Power distribution network recovery method, apparatus and device, and storage medium

Abstract

The invention discloses a power distribution network recovery method, device and equipment and a storage medium, and the method comprises the steps: setting a distributed power supply of a microgrid based on a synchronous generator, and constructing a dynamic model; determining an optimal recovery path from the micro-grid to all key load areas according to a topological structure corresponding to a power system of the power distribution network to be recovered, and combining loads on the optimal recovery path to obtain a plurality of groups of load groups; establishing a recovery optimization model based on the optimal load group, and solving the recovery optimization model according to preset constraints to obtain a recovery scheme; and inputting the recovery scheme into the dynamic model for simulation verification, and after verification is passed, recovering the power distribution network based on the recovery scheme. The technical problems that in the prior art, the optimization problem is difficult to solve quickly in the presence of frequency voltage constraints, and transient safety and stability under the implementation of the obtained recovery decision cannot be guaranteed are solved.

Description

Technical Field

[0001] The present invention relates to the field of microgrid technology, and in particular to a distribution network recovery method, device, equipment and storage medium. Background Art

[0003] The existing post-disaster recovery methods do not take into account the stability of microgrids and the dynamic performance of distributed power sources during the recovery process. This leads to many solutions in common microgrid-based load recovery methods that do not meet the frequency deviation limit and the transient voltage and current limit of distributed power sources. Their corresponding recovery methods may not be used in reality. At the same time, it is also impossible to simulate the imbalance of microgrids, resulting in the inability to guarantee transient safety and stability under the implementation of all recovery decisions. There is a problem that optimization problems are difficult to solve quickly under frequency and voltage constraints. Summary of the invention

[0004] The present invention provides a distribution network restoration method, device, equipment and storage medium to solve the technical problems in the prior art that the optimization problem under the existence of frequency and voltage constraints is difficult to solve quickly and the transient safety and stability under the implementation of the resulting restoration decision cannot be guaranteed.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a distribution network restoration method, comprising:

[0006] Set up distributed power generation for microgrid based on synchronous generators and build a dynamic model;

[0007] According to the topological structure corresponding to the power system of the distribution network to be restored, determine the optimal restoration path from the microgrid to all key load areas, and combine the loads on the optimal restoration path to obtain multiple load groups;

[0008] Establishing a restoration optimization model based on the optimal set of load groups, and solving the restoration optimization model according to preset constraints to obtain a restoration plan;

[0009] The restoration plan is input into the dynamic model for simulation verification, and after the verification passes, the distribution network is restored based on the restoration plan.

[0010] As a preferred solution, the distributed power supply of the microgrid is set based on the synchronous generator, and a dynamic model is constructed, which specifically includes:

[0011] Set up the distributed power supply of the microgrid based on synchronous generators, and build an electromechanical transient model based on synchronous generators;

[0012] According to the mechanical angular velocity of the synchronous generator, the mechanical torque of the synchronous generator is determined, and then a speed regulator model that maintains a constant frequency in a steady state is constructed;

[0013] According to the excitation voltage of the synchronous generator, an exciter model is constructed;

[0014] A dynamic model is constructed based on the electromechanical transient model, the speed regulator model and the exciter model.

[0015] As a preferred solution, the electromechanical transient model is constructed based on the synchronous generator, specifically including:

[0016] According to the relationship between the magnetic flux and the transient voltage of the synchronous generator between the first shaft and the second shaft, a first shaft magnetic flux transient voltage relationship equation and a second shaft magnetic flux transient voltage relationship equation are established;

[0017] According to the sub-transient voltage process corresponding to the differential components of the synchronous generator on the first axis and the second axis respectively, the magnetic flux current component equation of the first axis and the magnetic flux current component equation of the second axis are established;

[0018] According to the rotor speed of the synchronous generator, the voltage at the first shaft end and the voltage at the second shaft end, an electromagnetic transient equation of the stator voltage is established;

[0019] Based on the first axis flux transient voltage relationship equation, the second axis flux transient voltage relationship equation, the first axis flux current component equation, the second axis flux current component equation and the electromagnetic transient equation, the unbalanced operating state of the synchronous generator is modeled to obtain an electromechanical transient model.

[0020] As a preferred solution, according to the topological structure corresponding to the power system of the distribution network to be restored, the optimal restoration path from the microgrid to all key load areas is determined, and the loads on the optimal restoration path are combined to obtain multiple groups of loads, specifically including:

[0021] Determine the key load area combination of each microgrid according to the topological structure corresponding to the power system of the distribution network to be restored;

[0022] Based on the single-source shortest path algorithm, the restoration path from the distribution network to all key load areas in each microgrid key load area combination is determined, and the optimal restoration path is determined according to the path weight;

[0023] The key loads on the optimal restoration path are combined, and power flow calculation and dynamic simulation are performed on the combined key loads to determine multiple load groups.

[0024] As a preferred solution, determining the optimal restoration path according to the path weight specifically includes:

[0025] Determine the intermediate recovery path according to the total load on each recovery path;

[0026] The feasibility of the intermediate recovery path is verified through power flow calculation and dynamic simulation, and the infeasible intermediate recovery path is deleted, so that the verified feasible intermediate recovery path is taken as the optimal recovery path.

[0027] As a preferred solution, the restoration optimization model is established based on the optimal group of load groups, and the restoration optimization model is solved according to preset constraints to obtain a restoration solution, which specifically includes:

[0028] Based on the optimal group of load groups, on the basis of the dynamic model, a restoration optimization model is established with the goal of maximizing the cumulative restoration of power supply;

[0029] The restoration optimization model is solved according to the power flow constraint and the topology constraint in the preset constraints to obtain a restoration plan with the maximum cumulative restored power supply.

[0030] As a preferred solution, the preset constraints include: microgrid constraints and restoration constraints;

[0031] The microgrid constraints include: power flow constraints, topology constraints and frequency and voltage constraints;

[0032] The recovery constraints include: steady-state frequency constraints, transient frequency constraints and transient voltage constraints at power generation terminals.

[0033] Accordingly, the present invention also provides a distribution network restoration device, comprising: a modeling module, a load module, a solution module and a restoration module;

[0034] The modeling module is used to set up a distributed power source of a microgrid based on a synchronous generator and to build a dynamic model;

[0035] The load module is used to determine the optimal restoration path from the microgrid to all key load areas according to the topological structure corresponding to the power system of the distribution network to be restored, and to combine the loads on the optimal restoration path to obtain multiple load groups;

[0036] The solution module is used to establish a restoration optimization model based on the optimal set of load groups, and solve the restoration optimization model according to preset constraints to obtain a restoration plan;

[0037] The recovery module is used to input the recovery plan into the dynamic model for simulation verification, and after the verification is passed, restore the distribution network based on the recovery plan.

[0038] Correspondingly, the present invention also provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the distribution network recovery method as described in any one of the above items is implemented.

[0039] Correspondingly, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the distribution network recovery methods described above.

[0040] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0041] The technical solution of the present invention sets up a distributed power source of a microgrid based on a synchronous generator and establishes a dynamic model, taking into account the operation of the microgrid under dynamic conditions so as to be suitable for unbalanced simulation, and then determines the optimal recovery path according to the topological structure corresponding to the power system of the distribution network to be restored, and obtains the load group on the corresponding recovery path. A recovery optimization model is established and solved based on the optimal group of load groups to obtain the corresponding recovery plan, and finally the recovery plan is input into the dynamic model for simulation verification, so that after the verification is passed, the recovery plan can be directly used to restore the distribution network. The dynamic characteristics of the microgrid when restoring the distribution system load are compared with the previous method of only considering steady-state constraints. The transient safety and stability under the implementation of the obtained recovery decision is guaranteed, and the stability and reliability of the distribution network recovery are ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 : A flow chart of the steps of a distribution network restoration method provided by an embodiment of the present invention;

[0043] Figure 2 : A control block diagram of a DEGOV speed regulator provided in an embodiment of the present invention;

[0044] Figure 3 : A control block diagram of a SEXS exciter provided in an embodiment of the present invention;

[0045] Figure 4 : A structural diagram of a distribution network restoration device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Embodiment 1

[0048] Please refer to Figure 1, a distribution network restoration method provided by an embodiment of the present invention includes the following steps S101-S103:

[0049] Step S101: Set up the distributed power supply of the microgrid based on the synchronous generator and build a dynamic model.

[0050] As a preferred solution of this embodiment, the distributed power supply of the microgrid is set based on the synchronous generator, and the dynamic model is constructed, which specifically includes:

[0051] A distributed power source of a microgrid is set up based on a synchronous generator, and an electromechanical transient model is constructed based on the synchronous generator; the mechanical torque of the synchronous generator is determined according to the mechanical angular velocity of the synchronous generator, and then a speed regulator model that maintains a constant frequency in a steady state is constructed; an exciter model is constructed according to the excitation voltage of the synchronous generator; a dynamic model is constructed based on the electromechanical transient model, the speed regulator model and the exciter model.

[0052] In this embodiment, a synchronous generator is used as a distributed power source to provide power for the microgrid. Due to the need to take dynamic analysis into account, the synchronous generator needs to adopt an electromechanical transient model suitable for unbalanced simulation.

[0053] In this embodiment, the speed governor can maintain a constant frequency in a steady state by using the Woodward diesel speed governor model (DEGOV), which modifies the mechanical torque of the synchronous machine in proportion to the mechanical angular velocity deviation of the synchronous machine, including an electronic control box, an actuator, a prime mover and a droop link. The control block diagram is shown in FIG. Figure 2 As shown; the exciter model is a simplified exciter system (SEXS), which controls the terminal voltage of the generator (the average voltage amplitude of each phase) by modifying the generator excitation voltage (i.e. reactive power), including transient filter and exciter inertia link. The control block diagram is shown in Figure 3 The speed regulator and exciter directly affect the frequency and voltage characteristics of the synchronous generator, which can help the synchronous generator to operate stably and maintain the reliability of the power grid.

[0054] As a preferred solution of this embodiment, the electromechanical transient model is constructed based on the synchronous generator, specifically including:

[0055] According to the relationship between the magnetic flux and transient voltage between the first axis and the second axis of the synchronous generator, the first axis magnetic flux transient voltage relationship equation and the second axis magnetic flux transient voltage relationship equation are established; according to the sub-transient voltage process corresponding to the differential components of the synchronous generator on the first axis and the second axis, the first axis magnetic flux current component equation and the second axis magnetic flux current component equation are established; according to the rotor speed of the synchronous generator, the first axis shaft end voltage and the second axis shaft end voltage, the electromagnetic transient equation of the stator voltage is established; based on the first axis magnetic flux transient voltage relationship equation, the second axis magnetic flux transient voltage relationship equation, the first axis magnetic flux current component equation, the second axis magnetic flux current component equation and the electromagnetic transient equation, the unbalanced operating state of the synchronous generator is modeled to obtain an electromechanical transient model.

[0056] In this embodiment, a simplified fundamental frequency model represented by a phasor is used to model the unbalanced operation of a three-phase synchronous motor, where the positive sequence represents the main electrical torque and the negative sequence current produces the opposite torque. The total electrical torque is constant, which is convenient for solving and determining the balance. The change in electrical torque caused by unbalanced operation is ignored. The electrodynamic equations of the generator are established on the d and q axes, where the d axis corresponds to the first axis and the q axis corresponds to the second axis. The d-axis equation shows the relationship between the d-axis flux and the transient voltage:

[0057]

[0058] The q-axis equation is as follows:

[0059]

[0060] Among them, T′ d0、 T″ d0 is the d-axis open-circuit transient and subtransient time constant, T′ q0 , T″ q0 is the q-axis open-circuit transient and subtransient time constant; E′ d , E′ q 、E fd is the d-axis and q-axis transient voltage and excitation voltage; x d , x′ d , x″ d is the d-axis synchronous reactance, transient reactance and subtransient reactance, x q , x′ q , x″ q 、x l They are q-axis synchronous reactance, transient reactance, subtransient reactance and leakage reactance respectively; ψ 1d , 2q is the flux linkage of the d-axis and q-axis damping windings; I d ,I q are the d-axis and q-axis currents.

[0061] In this embodiment, the differential components of the d and q axes lead to the sub-transient voltage process of the generator. The following equation describes the relationship between the voltage and the magnetic flux and current components of the generator:

[0062]

[0063] E″0=-x0I0

[0064] Among them, E″ d , E″ q is the subtransient voltage of d-axis and q-axis; E″0 is the zero-sequence subtransient voltage; x0 is the zero-sequence reactance; I0 is the stator zero-sequence current.

[0065] The electromagnetic transient equation of the stator voltage is as follows:

[0066]

[0067] Where ω=ω s is the actual and rated mechanical speed of the rotor; R a , R0 is the armature resistance and zero-sequence resistance, V d 、V q , V0 is the voltage at the generator d, q, 0 axis terminals. The typical electromechanical transient model ignores the stator dynamics of the synchronous generator, that is, the derivative on the left side of the electromagnetic transient equation of the stator voltage in the above formula is replaced by the Laplace transform of the fundamental frequency. The speed dependence of the stator voltage is also usually ignored. Under the above assumptions, the electromagnetic transient equation of the stator voltage in the above formula has ω=ω s .

[0068] Step S102: Determine the optimal restoration path from the microgrid to all key load areas according to the topological structure corresponding to the power system of the distribution network to be restored, and combine the loads on the optimal restoration path to obtain multiple load groups.

[0069] As a preferred solution of this embodiment, the optimal restoration path from the microgrid to all key load areas is determined according to the topological structure corresponding to the power system of the distribution network to be restored, and the loads on the optimal restoration path are combined to obtain multiple groups of loads, specifically including:

[0070] According to the topological structure corresponding to the power system of the distribution network to be restored, the key load area combination of each microgrid is determined; based on the single-source shortest path algorithm, the recovery path from the distribution network to all key load areas in each microgrid key load area combination is determined, and the optimal recovery path is determined according to the path weight; the key loads on the optimal recovery path are combined, and the combined key loads are subjected to flow calculation and dynamic simulation to determine multiple load groups.

[0071] As a preferred solution of this embodiment, determining the optimal restoration path according to the path weight specifically includes:

[0072] According to the total load on each restoration path, the intermediate restoration path is determined; the feasibility of the intermediate restoration path is verified through power flow calculation and dynamic simulation, and the infeasible intermediate restoration path is deleted, so that the verified feasible intermediate restoration path is used as the optimal restoration path.

[0073] In this embodiment, the optimal recovery path can be determined according to its topology, and then the loads on the path can be combined to obtain multiple load groups. Specifically, a recovery tree can be constructed. For each "microgrid-critical load area" combination, a single-source shortest path algorithm is used to determine the recovery path from the microgrid to all critical load areas. The path with the smallest path weight (i.e., the total amount of load on the path) is the recovery path. The feasibility of the recovery path is verified through power flow calculation and dynamic simulation, and infeasible paths are deleted. A feasible recovery tree is constructed. Preferably, the single-source shortest path algorithm can be Dijkstra's algorithm.

[0074] In this embodiment, for each microgrid, all combinations of critical loads on its restoration tree are explored to find a set of load groups. Each load group consists of the critical load area restored by the microgrid through the restoration path. Each load group needs to be evaluated for its feasibility through power flow calculation and dynamic simulation. Infeasible load groups will be removed from the set to form load groups.

[0075] Step S103: Establish a restoration optimization model based on the optimal group of load groups, and solve the restoration optimization model according to preset constraints to obtain a restoration plan.

[0076] In this embodiment, the critical load restoration problem is formulated as a maximum coverage problem, and the goal is to maximize the service time of the microgrid for the loads on the distribution feeder, which is weighted by their priority. By solving the maximum coverage problem, the set of critical load areas that can be restored and the set of load groups covering these critical load areas are determined, and then the corresponding restoration paths are identified from the restoration tree, and a set of optimal load groups are selected on the path that should be restored to form a restoration plan, that is, to construct an optimization problem, set the objective function and the flow constraints and topology constraints, so that the restoration plan with the maximum cumulative restoration of power supply can be solved, that is, by solving the maximum coverage problem, the critical load areas that can be restored and the corresponding restoration paths are determined. For each restoration path, a series of switching operations will be performed to isolate the restoration path and restore the critical load area, and finally the restoration action will be determined as the restoration plan.

[0077] In this embodiment, a recovery path and action plan are determined, and there is more than one path and plan that meet the operation, topology and frequency constraints. According to the objective function, the plan that restores the most node loads the earliest can be selected, that is, the objective function is linked.

[0078] As a preferred solution of this embodiment, the restoration optimization model is established based on the optimal group of load groups, and the restoration optimization model is solved according to preset constraints to obtain a restoration solution, which specifically includes:

[0079] Based on the optimal group of load groups, on the basis of the dynamic model, a restoration optimization model is established with the goal of maximizing the cumulative restoration of power supply; according to the flow constraints and topology constraints in the preset constraints, the restoration optimization model is solved to obtain a restoration plan with the maximum cumulative restoration of power supply.

[0080] In this embodiment, based on the dynamic model corresponding to the synchronous generator, taking into account the load priority, with the goal of maximizing the cumulative power restoration, an optimization model for the restoration strategy under extreme disasters (corresponding to the restoration optimization model) is established:

[0081]

[0082] Where L is the set of all loads in the distribution network; ρ i is the weight factor of load i, reflecting the importance of the load, which is a given parameter; φ is the phase of the line; P L，i，t，φ is the active load parameter of node i in phase φ during period t. Assuming that the load switch cannot be remotely controlled, the load access state is determined by the line topology and is the decision variable parameter.

[0083] In this embodiment, the decision variable P refers to the load of the node. Only when the node is restored, P is not 0, otherwise P = 0. After the restoration path is determined, there is more than one load group and corresponding complex action that meet the frequency constraint, including only restoring the next load on the path each time. In order to restore more loads as quickly as possible, that is, under the condition of meeting the distribution network operation constraints, topology constraints and frequency constraints, as many nodes as possible are restored faster (that is, P is not 0 earlier), so as to link the decision variable P with the restoration strategy.

[0084] As a preferred solution of this embodiment, the preset constraints include: microgrid constraints and recovery constraints; the microgrid constraints include: power flow constraints, topology constraints and frequency and voltage constraints; the recovery constraints include: steady-state frequency constraints, transient frequency constraints and transient voltage constraints of power generation terminals.

[0085] In this embodiment, the microgrid constraints, i.e., the constraints satisfied by the microgrid, include: power flow constraints, topology constraints, and frequency and voltage constraints. Among them, the frequency and voltage feasibility will be achieved through constraint verification rather than solving optimization problems. The recovery constraints are essentially frequency and voltage constraints.

[0086] Considering the power flow constraints, the power flow equation must be satisfied, and its linearized form is as follows:

[0087]

[0088] P DG,j,t -P L,j,t =∑ s∈δ(j) H js,t -∑ i∈n(j) H ij，t |

[0089] Q DG，j，t -Q L，j，t =∑ s∈δ(j) G js,t -∑ i∈π(j)，t

[0090] Among them, U i，t and U j，t are the square matrices of the three-phase voltage amplitudes at node i and node j at time t, is the φ phase voltage of node i at time period t; S ij，t is the complex power flowing through line ij at time t, For S ij，t The conjugate value of is the line impedance matrix and The matrix obtained by multiplying the elements at corresponding positions, for The conjugate value of P L，j，t =[P L，j，t，a , P L，j，t，b , P L，j，t，c ] T , is the three-phase active load matrix of node j in period t; x ij，t Indicates line connection status; H ij，t , G ij，t P is the active and reactive power flowing through the line; DG，j，t , Q DG，j，t is the active and reactive power output of the distributed power supply; π(j), δ(j) is the set of parent and child nodes of node j; M is a large positive constant.

[0091] The radial topology constraints must also be met during recovery. While ensuring connectivity within each partition, the number of closed paths is equal to the number of nodes minus the number of partitions:

[0092] ∑s∈δ(j) F js -∑ i∈π(j) F ij = -1, j∈V / {DG}

[0093] ∑ s∈δ(j) F js -∑ i∈π(j) F ij =W j , j∈{DG}

[0094]

[0095] ∑ ij∈E x ij =|V|-|{DG}|

[0096] Among them, F ij Indicates the virtual power flowing through the line; W j is an unrestricted real number; E, V, {DG} are the sets of lines, nodes, and nodes where DG is located. Explain the meaning of the first two equations: the virtual load of each load node is 1, then the virtual power Σ flowing from node j to its child nodes s∈δ(j) F js , minus all virtual power Σ flowing from the parent node i∈π(j) F ij =-1, that is, 1 is consumed in the form of virtual load; for the power node, the virtual power flowing to the child node is greater than the virtual power flowing from the parent node (because it is power generation), which is set to an unrestricted real number W j .

[0097] Step S104: input the recovery plan into the dynamic model for simulation verification, and after the verification passes, restore the distribution network based on the recovery plan.

[0098] In this embodiment, the recovery plan is input into the dynamic model for simulation verification, and the failure is detected by frequency and voltage constraints. After the recovery action is performed, the microgrid should reach a stable state with acceptable frequency. The constraints are:

[0099]

[0100] in, are the upper and lower limits of the steady-state frequency; M is the set of available microgrids.

[0101] During the transient recovery process, the microgrid frequency should be within the acceptable range, that is, the transient frequency satisfy:

[0102]

[0103] in, is the upper and lower limits of transient frequency; G is the transient voltage at the generator terminal It should not exceed the preset upper limit, namely:

[0104]

[0105] in, The upper and lower limits of the transient voltage at the generator terminal; G is the set of generators in use.

[0106] In this embodiment, the feasibility of the restoration plan is evaluated through dynamic simulation. For infeasible plans, the constraints of their topological infeasibility are returned, and the optimization problem is solved again, that is, a group of second-best load groups are selected again to form a restoration plan, that is, the optimization problem is constructed, the objective function and the flow constraints and topological constraints are set, and the restoration plan with the largest cumulative restored power supply can be solved as the next restoration plan to be tested.

[0107] In this embodiment, there are multiple paths from each given microgrid to the load to be restored, and there are multiple load nodes on each restoration path. These loads can be restored in various combinations. Find the restoration paths from all microgrids to the critical loads, and the load nodes on the combined paths are load groups. Find the optimal solution (including path and load group) for the objective function, and then perform simulation verification through power flow, topology and frequency constraints to ensure that the optimal solution is feasible.

[0108] Implementing the above embodiments has the following effects:

[0109] The technical solution of the present invention sets up a distributed power source of a microgrid based on a synchronous generator and establishes a dynamic model, taking into account the operation of the microgrid under dynamic conditions so as to be suitable for unbalanced simulation, and then determines the optimal recovery path according to the topological structure corresponding to the power system of the distribution network to be restored, and obtains the load group on the corresponding recovery path. A recovery optimization model is established and solved based on the optimal group of load groups to obtain the corresponding recovery plan, and finally the recovery plan is input into the dynamic model for simulation verification, so that after the verification is passed, the recovery plan can be directly used to restore the distribution network. The dynamic characteristics of the microgrid when restoring the distribution system load are compared with the previous method of only considering steady-state constraints. The transient safety and stability under the implementation of the obtained recovery decision is guaranteed, and the stability and reliability of the distribution network recovery are ensured.

[0110] Embodiment 2

[0111] See also Figure 4 , which is a distribution network restoration device provided by the present invention, comprising: a modeling module 201, a load module 202, a solution module 203 and a restoration module 204;

[0112] The modeling module 201 is used to set up a distributed power source of a microgrid based on a synchronous generator and build a dynamic model;

[0113] The load module 202 is used to determine the optimal restoration path from the microgrid to all key load areas according to the topological structure corresponding to the power system of the distribution network to be restored, and combine the loads on the optimal restoration path to obtain multiple load groups;

[0114] The solution module 203 is used to establish a restoration optimization model based on the optimal group of load groups, and solve the restoration optimization model according to preset constraints to obtain a restoration plan;

[0115] The recovery module 204 is used to input the recovery plan into the dynamic model for simulation verification, and after the verification passes, restore the distribution network based on the recovery plan.

[0116] As a preferred solution, the distributed power supply of the microgrid is set based on the synchronous generator, and a dynamic model is constructed, which specifically includes:

[0117] Set up the distributed power supply of the microgrid based on synchronous generators, and build an electromechanical transient model based on synchronous generators;

[0118] According to the mechanical angular velocity of the synchronous generator, the mechanical torque of the synchronous generator is determined, and then a speed regulator model that maintains a constant frequency in a steady state is constructed;

[0119] According to the excitation voltage of the synchronous generator, an exciter model is constructed;

[0120] A dynamic model is constructed based on the electromechanical transient model, the speed regulator model and the exciter model.

[0121] As a preferred solution, the electromechanical transient model is constructed based on the synchronous generator, specifically including:

[0122] According to the relationship between the magnetic flux and the transient voltage of the synchronous generator between the first shaft and the second shaft, a first shaft magnetic flux transient voltage relationship equation and a second shaft magnetic flux transient voltage relationship equation are established;

[0123] According to the sub-transient voltage process corresponding to the differential components of the synchronous generator on the first axis and the second axis respectively, the magnetic flux current component equation of the first axis and the magnetic flux current component equation of the second axis are established;

[0124] According to the rotor speed of the synchronous generator, the voltage at the first shaft end and the voltage at the second shaft end, an electromagnetic transient equation of the stator voltage is established;

[0125] Based on the first axis flux transient voltage relationship equation, the second axis flux transient voltage relationship equation, the first axis flux current component equation, the second axis flux current component equation and the electromagnetic transient equation, the unbalanced operating state of the synchronous generator is modeled to obtain an electromechanical transient model.

[0126] As a preferred solution, according to the topological structure corresponding to the power system of the distribution network to be restored, the optimal restoration path from the microgrid to all key load areas is determined, and the loads on the optimal restoration path are combined to obtain multiple groups of loads, specifically including:

[0127] Determine the key load area combination of each microgrid according to the topological structure corresponding to the power system of the distribution network to be restored;

[0128] Based on the single-source shortest path algorithm, the restoration path from the distribution network to all key load areas in each microgrid key load area combination is determined, and the optimal restoration path is determined according to the path weight;

[0129] The key loads on the optimal restoration path are combined, and power flow calculation and dynamic simulation are performed on the combined key loads to determine multiple load groups.

[0130] As a preferred solution, determining the optimal restoration path according to the path weight specifically includes:

[0131] Determine the intermediate recovery path according to the total load on each recovery path;

[0132] The feasibility of the intermediate recovery path is verified through power flow calculation and dynamic simulation, and the infeasible intermediate recovery path is deleted, so that the verified feasible intermediate recovery path is taken as the optimal recovery path.

[0133] As a preferred solution, the restoration optimization model is established based on the optimal group of load groups, and the restoration optimization model is solved according to preset constraints to obtain a restoration solution, which specifically includes:

[0134] Based on the optimal group of load groups, on the basis of the dynamic model, a restoration optimization model is established with the goal of maximizing the cumulative restoration of power supply;

[0135] The restoration optimization model is solved according to the power flow constraint and the topology constraint in the preset constraints to obtain a restoration plan with the maximum cumulative restored power supply.

[0136] As a preferred solution, the preset constraints include: microgrid constraints and restoration constraints;

[0137] The microgrid constraints include: power flow constraints, topology constraints and frequency and voltage constraints;

[0138] The recovery constraints include: steady-state frequency constraints, transient frequency constraints and transient voltage constraints at power generation terminals.

[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0140] Implementing the above embodiments has the following effects:

[0141] The technical solution of the present invention sets up a distributed power source of a microgrid based on a synchronous generator and establishes a dynamic model, taking into account the operation of the microgrid under dynamic conditions so as to be suitable for unbalanced simulation, and then determines the optimal recovery path according to the topological structure corresponding to the power system of the distribution network to be restored, and obtains the load group on the corresponding recovery path. A recovery optimization model is established and solved based on the optimal group of load groups to obtain the corresponding recovery plan, and finally the recovery plan is input into the dynamic model for simulation verification, so that after the verification is passed, the recovery plan can be directly used to restore the distribution network. The dynamic characteristics of the microgrid when restoring the distribution system load are compared with the previous method of only considering steady-state constraints. The transient safety and stability under the implementation of the obtained recovery decision is guaranteed, and the stability and reliability of the distribution network recovery are ensured.

[0142] Embodiment 3

[0143] Correspondingly, the present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the distribution network recovery method described in any one of the above embodiments is implemented.

[0144] The terminal device of this embodiment includes: a processor, a memory, and a computer program and a computer instruction stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned embodiment 1 is implemented, for example: Figure 1 Alternatively, the processor implements the functions of each module / unit in the above device embodiment, such as the load module 202, when executing the computer program.

[0145] Exemplarily, the computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device. For example, the load module 202 is used to determine the optimal restoration path from the microgrid to all key load areas according to the topological structure corresponding to the power system of the distribution network to be restored, and combine the loads on the optimal restoration path to obtain multiple groups of load groups.

[0146] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the terminal device may also include an input / output device, a network access device, a bus, etc.

[0147] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0148] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile terminal, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0149] Wherein, if the module / unit integrated in the terminal device 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 such an 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, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, 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. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0150] Embodiment 4

[0151] Correspondingly, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network recovery method described in any one of the above embodiments.

[0152] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distribution network restoration method, characterized in that: include: Set up distributed power sources for microgrids based on synchronous generators and build dynamic models; According to the topological structure corresponding to the power system of the distribution network to be restored, determine the optimal restoration path from the microgrid to all key load areas, and combine the loads on the optimal restoration path to obtain multiple load groups; Establishing a restoration optimization model based on the optimal set of load groups, and solving the restoration optimization model according to preset constraints to obtain a restoration plan; The restoration plan is input into the dynamic model for simulation verification, and after the verification passes, the distribution network is restored based on the restoration plan.

2. A distribution network restoration method according to claim 1, characterized in that: The distributed power supply of the microgrid is set based on the synchronous generator, and a dynamic model is constructed, which specifically includes: Set up the distributed power supply of the microgrid based on synchronous generators, and build an electromechanical transient model based on synchronous generators; According to the mechanical angular velocity of the synchronous generator, the mechanical torque of the synchronous generator is determined, and then a speed regulator model that maintains a constant frequency in a steady state is constructed; According to the excitation voltage of the synchronous generator, an exciter model is constructed; A dynamic model is constructed based on the electromechanical transient model, the speed regulator model and the exciter model.

3. A distribution network restoration method as claimed in claim 2, characterized in that: The electromechanical transient model is constructed based on the synchronous generator, specifically including: According to the relationship between the magnetic flux and the transient voltage of the synchronous generator between the first shaft and the second shaft, a first shaft magnetic flux transient voltage relationship equation and a second shaft magnetic flux transient voltage relationship equation are established; According to the sub-transient voltage process corresponding to the differential components of the synchronous generator on the first axis and the second axis respectively, the magnetic flux current component equation of the first axis and the magnetic flux current component equation of the second axis are established; According to the rotor speed of the synchronous generator, the voltage at the first shaft end and the voltage at the second shaft end, an electromagnetic transient equation of the stator voltage is established; Based on the first axis flux transient voltage relationship equation, the second axis flux transient voltage relationship equation, the first axis flux current component equation, the second axis flux current component equation and the electromagnetic transient equation, the unbalanced operating state of the synchronous generator is modeled to obtain an electromechanical transient model.

4. A distribution network restoration method according to claim 1, characterized in that: The method determines the optimal restoration path from the microgrid to all key load areas according to the topological structure corresponding to the power system of the distribution network to be restored, and combines the loads on the optimal restoration path to obtain multiple load groups, specifically including: Determine the key load area combination of each microgrid according to the topological structure corresponding to the power system of the distribution network to be restored; Based on the single-source shortest path algorithm, the restoration path from the distribution network to all key load areas in each microgrid key load area combination is determined, and the optimal restoration path is determined according to the path weight; The key loads on the optimal restoration path are combined, and power flow calculation and dynamic simulation are performed on the combined key loads to determine multiple load groups.

5. A distribution network restoration method as claimed in claim 4, characterized in that: Determining the optimal restoration path according to the path weight specifically includes: Determine the intermediate recovery path according to the total load on each recovery path; The feasibility of the intermediate recovery path is verified through power flow calculation and dynamic simulation, and the infeasible intermediate recovery path is deleted, so that the verified feasible intermediate recovery path is taken as the optimal recovery path.

6. A distribution network restoration method according to any one of claim 1, characterized in that: The restoration optimization model is established based on the optimal group of load groups, and the restoration optimization model is solved according to preset constraints to obtain a restoration plan, which specifically includes: Based on the optimal group of load groups, on the basis of the dynamic model, a restoration optimization model is established with the goal of maximizing the cumulative restoration of power supply; The restoration optimization model is solved according to the power flow constraint and the topology constraint in the preset constraints to obtain a restoration plan with the maximum cumulative restored power supply.

7. A distribution network restoration method according to any one of claims 1 to 6, characterized in that: The preset constraints include: microgrid constraints and restoration constraints; The microgrid constraints include: power flow constraints, topology constraints and frequency and voltage constraints; The recovery constraints include: steady-state frequency constraints, transient frequency constraints and transient voltage constraints at power generation terminals.

8. A distribution network restoration device, characterized in that: include: Modeling module, load module, solution module and recovery module; The modeling module is used to set up a distributed power source of a microgrid based on a synchronous generator and to build a dynamic model; The load module is used to determine the optimal restoration path from the microgrid to all key load areas according to the topological structure corresponding to the power system of the distribution network to be restored, and to combine the loads on the optimal restoration path to obtain multiple load groups; The solution module is used to establish a restoration optimization model based on the optimal set of load groups, and solve the restoration optimization model according to preset constraints to obtain a restoration plan; The recovery module is used to input the recovery plan into the dynamic model for simulation verification, and after the verification is passed, restore the distribution network based on the recovery plan.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the distribution network restoration method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the distribution network restoration method according to any one of claims 1 to 7.

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