,u,t ≤f i,max

[0102] Line transmission power safety constraints include:

[0103]

[0104] in, is the maximum transmission capacity allowed for line ij; where b ij,t The distribution network topology constraints need to be met. When the line is disconnected, the line transmission power safety constraint is 0.

[0105] It can be seen from the above objective functions and constraints that after encountering extreme disasters, the active distribution network loses its power support capability. The embodiment of the present invention provides power support to each microgrid after the dynamic division of the microgrid by optimizing the prior energy storage and intelligent soft switches, combined with the access of existing distributed power sources. At the same time, the distributed power sources fully output power, the energy storage that does not participate in the droop control performs optimal power operation, and the intelligent soft switches perform power exchange between microgrids. The above micro sources jointly participate in power support, and the operation process also always takes into account the frequency, voltage and various safe operation conditions between the microgrids. The power transmission of the intelligent soft switch increases the power support capability of the active distribution network and improves the flexibility of the power system; the construction of energy storage and intelligent soft switches provides more possibilities for the division of microgrids, and essentially provides more optimal operation solutions.

[0106] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0107] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0108] Figure 3 The schematic diagram of the structure of the distribution and micro-cooperative restoration device for power grid failure provided by an embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0109] like Figure 3 As shown, the distribution and micro-coordinated restoration device for power grid failure includes:

[0110] The parameter acquisition module 21 is used to obtain the current operating parameters of the power grid system;

[0111] A model building module 22, used to consider the influence of the intelligent soft switch, determine the constraint function based on the operating parameters, and establish a fault recovery model;

[0112] The model solving module 23 is used to solve the fault recovery model with the lowest total cost of completing power support as the objective function to obtain the fault recovery control strategy.

[0113] In a possible implementation, the model solving module 23 may include:

[0114] A problem conversion unit is used to perform second-order cone relaxation on the fault recovery model and convert the fault recovery model solution into a convex optimization problem;

[0115] The convex optimization solving unit is used to solve the convex optimization problem after convex relaxation to obtain a fault recovery control strategy.

[0116] In one possible implementation, the second-order cone constraint of the convex optimization problem may be:

[0117]

[0118] Among them, P ij,u,t and Q ij,u,t are the active power and reactive power flowing through branch ij at time t, respectively, ij,u,t is the square of the current amplitude of branch ij at time t, v i,u,t is the voltage amplitude of node i; and are the active power and reactive power of the intelligent soft-switching converter at node i, and are the active power and reactive power of the intelligent soft-switching converter at node j, is the loss coefficient of the SOP converter at node i, is the loss coefficient of the SOP converter at node j; and They are the active power losses of the energy storage converter at node i and node j in scenario u at time t respectively.

[0119] In a possible implementation, the constraints may include: active distribution network reconstruction constraints, intelligent soft switch installation constraints, energy storage installation constraints, power support operation constraints, power flow constraints, frequency constraints and safety constraints.

[0120] In a possible implementation, the power support operation constraints may include: photovoltaic power generation and wind power generation constraints, energy storage constraints participating in microgrid power support, energy storage and gas turbine constraints operating in droop control mode, and intelligent soft switch power support constraints;

[0121] Solar and wind power generation constraints can include:

[0122]

[0123]

[0124]

[0125]

[0126] in, and are the active power outputs of photovoltaic power generation and wind power generation at node i at time t in scenario u; and are the reactive power outputs of photovoltaic power generation and wind power generation at node i at time t in scenario u; and are the maximum active powers of photovoltaic power generation and wind power generation at node i under scenario u; and are the power factors of photovoltaic power generation and wind power generation at node i respectively; Ω PVG and Ω WTG They are respectively the collection of nodes to be constructed for photovoltaic power generation and wind power generation;

[0127] Energy storage constraints involved in microgrid power support can include:

[0128]

[0129]

[0130]

[0131] in, and are the energy storage installation power and capacity at node i, and They are respectively the active output and reactive output of the energy storage at node i at time t in scenario u; and are the lower limit coefficient and upper limit coefficient of the charge state of the energy storage at node i respectively;

[0132] Energy storage and gas turbine constraints for droop control operation can include:

[0133] P i,t,Dref -P i,u,t,D =K d,DP (f ref -f i,u,t )

[0134] Q i,t,Dref -Q i,u,t,D =K d,DQ (U d,ref -U i,u,t )

[0135] P i,D min ≤P i,t,Dref ≤P i,D max

[0136] Q i,D min ≤Q i,t,Dref ≤Q i,D max

[0137] P i,D min ≤Pi,u,t,D ≤P i,D max

[0138] Q i,D min ≤Q i,u,t,D ≤Q i,D max

[0139] Among them, P i,t,Dref and Q i,t,Dref are respectively the reference values ​​of active output and reactive output given by the droop control element at node i at time t; P i,u,t,D and Q i,u,t,D are the actual active output and actual reactive output of the droop control element at node i at time t in scenario u; f ref is the system rated frequency; U d,ref is the reference voltage at node i; f i,u,t and U i,u,t are the frequency and voltage amplitude of node i in scenario u at time t; K d,DP and K d,DQ are the active frequency-frequency droop coefficient and reactive frequency-voltage droop coefficient of the droop control element at node i respectively; P i,D max and P i,D min are the maximum and minimum active outputs of the droop control element at node i, respectively; Q i,D max and Q i,D min are the maximum output reactive power and minimum output reactive power of the droop control element at node i, respectively;

[0140] Intelligent soft switching power support constraints can include:

[0141]

[0142]

[0143] in, and are the active power and reactive power of the intelligent soft-switching converter at node i, and are the active power and reactive power of the intelligent soft-switching converter at node j, is the loss coefficient of the intelligent soft-switching converter at node i, is the loss coefficient of the intelligent soft-switching converter at node j; and are the active power losses of the smart soft-switching converter at node i and node j in scenario u at time t, is the capacity of the intelligent soft switch on branch ij.

[0144] In a possible implementation, the smart soft switch installation constraints may include:

[0145]

[0146]

[0147] in, Indicates whether the supporting contact line at the location where the intelligent soft switch is to be installed on line ij is newly built. 1 indicates construction, and 0 indicates no construction. is the capacity of the intelligent soft switch on branch ij, is the maximum number of installed intelligent soft switches of unit capacity on line ij; is the number of installed intelligent soft switches of unit capacity on line ij, It is the unit installation capacity of the intelligent soft switch; SOP It is a collection of nodes to be built for intelligent soft switches.

[0148] In a possible implementation, energy storage installation constraints may include:

[0149]

[0150] in, and are the energy storage installation power and capacity at node i respectively; and are the upper limit of energy storage power and the upper limit of capacity installation at node i respectively; β BESS is the energy rate coefficient of the energy storage system; and The upper limit of energy storage power and capacity installation in the distribution network; BESS A collection of energy storage nodes to be built.

[0151] In one possible implementation, the power flow constraints may include:

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159] Among them, P ij,u,t and Q ij,u,t are the active power and reactive power flowing through branch ij at time t, P ij,u,t and Q ij,u,t are the active power and reactive power flowing through branch ij at time t respectively; v i,u,t and v j,u,t are the voltage amplitudes at node i and node j respectively; R ij and X ij are the resistance and reactance of branch ij respectively; and are respectively the active power and reactive power injected from node i in scenario u at time t, M is a sufficiently large positive number; l ij,u,t is the square of the current amplitude of branch ij at time t, P i,u,t,D and Q i,u,t,D are the actual active output and actual reactive output of the droop control element at node i at time t in scenario u, respectively; and are the load reactive power and load active power of node i in scenario u at time t, respectively, N is a set of nodes, Ψ L A collection of lines.

[0160] In a possible implementation, the objective function may be:

[0161] minC=C USE +C INV +C OM

[0162] Among them, C is the total cost of completing power support, C USE is the electricity cost, C INV is the construction cost of intelligent soft switch and energy storage, C OM For operation and maintenance costs.

[0163] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0164] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0165] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the microgrid central controller, it can implement the steps of the above-mentioned micro-grid coordinated recovery method embodiment for each power grid fault. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0166] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for coordinated restoration of power grid failure by distribution and microelectronics, characterized in that: include: Obtain the current operating parameters of the power grid system; Considering the influence of intelligent soft switch, determining constraint conditions based on the operating parameters, and establishing a fault recovery model; Taking the minimum total cost of completing power support as the objective function, the fault recovery model is solved to obtain a fault recovery control strategy.

2. The method for coordinated restoration of power grid failure according to claim 1, characterized in that: The step of solving the fault recovery model to obtain a fault recovery control strategy includes: Performing second-order cone relaxation on the fault recovery model, and converting the solution of the fault recovery model into a convex optimization problem; The convex optimization problem after convex relaxation is solved to obtain the fault recovery control strategy.

3. The method for coordinated restoration of power grid failure according to claim 2, characterized in that: The second-order cone constraints of the convex optimization problem are: ||[2P ij,u,t 2Q ij,u,t l ij,u,t -in i,u,t ] T ||2≤l ij,u,t +v i,u,t Among them, P ij,u,t and Q ij,u,t are the active power and reactive power flowing through branch ij at time t, respectively, ij,u,t is the square of the current amplitude of branch ij at time t, v i,u,t is the voltage amplitude of node i; and are the active power and reactive power of the intelligent soft-switching converter at node i, and are the active power and reactive power of the intelligent soft-switching converter at node j, is the loss coefficient of the SOP converter at node i, is the loss coefficient of the SOP converter at node j; and They are the active power losses of the energy storage converter at node i and node j in scenario u at time t respectively.

4. The method for coordinated restoration of power grid failure by distribution and microelectronics according to any one of claims 1 to 3, characterized in that: The constraints include: active distribution network reconstruction constraints, intelligent soft switch installation constraints, energy storage installation constraints, power support operation constraints, power flow constraints, frequency constraints and safety constraints.

5. The method for coordinated restoration of power grid failure by distribution and microelectronics according to claim 4 is characterized in that: The power support operation constraints include: photovoltaic power generation and wind power generation constraints, energy storage constraints participating in microgrid power support, energy storage and gas turbine constraints operating in droop control mode, and intelligent soft switch power support constraints; The photovoltaic power generation and wind power generation constraints include: in, and are the active power outputs of photovoltaic power generation and wind power generation at node i at time t in scenario u; and are the reactive power outputs of photovoltaic power generation and wind power generation at node i at time t in scenario u; and are the maximum active powers of photovoltaic power generation and wind power generation at node i under scenario u; and are the power factors of photovoltaic power generation and wind power generation at node i respectively; Ω PVG and Ω WTG They are respectively the collection of nodes to be constructed for photovoltaic power generation and wind power generation; The energy storage constraints participating in the microgrid power support include: in, and are the energy storage installation power and capacity at node i, and They are respectively the active output and reactive output of the energy storage at node i at time t in scenario u; and are the lower limit coefficient and upper limit coefficient of the charge state of the energy storage at node i respectively; The energy storage and gas turbine constraints for droop control operation include: P i,t,Dref -P i,u,t,D =K d,DP (f ref -f i,u,t ) Q i,t,Dref -Q i,u,t,D =K d,DQ (U d,ref -U i,u,t ) P i,Dmin ≤P i,t,Dref ≤P i,Dmax Q i,Dmin ≤Q i,t,Dref ≤Q i,Dmax P i,Dmin ≤P i,u,t,D ≤P i,Dmax Q i,Dmin ≤Q i,u,t,D ≤Q i,Dmax Among them, P i,t,Dref and Q i,t,Dref are respectively the reference values ​​of active output and reactive output given by the droop control element at node i at time t; P i,u,t,D and Q i,u,t,D are the actual active output and actual reactive output of the droop control element at node i at time t in scenario u; f ref is the system rated frequency; U d,ref is the reference voltage at node i; f i,u,t and U i,u,t are the frequency and voltage amplitude of node i in scenario u at time t; K d,DP and K d,DQ are the active frequency-frequency droop coefficient and reactive frequency-voltage droop coefficient of the droop control element at node i respectively; P i,Dmax and P i,Dmin are the maximum and minimum active outputs of the droop control element at node i, respectively; Q i,Dmax and Q i,Dmin are the maximum output reactive power and minimum output reactive power of the droop control element at node i, respectively; The intelligent soft switch power support constraint includes: in, and are the active power and reactive power of the intelligent soft-switching converter at node i, and are the active power and reactive power of the intelligent soft-switching converter at node j, is the loss coefficient of the intelligent soft-switching converter at node i, is the loss coefficient of the intelligent soft-switching converter at node j; and are the active power losses of the smart soft-switching converter at node i and node j in scenario u at time t, is the capacity of the intelligent soft switch on branch ij.

6. The method for coordinated restoration of power grid failure by distribution and microelectronics according to claim 4, characterized in that: The intelligent soft switch installation constraints include: in, Indicates whether the supporting contact line at the location where the intelligent soft switch is to be installed on line ij is newly built. 1 indicates construction, and 0 indicates no construction. is the capacity of the intelligent soft switch on branch ij, is the maximum number of installed intelligent soft switches of unit capacity on line ij; is the number of installed intelligent soft switches of unit capacity on line ij, It is the unit installation capacity of the intelligent soft switch; SOP It is a collection of nodes to be built for intelligent soft switches.

7. The method for coordinated restoration of power grid failure by distribution and microelectronics according to claim 4, characterized in that: The energy storage installation constraints include: in, and are the energy storage installation power and capacity at node i respectively; and are the upper limit of energy storage power and the upper limit of capacity installation at node i respectively; β BESS is the energy rate coefficient of the energy storage system; and The upper limit of energy storage power and capacity installation in the distribution network; BESS A collection of energy storage nodes to be built.

8. The method for coordinated restoration of power grid failure by distribution and microelectronics according to claim 4, characterized in that: The power flow constraints include: Among them, P ij,u,t and Q ij,u,t are the active power and reactive power flowing through branch ij at time t, P ij,u,t and Q ij,u,t are the active power and reactive power flowing through branch ij at time t respectively; v i,u,t and v j,u,t are the voltage amplitudes at node i and node j respectively; R ij and X ij are the resistance and reactance of branch ij respectively; and are respectively the active power and reactive power injected from node i in scenario u at time t, M is a sufficiently large positive number; l ij,u,t is the square of the current amplitude of branch ij at time t, P i,u,t,D and Q i,u,t,D are the actual active output and actual reactive output of the droop control element at node i at time t in scenario u, respectively; and are the load reactive power and load active power of node i in scenario u at time t, respectively, N is a set of nodes, Ψ L A collection of lines.

9. The method for coordinated restoration of power grid failure by distribution and microelectronics according to any one of claims 1 to 3, characterized in that: The objective function is: minC=C USE +C INV +C OM Among them, C is the total cost of completing power support, C USE is the electricity cost, C INV is the construction cost of intelligent soft switch and energy storage, C OM For operation and maintenance costs.

10. A distribution and micro-cooperative restoration device for power grid failure, characterized in that: include: A parameter acquisition module is used to obtain the current operating parameters of the power grid system; A model building module, used to consider the influence of the intelligent soft switch, determine the constraint function based on the operating parameters, and establish a fault recovery model; The model solving module is used to solve the fault recovery model with the lowest total cost of completing power support as the objective function to obtain a fault recovery control strategy.

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