Coordinated operation method for post-disaster recovery of multi-energy coupled distribution network based on TESS configuration

Abstract

The present invention relates to a coordinated operation method for post-disaster recovery of a multi-energy coupled distribution network based on TESS configuration, which belongs to the field of coordinated operation control technology for multi-energy coupled distribution networks, and solves the problem of the lack of an effective coordinated operation method for post-disaster recovery of a multi-energy coupled distribution network in the prior art. The method includes: constructing an objective function of a post-disaster recovery model of a multi-energy coupled distribution network that comprehensively considers user disconnection, TESS transportation, and microgrid power generation, wherein the objective function is used to minimize the sum of user disconnection costs, TESS transportation costs, and microgrid power generation costs; constructing constraints for the post-disaster recovery model of the multi-energy coupled distribution network, wherein the constraints include: TESS spatiotemporal constraints, TESS operation constraints, distribution network reconstruction topology constraints, microgrid operation constraints, and Kirchhoff's law constraints; solving the post-disaster recovery model of the multi-energy coupled distribution network, and obtaining the TESS optimization scheduling results during the post-disaster recovery process of the multi-energy coupled distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated operation control of multi-energy coupling distribution networks, and in particular to a coordinated operation method for post-disaster recovery of multi-energy coupling distribution networks based on TESS configuration. Background Art

[0002] With the frequent occurrence of natural disasters in recent years, how to deal with power emergencies in multi-energy coupled distribution networks is an urgent problem for power companies to solve. At present, cities contain a large number of important power users. Failures in distribution networks will bring immeasurable losses to various industries. The construction of a highly reliable multi-energy coupled distribution network requires a complete and efficient emergency system and mechanism to deal with power emergencies. The reasonable and effective configuration of power emergency materials will determine the guarantee capacity of power emergency materials and the construction of the emergency mechanism of the entire power system. Therefore, from the perspective of the post-disaster recovery stage, it is necessary and of practical application value to study the reliability improvement strategy of multi-energy coupled distribution networks according to the nature of different users.

[0003] Distribution network reconstruction and autonomous power supply unit formation are two important strategies for post-disaster recovery of multi-energy coupled distribution networks, among which remote feeder switches and mobile power equipment play an important role. Remote feeder switches can rely on intelligent control technology. When a large-scale fault occurs in the multi-energy coupled distribution network, the distribution network topology structure can be adjusted to improve the supply environment of important loads. Mobile power equipment can adjust the access point of distributed power sources by rerouting, providing more solutions for distribution network connection and power supply unit formation. Therefore, the coordination of remote feeder switches and mobile power equipment is one of the keys to post-disaster recovery of multi-energy coupled distribution networks.

[0004] At present, the methods for solving the distribution network reconstruction and fault recovery problems include heuristic algorithms and artificial intelligence algorithms. The calculation results of heuristic algorithms are greatly affected by the initial power flow distribution of the network and the processing order of the ring network; artificial intelligence algorithms are not affected by the initial state of the network. However, all of the above methods have the problem of not being able to guarantee a global optimal solution.

[0005] Compared with fixed energy storage systems, transportable energy storage systems (TESS) have certain advantages in terms of transportability and operational flexibility, and can provide power supply to critical loads in emergency situations. In addition, the investment cost of TESS is expected to drop to a competitive level in about 5 to 10 years. TESS has the potential to compete with distributed power generation devices such as fixed fuel cells and micro turbines in the next few years. How to formulate a mobile strategy and discharge strategy for TESS as an emergency power source after a disaster is also a technical problem that needs to be solved urgently. Summary of the invention

[0006] In view of the above analysis, an embodiment of the present invention aims to provide a coordinated operation method for post-disaster recovery of a multi-energy coupled distribution network based on a TESS configuration, so as to solve the problem of lack of an effective coordinated operation method for post-disaster recovery of a multi-energy coupled distribution network in the prior art.

[0007] The present invention discloses a coordinated operation method for post-disaster recovery of a multi-energy coupled distribution network based on a TESS configuration, the method comprising:

[0008] Construct an objective function of a multi-energy coupled distribution network post-disaster recovery model that comprehensively considers user disconnection, TESS transportation, and microgrid power generation. The objective function is used to minimize the sum of user disconnection costs, TESS transportation costs, and microgrid power generation costs.

[0009] Constructing the constraint conditions of the multi-energy coupled distribution network post-disaster recovery model, the constraint conditions include: TESS spatiotemporal constraint, TESS operation constraint, distribution network reconstruction topology constraint, microgrid operation constraint and Kirchhoff's law constraint;

[0010] The multi-energy coupled distribution network post-disaster recovery model is solved to obtain the TESS optimization dispatch result during the multi-energy coupled distribution network post-disaster recovery process.

[0011] On the basis of the above method, the present invention also makes the following improvements:

[0012] Furthermore, the objective function of the multi-energy coupled distribution network post-disaster recovery model is expressed as:

[0013] (1)

[0014] in, For Node load interruption costs; For Node exist The load active power of the time period, For Node exist Active power of loads restored during the period, Represents a set of distribution network nodes; Indicates the time scale; Indicates the total optimization period; Represents microgrid node The power generation cost of distributed generators is Represents microgrid node Distributed generator sets in Output during the period, represents the set of microgrid nodes obtained by adjusting the feeder switches to decompose the distribution network; , Respectively represent The transportation and maintenance costs of each TESS, represents the set of TESS; The set of transport routes scheduled for TESS routing, For the TESS and microgrid nodes , Transport routes between A binary variable of the relevant transport status, when its value is 1, it means TESS in The time period is located on the transport route When its value is 0, it means TESS in The time period is not located on the transport route superior; , Respectively represent TESS in Microgrid nodes Charging power and discharging power.

[0015] Furthermore, the TESS space-time constraints are expressed as:

[0016] (2)

[0017] (3)

[0018] (4)

[0019] (5)

[0020] in, From microgrid node The set of transport routes for departure, Microgrid Node is the set of transport routes to the destination; It is a set of virtual microgrid nodes in the TSN model.

[0021] Furthermore, the TESS operation constraints are expressed as:

[0022] (6)

[0023] (7)

[0024] (8)

[0025] (9)

[0026] (10)

[0027] (11)

[0028] In the formula, , Respectively represent The maximum charging power and maximum discharging power of each TESS; is a binary variable, indicating TESS in Whether the time period is in charging state, 1 means it is in charging state, 0 means it is not in charging state; is a binary variable, indicating TESS in Whether the time period is in the discharge state, 1 means it is in the discharge state, and 0 means it is not in the discharge state; Indicates TESS in The state of charge during the time period, represents the self-discharge parameter of TESS; , Respectively represent The charging efficiency and discharging efficiency of each TESS; , They represent the maximum and minimum values ​​of TESS charge capacity, respectively.

[0029] Furthermore, the distribution network reconstruction topology constraint is expressed as:

[0030] (12)

[0031] (13)

[0032] (14)

[0033] (15)

[0034] in, is the distribution network branch set, For the node The set of all adjacent nodes; is a binary variable. If the line In running state, its value is 1. Disconnected, its value is 0; is a binary variable. For Node When it is the root node, its value is 1, otherwise its value is 0; , Respectively , The cardinality of .

[0035] Furthermore, the microgrid operation constraints are expressed as:

[0036] (16)

[0037] (17)

[0038] (18)

[0039] (19)

[0040] (20)

[0041] (twenty one)

[0042] in, , Represent microgrid nodes The active power and reactive power of , Represent microgrid nodes Active load and reactive load in; Represents microgrid node Distributed generator sets in Reactive power during the time period; , Represent microgrid nodes The upper limit of active power and reactive power of distributed generator sets; Represents microgrid node Distributed generator sets in The capacity of the time period, , Represent microgrid nodes The lower and upper capacity limits of distributed generator sets.

[0043] Further, the Kirchhoff's law constraint is expressed as:

[0044] (twenty two)

[0045] in, Indicates line The meritorious trend on Indicates line The reactance value of , Respectively indicate lines The phase angles of the start and end nodes, Represents a collection of lines in a distribution network; Indicates line Fault judgment, when its value is 1, it means that the line A fault occurs when a disaster occurs. A value of 0 indicates that the line No failures occurred when the disaster occurred; For Line The protection decision variable, when its value is 1, it means the line is protected, when its value is 0, it means the line is not protected;

[0046] line Binary variable with line status Associated.

[0047] Further, when When The remote feeder switch on the line is closed. In operation; when When The remote feeder switch on the line is in the open state. Out of operation.

[0048] Furthermore, the decision variables , and As the TESS optimization scheduling result.

[0049] Furthermore, the gurobi solver is used to solve the multi-energy coupled distribution network post-disaster recovery model.

[0050] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0051] The collaborative operation method for post-disaster recovery of a multi-energy coupled distribution network based on TESS configuration provided by the present invention constructs an objective function of a post-disaster recovery model of a multi-energy coupled distribution network that comprehensively considers user disconnection, TESS transportation, and microgrid power generation, and constructs constraint conditions of a post-disaster recovery model of a multi-energy coupled distribution network that considers TESS spatiotemporal constraints, TESS operation constraints, distribution network reconstruction topology constraints, microgrid operation constraints, and Kirchhoff's law constraints; thereby a complete post-disaster recovery model of a multi-energy coupled distribution network is constructed to simulate the post-disaster recovery of a multi-energy coupled distribution network. By solving the post-disaster recovery model of the multi-energy coupled distribution network, the TESS optimization scheduling result in the post-disaster recovery process of the multi-energy coupled distribution network can be obtained. In summary, this embodiment aims to maximize the supply capacity of critical loads, makes decisions on the access status of mobile power supply equipment, and separates binary variables from continuous variables through mathematical algorithms to reduce the difficulty of solving the problem.

[0052] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0054] Figure 1 A flowchart of a method for coordinated operation of a multi-energy coupled distribution network after disaster recovery provided by an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of a post-disaster recovery strategy for a multi-energy coupled distribution network provided by an embodiment of the present invention;

[0056] Figure 3 A schematic diagram of a TSN model provided in an embodiment of the present invention;

[0057] Figure 4 A schematic diagram of a TESS scheduling model based on a TSN model considering the travel between different microgrid nodes provided in an embodiment of the present invention;

[0058] Figure 5 A schematic diagram of the structure of a partial area of ​​a 10kV distribution network provided in an embodiment of the present invention;

[0059] Figure 6 Schematic diagram of the change of the residual power of the TESS system during the entire simulation process. DETAILED DESCRIPTION

[0060] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0061] A specific embodiment of the present invention combines the post-disaster rerouting scheduling of the mobile energy storage system with the multi-microgrid structure formed by the feeder switch action, and provides a post-disaster recovery and coordinated operation method for a multi-energy coupled distribution network based on TESS configuration. The flow chart is as follows: Figure 1 As shown, the method includes the following steps.

[0062] Step S1: constructing an objective function of a multi-energy coupled distribution network post-disaster recovery model that comprehensively considers user disconnection, TESS transportation, and microgrid power generation, wherein the objective function is used to minimize the sum of user disconnection costs, TESS transportation costs, and microgrid power generation costs;

[0063] Step S2: constructing the constraint conditions of the multi-energy coupled distribution network post-disaster recovery model, wherein the constraint conditions include: TESS spatiotemporal constraint, TESS operation constraint, distribution network reconstruction topology constraint, microgrid operation constraint and Kirchhoff's law constraint;

[0064] Step S3: solving the multi-energy coupled distribution network post-disaster recovery model to obtain the TESS optimization scheduling result during the multi-energy coupled distribution network post-disaster recovery process.

[0065] After an extreme event occurs, the main grid will lose its ability to supply power to important loads due to possible failures in substations and feeders in the power system. At this time, on the one hand, by controlling the switch state of the remote feeder switch, the multi-energy coupled distribution network is reconfigured and decomposed into multiple microgrids. Each microgrid is in an isolated operation state. On each isolated island, the microgrid is the root node for supplying power to critical loads and serves as the interface between the distribution system and the TESS transportation system. On the other hand, the mobile energy storage system can be moved between different microgrids through the transportation network to alleviate the energy imbalance problem of the distribution network caused by operation and topological constraints in multiple fault scenarios, so as to improve the post-disaster recovery capability of the multi-energy coupled distribution network. Figure 2 This is a schematic diagram of the post-disaster recovery strategy for the multi-energy coupled distribution network proposed in this embodiment. In the specific implementation process, TESS is introduced into the multi-energy coupled distribution network. Through the coordination of TESS and the topology reconstruction of the distribution network, a more flexible recovery solution can be obtained. In the post-disaster recovery model of the multi-energy coupled distribution network provided in this embodiment, the objective function is constructed with the minimum total cost as the goal, and the constraint conditions are constructed with the spatiotemporal constraints of the mobile energy storage system, the operation constraints of the mobile energy storage system, the distribution network reconstruction topology constraints, the microgrid operation constraints and the Kirchhoff's law constraints as constraints, as described below.

[0066] In the objective function constructed with the goal of minimizing the total cost, the user disconnection cost, TESS transportation cost, and microgrid power generation cost are comprehensively considered.

[0067] (1)

[0068] in, For Node load interruption costs; For Node exist The load active power of the time period, For Node exist Active power of loads restored during the period, Represents a set of distribution network nodes; Indicates the time scale, such as 1h; Indicates the total optimization period, such as 24 hours a day; Represents microgrid node The power generation cost of distributed generators is Represents microgrid node Distributed generator sets in Output during the period, represents the set of microgrid nodes obtained by adjusting the feeder switches to decompose the distribution network; , Respectively represent The transportation and maintenance costs of each TESS, represents the set of TESS; The set of transport routes scheduled for TESS routing, For the TESS and microgrid nodes , Transport routes between A binary variable of the relevant transport status, when its value is 1, it means TESS in The time period is located on the transport route When its value is 0, it means TESS in The time period is not located on the transport route Therefore, Describes whether the transportation cost exists. When its value is 1, TESS in The time period is in the transportation state, and the transportation cost is generated; when its value is 0, TESS in There will be no transportation costs during the time slot; , Respectively represent TESS in Microgrid nodes In formula (1), the decision variables are the TESS optimization scheduling results, including: , and .

[0069] The constraints of this embodiment are described in detail as follows.

[0070] (1) Temporal and spatial constraints of mobile energy storage systems

[0071] The mobile energy storage system can be transmitted to the designated location through the transportation network. In order to better handle the scheduling problems at both time and space scales, this embodiment uses the time-space scheduling network (TSN) model to model the routing scheduling of TESS and study its impact on the recovery of multi-energy coupled distribution networks. The TSN model is more suitable for scheduling problems involving both spatial decision-making and discrete time scales, and the TSN-based scheduling model can be expressed as a MILP problem, which can be solved using an off-the-shelf solver. The schematic diagram of the TSN model is shown in the figure. Figure 3 As shown, a transportation network connecting multiple microgrids is considered, and each transportation line has a corresponding transportation time. For example, transportation between TESS microgrids 2 and 3 requires two time steps, while transportation between other microgrids only requires one time step. In order to make the time step represented by each line segment the same, a virtual node, namely MG4, is introduced between MG2 and MG3 so that the travel time of each arc in the network is exactly one time step. This embodiment proposes a TESS scheduling model based on the TSN model considering the travel between different microgrid nodes. Figure 4 As shown. The solid arrow indicates that the TESS remains in the same position within a time step, and the dotted arrow indicates the transportation route of the TESS within a time step, transmitting energy between different microgrid nodes, thereby showing the spatiotemporal state of the TESS. TSN consists of microgrid nodes and line segments connecting each microgrid node, representing all possible TESS transportation routes. This model introduces the problem of resilience improvement and effectively simulates the spatiotemporal operation of mobile electric energy equipment. The mathematical expression of the TESS spatiotemporal scheduling model based on TSN (that is, the spatiotemporal constraints of the mobile energy storage system) is as follows:

[0072] (2)

[0073] (3)

[0074] (4)

[0075] (5)

[0076] in, From microgrid node The set of transport routes for departure, Microgrid Node is the set of transport routes to the destination; It is a set of virtual microgrid nodes in the TSN model.

[0077] In the above formula, formula (2) ensures that TESS can only be in one state of transportation or fixed position within a time scale. Formulas (2) and (3) represent the conservation of transportation flow of microgrid nodes and virtual nodes in the TSN model. Formula (4) represents During the period, TESS is transported to the microgrid node through a transmission line whose terminal is microgrid node m. , then in During the time period, TESS must be located at the starting point of the microgrid node Formula (5) constrains that TESS cannot make a round trip immediately, that is, after moving to another microgrid node, it cannot directly return to the previous microgrid node.

[0078] (2) Operation constraints of mobile energy storage systems

[0079] When TESS reaches a microgrid, it can perform charging or discharging operations, and the following operating constraints need to be met:

[0080] (6) (7) (8)

[0081] (9)

[0082] (10)

[0083] (11)

[0084] In the formula, , Respectively represent The maximum charging power and maximum discharging power of each TESS; is a binary variable, indicating TESS in Whether the time period is in charging state, 1 means it is in charging state, 0 means it is not in charging state; is a binary variable, indicating TESS in Whether the time period is in the discharge state, 1 means it is in the discharge state, and 0 means it is not in the discharge state; Indicates TESS in The state of charge during the time period, represents the self-discharge parameter of TESS; , Respectively represent The charging efficiency and discharging efficiency of each TESS; , They represent the maximum and minimum values ​​of TESS charge capacity, respectively.

[0085] In the above formula, formula (6) and (7) represent the TESS to the microgrid node The charging / discharging power constraint of TESS is shown in Equations (8) and (9). The TESS can exchange power with the microgrid through charging and discharging only when it is in a fixed position. Equations (10) and (11) represent the equality constraint and capacity constraint of the TESS charge level, respectively.

[0086] (3) Distribution network reconstruction topology constraints

[0087] Undirected Graph When modeling the distribution network, when an extreme event occurs and causes a distribution network failure, there may be some isolated areas that are not connected to any node. These areas will be separated and form an island operation state.

[0088] The topological reconstruction of the distribution network should retain the radial structure, with only one microgrid in each island, and no ring structure or overlapping area. The mathematical expression is:

[0089] (12)

[0090] (13)

[0091] (14)

[0092] (15)

[0093] in, is the distribution network branch set, For the node The set of all adjacent nodes. is a binary variable. If the line In running state, its value is 1. Disconnected, its value is 0. is a binary variable. For Node When it is the root node, its value is 1, otherwise its value is 0. , Respectively , The cardinality of .

[0094] In the above equation, network reconstruction is achieved through a switching operation decision. The necessary conditions for the spanning tree are expressed in equation (12). Equation (13) is verified by or To determine whether the line is connected. Formula (14) indicates that all nodes except microgrid nodes have only one root node, and formula (15) indicates that there is no root node in the microgrid node.

[0095] (4) Microgrid operation constraints

[0096] A microgrid is an entity that coordinates distributed generators. From the perspective of the power grid, the microgrid operates as a single equivalent unit or load. In this embodiment, the microgrid is modeled as a single node with an equivalent dispatchable distributed generator set that aggregates the generation resources, equivalent local loads, and charging / discharging facilities within the entire microgrid. After connecting the TESS to the distribution network, the operating constraints of the microgrid can be expressed as:

[0097] (16)

[0098] (17)

[0099] (18)

[0100] (19)

[0101] (20)

[0102] (twenty one)

[0103] in, , Represent microgrid nodes The active power and reactive power of , Represent microgrid nodes Active load and reactive load in; Represents microgrid node Distributed generator sets in Reactive power during the time period; , Represent microgrid nodes The upper limit of active power and reactive power of distributed generator sets; Represents microgrid node Distributed generator sets in The capacity of the time period, , Represent microgrid nodes The lower and upper capacity limits of distributed generator sets.

[0104] In the above equations, equations (16) and (17) indicate that TESS charges and discharges the microgrid, and the microgrid provides active and reactive support to the distribution system through the PCC node. Equations (18) and (19) represent the upper and lower limits of the active and reactive power of the dispatchable distributed power sources of each microgrid. Combined with the mathematical model of TESS, the post-disaster joint recovery plan of the multi-energy coupled distribution network is abstracted as a MILP problem, which can be solved using the commercial solver gurobi. Since the voltage level of the distribution network system is relatively low, its power supply radius is only a dozen kilometers to dozens of kilometers, so and All are within the acceptable range.

[0105] (5) Kirchhoff’s law constraints

[0106] In this embodiment, considering that relying on remote feeder switches for intelligent regulation can greatly improve the post-disaster recovery capability of the multi-energy coupled distribution network, the active defense strategy of the distribution network and the distribution network reconstruction decision are integrated into the defender-attacker-defender (DAD) model. Reliability resources are fully utilized by formulating a post-disaster multi-energy coupled distribution network topology reconstruction strategy. The challenge brought about by the introduction of remote feeder switches is how to model the distribution network topology change process and island operation in the DAD framework. The existing DAD model solution algorithm relies heavily on the strong duality of the innermost linear programming problem. However, when the binary variable of the operating state of the remote feeder switch is introduced, the inner optimization will be a non-convex nonlinear problem. This embodiment regards line switching as an emergency defense and recovery operation, and uses binary transmission line switching variables in the internal DC optimal power flow model. Specifically, in the distribution network, the line Binary variable with line status When When The remote feeder switch on the line is closed. In operation; when When The remote feeder switch on the line is in the open state. Therefore, in the post-disaster topology reconstruction strategy of the multi-energy coupled distribution network in the DAD model, the Kirchhoff law constraint can be written as the following mixed integer nonlinear equation:

[0107] (twenty two)

[0108] in, Indicates line The meritorious trend on Indicates line The reactance value. , Respectively indicate lines The phase angles of the start and end nodes, Represents a collection of lines in a distribution network. Indicates line Fault judgment, when its value is 1, it means that the line A fault occurs when a disaster occurs. A value of 0 indicates that the line There were no failures when the disaster occurred. For Line The protection decision variable is 1, which means the line is protected, and 0, which means the line is not protected. Indicates line The logical relationship between the protected and fault decisions is if is 1, indicating that the line will not be attacked. It is worth noting that if a line fails after a disaster, it must be an unprotected line, i.e. is 0, which is equivalent to is 0, then the active power flow value on the line is 0. If the line does not fail during the disaster, equation (22) can be simplified to ,Therefore, the action of the remote feeder switch will have an impact on the line topology.

[0109] During the model solving process, the gurobi solver can be used to solve the above model to obtain the corresponding decision variables.

[0110] In summary, this embodiment aims to maximize the critical load supply capacity, makes decisions on the feeder switch state and the mobile power device access point, and separates binary variables from continuous variables through mathematical algorithms to reduce the difficulty of solving the problem.

[0111] In order to verify the effect of the above method, this embodiment selects a part of the 10kV distribution network as an actual example for analysis. The structural diagram of the part of the 10kV distribution network is as follows: Figure 5 shown.

[0112] (1) Parameter setting

[0113] The specific parameter settings of some areas of the 10kV distribution network and the branch parameter settings are shown in Table 1 and Table 2 respectively.

[0114] Table 1 Node parameter settings for some areas of 10kV distribution network

[0115]

[0116] Table 2 Branch parameter settings for some areas of 10kV distribution network

[0117]

[0118] The technical parameters of DG and TESS are shown in Table 3 and Table 4. The initial SOC of each mobile electric energy device is set to 1.0, the upper limit of energy storage capacity configuration is 500MW, the capacity configuration cost of energy storage unit is 199.1$ / MW, the unit single operation cost is 2.6$, and the load shedding cost is 1.53$ / MW.

[0119] Table 3 DG parameters

[0120]

[0121] Table 4 TESS parameters

[0122]

[0123] For comparative analysis, this example will set up two scenarios:

[0124] 1) TESS and DG scenarios

[0125] 2) DG scene only

[0126] (2) Results analysis

[0127] The above two schemes are optimized and solved respectively, and the example results are obtained. Table 5 shows the initial capacity configuration of TESS.

[0128] Table 5 TESS initial capacity configuration

[0129]

[0130] From the perspective of distribution network resilience, the load power failure rate index values ​​of the distribution network in scenario 1 and scenario 2 are 0.1246 and 0.1753 respectively, and scenario 1 is 28.92% lower than scenario 2. It can be seen that the TESS configuration method can improve the resilience of the distribution network. From the perspective of the economic efficiency of post-disaster operation of the distribution network, the addition of TESS reduces the cost of load shedding. Even if the configuration cost of TESS increases, the average total cost of post-disaster operation of the distribution network is reduced from $23,615.22 to $19,548.15, a decrease of 17.22%.

[0131] Therefore, it can be concluded that the multi-energy coupled distribution network post-disaster recovery collaborative operation method based on TESS configuration proposed in this embodiment is helpful to improve the resilience of the actual distribution network and reduce the economic losses of the distribution network under disasters.

[0132] Figure 6 The figure is a schematic diagram of the change process of the TESS system residual power during the entire simulation process, which fully reflects the charging and discharging behavior of each TESS system after the distribution network disaster occurs. It can be seen that the residual power level of each TESS basically drops to a low level in the later stage of operation, indicating that each TESS plays a positive supporting role in the distribution network in the post-disaster stage.

[0133] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0134] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A coordinated operation method for post-disaster recovery of a multi-energy coupled distribution network based on TESS configuration, characterized in that: The method comprises: Construct an objective function of a multi-energy coupled distribution network post-disaster recovery model that comprehensively considers user disconnection, TESS transportation, and microgrid power generation. The objective function is used to minimize the sum of user disconnection cost, TESS transportation cost, and microgrid power generation cost; Constructing the constraint conditions of the multi-energy coupled distribution network post-disaster recovery model, the constraint conditions include: TESS spatiotemporal constraint, TESS operation constraint, distribution network reconstruction topology constraint, microgrid operation constraint and Kirchhoff's law constraint; Solving the post-disaster recovery model of the multi-energy coupled distribution network to obtain the TESS optimization dispatch result during the post-disaster recovery process of the multi-energy coupled distribution network; The objective function of the multi-energy coupled distribution network post-disaster recovery model is expressed as: (1) in, For Node load interruption costs; For Node exist The load active power of the time period, For Node exist Active power of loads restored during the period, Represents a set of distribution network nodes; Indicates the time scale; Indicates the total optimization period; Represents microgrid node The power generation cost of distributed generators is Represents microgrid node Distributed generator sets in Output during the period, represents the set of microgrid nodes obtained by adjusting the feeder switches to decompose the distribution network; , Respectively represent The transportation and maintenance costs of each TESS, represents the set of TESS; The set of transport routes scheduled for TESS routing, For the TESS and microgrid nodes , Transport routes between A binary variable of the relevant transport status, when its value is 1, it means TESS in The time period is located on the transport route When its value is 0, it means TESS in The time period is not located on the transport route superior; , Respectively represent TESS in Microgrid nodes Charging power and discharging power; The TESS space-time constraints are expressed as: (2) (3) (4) (5) in, From microgrid node The set of transport routes for departure, Microgrid Node is the set of transport routes to the destination; It is a set of virtual microgrid nodes in the TSN model; The TESS operation constraints are expressed as: (6) (7) (8) (9) (10) (11) In the formula, , Respectively represent The maximum charging power and maximum discharging power of each TESS; is a binary variable, indicating TESS in Whether the time period is in charging state, 1 means it is in charging state, 0 means it is not in charging state; is a binary variable, indicating TESS in Whether the time period is in the discharge state, 1 means it is in the discharge state, and 0 means it is not in the discharge state; Indicates TESS in The state of charge during the time period, represents the self-discharge parameter of TESS; , Respectively represent The charging efficiency and discharging efficiency of each TESS; , They represent the maximum and minimum values ​​of TESS charge capacity respectively; The distribution network reconstruction topology constraint is expressed as: (12) (13) (14) (15) in, is the distribution network branch set, For the node The set of all adjacent nodes; is a binary variable. If the line In running state, its value is 1. Disconnected, its value is 0; is a binary variable. For Node When it is the root node, its value is 1, otherwise its value is 0; , Respectively , The cardinality of The microgrid operation constraints are expressed as: (16) (17) (18) (19) (20) (21) in, , Represent microgrid nodes The active power and reactive power of , Represent microgrid nodes Active load and reactive load in; Represents microgrid node Distributed generator sets in Reactive power during the time period; , Represent microgrid nodes The upper limit of active power and reactive power of distributed generator sets; Represents microgrid node Distributed generator sets in The capacity of the time period, , Represent microgrid nodes The lower and upper capacity limits of the distributed generating units; The Kirchhoff's law constraints are expressed as: (22) in, Indicates line The meritorious trend on Indicates line The reactance value of , Respectively indicate lines The phase angles of the start and end nodes, Represents a collection of lines in a distribution network; Indicates line Fault judgment, when its value is 1, it means that the line A fault occurs when a disaster occurs. A value of 0 indicates that the line No failures occurred when the disaster occurred; For Line The protection decision variable, when its value is 1, it means the line is protected, when its value is 0, it means the line is not protected; line Binary variable with line status Associated.

2. The method for coordinated operation of multi-energy coupled distribution network post-disaster recovery based on TESS configuration according to claim 1 is characterized in that: when When The remote feeder switch on the line is closed. In operation; when When The remote feeder switch on the line is in the open state. Out of operation.

3. The method for coordinated operation of multi-energy coupled distribution network post-disaster recovery based on TESS configuration according to claim 2 is characterized in that: The decision variable , and As the TESS optimization scheduling result.

4. The method for coordinated operation of multi-energy coupled distribution network post-disaster recovery based on TESS configuration according to claim 3 is characterized in that: The gurobi solver is used to solve the multi-energy coupled distribution network post-disaster recovery model.

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