Scheduling method of mobile energy storage device after extreme disaster
By constructing a scheduling model with the minimum scheduling cost for mobile energy storage devices and the minimum equivalent loss cost for distribution networks, and considering multiple constraints, the particle swarm algorithm is used to solve the scheduling of mobile energy storage devices after extreme disasters. Economic issues have achieved the optimal economic scheduling plan.
Patent Information
- Application Number
- CN202411625624.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-14
- Publication Date
- 2025-02-18
AI Technical Summary
After extreme disasters, the affected nodes of the distribution network are inconsistent with the estimated nodes, resulting in the need of secondary dispatch of mobile energy storage devices. The existing technology is difficult to effectively solve this problem.
Construct a scheduling model with the objective function of the minimum scheduling cost of mobile energy storage devices and the minimum equivalent loss cost of distribution network, considering traffic network constraints, radial network constraints, current constraints, node balance constraints, and mobile energy storage device scheduling constraints and distribution line repair constraints, and use particle swarm algorithm to solve them.
The most economical dispatch of mobile energy storage devices after extreme disasters is achieved. The dispatching plan obtained is the best economical and can effectively deal with the impact of extreme disasters on traffic conditions.
Smart Images
Figure QLYQS_13 
Figure QLYQS_27 
Figure QLYQS_79
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of mobile energy storage device scheduling, and in particular relates to a scheduling method for a mobile energy storage device after an extreme disaster. Background Art
[0002] As an important power infrastructure, the safe and stable operation of the distribution network system is extremely vulnerable to extreme weather. When dealing with extreme natural disasters, the disaster resistance of the distribution network is weaker than that of the main network and is more sensitive to disaster events. Therefore, according to the pre-disaster risk assessment, the staff will pre-dispatch the mobile energy storage devices used for emergency to the load nodes of the distribution network that are prone to failure.
[0003] After an extreme disaster occurs, if the affected nodes of the distribution network are inconsistent with the estimated nodes, or other important load nodes are affected by the disaster, the mobile energy storage device will need to be re-dispatched (post-disaster dispatch). Summary of the invention
[0004] In view of the deficiencies in the prior art, the present invention provides a method for dispatching a mobile energy storage device after an extreme disaster.
[0005] In order to solve one, part or all of the above technical problems, the technical solution adopted by the present invention is: A method for dispatching a mobile energy storage device after an extreme disaster comprises: constructing an objective function of a dispatching model with the goal of minimizing the dispatching cost of the mobile energy storage device and minimizing the equivalent loss load cost of the distribution network; constructing constraint conditions of the dispatching model; solving the dispatching model, and dispatching the mobile energy storage device based on the solution; the constraint conditions include: traffic network constraints, radial network constraints, flow constraints, node balance constraints, mobile energy storage device dispatching constraints, and distribution line repair constraints.
[0006] Furthermore, the objective function is expressed as , T represents the number of time sub-segments after the complete scheduling cycle is evenly divided, t represents the starting point of each time sub-segment; i represents the load node of the distribution network that moves out of the mobile energy storage device, m represents the load node of the distribution network that moves into the mobile energy storage device, I is the number of load nodes that move out of the mobile energy storage device, and M is the number of load nodes that move into the mobile energy storage device; n represents the number of mobile energy storage devices to be scheduled; represents the cost of reducing unit load; Represents the state characterization of load node m. When the failure rate at m exceeds the limit, the value is 1, otherwise the value is 0; Indicates the load size of load node m; represents the time required for the mobile energy storage device to be dispatched from load node i to load node m; represents the unit dispatch cost of the mobile energy storage device; represents the road distance between load nodes i and m; φ is the depreciation coefficient of the energy storage device; Indicates the cost of charging and discharging a unit of electricity of a mobile energy storage device; and The distribution represents the charging and discharging power of the mobile energy storage device; Δt is the time required for charging and discharging.
[0007] Furthermore, the traffic network constraint is expressed as , where Q represents the traffic flow of the road, and , is the traffic volume of the road under normal circumstances; Indicates wind speed level; Indicates visibility. , and is the correlation coefficient; μ represents the friction coefficient of the road surface, which is related to the traffic volume on the road; g represents the acceleration of gravity; Indicates the length of the vehicle transporting the mobile energy storage device; The permissible traffic capacity of roads in extreme weather conditions; and is the calibration coefficient, , ; Represents the road distance between load nodes i and m.
[0008] Furthermore, the radial network constraint is expressed as , ,in represents the virtual power flow of line ij; Indicates the feasible branch after fault isolation; Indicates the branch where the fault occurs; and They respectively represent the number of load nodes and power sources involved in network reconstruction.
[0009] Furthermore, the power flow constraint is expressed as ,in is the power value input into load node j; It represents the feasible branch after fault isolation. and Respectively represent the power values of line jk and line ij; Represents the square of the maximum current of line ij; Represents the impedance in line ij.
[0010] Furthermore, the power flow constraint is expressed as , ,in and They represent the active power and reactive power of the input load node j respectively; It represents the feasible branch after fault isolation. and They represent the active power and reactive power of line ij respectively, and They represent the active power and reactive power of line jk respectively; represents the square of the maximum current of line ij, and They represent the resistance and reactance of line ij respectively.
[0011] Furthermore, the node balance constraint is expressed as , , , , ,in , Respectively represent the active power input from the load node power supply and the mobile energy storage device; , Respectively represent the reactive power input from the load node power supply and the mobile energy storage device; is the predicted expected value; and Respectively represent the maximum deviation value of power, A binary variable indicating whether the node j load has restored power supply, and They represent the active power and reactive power of the load at node j, , and Indicates the deviation control amount.
[0012] Furthermore, the dispatch constraint of the mobile energy storage device is expressed as , , ,in represents the location of the mobile energy storage device; Ω represents the set of all pre-scheduled energy storage devices, T represents the number of time sub-segments after the complete scheduling cycle is evenly divided, n represents the number of mobile energy storage devices to be scheduled, and B represents all load nodes; Indicates the time required for scheduling; Indicates the current location of the mobile energy storage device.
[0013] Furthermore, the distribution line repair constraint is expressed as , , , ,in and They respectively represent the time when the fault of the distribution line is discovered and the time when repair begins; and Respectively represent the number of lines with faults and the number of lines that have been repaired; Indicates a time interval; It is the time from when a fault occurs to when no more faults occur on the line; Indicates the time from when a fault occurs until all lines are repaired.
[0014] Furthermore, the scheduling model is solved using a particle swarm algorithm.
[0015] Compared with the prior art, the present invention has the following beneficial effects: The dispatching model constructed by the present invention aims to minimize the dispatching cost of mobile energy storage devices and the equivalent loss load cost of the distribution network, and takes into account constraints such as network constraints, radial network constraints, flow constraints, node balance constraints, mobile energy storage device dispatching constraints and distribution line repair constraints, so as to solve the most economical transportation path of mobile energy storage devices in the transportation network.
[0016] The present invention performs real-time dispatching of mobile energy storage devices while taking into account the impact of extreme disasters on traffic conditions, and the obtained dispatching scheme has the best economy. DETAILED DESCRIPTION
[0017] In order to better understand the present invention, the content of the present invention is further clearly set forth in conjunction with the embodiments below, but the protection content of the present invention is not limited to the following embodiments. In the following description, a large number of specific details are provided in order to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details.
[0018] Embodiment 1: The purpose of this embodiment is to provide a method for dispatching a mobile energy storage device after an extreme disaster.
[0019] S1. Construct a scheduling model for mobile energy storage devices.
[0020] Before an extreme disaster occurs, the staff will pre-dispatch the mobile energy storage devices used for emergency to the risk nodes of the distribution network according to the weather forecast and risk assessment. However, after an extreme disaster occurs, if the affected nodes of the distribution network are inconsistent with the estimated nodes, the mobile energy storage devices need to be dispatched again (post-disaster dispatch). Therefore, this step constructs a dispatch model for post-disaster dispatch of mobile energy storage devices after extreme disasters.
[0021] Specifically, step S1 includes: S11, constructing the objective function of the scheduling model.
[0022] The objective function is constructed with the goal of minimizing the dispatching cost of mobile energy storage devices and the equivalent loss of load cost of the distribution network: ; The formula divides a complete dispatch cycle of the mobile energy storage device into T sub-segments on average, t represents the starting point of each time sub-segment; i represents the load node of the distribution network that moves out of the mobile energy storage device, m represents the load node of the distribution network that moves into the mobile energy storage device, I represents the number of load nodes that move out of the mobile energy storage device, and M represents the number of load nodes that move into the mobile energy storage device; n represents the number of mobile energy storage devices to be dispatched; represents the cost of reducing unit load; Represents the state characterization of load node m. When the failure rate at m exceeds the limit, the value is 1, otherwise the value is 0; Indicates the load size of load node m; represents the time required for the mobile energy storage device to be dispatched from load node i to load node m; represents the unit dispatch cost of the mobile energy storage device; represents the road distance between load nodes i and m; φ is the depreciation coefficient of the energy storage device; Indicates the cost of charging and discharging a unit of electricity of a mobile energy storage device; and The distribution represents the charging and discharging power of the mobile energy storage device; Δt is the time required for charging and discharging.
[0023] S12. Constructing the traffic network constraints of the scheduling model.
[0024] Traffic conditions will be more congested than normal under the influence of extreme disaster weather, which will extend the time it takes to transport mobile energy storage devices to their destinations. Therefore, the traffic network constraints are constructed as follows: .
[0025] Where Q represents the traffic flow of the road, and , is the traffic volume of the road under normal circumstances; Indicates wind speed level; Indicates visibility. , and is the correlation coefficient; μ represents the friction coefficient of the road surface, which is related to the traffic volume on the road; g represents the acceleration of gravity; Indicates the length of the vehicle transporting the mobile energy storage device; The permissible traffic capacity of roads in extreme weather conditions; and is the calibration factor, and the relevant specifications stipulate , ; Represents the road distance between load nodes i and m.
[0026] S13: construct radial network constraints of the scheduling model.
[0027] The radial structure of the distribution network will affect the network reconstruction, so radial network constraints are also constructed for the dispatch model. To ensure that the island division form can be realized, the power flow constraint method is used to describe the radial constraints of the distribution network. The radial network constraint conditions are: , .
[0028] in represents the virtual power flow of line ij; Indicates the feasible branch after fault isolation; Indicates the branch where the fault occurs; and They respectively represent the number of load nodes and power sources involved in network reconstruction.
[0029] S14. Constructing power flow constraints of the scheduling model.
[0030] In order to improve the iteration efficiency, the parallel admittance is not considered and the power flow constraint condition is constructed for the scheduling model: ,in is the power value input into load node j; It represents the feasible branch after fault isolation. and Respectively represent the power values of line jk and line ij; Represents the square of the maximum current of line ij; Represents the impedance in line ij.
[0031] The formula for power flow constraint can be decomposed into the form of active power and reactive power: , .
[0032] in and They represent the active power and reactive power of the input load node j respectively; It represents the feasible branch after fault isolation. and They represent the active power and reactive power of line ij respectively, and They represent the active power and reactive power of line jk respectively; represents the square of the maximum current of line ij, and They represent the resistance and reactance of line ij respectively.
[0033] S15. Constructing node balance constraints of the scheduling model.
[0034] The equilibrium constraints are: ; ; ; ; .
[0035] in , Respectively represent the active power input from the load node power supply and the mobile energy storage device; , Respectively represent the reactive power input from the load node power supply and the mobile energy storage device; is the predicted expected value; and Respectively represent the maximum deviation value of power, A binary variable indicating whether the node j load has restored power supply, and They represent the active power and reactive power of the load at node j, , and Indicates the deviation control amount.
[0036] The big M method is introduced to simplify the equilibrium constraints: ; ; .
[0037] in and Respectively represent the square of the maximum voltage of load nodes i and j; M represents a particularly large integer; Is a characterization quantity indicating whether the branch is closed. A binary variable indicating whether the branch is closed, and They represent the active power and reactive power of line ij respectively, and They represent the resistance and reactance of line ij respectively.
[0038] S15. Constructing the dispatch constraints of the mobile energy storage device of the dispatch model.
[0039] Considering the dispatching situation of mobile energy storage devices, the dispatching constraints are constructed as follows: , , .
[0040] in represents the location of the mobile energy storage device; Ω represents the set of all pre-scheduled energy storage devices, T represents the number of time sub-segments after the complete scheduling cycle is evenly divided, n represents the number of mobile energy storage devices to be scheduled, and B represents all load nodes; Indicates the time required for scheduling; Indicates the current location of the mobile energy storage device.
[0041] S16. Constructing distribution line repair constraints of the scheduling model.
[0042] Post-disaster dispatching needs to consider the repair constraints of distribution lines, because extreme weather of different intensities will cause different levels of damage to the lines, and repairs will require different time costs. The damage levels are classified into three levels: green, yellow, and red, and the corresponding repair times are 1 hour, 2 hours, and 4 hours. The distribution line repair constraints are constructed as follows: , , , .
[0043] in and They respectively represent the time when the fault of the distribution line is discovered and the time when repair begins; and Respectively represent the number of lines with faults and the number of lines that have been repaired; Indicates a time interval; It is the time from when a fault occurs to when no more faults occur on the line; Indicates the time from when a fault occurs until all lines are repaired.
[0044] S2. Solve the scheduling model and use the solution to schedule the mobile energy storage device.
[0045] Based on the MATLAB R2018b platform, the scheduling model is solved in combination with the particle swarm algorithm. The solution result is the most economical scheduling plan for the mobile energy storage device, and the mobile energy storage device is scheduled based on the solution result.
[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in the field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.
Claims
1. A method for dispatching a mobile energy storage device after an extreme disaster, characterized in that: include: The objective function of the dispatch model is constructed with the goal of minimizing the dispatch cost of mobile energy storage devices and the equivalent loss of load cost of the distribution network; Constructing constraints of the scheduling model; Solving the scheduling model, and using the solution results to schedule the mobile energy storage device; The constraints include: traffic network constraints, radial network constraints, power flow constraints, node balance constraints, mobile energy storage device scheduling constraints and distribution line repair constraints.
2. The dispatching method of mobile energy storage device after extreme disasters according to claim 1 is characterized in that: The objective function is expressed as , T represents the number of time sub-segments after the complete scheduling cycle is evenly divided, and t represents the starting point of each time sub-segment; i represents the load node of the distribution network that has been moved out of the mobile energy storage device, m represents the load node of the distribution network that has been moved into the mobile energy storage device, I is the number of load nodes that have been moved out of the mobile energy storage device, and M is the number of load nodes that have been moved into the mobile energy storage device; n represents the number of mobile energy storage devices to be dispatched; represents the cost of reducing unit load; Represents the state characterization of load node m. When the failure rate at m exceeds the limit, the value is 1, otherwise the value is 0; Indicates the load size of load node m; represents the time required for the mobile energy storage device to be dispatched from load node i to load node m; represents the unit dispatch cost of the mobile energy storage device; represents the road distance between load nodes i and m; φ is the depreciation coefficient of the energy storage device; Indicates the cost of charging and discharging a unit of electricity of a mobile energy storage device; and The distribution represents the charging and discharging power of the mobile energy storage device; Δt is the time required for charging and discharging.
3. The method for dispatching a mobile energy storage device after an extreme disaster according to claim 1, characterized in that: The traffic network constraints are expressed as , where Q represents the traffic flow of the road, and , is the traffic volume of the road under normal circumstances; Indicates wind speed level; Indicates visibility. , and is the correlation coefficient; μ represents the friction coefficient of the road surface, which is related to the traffic volume on the road; g represents the acceleration of gravity; Indicates the length of the vehicle transporting the mobile energy storage device; The permissible traffic capacity of roads in extreme weather conditions; and is the calibration coefficient, , ; Represents the road distance between load nodes i and m.
4. The method for dispatching a mobile energy storage device after an extreme disaster according to claim 1, characterized in that: The radial network constraint is expressed as , ,in represents the virtual power flow of line ij; Indicates the feasible branch after fault isolation; Indicates the branch where the fault occurs; and They respectively represent the number of load nodes and power sources involved in network reconstruction.
5. The method for dispatching a mobile energy storage device after an extreme disaster according to claim 1, characterized in that: The power flow constraint is expressed as ,in is the power value input into load node j; It represents the feasible branch after fault isolation. and Respectively represent the power values of line jk and line ij; Represents the square of the maximum current of line ij; Represents the impedance in line ij.
6. The method for dispatching a mobile energy storage device after an extreme disaster according to claim 1, characterized in that: The power flow constraint is expressed as , ,in and They represent the active power and reactive power of the input load node j respectively; It represents the feasible branch after fault isolation. and They represent the active power and reactive power of line ij respectively, and They represent the active power and reactive power of line jk respectively; represents the square of the maximum current of line ij, and They represent the resistance and reactance of line ij respectively.
7. The method for dispatching a mobile energy storage device after an extreme disaster according to claim 1, characterized in that: The node balance constraint is expressed as , , , , ,in , Respectively represent the active power input from the load node power supply and the mobile energy storage device; , Respectively represent the reactive power input from the load node power supply and the mobile energy storage device; is the predicted expected value; and Respectively represent the maximum deviation value of power, A binary variable indicating whether the node j load has restored power supply, and They represent the active power and reactive power of the load at node j, , and Indicates the deviation control amount.
8. The method for dispatching a mobile energy storage device after an extreme disaster according to claim 1, characterized in that: The dispatch constraint of the mobile energy storage device is expressed as , , ,in represents the location of the mobile energy storage device; Ω represents the set of all pre-scheduled energy storage devices, T represents the number of time sub-segments after the complete scheduling cycle is evenly divided, n represents the number of mobile energy storage devices to be scheduled, and B represents all load nodes; Indicates the time required for scheduling; Indicates the current location of the mobile energy storage device.
9. The method for dispatching a mobile energy storage device after an extreme disaster according to claim 1, characterized in that: The distribution line repair constraint is expressed as , , , ,in and They respectively represent the time when the fault of the distribution line is discovered and the time when repair begins; and Respectively represent the number of lines with faults and the number of lines that have been repaired; Indicates a time interval; It is the time from when a fault occurs to when no more faults occur on the line; Indicates the time from when a fault occurs until all lines are repaired.
10. The method for dispatching a mobile energy storage device after an extreme disaster according to claim 1, characterized in that: The scheduling model is solved using a particle swarm algorithm.