Method and system for coordinated source-grid-load-storage dispatch optimization during post-disaster restoration of power distribution system

Abstract

The present invention relates to the technical field of power systems and operations research. Disclosed are a method and system for coordinated source-grid-load-storage dispatch optimization during post-disaster restoration of a power distribution system. The method comprises: acquiring network topology constraint data for a power distribution system, wherein network topology constraints for the power distribution system comprise a virtual network topology constraint for the power distribution system, a geometric network topology constraint for the power distribution system, and an electrical network topology constraint for the power distribution system; on the basis of fault situations of a power source device, a load device and an energy storage device that are caused by a disaster, generating operational characteristic constraint data for devices in the power distribution system, wherein the operational characteristic constraint data for the devices in the power distribution system comprises operational characteristic constraints for the power source device, the load device and the energy storage device; and inputting the network topology constraint data for the power distribution system and the operational characteristic constraint data for the devices in the power distribution system into a pre-established coordinated source-grid-load-storage dispatch optimization model for power distribution systems in a post-disaster restoration scenario, such that a coordinated dispatch optimization result is output.

Description

A post-disaster recovery source-network-load-storage collaborative scheduling optimization method and system for a power distribution system TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems and operations research, and in particular to a post-disaster recovery source-network-load-storage collaborative scheduling optimization method and system for a power distribution system. BACKGROUND

[0002] Since the 21st century, extreme events worldwide have caused several serious large-scale power system blackouts. In the past decade, with the continuous advancement of new power system construction, the proportion of distributed photovoltaic, wind power and other renewable energy power generation equipment, as well as virtual power plant load aggregators coordinating the management of air conditioners, electric vehicles and other flexible loads in the power distribution system has been increasing. The enrichment of source-load-storage resources in the power distribution system provides a large amount of potential resilience support capacity for disaster scenarios.

[0003] The existing post-disaster recovery scheduling methods for power distribution systems mainly have two problems: on the one hand, a modern power distribution system is an organic whole composed of power grid topology, power supply equipment, load equipment and energy storage equipment. However, most existing methods only focus on one or several types of controllable devices, making it difficult to fully mobilize all power resources in the power distribution system to promote the power recovery of important loads. On the other hand, traditional power distribution system topology control optimization techniques are mostly designed for normal operation scenarios. When applied to post-disaster recovery scenarios with more device failures, they may produce loops that violate safety operation requirements. New topology control optimization techniques proposed for post-disaster recovery scenarios mostly assume that there are no passive islands in the power distribution system. This simplification will lead to the fact that the model used by existing techniques will not be able to give a feasible scheduling scheme once a passive island is generated due to line breakage and device failure in the real scenario.

[0004] SUMMARY

[0005] To solve the problems mentioned in the background, the purpose of the present application is to provide a post-disaster recovery source-network-load-storage collaborative scheduling optimization method and system for a power distribution system, which coordinates and controls the power grid topology, power supply equipment, load equipment and energy storage equipment in the power distribution system, reduces the economic losses caused by disasters, and restores the power supply capacity of the power distribution system to the pre-disaster level until all faults in the power distribution system are repaired.

[0006] In the first aspect, the purpose of the present application can be achieved by the following technical solution: a post-disaster recovery source-network-load-storage collaborative scheduling optimization method for a power distribution system, the method comprising the following steps:

[0007] obtaining network topology constraint data of the power distribution system, wherein the network topology constraint of the power distribution system comprises a virtual network topology constraint of the power distribution system, a geometric network topology constraint of the power distribution system, and an electrical network topology constraint of the power distribution system;

[0008] generating power distribution system equipment operation characteristic constraint data according to the fault conditions of the power supply equipment, the load equipment and the energy storage equipment caused by the disaster, wherein the power distribution system equipment operation characteristic constraint data comprises operation characteristic constraints of the power supply equipment, the load equipment and the energy storage equipment;

[0009] inputting the network topology constraint data of the power distribution system and the power distribution system equipment operation characteristic constraint data into a pre-established power distribution system source-network-load-storage collaborative scheduling optimization model for a post-disaster recovery scenario, and outputting a collaborative scheduling optimization result.

[0010] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the obtaining process of the network topology constraint of the power distribution system is as follows: receiving connectivity constraints and radiation constraints required to be met by the network topology of the power distribution system in normal operation, and generating the virtual network topology constraint of the power distribution system according to the connectivity constraints and the radiation constraints.

[0011] relaxing the connectivity constraint of the virtual topology of the power distribution system to generate the geometric network topology constraint of the power distribution system, receiving electrical constraints required to be met by the grid voltage, current and power in the power distribution system to generate the electrical network topology constraint of the power distribution system, and thus integrating the virtual network topology constraint of the power distribution system, the geometric network topology constraint of the power distribution system and the electrical network topology constraint of the power distribution system to generate the network topology constraint of the power distribution system.

[0012] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the connectivity constraint is constructed based on a virtual commodity flow model, wherein the formula of the connectivity constraint is as follows:

[0013] wherein i and j are node numbers in the power distribution system; I S is a set of main grid substation nodes in the power distribution system; I N is a set of nodes in the power distribution system except the main grid substation nodes; t is a number of a decision time; T is a total number of decision time steps; L is a set of lines in the power distribution system, each line is counted in L in the form of a two-dimensional vector composed of two end node numbers, and the smaller number in the two end node numbers is the first dimension of the two-dimensional vector and the larger number is the second dimension of the two-dimensional vector; f i,j,t is a virtual commodity flow from node i to node j on line (i, j) in the virtual network topology at t; f j,i,tis the virtual commodity flow from node j to node i on the line (j, i) in the virtual network topology at time t; d i,j,t is the on-off state variable of the virtual line (i, j) at time t; M is a linear relaxation coefficient, and the value is set as the number of nodes in the power distribution system;

[0014] The formula of the radiation constraint is as follows:

[0015] In the formula, |I N is the number of nodes in the power distribution system except the main grid substation node.

[0016] With reference to the first aspect, in some implementations of the first aspect, the method further comprises: the connectivity constraint of the virtual topology of the power distribution system is relaxed to generate a geometric network topology constraint of the power distribution system, the line fault condition of the post-disaster power distribution system is considered in generating the geometric network topology constraint, and a subgraph principle is used for generation, wherein the constraint considering the line fault condition of the post-disaster power distribution system is as follows:

[0017] In the formula, c i,j,t is the on-off state variable of the physical line (i, j) at time t; is the period in which the physical line (i, j) is in an open state due to a fault; is the period in which the physical line (i, j) is not faulty or has been repaired and can work normally; L U is a line set without a switch device;

[0018] The geometric network topology constraint of the power distribution system is as follows:

[0019] With reference to the first aspect, in some implementations of the first aspect, the method further comprises: the electrical network topology constraint of the power distribution system is constructed based on a DistFlow model and combined with a second-order cone relaxation technique and a large M relaxation technique, electrical constraints required to be met by grid voltage, current and power in the power distribution system are constructed, and thus an electrical network topology constraint of the power distribution system is formed, wherein the electrical constraints required to be met by grid voltage, current and power in the power distribution system are as follows:

[0020] In the formula, I is a node number set of the power distribution system; p i,t is the net active power demand of the device connected to node i in the electrical network topology at time t; p i,j,tPij(t) is the active power flowing from bus i to bus j on line (i, j) at time t; p j,i,t Pji(t) is the active power flowing from bus j to bus i on line (j, i) at time t; Rijis the resistance of line (i, j); r i,j Rijis the resistance of line (i, j); r i,t Qi(t) is the net reactive power demand of the devices connected to bus i at time t; q i,j,t Qi(t) is the net reactive power demand of the devices connected to bus i at time t; q j,i,t Qi(t) is the net reactive power demand of the devices connected to bus i at time t; q i,j Xiis the reactance of line (i, j); x Vi(t) is the square of the voltage at bus i at time t; Vi(t) is the square of the voltage at bus i at time t;

[0021] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the power system equipment operation characteristic constraints are constructed for power supply equipment represented by main grid substations, distributed fossil fuel units, and distributed renewable energy units in the power distribution system in combination with equipment failure conditions caused by disasters, wherein the main grid substation operation characteristic constraints are as follows:

[0022] wherein, Pmi(t) is the active power output by the main grid substation at bus i at time t; Fmi(t) is a binary variable representing the fault state of the main grid substation at bus i at time t; Pmi,maxis the upper limit of the active power output by the main grid substation at bus i; Qmi(t) is the reactive power output by the main grid substation at bus i at time t; Qmi,maxis the upper limit of the reactive power output by the main grid substation at bus i;

[0023] The constructed distributed fossil fuel unit operation characteristic constraints are as follows:

[0024] wherein, I DG I is the set of node numbers of the power distribution system equipped with distributed fossil fuel units; Pi(t) is the active power output of the distributed fossil fuel unit at node i at time t; Pi(t) is the active power output of the distributed fossil fuel unit at node i at time t; Pi(t) is the active power output of the distributed fossil fuel unit at node i at time t; Pi(t) is the active power output of the distributed fossil fuel unit at node i at time t; Pi(t) is the active power output of the distributed fossil fuel unit at node i at time t;

[0025] The constructed operating characteristic constraints of the distributed renewable energy unit are as follows:

[0026] wherein, I RES I is the set of node numbers of the power distribution system equipped with the distributed renewable energy unit; Pi(t) is the active power output of the distributed renewable energy unit at node i at time t; Pi(t) is the active power output of the distributed renewable energy unit at node i at time t; Pi(t) is the active power output of the distributed renewable energy unit at node i at time t.

[0027] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: constructing operating characteristic constraints of load devices in the power distribution system through three different types of load devices, i.e., removable load, transferable load and non-adjustable load, in the power distribution system, wherein the constructed operating characteristic constraints of the removable load are as follows:

[0028] wherein, Pi(t) is the active power output of the distributed renewable energy unit at node i at time t; Pi(t) is the active power output of the distributed renewable energy unit at node i at time t; Pi(t) is the active power output of the distributed renewable energy unit at node i at time t; Pi(t) is the active power output of the distributed renewable energy unit at node i at time t;

[0029] The constructed operating characteristic constraints of the transferable load are as follows:

[0030] wherein, Pi(t) is the active power output of the distributed renewable energy unit at node i at time t; Pi(t) is the active power output of the distributed renewable energy unit at node i at time t; is the active power absorbed by the shiftable load at node i at time t; is the original demand of the active power of the shiftable load by the user at node i at time t; is the reactive power absorbed by the shiftable load at node i at time t; is the original demand of the reactive power of the shiftable load by the user at node i at time t;

[0031] The active power and the reactive power absorbed by the non-adjustable load at node i at time t are given as constants, respectively, as follows: and .

[0032] In combination with the first aspect, in some implementations of the first aspect, the method further comprises that the operating characteristic constraint of the energy storage device in the power distribution system is as follows:

[0033] wherein I ES is a node number set of the power distribution system equipped with the energy storage device; is the active power output by the energy storage device at node i at time t; is a binary variable representing the fault state of the energy storage device at node i at time t; is a binary variable representing the charging and discharging state of the energy storage device at node i at time t; is the upper limit of the active power output by the energy storage device at node i; is the active power absorbed by the energy storage device at node i at time t; i ch is the upper limit of the active power absorbed by the energy storage device at node i; is the residual power of the energy storage device at node i at time t after decision, and the initial residual power of the energy storage device at node i at time 0 is given as a constant; and are the discharging efficiency and the charging efficiency of the energy storage device at node i, respectively; and Δt is a decision time step; and are the upper limit and the lower limit of the power storage of the energy storage device at node i, respectively.

[0034] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the pre-established power distribution system source-grid-load-storage collaborative scheduling optimization model is constructed by minimizing an economic dispatch cost optimization objective, and coupled operation constraints of the power distribution system source-grid-load-storage are constructed, wherein the minimizing economic dispatch cost optimization objective is as follows:

[0035] wherein, and are the adjustment prices of the main grid substation, the distributed fossil fuel unit, the removable load and the transferable load, respectively;

[0036] The coupled operation constraints of the power distribution system source-grid-load-storage are as follows:

[0037] In the second aspect, to achieve the above object, the application discloses a power distribution system post-disaster recovery source-grid-load-storage collaborative scheduling optimization system, comprising:

[0038] A data acquisition module is configured to acquire network topology constraint data of the power distribution system, wherein the network topology constraint of the power distribution system comprises virtual network topology constraint of the power distribution system, geometric network topology constraint of the power distribution system and electrical network topology constraint of the power distribution system;

[0039] A data generation module is configured to generate power distribution system equipment operation characteristic constraint data according to fault conditions of power supply equipment, load equipment and energy storage equipment caused by disasters, wherein the power distribution system equipment operation characteristic constraint data comprises operation characteristic constraints of the power supply equipment, the load equipment and the energy storage equipment;

[0040] A collaborative scheduling optimization module is configured to input the network topology constraint data of the power distribution system and the power distribution system equipment operation characteristic constraint data into a pre-established power distribution system source-grid-load-storage collaborative scheduling optimization model for post-disaster recovery scenarios, and output a collaborative scheduling optimization result.

[0041] The application has the following beneficial effects:

[0042] In one aspect of the application, the power grid topology, the power supply equipment, the load equipment and the energy storage equipment in the power distribution system are comprehensively considered in terms of adjustable potential and constraint characteristics, so that all power resources in the power distribution system are fully mobilized to promote power recovery of important loads. On the other hand, the power distribution system topology control optimization model used can adapt to various complex post-disaster complex scenarios and give an optimal scheduling scheme under the corresponding scenario, and has stronger robustness. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

[0044] Fig. 1 is a flowchart of the method of the present application;

[0045] Fig. 2 is a schematic diagram of the workflow of the present application;

[0046] Fig. 3 is a schematic diagram of the normal operation state of the test system used in the specific embodiment of the present application;

[0047] Fig. 4 is a schematic diagram of the operation state of a power distribution system in a test scenario of the specific embodiment of the present application;

[0048] Fig. 5 is a schematic diagram of the operation state of a power distribution system in a test scenario of the specific embodiment of the present application;

[0049] Fig. 6 is a schematic diagram of the operation state of a power distribution system in a test scenario of the specific embodiment of the present application;

[0050] Fig. 7 is a schematic diagram of the operation state of a power distribution system in a test scenario of the specific embodiment of the present application;

[0051] Fig. 8 is a schematic diagram of the system structure of the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort fall within the scope of the present application.

[0053] Embodiment one:

[0054] Next, the related terms involved in the embodiments of the present application will be introduced:

[0055] Power distribution system: the section of the power system from the outlet of the step-down distribution substation (high-voltage distribution substation) to the user end is called the power distribution system. The power distribution system is a power network system composed of various power distribution equipment (or elements) and power distribution facilities, which transforms voltage and directly distributes electric energy to the end user.

[0056] Source-grid-load-storage is mainly composed of source, grid, load (storage):

[0057] One, the source of "source-grid-load-storage" corresponds to the power generation side energy storage.

[0058] The power generation side energy storage mainly refers to the common photovoltaic, wind power, and hydropower with storage, which is mainly operated in the mode of cooperating with thermal power plants and power grid peak regulation and frequency regulation and obtaining benefits. It can greatly reduce the local light and wind abandonment rate.

[0059] Second, the network of "source network load storage" corresponds to the grid side energy storage.

[0060] The grid side energy storage can be directly used for computer, mobile phone, refrigerator and other electrical equipment. The grid side energy storage represents pumped storage. The power grid can balance the electricity price by itself. Compared with the power generation side, the grid side does not need to fight for additional compensation mechanism for energy storage, such as price discount, subsidy, etc.

[0061] Third, the load (storage) of "source network load storage" corresponds to the user side energy storage.

[0062] Usually, household storage, industrial and commercial storage, and energy storage charging piles are all classified as user side, and the main object is the power consumer.

[0063] As shown in FIG. 1, a power distribution system post-disaster recovery source network load storage collaborative scheduling optimization method, characterized in that the method comprises the following steps:

[0064] Obtain the network topology constraint data of the power distribution system, wherein the network topology constraint of the power distribution system includes the virtual network topology constraint of the power distribution system, the geometric network topology constraint of the power distribution system, and the electrical network topology constraint of the power distribution system;

[0065] Wherein, the process of obtaining the network topology constraint of the power distribution system is as follows: receiving the connectivity constraint and the radiance constraint required to be met by the network topology of the power distribution system in normal operation, generating the virtual network topology constraint of the power distribution system according to the connectivity constraint and the radiance constraint;

[0066] Relax the connectivity constraint of the virtual topology of the power distribution system to generate the geometric network topology constraint of the power distribution system, generate the electrical network topology constraint of the power distribution system by receiving the electrical constraint required to be met by the grid voltage, current and power in the power distribution system, and integrate the virtual network topology constraint of the power distribution system, the geometric network topology constraint of the power distribution system and the electrical network topology constraint of the power distribution system to generate the network topology constraint of the power distribution system.

[0067] Further, the connectivity constraint is constructed based on a virtual commodity flow model, wherein the formula of the connectivity constraint is as follows:

[0068] In the formula, i and j are node numbers in the power distribution system; I S is the set of main grid substation nodes in the power distribution system; I Nis the set of nodes in the distribution system except for the main grid substation nodes; t is the number of decision time; T is the total number of decision time steps; L is the set of lines in the distribution system, each line is counted in L in the form of a two-dimensional vector composed of two end node numbers, and the smaller number in the two end node numbers is the first dimension of the two-dimensional vector and the larger number is the second dimension of the two-dimensional vector; f i,j,t is the virtual commodity flow from node i to node j on line (i, j) in the virtual network topology at time t; f j,i,t is the virtual commodity flow from node j to node i on line (j, i) in the virtual network topology at time t; d i,j,t is the on-off state variable of virtual line (i, j) at time t, which is 1 when virtual line (i, j) is connected and 0 when virtual line (i, j) is disconnected; M is the linear relaxation coefficient, which can be generally set as the number of nodes in the distribution system;

[0069] The formula of the radiation constraint is as follows:

[0070] In the formula, |I N | is the number of nodes in the distribution system except for the main grid substation nodes.

[0071] Among them, the connectivity constraint of the virtual topology of the distribution system is relaxed to generate the geometric network topology constraint of the distribution system, which needs to consider the line fault condition of the post-disaster distribution system and is based on the generation subgraph principle, wherein the constraint constructed by considering the line fault condition of the post-disaster distribution system is as follows:

[0072] In the formula, c i,j,t is the on-off state variable of physical line (i, j) at time t; is the period when physical line (i, j) is in a disconnected state due to failure; is the period when physical line (i, j) is not faulty or has been repaired and can work normally; L U is the set of lines without switch devices;

[0073] The geometric network topology constraint of the distribution system is as follows:

[0074] The electrical network topology constraint of the distribution system is based on the DistFlow model and combined with the second-order cone relaxation technology and the large M relaxation technology, and constructs the electrical constraint required to be met by the grid voltage, current and power in the distribution system, thereby forming the electrical network topology constraint of the distribution system, wherein the electrical constraint required to be met by the grid voltage, current and power in the distribution system is as follows:

[0075] where I is the set of node indices of the power distribution system; p i,t is the net active power demand of the device connected to node i in the electrical network topology at time t; p i,j,t is the active power sent from node i to node j on line (i, j) in the electrical network topology at time t; p j,i,t is the active power sent from node j to node i on line (j, i) in the electrical network topology at time t; p is the square of the current on line (j, i) in the electrical network topology at time t; r i,j is the resistance of line (i, j); q i,t is the net reactive power demand of the device connected to node i in the electrical network topology at time t; q i,j,t is the reactive power sent from node i to node j on line (i, j) in the electrical network topology at time t; q j,i,t is the reactive power sent from node j to node i on line (j, i) in the electrical network topology at time t; x i,j is the reactance of line (i, j); is the square of the voltage at node i in the electrical network topology at time t; is the square of the voltage at node j in the electrical network topology at time t; ||·||2is the two-norm operator.

[0076] According to the fault conditions of power supply equipment, load equipment and energy storage equipment caused by disasters, power distribution system equipment operation characteristic constraint data is generated, wherein the power distribution system equipment operation characteristic constraint data includes the operation characteristic constraints of power supply equipment, load equipment and energy storage equipment;

[0077] The power distribution system equipment operation characteristic constraint is constructed for power supply equipment represented by main grid substations, distributed fossil fuel units and distributed renewable energy units in the power distribution system in combination with the equipment fault conditions caused by disasters, wherein the main grid substation operation characteristic constraint is as follows:

[0078] where, is the active power output by the main grid substation at node i at time t; is a binary variable representing the fault state of the main grid substation at node i at time t, taking 1 when the main grid substation can normally output power and 0 otherwise; is the upper limit of the active power that can be output by the main grid substation at node i; is the reactive power output by the main grid substation at node i at time t; Qgi, t is the upper limit of the reactive power that the main grid substation at node i can output at time t;

[0079] The constructed operation characteristic constraints of the distributed fossil fuel units are as follows:

[0080] wherein, I DG is a set of node numbers of the distribution system equipped with the distributed fossil fuel units; Qgi, t is the active power output by the distributed fossil fuel unit at node i at time t; is a binary variable representing the fault state of the distributed fossil fuel unit at node i at time t, taking 1 when the distributed fossil fuel unit can normally output power, otherwise taking 0; Qgi, t is the upper limit of the active power that the distributed fossil fuel unit at node i can output at time t; Qgi, t is the reactive power output by the distributed fossil fuel unit at node i at time t; Qgi, t is the upper limit of the reactive power that the distributed fossil fuel unit at node i can output at time t;

[0081] The constructed operation characteristic constraints of the distributed renewable energy units are as follows:

[0082] wherein, I RES is a set of node numbers of the distribution system equipped with the distributed renewable energy units; Qgi, t is the active power output by the distributed renewable energy unit at node i at time t; is a binary variable representing the fault state of the distributed renewable energy unit at node i at time t, taking 1 when the distributed renewable energy unit can normally output power, otherwise taking 0; Qgi, t is the upper limit of the active power that the distributed renewable energy unit at node i can output at time t.

[0083] The operation characteristic constraints of the load devices in the distribution system are constructed through three different types of load devices, i.e., the removable load, the transferable load and the non-adjustable load, wherein the constructed operation characteristic constraints of the removable load are as follows:

[0084] wherein, Qgi, t is the active power absorbed by the removable load at node i at time t; Qgi, t is the upper limit of the active power absorbed by the removable load at node i at time t; Qgi, t is the reactive power absorbed by the removable load at node i at time t; The upper limit of the reactive power absorbed by the adjustable load at node i at time t;

[0085] The constructed transferable load operating characteristic constraints are as follows:

[0086] Wherein, The active power adjustment amount of the transferable load at node i at time t; The upper limit of the active power of the transferable load at node i at time t; The active power absorbed by the transferable load at node i at time t; The original demand of the user for the active power of the transferable load at node i at time t; The reactive power absorbed by the transferable load at node i at time t; The original demand of the user for the reactive power of the transferable load at node i at time t;

[0087] The active power and the reactive power absorbed by the non-adjustable load at node i at time t are given as constants, respectively using And Indicate.

[0088] The operating characteristic constraints of the energy storage device in the power distribution system are as follows:

[0089] Wherein, I ES The node number set of the power distribution system equipped with the energy storage device; The active power output by the energy storage device at node i at time t; A binary variable representing the fault state of the energy storage device at node i at time t, taking 1 when the energy storage device can normally charge and discharge, and 0 otherwise; A binary variable representing the charge and discharge state of the energy storage device at node i at time t, taking 1 when the energy storage device is in the charging state, and 0 when the energy storage device is in the discharging state; The upper limit of the active power output by the energy storage device at node i; The active power absorbed by the energy storage device at node i at time t; The upper limit of the active power absorbed by the energy storage device at node i; The remaining capacity of the energy storage device at node i after decision at time t, and the initial remaining capacity of the energy storage device at node i at time 0 Is given as a constant. and respectively are the discharge efficiency and the charge efficiency of the energy storage device on node i; Δt is the decision time step; and respectively are the upper limit and the lower limit of the power storage of the energy storage device on node i.

[0090] The network topology constraint data and the power distribution system equipment operation characteristic constraint data of the power distribution system are input into a pre-established power distribution system source network load storage collaborative scheduling optimization model for post-disaster recovery scenarios, and a collaborative scheduling optimization result is output.

[0091] The pre-established power distribution system source network load storage collaborative scheduling optimization model for post-disaster recovery scenarios is constructed by minimizing the economic scheduling cost optimization target, and the coupled operation constraints of the power distribution system source network load storage are constructed, wherein the minimum economic scheduling cost optimization target is as follows:

[0092] wherein, and respectively are the adjustment prices of the main network substation, the distributed fossil fuel unit, the removable load and the transferable load, and the adjustment cost of the distributed renewable energy unit and the energy storage device is ignored.

[0093] The coupled operation constraints of the power distribution system source network load storage are as follows:

[0094] Specifically, the following embodiments are further described:

[0095] This embodiment adopts a double-substation coupled power distribution system for testing, and its normal operation state is shown in FIG. 3. Among them, the important load nodes are all equipped with distributed power or distributed energy storage devices, and the non-adjustable load on the node is not 0; the non-adjustable load on the secondary load node is 0, so that a passive island is allowed to be formed in an extreme scenario.

[0096] Test scenario one is shown in FIG. 4. The main network substation of node 1 cannot normally supply power due to failure, and after optimization by the method proposed in the application, the load originally supplied by the main network substation of node 1 is transferred to the main network substation of node 18 through the communication line (10, 19), thereby ensuring sufficient power supply for half of the load nodes in the power distribution system.

[0097] Test scenario two is shown in FIG. 5. On the basis of test scenario one, a failure of line (14, 15) also occurs, and after optimization by the method proposed in the application, in addition to the line (10, 19) in test scenario one, the line (7, 17) is also connected, thereby ensuring sufficient power supply for the loads on nodes 15, 16 and 17.

[0098] Test scenario three, as shown in FIG. 6, in addition to the test scenario one, the line (15, 16), (6, 7), (8, 9) fault also occurs, after the method is optimized, in addition to the line (10, 19) in the communication test scenario one, the line (7, 17) and the line (9, 26) are also connected, thereby forming an island composed of nodes 6, 7, 16, 17. The load on the island is powered by the distributed power supply on node 16. Since the distributed power supply is small in size, the load of the island is cut off or translated by a small amount.

[0099] Test scenario four, as shown in FIG. 7, in addition to the test scenario one, the line (7, 8), (8, 9) fault also occurs, in addition to the line (10, 19) in the communication test scenario one, the line (9, 26) is also connected, at this time node 8 alone forms a passive island, and its load is cut off or translated. It is worth mentioning that the prior art usually cannot normally process such a scene that must form a passive island, and will be mistaken as there is no feasible scheduling scheme under this scene.

[0100] Embodiment two: the second aspect, as shown in FIG. 8, in order to achieve the above purpose, the application discloses a power distribution system post-disaster recovery source network load storage collaborative scheduling optimization system, comprising:

[0101] A data acquisition module is configured to acquire network topology constraint data of a power distribution system, wherein the network topology constraint of the power distribution system includes virtual network topology constraint of the power distribution system, geometric network topology constraint of the power distribution system, and electrical network topology constraint of the power distribution system.

[0102] A data generation module is configured to generate power distribution system equipment operation characteristic constraint data according to fault conditions of power supply equipment, load equipment and energy storage equipment caused by disasters, wherein the power distribution system equipment operation characteristic constraint data includes operation characteristic constraints of power supply equipment, load equipment and energy storage equipment.

[0103] A collaborative scheduling optimization module is configured to input the network topology constraint data of the power distribution system and the power distribution system equipment operation characteristic constraint data into a pre-established power distribution system source network load storage collaborative scheduling optimization model for post-disaster recovery scenarios, and output a collaborative scheduling optimization result.

[0104] Based on the same inventive concept, the present application further provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the program comprises program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are configured to implement one or more instructions, and are specifically configured to load and execute one or more instructions in the computer storage medium to implement the above method.

[0105] It needs to be further explained that, based on the same inventive concept, the present application further provides a computer storage medium, which stores a computer program, and the computer program is executed by the processor to perform the above method. The storage medium can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, be but is not limited to an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0106] In the description of the present application, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in one or more embodiments or examples.

[0107] The foregoing presents and describes the basic principles, main features and advantages of the present disclosure. It should be understood by those skilled in the art that the present disclosure is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, various changes and improvements can be made to the present disclosure, and all these changes and improvements fall within the scope of the present disclosure.

Claims

1. A method for optimizing source-grid-load-storage coordinated scheduling for post-disaster recovery of a distribution system, characterized in that: The method comprises the following steps: Acquire network topology constraint data of the power distribution system, wherein the network topology constraint of the power distribution system includes a virtual network topology constraint of the power distribution system, a geometric network topology constraint of the power distribution system, and an electrical network topology constraint of the power distribution system; Generate distribution system equipment operating characteristic constraint data based on the fault conditions of power supply equipment, load equipment, and energy storage equipment caused by the disaster, where the distribution system equipment operating characteristic constraint data includes operating characteristic constraints of power supply equipment, load equipment, and energy storage equipment; The network topology constraint data of the distribution system and the distribution system equipment operating characteristic constraint data are input into a pre-established distribution system source-grid-load-storage collaborative scheduling optimization model for post-disaster recovery scenarios, and the collaborative scheduling optimization results are output.

2. The method for optimizing source-grid-load-storage coordinated scheduling for post-disaster recovery of a power distribution system according to claim 1, characterized in that: The process of acquiring the network topology constraints of the power distribution system is as follows: receiving the connectivity constraints and the radiation constraints that the network topology needs to satisfy when the power distribution system operates normally, and generating the virtual network topology constraints of the power distribution system according to the connectivity constraints and the radiation constraints; The connectivity constraints of the virtual topology of the distribution system are relaxed to generate the geometric network topology constraints of the distribution system. By receiving the electrical constraints that the grid voltage, current and power in the distribution system need to satisfy, the electrical network topology constraints of the distribution system are generated. Thus, the virtual network topology constraints of the distribution system, the geometric network topology constraints of the distribution system and the electrical network topology constraints of the distribution system are integrated to generate the network topology constraints of the distribution system.

3. The method for optimizing source-grid-load-storage coordinated scheduling for post-disaster recovery of a power distribution system according to claim 2, characterized in that: The connectivity constraint is constructed based on the virtual commodity flow model, wherein the formula of the connectivity constraint is as follows: Where i and j are node numbers in the distribution system; I S is the set of main grid substation nodes in the power distribution system; I N is the set of nodes in the distribution system except the main grid substation nodes; t is the number of the decision moment; T is the total number of time steps for the decision; L is the set of lines in the distribution system, and each line is counted in L as a two-dimensional vector consisting of the node numbers at both ends, with the smaller number of the two-dimensional vector as the first dimension and the larger number as the second dimension; f i,j,t is the virtual commodity flow from node i to node j on line (i, j) in the virtual network topology at time t; f j,i,t is the virtual commodity flow from node j to node i on line (j, i) in the virtual network topology at time t; d i,j,t is the on-off state variable of the virtual circuit (i, j) at time t; M is the linear relaxation coefficient; The formula for the radiation constraint is as follows: In the formula, |I N | is the number of nodes in the distribution system excluding the main grid substation nodes.

4. A method for optimizing source-grid-load-storage coordinated scheduling for post-disaster recovery of a power distribution system according to claim 3, characterized in that: The connectivity constraints of the virtual topology of the power distribution system are relaxed. When generating the geometric network topology constraints of the power distribution system, the line failure conditions of the power distribution system after the disaster need to be considered. This is done based on the principle of generating subgraphs. The constraints constructed considering the line failure conditions of the power distribution system after the disaster are as follows: Where c i,j,t is the on-off state variable of the physical line (i, j) at time t; is the period during which the physical line (i, j) is disconnected due to a fault; L is the period during which the physical line (i, j) is not faulty or has been repaired and can work normally; U A collection of circuits that are not equipped with switchgear; The geometric network topology constraints of the power distribution system are as follows:

5. The method for optimizing source-grid-load-storage coordinated scheduling for post-disaster recovery of a power distribution system according to claim 2, characterized in that: The electrical network topology constraints of the distribution system are constructed based on the DistFlow model and combined with the second-order cone relaxation technique and the large-M relaxation technique to construct the electrical constraints that the grid voltage, current, and power in the distribution system need to satisfy, thereby forming the electrical network topology constraints of the distribution system. The electrical constraints that the grid voltage, current, and power in the distribution system need to satisfy are as follows: Where, I is the node number set of the distribution system; p i,t is the net active power demand of the equipment connected to node i in the electrical network topology at time t; p i,j,t is the active power delivered from node i to node j by line (i, j) in the electrical network topology at time t; p j,i,t is the active power delivered from node j to node i by line (j, i) in the electrical network topology at time t; is the square of the current on line (j,i) in the electrical network topology at time t; r i,j is the resistance of line (i, j); q i,t is the net reactive power demand of the equipment connected to node i in the electrical network topology at time t; i,j,t is the reactive power delivered from node i to node j by line (i, j) in the electrical network topology at time t; j,i,t is the reactive power delivered from line (j, i) at node j to node i in the electrical network topology at time t; i,j is the reactance of line (i, j); is the square of the voltage on node i in the electrical network topology at time t; is the square of the voltage at node j in the electrical network topology at time t; ||·||2 is the two-norm operator.

6. The method for optimizing source-grid-load-storage coordinated scheduling for post-disaster recovery of a power distribution system according to claim 1, characterized in that: The distribution system equipment operating characteristic constraints are constructed for power supply equipment in the distribution system, represented by main grid substations, distributed fossil fuel units, and distributed renewable energy units, taking into account equipment failures caused by disasters. The main grid substation operating characteristic constraints are as follows: in, is the active power output by the main grid substation at node i at time t; is a binary variable representing the fault status of the main grid substation at node i at time t; is the upper limit of active power that can be output by the main grid substation at node i; is the reactive power output by the main grid substation at node i at time t; is the upper limit of reactive power that can be output by the main grid substation at node i; The operating characteristic constraints of the constructed distributed fossil fuel units are as follows: Among them, I DG A set of node numbers for the distribution system equipped with distributed fossil fuel units; is the active power output of the distributed fossil fuel unit at node i at time t; is a binary variable representing the fault status of the distributed fossil fuel unit at node i at time t; The distributed fossil fuel unit on node i can The upper limit of the output active power; is the reactive power output by the distributed fossil fuel unit at node i at time t; is the upper limit of reactive power that can be output by the distributed fossil fuel unit at node i; The operating characteristic constraints of the constructed distributed renewable energy units are as follows: Among them, I RES A set of node numbers for a distribution system equipped with distributed renewable energy units; is the active power output by the distributed renewable energy unit at node i at time t; is a binary variable representing the fault status of the distributed renewable energy unit at node i at time t; is the upper limit of the active power that the distributed renewable energy unit at node i can output at time t.

7. A method for optimizing source-grid-load-storage coordinated scheduling for post-disaster recovery of a power distribution system according to claim 6, characterized in that: In the power distribution system, three different types of load devices, namely, removable loads, transferable loads, and non-adjustable loads, are used to construct operating characteristic constraints for the load devices in the power distribution system. The constructed removable load operating characteristic constraints are as follows: in, is the active power absorbed by the removable load on node i at time t; is the upper limit of active power absorbed by the load that can be removed at node i at time t; is the reactive power absorbed by the removable load at node i at time t; is the upper limit of reactive power absorbed by the load that can be removed at node i at time t; The constructed transferable load operation characteristic constraints are as follows: in, is the active power regulation of the transferable load on node i at time t; is the upper limit of the active power of the transferable load on node i at time t; is the active power absorbed by the transferable load at node i at time t; is the original demand for transferable load active power by the user at node i at time t; is the reactive power absorbed by the transferable load at node i at time t; is the original demand for transferable load reactive power by the user at node i at time t; The active power and reactive power absorbed by the non-adjustable load at node i at time t are given as constants, respectively. and express.

8. The method for optimizing source-grid-load-storage coordinated scheduling for post-disaster recovery of a power distribution system according to claim 7, characterized in that: The operating characteristics of the energy storage equipment in the power distribution system are constrained as follows: Among them, I ES A set of node numbers for a power distribution system equipped with energy storage equipment; is the active power output by the energy storage device at node i at time t; is a binary variable representing the fault status of the energy storage device at node i at time t; is a binary variable representing the charging and discharging status of the energy storage device at node i at time t; is the upper limit of the active power output of the energy storage device at node i; is the active power absorbed by the energy storage device at node i at time t; is the upper limit of active power absorbed by the energy storage device at node i; is the remaining capacity of the energy storage device at node i after the decision at time t, and the initial remaining capacity of the energy storage device at node i at time 0 Given as a constant; and are the discharge efficiency and charging efficiency of the energy storage device at node i, respectively; Δt is the decision time step; and are the upper and lower limits of the energy storage capacity of the energy storage device at node i, respectively.

9. The method for optimizing source-grid-load-storage coordinated scheduling for post-disaster recovery of a power distribution system according to claim 1, characterized in that: The pre-established distribution system source-grid-load-storage coordinated dispatch optimization model for post-disaster recovery scenarios is constructed by minimizing the economic dispatch cost optimization objective and constructing the coupled operation constraints of the distribution system source-grid-load-storage, wherein the minimization of the economic dispatch cost optimization objective is as follows: in, and They are the regulation prices for main grid substations, distributed fossil fuel units, removable loads and transferable loads; The coupled operation constraints of the distribution system's source, grid, load, and storage are as follows:

10. A distribution system post-disaster recovery source grid load storage coordinated dispatch optimization system, characterized by: include: A data acquisition module is used to acquire network topology constraint data of the power distribution system, wherein the network topology constraint of the power distribution system includes a virtual network topology constraint of the power distribution system, a geometric network topology constraint of the power distribution system, and an electrical network topology constraint of the power distribution system; A data generation module is used to generate distribution system equipment operating characteristic constraint data based on the fault conditions of power supply equipment, load equipment, and energy storage equipment caused by the disaster, wherein the distribution system equipment operating characteristic constraint data includes operating characteristic constraints of power supply equipment, load equipment, and energy storage equipment; The collaborative scheduling optimization module is used to input the network topology constraint data of the distribution system and the distribution system equipment operating characteristic constraint data into a pre-established distribution system source-grid-load-storage collaborative scheduling optimization model for post-disaster recovery scenarios, and output the collaborative scheduling optimization results.

Images