CN114004550A - Power grid emergency repair and restoration scheduling cooperative decision method and system under natural disaster - Google Patents
Power grid emergency repair and restoration scheduling cooperative decision method and system under natural disaster Download PDFInfo
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Abstract
The invention belongs to the technical field of power system recovery decision-making, and provides a cooperative decision-making method and a cooperative decision-making system for power grid emergency repair and recovery scheduling under natural disasters, wherein the method considers the influence of equipment damage on the recovery scheduling, adjusts the emergency repair scheme on line, completes the repair of a power transmission line, a communication link and a transformer substation load outgoing line, effectively processes the mutual influence between the emergency repair and the recovery scheduling, and realizes the effective cooperation between the emergency repair and the recovery scheduling; the Monte Carlo tree search and the mathematical planning method are combined, so that emergency repair and scheduling recovery decisions can be completed in effective time, and the decision is ensured to be realized on line.
Description
Technical Field
The invention belongs to the technical field of power system restoration decision-making, and particularly relates to a cooperative decision-making method and system for emergency repair and restoration scheduling of a power grid under natural disasters.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the warming of climate, extreme natural disasters such as typhoon, rainstorm and ice disaster are frequently generated in the global scope in recent years, which may damage information equipment and physical equipment of the power grid, and further affect the power supply of users. Extreme natural disasters can directly cause severe changes to the operating environment of the electric power information physical equipment, threaten the safe operation of a power grid, and cause equipment damage and load loss. Although the application of new technology and new equipment in modern power systems can improve the stability and reliability of system operation, the occurrence of power failure accidents, especially power failure caused by manpower-irresistible factors, still cannot be avoided.
Modern society power supply influences the civilian basis such as water supply, heat supply and air supply, and the normal operation of society and the basic life of people can seriously be influenced to the power failure accident. In particular, the superposition of extreme natural disasters and power failure accidents can further aggravate the severity of the adverse effect and even threaten the life safety of people. The method accelerates the power supply recovery after the extreme natural disaster, is an important measure for improving the power grid to resist the extreme natural disaster, and is very important for guaranteeing social production and life. In order to realize the rapid recovery of the power grid from the disturbance of the extreme natural disaster as soon as possible, the emergency repair of the damaged information physical equipment needs to be completed rapidly, and meanwhile, the input of the power failure load is completed through the dispatching of the power grid.
Emergency repair and restoration scheduling are key measures for power grid restoration after extreme natural disasters, damage of the extreme natural disasters to power transmission lines, communication links and transformer substation load outgoing lines is considered, emergency repair and restoration scheduling are coordinated, repair paths, load outgoing line switching and power generation scheduling are reasonably arranged, and rapid and reliable power supply restoration after the extreme natural disasters can be effectively achieved.
The conventional method for restoring the power grid after the extreme natural disaster relatively breaks under consideration of emergency repair and restoration scheduling, and a reasonable cooperative mechanism is not formed, so that the emergency repair and restoration scheduling belonging to different departments after the extreme natural disaster are difficult to effectively cooperate. In addition, the related method is limited in the aspect of offline plan formulation, and is difficult to effectively deal with sudden uncertain conditions in the recovery process, so that the power supply recovery speed is influenced.
Disclosure of Invention
The invention provides a cooperative decision method and a cooperative decision system for emergency repair and restoration scheduling of a power grid under natural disasters, which consider the influence of equipment damage on restoration scheduling, adjust an emergency repair scheme on line, finish the repair of a power transmission line, a communication link and a transformer substation load outgoing line, process the mutual influence between the emergency repair and the restoration scheduling, and realize the effective cooperation between the emergency repair and the restoration scheduling.
According to some embodiments, the invention adopts the following technical scheme:
a power grid emergency repair and restoration scheduling cooperative decision method under natural disasters comprises the following steps:
acquiring the running state of a power grid, the state of a traffic network, equipment faults and position information of an emergency maintenance team;
evaluating the traffic time and equipment repair time of emergency repair based on the traffic network state, equipment faults and position information of an emergency repair team;
considering the current power grid running state, traffic time obtained by evaluation and equipment repair time, and adopting a Monte Carlo tree search algorithm to decide an emergency repair plan in a set time period in the future;
acquiring the state change condition of the power grid and the duration of each state in the future based on the emergency repair plan, establishing a load recovery dynamic optimization model, and solving by adopting a mathematical programming method to obtain a recovery scheduling plan;
and carrying out emergency repair operation arrangement and material planning arrangement according to the emergency repair plan, and carrying out generator output adjustment and load outlet closing operation according to the recovery scheduling plan.
As an alternative embodiment, the specific process of evaluating the traffic time of emergency repair and the equipment repair time based on the traffic network state, the equipment failure and the repair team position information includes:
estimating the traffic time required by each emergency maintenance team to each fault according to the specific position of the current emergency maintenance team and the state of a traffic network;
and predicting the fault repair time of the corresponding equipment according to the fault condition of each equipment.
As an alternative embodiment, the specific process of using the monte carlo tree search algorithm to decide the emergency repair plan in the future set time period includes:
starting from the root node, after the upper confidence interval index value of each node is calculated, sequentially selecting the node with the maximum upper confidence interval index value to perform the next expansion or simulation;
analyzing the current node into a system emergency repair state, searching all possible emergency repair states in a set time period in the future, setting the possible emergency repair states as child nodes of the current node, and setting the index value of the upper limit confidence interval of the child nodes as infinity;
analyzing the current node into a system emergency repair state, randomly generating a possible future repair process, establishing a plurality of recovery scheduling single time-step optimization models according to the repair process, and solving to obtain a corresponding decision index value;
and updating the parameters of each node in the search tree based on the simulation result.
As a further limitation, the optimization goal of the recovery scheduling single-time-step optimization model is to maximize the single-time-step weighted load recovery yield, and the constraints include node voltage constraint, branch power flow constraint, generator climbing constraint and generator output constraint.
By way of further limitation, the upper limit confidence interval index value is determined by the average value of the simulation results of each node, the number of times the node is accessed and the number of times the parent node is accessed.
As an alternative embodiment, the specific process of establishing a load recovery dynamic optimization model and solving and obtaining a recovery scheduling plan by using a mathematical programming method includes:
judging whether the emergency repair plan can cause the change of the power grid state or not based on the emergency repair plan in a set time period in the future, and if so, acquiring the sequence and the state duration of each state;
according to the state change and the duration of the power grid, a dynamic optimization model in a set time period in the future is established by taking the set time period as an optimization time step, wherein the set time period can be divided into a plurality of set time periods;
and solving the dynamic optimization model by adopting a mathematical programming algorithm to obtain the recovery scheduling plans of each optimization time step in the future, and sequentially executing the recovery scheduling plans of each optimization time step.
As a further limitation, the optimization goal of the load recovery dynamic optimization model is the weighted load recovery gain of the time period, and the constraints include node voltage constraint, branch power flow constraint, generator ramp constraint, generator output constraint, and scheduling association constraint at each time step.
As an alternative embodiment, the specific process of performing emergency repair work arrangement and material planning arrangement according to the emergency repair plan includes:
and issuing an emergency repair plan of a set time period in the future every set time, and arranging the action route of a corresponding emergency repair team and the action route of material allocation according to the emergency repair plan.
As an alternative embodiment, the specific process of performing the generator output adjustment and the load outlet closing operation according to the recovery scheduling plan includes:
and generating a closing operation order of the power plant and the transformer substation according to the recovery scheduling plan of the next optimized time step, and issuing the closing operation order to a corresponding dispatcher or an actuator for execution.
A power grid emergency repair and restoration scheduling cooperative decision system under natural disasters comprises:
the parameter acquisition module is configured to acquire a power grid running state, a traffic network state, equipment faults and position information of an emergency maintenance team;
the time factor determination module is configured to evaluate the traffic time of emergency repair and the equipment repair time based on the traffic network state, the equipment fault and the position information of the emergency repair team;
the emergency repair decision module is configured to consider the current power grid operation state, traffic time obtained through evaluation and equipment repair time, and decide an emergency repair plan in a set time period in the future by adopting a Monte Carlo tree search algorithm;
the recovery scheduling decision module is configured to acquire the state change condition of the power grid and the duration of each state in the future based on the emergency repair plan, establish a load recovery dynamic optimization model, and solve by adopting a mathematical programming method to acquire a recovery scheduling plan;
and the execution module is configured to carry out emergency repair operation arrangement and material planning arrangement according to the emergency repair plan, and carry out generator output adjustment and load outlet closing operation according to the recovery scheduling plan.
Compared with the prior art, the invention has the beneficial effects that:
(1) according to the cooperative decision method for the emergency repair and restoration scheduling of the power grid under the natural disaster, provided by the invention, the emergency repair scheme can be adjusted on line according to the real-time information of the emergency repair and restoration scheduling and the restoration scheduling plan can be decided, the uncertain change of the system in the restoration process can be adapted, and the power supply restoration work after the disaster can be quickly completed by coordinating the emergency repair center and the scheduling center;
(2) the cooperative decision method for the emergency first-aid repair and the recovery scheduling of the power grid under the natural disaster establishes a cooperative mechanism of the emergency first-aid repair and the recovery scheduling, considers the independent coupling of the emergency first-aid repair and the recovery scheduling implementation and the target, and is matched with the actual application process;
(3) according to the cooperative decision method for the emergency first-aid repair and the restoration scheduling of the power grid under the natural disaster, the influence on the restoration scheduling is considered when an emergency first-aid repair plan is prepared, so that the emergency first-aid repair can effectively serve for load restoration;
(4) according to the cooperative decision method for emergency repair and restoration scheduling of the power grid under natural disasters, provided by the invention, the continuous change of the running state of the power grid in a period of time in the future is considered, the output adjustment and load input plan of a generator is made, and the power supply restoration efficiency is improved;
(5) according to the cooperative decision method for emergency repair and restoration scheduling of the power grid under natural disasters, provided by the invention, Monte Carlo tree search and a mathematical planning method are combined, so that emergency repair and restoration scheduling decisions can be completed in effective time, and the on-line implementation of the decisions is ensured.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic diagram of multi-source real-time information processing;
FIG. 2 is a flowchart of an emergency repair online decision making process based on Monte Carlo tree search;
FIG. 3 is a schematic diagram of a model predictive control-based recovery scheduling process;
FIG. 4 is a schematic diagram of a decision-making and issuing execution mechanism of an emergency repair and dispatch plan;
fig. 5 is a power grid structure diagram of a certain area provided in the present embodiment;
FIG. 6 shows the state of the power grid in this embodiment after a natural disaster;
fig. 7 is a result of cooperative decision of emergency repair and restoration scheduling of the power grid according to this embodiment after a natural disaster.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Firstly, the invention provides a cooperative decision method for emergency repair and restoration scheduling of a power grid under natural disasters, which comprises the following specific steps:
(1) real-time information such as the running state of a power grid, the state of a traffic network, equipment faults, the positions of emergency repair personnel and the like is acquired through an energy management system, a geographic information system and the feedback information of the emergency repair personnel;
(2) evaluating the traffic time and equipment repair time of emergency repair based on real-time information such as the state of a traffic network, equipment faults, the positions of emergency repair personnel and the like;
(3) considering the current power grid running state, traffic time and equipment repair time, and adopting a Monte Carlo tree search algorithm to make an on-line decision on an emergency repair plan for 1 hour in the future;
(4) based on a future 1-hour (1 hour in the embodiment, in other embodiments, the time can be modified into other time periods, the same applies below) emergency repair plan, obtaining the state change condition of the future power grid and the duration of each state, establishing a load recovery dynamic optimization model, and solving by adopting mathematical programming to obtain a recovery scheduling plan;
(5) and issuing the emergency repair plan to an emergency repair center to execute the planning arrangement of emergency repair personnel and materials, issuing the recovery scheduling plan to a scheduling center to execute the output adjustment of the generator and the load outlet switching-on operation, and entering the next time step.
In the step (1), according to the real-time information, a specific power grid affected by the natural disaster is determined, and the power grid restoration scheduling information is associated with the emergency repair information, and the method specifically includes the following steps:
and (1-1) determining an information link, a power transmission line and a load outgoing line of a specific fault, and establishing a mapping relation between equipment faults and scheduling resources. The failure of the information link causes the unobservable and uncontrollable corresponding scheduling resources, the failure of the power transmission line causes the corresponding line to be unavailable, and the failure of the load outgoing line causes the corresponding load to be unavailable.
And (1-2) determining the weight of each load outgoing line in the power grid according to the importance degree of the load. And establishing a uniform optimization decision index, specifically a weighted load recovery gain.
The weighted load recovery yield expression is shown as follows:
wherein,
the weighted load recovery yield is expressed in terms of,
the number of time steps is represented by,Nthe number of the bus bars is shown,
indicating busiThe number of the load lines is increased,
indicating busiLoad outletmIn thattThe status of the time step, if
=1, indicating that the load line has been dropped, otherwise, not dropped,
indicating busiLoad outletmThe weight of (a) is determined,
indicating busiLoad outletmThe power of (a) is determined,
representing the time step length.
The multi-source real-time information processing flow is shown in fig. 1.
In the step (2), the traffic time of emergency repair and the equipment repair time are evaluated, and the emergency repair scheme and the restoration scheduling plan are made as the basis. According to the geographic information system and the feedback information of the emergency maintenance personnel, the specific position and the traffic network state of the current emergency maintenance team are obtained, the traffic time required by each emergency maintenance team to each fault is calculated, and the repair time of each fault equipment is evaluated according to the damage condition of the equipment.
In the step (3), a Monte Carlo tree search algorithm is adopted to make an on-line decision on an emergency repair plan for 1 hour in the future, and the specific steps are as follows:
1. and (4) selecting. And starting from the root node, after the upper limit confidence interval index value of each node is calculated, sequentially selecting the node with the maximum upper limit confidence interval index value to perform the next expansion or simulation. The upper confidence interval index is shown as follows:
wherein UTC is an upper confidence interval index,
the mean value of the simulation index is shown,
for the number of times the parent node is accessed,
the number of times the current node is accessed.
2. And (5) expanding. And resolving the current node into a system emergency repair state, searching all possible emergency repair states in the future 1 hour, setting the possible emergency repair states as child nodes of the current node, and setting the index value of the upper limit confidence interval of the child nodes as infinity.
3. And (6) simulating. Analyzing the current node into a system emergency repair state, randomly generating a possible future repair process, establishing a plurality of recovery scheduling single time step optimization models according to the repair process, and synthesizing the solving results of the plurality of single time step optimization models to obtain the corresponding decision index value. And recovering the scheduling single-time-step optimization model, wherein the optimization target is the maximization of the single-time-step weighted load recovery yield, and the constraints comprise node voltage constraint, branch flow constraint, generator climbing constraint and generator output constraint. The corresponding expression is as follows:
wherein,
representing the weighted load recovery gain for the current time step, Nthe number of the bus bars is shown,
indicating busiThe number of the load lines is increased,
indicating busiLoad outletmAt the current time step, if
=1, indicating that the load line has been dropped, otherwise, not dropped,
indicating busiLoad outletmThe weight of (a) is determined,
indicating busiLoad outletmThe power of (a) is determined,
indicating busiThe voltage is applied to the surface of the substrate,
and
respectively representing bus-barsiThe upper and lower limits of the voltage are,
indicating lineijThe active power of the power converter is set,
indicating lineijThe upper limit of the active power is set,
and
respectively representing the active and the reactive power output of the generator,
，
，
and
the upper and lower limits of the active and reactive power output of the generator are respectively represented.
4. And (6) backtracking. And updating the parameters of each node in the search tree based on the simulation result.
An emergency repair online decision flow based on monte carlo tree search is shown in fig. 2.
In the step (4), a model predictive control technology is adopted to obtain a recovery scheduling plan for 15 minutes in the future, and the specific method is as follows:
1. judging whether the emergency repair plan can cause the change of the power grid state or not based on the emergency repair plan of 1 hour in the future, and if so, acquiring the sequence and the state duration of each state;
2. according to the state change and the duration of the power grid, a dynamic optimization model of 1 hour in the future is established by taking 15 minutes as an optimization time step;
3. and solving the dynamic optimization model by adopting a mathematical programming algorithm to obtain the recovery scheduling plans of 4 time steps in the future, and sending the recovery scheduling plan of the 1 st time step to an execution mechanism.
The model predictive control based recovery scheduling process is illustrated in fig. 3.
In the step (4), a load recovery dynamic optimization model is solved to obtain a recovery scheduling plan in the future 15 minutes, and the dynamic optimization model mainly considers the correlation condition between different time steps.
In the step (5), the emergency repair plan is issued to the emergency repair center to execute planning arrangement of emergency repair personnel and materials, the emergency repair plan for the next 1 hour is issued every 15 minutes, and after the emergency repair center receives the emergency repair plan, action routes of different emergency repair teams are arranged. And issuing the recovery scheduling plan to a scheduling center to execute the output adjustment of the generator and the closing operation of the load outgoing line, generating closing operation tickets of the power plant and the transformer substation according to the recovery scheduling plan of 15 minutes in the future, and issuing the closing operation tickets to a power system dispatcher to execute.
The emergency repair and dispatch plan decision and delivery execution mechanism is shown in fig. 4.
The invention also provides a cooperative decision making system for emergency repair and restoration scheduling of the power grid under natural disasters, which comprises:
the module is used for inputting real-time information into an information layer for processing, and constructing an emergency repair and restoration scheduling cooperative decision model;
the module is used for evaluating the traffic time of emergency repair and the equipment repair time;
a module for online decision-making of an emergency repair plan for 1 hour in the future by adopting a Monte Carlo tree search algorithm;
the module is used for acquiring the change condition of the future power grid state and the duration of each state;
a module for obtaining a recovery scheduling plan for 15 minutes in the future by using a model predictive control technique;
the module is used for issuing the emergency repair plan to the emergency repair center for execution;
and the module is used for sending the recovered scheduling plan to the scheduling center for execution.
The module may also be a device.
The following description is directed to a simulation of an actual system of a power grid (a power grid referred to as a yanwei power grid for short) in the weihai region of the china tobacco pipe, and the specific process of the cooperative decision method for emergency repair and restoration scheduling of the power grid provided by the present invention is described.
The structure of the electric network of the Yanwei is shown in FIG. 5, and the electric network of the Shandong in the Yanwei area is influenced by the snowstorm and the typhoon. It is assumed that the area is affected by the snow storm, which causes the faults of partial transmission lines, communication links and load outgoing lines, and further causes the outage of partial load. The load is 5242.8MW before the power failure in the area, and 1899.8MW load power failure is caused by accidents. The area has 3 first-aid repair teams, wherein, the first-aid repair center 1 has 2 teams, and the first-aid repair center 2 has 1 team. The method for realizing emergency repair of the fault equipment and power failure load power restoration by adopting a power grid emergency repair and restoration scheduling cooperative decision method comprises the following specific steps:
s1: real-time information such as the running state of a power grid, the state of a traffic network, equipment faults, the positions of emergency repair personnel and the like is acquired through an energy management system, a geographic information system and the feedback information of the emergency repair personnel.
After an extreme natural disaster occurs, the affected specific power grid is determined to be the Wien power grid, and when the recovery just starts, a cooperative decision model of emergency repair and recovery scheduling is formed. The specific fault positions of the power transmission line, the communication link and the load outgoing line are obtained based on the energy management system, the geographic information system and the feedback information of the emergency repair personnel, as shown in fig. 5, and 6 power transmission line faults, 2 load outgoing line faults and 2 communication link faults are total.
Based on the information, the fact that the Laizhou power plant is uncontrollable and invisible due to the influence of external faults can be further confirmed; off-line of the Weihai power plant in stage I and the Haiyang power plant; and the corresponding power grid states of the transformer substations of the ancient willows, the clean water, the Wanhua and the bamboo forest are off-grid.
And determining the weight of each load outgoing line in the power grid by considering the importance degree of the load, wherein the set value is between 0.2 and 0.6.
The objective of the cooperative decision of emergency repair and restoration scheduling is that the weighted load restoration yield is the maximum, and the expression of the weighted load restoration yield is shown as a formula (1);
s2: and evaluating the traffic time and equipment repair time of emergency repair based on real-time information such as the state of a traffic network, equipment faults, the positions of emergency repair personnel and the like.
According to the geographic information system and the feedback information of the emergency maintenance personnel, the specific position and the traffic network state of the current emergency maintenance team are obtained, the traffic time required by each emergency maintenance team to each fault is calculated, and the repair time of each fault equipment is evaluated according to the damage condition of the equipment. The final estimated traffic time is 0.5 to 4 hours, depending on distance; the final estimated breakdown time was 1 hour or 2 hours depending on the degree of damage.
S3: and (4) taking the current power grid running state, the traffic time and the equipment repair time into consideration, and adopting a Monte Carlo tree search algorithm to make an on-line decision on an emergency repair plan for 1 hour in the future.
A Monte Carlo tree search algorithm is adopted to make an on-line decision on an emergency repair plan for 1 hour in the future, and the method specifically comprises the following steps:
1. and (4) selecting. And starting from the root node, after the upper limit confidence interval index value of each node is calculated, sequentially selecting the node with the maximum upper limit confidence interval index value to perform the next expansion or simulation. The upper confidence interval index is shown in equation (2).
2. And (5) expanding. And resolving the current node into a system emergency repair state, searching all possible emergency repair states in the future 1 hour, setting the possible emergency repair states as child nodes of the current node, and setting the index value of the upper limit confidence interval of the child nodes as infinity.
3. And (6) simulating. Analyzing the current node into a system emergency repair state, randomly generating a possible future repair process, establishing a plurality of recovery scheduling single time step optimization models according to the repair process, and synthesizing the solving results of the plurality of single time step optimization models to obtain the corresponding decision index value. And recovering the scheduling single-time-step optimization model, wherein the optimization target is the maximization of the single-time-step weighted load recovery yield, and the constraints comprise node voltage constraint, branch flow constraint, generator climbing constraint and generator output constraint. The corresponding expression is shown in equation (3).
4. And (6) backtracking. And updating the parameters of each node in the search tree based on the simulation result.
The search time of the Monte Carlo tree search is set to 10 minutes (each time step is 15 minutes), and the final obtained results are that the emergency maintenance team 1 is sent to the bamboo forest station, the emergency maintenance team 2 is sent to the Wanhua station, and the emergency maintenance team 3 is sent to the transmission line Laiyang-Haiyang.
S4: based on the emergency repair plan of 1 hour in the future, the state change condition of the power grid and the duration of each state in the future are obtained, a load recovery dynamic optimization model is established, and a recovery scheduling plan is obtained by adopting mathematical programming solution.
A model predictive control technology is adopted to obtain a recovery scheduling plan in the future 15 minutes, and the specific steps are as follows:
1. and judging whether the emergency repair plan can cause the change of the power grid state or not based on the future 1-hour emergency repair plan, and if so, acquiring the sequence and the state duration of each state.
According to the emergency repair plan of the future 1 hour obtained in the step S3, all the repair teams are on the maintenance and repair road in the future 1 hour, and the power grid state is not affected, so that the power grid state in the future 1 hour is the same as the initial state of the system after power failure.
2. And according to the state change and the duration of the power grid, taking 15 minutes as an optimization time step, and establishing a dynamic optimization model for 1 hour in the future.
And the dynamic optimization model is used for optimizing the load recovery gain with the target of weighting in the time interval, and the constraints comprise node voltage constraint, branch power flow constraint, generator climbing constraint, generator output constraint and scheduling association constraint among time steps. Scheduling association constraints with invisible formats mainly include: the load can not be cut off after the load is put into operation, and the output of the generator is limited by the output of the last period of time and the climbing rate.
3. And solving the dynamic optimization model by adopting a mathematical programming algorithm to obtain the recovery scheduling plans of 4 time steps in the future, and sending the recovery scheduling plan of the 1 st time step to an execution mechanism.
Finally, the load recovered at the 1 st time step in the future includes: the line is drawn from the Fenglin station #2, the line is drawn from the Zetou station #3, the line is drawn from the Shawang station #3, and the line is drawn from the Yishun stations #6 and # 7.
S5: and issuing the emergency repair plan to an emergency repair center to execute the planning arrangement of emergency repair personnel and materials, issuing the recovery scheduling plan to a scheduling center to execute the output adjustment of the generator and the load outlet switching-on operation, and entering the next time step.
The emergency repair plan is issued to an emergency repair center to execute planning and arrangement of emergency repair personnel and materials, the emergency repair plan for the next 1 hour is issued every 15 minutes, after the emergency repair center receives the emergency repair plan, the emergency repair team 1 is arranged to be an emergency repair center 1 → a bamboo forest station, the emergency repair team 2 is arranged to be the emergency repair center 1 → a Wanhua station, and the emergency repair team 3 is arranged to be the emergency repair center 2 → a circuit Laiyang-Haiyang. And issuing the recovery scheduling plan to a scheduling center to execute power output adjustment and load outgoing line switching-on operation of the generator, generating a switching-on operation ticket of the power plant and the transformer substation according to the recovery scheduling plan of 15 minutes in the future, wherein a switching-on feeder line comprises a phoenix forest station #2 outgoing line, a sun station #3 outgoing line, a shawang station #3 outgoing line, a Yishun station #6 outgoing line and a #7 outgoing line, and issuing the switching-on feeder line to a power system dispatcher for execution.
Finally, power restoration for the full 1899.8MW load is completed in 315 minutes. The first-aid repair scheme of the first-aid repair team 1 is as follows: the emergency repair center 1-bamboo forest station-phoenix forest station-Weihai power plant stage I-Wendeng station-Weihai power plant stage I-Kunyu station-Weihai power plant stage I. The emergency repair scheme of the emergency repair team 2 is as follows: the emergency repair center 1-the kaleidoscope station-the percha station-the lazhou power plant-the Guangzhou station-the lazhou power plant. The emergency repair scheme of the emergency repair team 3 is as follows: the emergency repair center 2-laiyang station-haiyang-ancient willow station-laiyang station-ancient willow station-hai-yang station. The overall repair sequence and load recovery results obtained are shown in figure 7.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A power grid emergency repair and restoration scheduling cooperative decision method under natural disasters is characterized by comprising the following steps: the method comprises the following steps:
acquiring the running state of a power grid, the state of a traffic network, equipment faults and position information of an emergency maintenance team;
evaluating the traffic time and equipment repair time of emergency repair based on the traffic network state, equipment faults and position information of an emergency repair team;
considering the current power grid running state, traffic time obtained by evaluation and equipment repair time, and adopting a Monte Carlo tree search algorithm to decide an emergency repair plan in a set time period in the future;
acquiring the state change condition of the power grid and the duration of each state in the future based on the emergency repair plan, establishing a load recovery dynamic optimization model, and solving by adopting a mathematical programming method to obtain a recovery scheduling plan;
and carrying out emergency repair operation arrangement and material planning arrangement according to the emergency repair plan, and carrying out generator output adjustment and load outlet closing operation according to the recovery scheduling plan.
2. The cooperative decision method for emergency repair and restoration scheduling of the power grid under natural disasters of claim 1, which is characterized in that: based on traffic network state, equipment trouble and the team position information of salvageing, the concrete process of the traffic time of aassessment emergency repair and equipment repair time includes:
estimating the traffic time required by each emergency maintenance team to each fault according to the specific position of the current emergency maintenance team and the state of a traffic network;
and predicting the fault repair time of the corresponding equipment according to the fault condition of each equipment.
3. The cooperative decision method for emergency repair and restoration scheduling of the power grid under natural disasters of claim 1, which is characterized in that: the specific process of adopting the Monte Carlo tree search algorithm to decide the emergency repair plan in the set time period in the future comprises the following steps:
starting from the root node, after the upper confidence interval index value of each node is calculated, sequentially selecting the node with the maximum upper confidence interval index value to perform the next expansion or simulation;
analyzing the current node into a system emergency repair state, searching all possible emergency repair states in a set time period in the future, setting the possible emergency repair states as child nodes of the current node, and setting the index value of the upper limit confidence interval of the child nodes as infinity;
analyzing the current node into a system emergency repair state, randomly generating a possible future repair process, establishing a plurality of recovery scheduling single time-step optimization models according to the repair process, and solving to obtain a corresponding decision index value;
and updating the parameters of each node in the search tree based on the simulation result.
4. The cooperative decision method for emergency repair and restoration scheduling of the power grid under natural disasters of claim 3, wherein the cooperative decision method comprises the following steps: the optimization target of the recovery scheduling single-time-step optimization model is the maximization of the single-time-step weighted load recovery yield, and the constraints comprise node voltage constraint, branch flow constraint, generator climbing constraint and generator output constraint.
5. The cooperative decision method for emergency repair and restoration scheduling of the power grid under natural disasters of claim 3, wherein the cooperative decision method comprises the following steps: and the index value of the upper limit confidence interval is determined by the average value of the simulation results of all the nodes, the number of times that the node is accessed and the number of times that the parent node is accessed.
6. The cooperative decision method for emergency repair and restoration scheduling of the power grid under natural disasters of claim 1, which is characterized in that: the specific process of establishing a load recovery dynamic optimization model and solving and obtaining a recovery scheduling plan by adopting a mathematical programming method comprises the following steps:
judging whether the emergency repair plan can cause the change of the power grid state or not based on the emergency repair plan in a set time period in the future, and if so, acquiring the sequence and the state duration of each state;
according to the state change and the duration of the power grid, a dynamic optimization model in a set time period in the future is established by taking the set time period as an optimization time step, wherein the set time period can be divided into a plurality of set time periods;
and solving the dynamic optimization model by adopting a mathematical programming algorithm to obtain the recovery scheduling plans of each optimization time step in the future, and sequentially executing the recovery scheduling plans of each optimization time step.
7. The cooperative decision method for emergency repair and restoration scheduling of the power grid under natural disasters of claim 6, wherein the cooperative decision method comprises the following steps: the optimization target of the load recovery dynamic optimization model is the weighted load recovery gain of the time interval, and the constraints comprise node voltage constraint, branch power flow constraint, generator climbing constraint, generator output constraint and scheduling association constraint among time steps.
8. The cooperative decision method for emergency repair and restoration scheduling of the power grid under natural disasters of claim 1, which is characterized in that: the concrete process of emergency repair operation arrangement and material planning arrangement according to the emergency repair plan comprises the following steps:
and issuing an emergency repair plan of a set time period in the future every set time, and arranging the action route of a corresponding emergency repair team and the action route of material allocation according to the emergency repair plan.
9. The cooperative decision method for emergency repair and restoration scheduling of the power grid under natural disasters of claim 1, which is characterized in that: the specific process of performing the power output adjustment and the load outlet closing operation of the generator according to the recovery scheduling plan comprises the following steps:
and generating a closing operation order of the power plant and the transformer substation according to the recovery scheduling plan of the next optimized time step, and issuing the closing operation order to a corresponding dispatcher or an actuator for execution.
10. A power grid emergency repair and restoration scheduling cooperative decision system under natural disasters is characterized in that: the method comprises the following steps:
the parameter acquisition module is configured to acquire a power grid running state, a traffic network state, equipment faults and position information of an emergency maintenance team;
the time factor determination module is configured to evaluate the traffic time of emergency repair and the equipment repair time based on the traffic network state, the equipment fault and the position information of the emergency repair team;
the emergency repair decision module is configured to consider the current power grid operation state, traffic time obtained through evaluation and equipment repair time, and decide an emergency repair plan in a set time period in the future by adopting a Monte Carlo tree search algorithm;
the recovery scheduling decision module is configured to acquire the state change condition of the power grid and the duration of each state in the future based on the emergency repair plan, establish a load recovery dynamic optimization model, and solve by adopting a mathematical programming method to acquire a recovery scheduling plan;
and the execution module is configured to carry out emergency repair operation arrangement and material planning arrangement according to the emergency repair plan, and carry out generator output adjustment and load outlet closing operation according to the recovery scheduling plan.
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