CN117236672A - Mobile energy storage robust site selection and path planning method considering emergency time reliability - Google Patents
Mobile energy storage robust site selection and path planning method considering emergency time reliability Download PDFInfo
- Publication number
- CN117236672A CN117236672A CN202311523042.9A CN202311523042A CN117236672A CN 117236672 A CN117236672 A CN 117236672A CN 202311523042 A CN202311523042 A CN 202311523042A CN 117236672 A CN117236672 A CN 117236672A
- Authority
- CN
- China
- Prior art keywords
- path
- emergency
- reliability
- maximum
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 50
- 238000000034 method Methods 0.000 title claims abstract description 40
- 230000008569 process Effects 0.000 claims abstract description 10
- 239000011159 matrix material Substances 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000004044 response Effects 0.000 claims description 5
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 3
- 230000007774 longterm Effects 0.000 abstract description 3
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 10
- 239000003345 natural gas Substances 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 2
- 238000010187 selection method Methods 0.000 description 2
- 101100401100 Caenorhabditis elegans mes-1 gene Proteins 0.000 description 1
- 101100512897 Caenorhabditis elegans mes-2 gene Proteins 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013524 data verification Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000010792 warming Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention belongs to the technical field of safe operation of an electric power system, and particularly relates to a mobile energy storage robust site selection and path planning method considering emergency time reliability. The whole process can carry out mobile energy storage emergency site selection and path planning only by knowing the upper and lower bounds of uncertain parameters, and has more practical value. The invention effectively avoids the long-term risk of the uncertain factor fluctuation on the site selection of the facilities, and simultaneously can better consider the problems of the network center and the median.
Description
Technical Field
The invention belongs to the technical field of safe operation of power systems, and particularly relates to a mobile energy storage robust site selection and path planning method considering emergency time reliability.
Background
In recent years, extreme climatic events frequently occur due to global warming. The operation mode of the power grid is more complex, the risk of large-scale power failure is obviously increased, and particularly when extreme weather disasters are met, the events such as power interruption and the like cause huge damage to the whole society, so that the construction of an emergency power supply is urgently required to be enhanced.
Due to the rapid growth of the power industry and the increasing sophistication of power systems, the requirements for ensuring the stability and reliability of the power supply are increasing. Therefore, after the emergency occurs, how to perform safe, reasonable and efficient rescue has long-term significance on the stability of the power system.
At present, the emergency facility path-site selection method adopted when an emergency occurs is required to rely on a large amount of statistical data for processing the parameter distribution of a probability or fuzzy model, but due to the characteristics of small emergency probability and small sample and information, the emergency facility path-site selection method has a problem in the aspect of uncertainty in processing. Therefore, the method has more practical significance in making a scientific decision on the initial position of the mobile energy storage device before an accident occurs.
Disclosure of Invention
According to the defects in the prior art, the mobile energy storage robust site selection and path planning method considering the reliability of emergency time is provided, the mobile energy storage emergency site selection and path planning can be performed only by knowing the upper and lower bounds of uncertain parameters, the practical value is higher, the long-term risk of the uncertain factor fluctuation on the site selection of facilities is effectively avoided, and the safe operation of an electric power system is ensured.
In order to achieve the above purpose, the invention provides a mobile energy storage robust site selection and path planning method considering emergency time reliability, comprising the following steps:
s1, setting an electric power node as an interval point in a traffic network, constructing a topological structure of the traffic network, and calculating the weight of each node according to the known information of each node;
s2, defining the reliability of rescue emergency time as: after the extreme event occurs, mobile energy storage (Mobile Energy Storage, MES, in the invention, short for mobile energy storage emergency power supply vehicle) reaches a demand point within the maximum rescue time range, so that an emergency time non-probability reliability model is established;
s3, establishing a maximum path of the non-probability reliability of the emergency time and a path weight matrix model thereof according to the non-probability reliability model of the emergency time;
s4, constructing a star network based on nodes by taking the power network as an absolute center in the traffic network, and calculating a maximum reliability path and a weight matrix thereof;
and S5, establishing an emergency facility deviation robust site selection model based on the minimum maximum remorse value, and solving to obtain a deviation robust optimal solution.
After an extreme event occurs, the power network emergency rescue time is affected by a plurality of factors, such as the distance from a rescue place to a departure place, road congestion, rescue cost, road safety and the like, so that when a reasonable road emergency rescue vehicle path planning model is established, reasonable quantitative analysis of each factor is very important. The invention assumes that the power nodes are interval points in the traffic network, and the number of the used intervals represents the influence of 7 factors such as the interval length of each node, the risk condition of the line and the surrounding environment, the frequency of historical emergency, the grade of disaster, the effectiveness degree of acquired information, the status of network communication, the demand intensity of rescue and the like on emergency rescue.
In the step S1, the method for constructing the topological structure of the traffic network comprises the following steps:
the topological relation of the traffic network is thatWherein->For a set of power nodes in the traffic network C, +.>Is an arc segment set for connecting each node, namely a traffic network road, and is provided with any node +.>The weight of (2) is +.>，/>、/>The lower bound and the upper bound of the weight of the node i are respectively +.>The value can be arbitrarily taken in the corresponding interval, and the formula is as follows:
（1）；
（2）；
（3）；
in the formula (1)The weight coefficients of the 7 factors relative to other factors are respectively,the quantized values of 7 factors are respectively, and the minimum value and the maximum value of the node weight interval are respectively 1 and 2 for the convenience of calculation.
The invention introduces the concept of reliability into the problem of site selection concept, and defines the reliability of rescue emergency time as follows: after the extreme event occurs, the mobile energy storage reaches the demand point within the maximum rescue time range.
In the step S2, the process of establishing the emergency time non-probability reliability model is as follows:
s21, any path between any two traffic network nodes i and j is as follows，/>In order to connect all possible path sets between the i and j nodes, the operation of the mobile energy storage usually encounters the phenomenon of road jam in consideration of the real situation, and therefore, the mobile energy storage passes through + ->Is +.>It is determined whether the mobile energy storage can pass the route +.>At maximum emergency time->The success of rescue in the range of (a) is:
（4）；
（5）；
wherein,for moving energy storage under ideal state>Time of (2)>Time spent for mobile energy storage due to road congestion; when->If the emergency rescue fails, the emergency rescue is not successful, otherwise, < +.>Then the rescue is successful;
s22, as can be seen from the formula (4), U is an interval value, and the center point thereof is:
the radius is as follows:
thus, the emergency time non-probability reliability is defined as:
（6）；
when (when)When in use, then->I.e. rescue success, when->Then->I.e. rescue fails, whenWhen it is, there is +.>、/>Two possibilities, in which case the rescue is not necessarily successful, but can be determined +.>The larger the value, the higher the likelihood of success of rescue;
s23, setting the maximum emergency response time for the convenience of calculationThe formula (5) is simplified as:
（7）；
the emergency time non-probability reliability between the nodes i and j is obtained. />The larger the mobile energy storage starts from the point i, the larger the success rate of reaching the point j in the maximum emergency rescue time.
In the step S3, the step of establishing the maximum path of the non-probability reliability of the emergency time and the path weight matrix model thereof is as follows:
s31, let l be the compositionIf the maximum emergency time non-probability reliability between the nodes i, j can be calculated as:
（8）；
s32, introducing an independent variable as to find a path with optimal time reliabilityFunction of->The time reliability parameter of the path between any two nodes i and j in the emergency rescue network is used as a new path weight;
s33, when the emergency time is abundant, the allowable time in the rescue process takes an upper limit value, if the time spent in the actual operation is less than the upper limit value, the result is very reliable, otherwise, if the emergency time is insufficient, each link in the rescue process must be more compact, and the time spent in the rescue process is as far as possible deviated from a lower limit value, so that the emergency time is very shortThe calculation formula is as follows:
（9）；
in the middle of、/>、/>Respectively an upper limit value, a central value and a lower limit value of a time-consuming interval of the kth road from the i node to the j node; as can be seen from formula (8), +.>The value of (2) depends on->The smaller the value of the interval parameter of the value interval and the time spent by the rescue route, the more reliable the rescue is through the route; at the same time +.>Consider a non-probabilistic reliability path between nodes i, j; with independent variable->When a certain rescue path has a minimum +.>I.e.=/>In this case, the path is the path corresponding to the non-probability reliability of the emergency time with the maximum value between the nodes i and j>Simply referred to as the maximum reliability path;
s34, obtaining a path with maximum reliability between any two nodes i and j by using Floyd algorithmAnd its path weight +.>The maximum reliability path matrix between the nodes in the emergency rescue network is +.>And its corresponding path weight matrix +.>Expressed as:
（10）；
（11）。
in the step S4, the absolute center of the electric power network in the traffic network has the smallest maximum distance from the electric power network to each power demand point, and the shortest distance from the absolute center to each farthest point is called the absolute radius of the traffic network; for any node i, i and other nodes are connected according to the shortest path to form a star network, namely a star network generated by i, a point exists in the star network, so that the maximum distance from the point to the farthest node is minimum, the point is called a local center, the distance value is called the local radius of the local center, and the maximum reliability path and the weight matrix thereof are solved by combining the concept of the maximum emergency time non-probability reliability corresponding path and the path weight matrix thereof provided by the invention.
Accordingly, the method for calculating the maximum reliability path and the weight moment thereof comprises the following steps:
s41, constructing a star network formed by nodes, and finding out corresponding local center points；
S42, orderCalculate and derive the initial maximum reliability path matrix +.>And a path weight matrix corresponding thereto>；
S43, slaveFind the maximum element +.>And (2) and (4) at->The corresponding maximum reliability path +.>；
S44, slaveFind the next largest element in row k>And is not in the way->The procedure is repeated until the n+1th element +.>Calculating local radius +.>：
；
；
……；
；
S45, ifS46, if not, directly transferring to S47;
s46, from the local center pointEdge->And->Direction (S)>Anddirection … …, < >>And->Direction is moved respectively +.>，/>，……，/>Units, record newly formed dots +.>；
S47, ifS43 is carried out, otherwise S48 is carried out;
s48, selecting n groups of newly formed points according to the local radius;
s49, new nodes are added to the original traffic network C, and the maximum reliability path and the path weight matrix thereof between the new nodes and the original traffic network nodes are recalculated.
In the step S5, the emergency facility deviation robust model is solved by the following steps:
s51, as known, a point x is required to be found on any arc in the traffic network C, so that the mobile energy storage is in an emergency response time limit periodIn the implementation of emergency facilities in various possible scenarios +.>The maximum deviation from the optimal solution of the lower (namely, all possible values of the node weights) to the sum of the weighted path weights of all nodes in the network is minimum, wherein the scenario set O is the interval +.>；
S52, defining a scenarioThe lower mobile energy storage emergency site selection is carried out at a local central point +.>The calculation formula of the cost function of (2) is as follows:
（12）；
s53, defining and using local center pointsAlternative node +.>And paying out a function of the maximum regret value of the cost:
（13）；
s54, obtaining the condition meeting the constraint condition by using the distance matrix method in the formula (13)Is +.>，/>The maximum element in the z-th row of the distance matrix is moved in the two directions of the second maximum element which is not on the path>A series of newly marked nodes, new nodes and the whole of the original nodes contained on the connection line of the new nodes, which are obtained by the difference between the local radius values, are recorded as an alternative node set->；
S55, setting:
（14）；
s56, establishing a minimum maximum remorse emergency facility deviation robust site selection model based on the formulas (11), (12) and (13):
（15）；
and S57, solving the model in the S56 to obtain a bias robust optimal solution.
And emergency site selection and path planning of mobile energy storage can be performed through the optimal solution.
In the S1-S5, MATLAB modeling and solving are used.
The model and algorithm related to the invention can be executed by an electronic device, the electronic device comprises a memory, a processor and a computer program stored on the memory and running on the processor, and the model solving and algorithm is realized by the execution of the program by the processor.
The invention has the beneficial effects that:
the invention sets the power node as an interval point in the traffic network, constructs a topological structure of the traffic network, establishes an emergency time non-probability reliability model, an emergency time non-probability reliability maximum path and a path weight matrix model thereof, then uses the power network as an absolute center in the traffic network, constructs a star network based on the node, calculates the maximum reliability path and a weight matrix thereof, finally establishes an emergency facility deviation robust site selection model based on the minimum maximum regret value and solves the model to obtain a deviation robust optimal solution, and the whole process can carry out mobile energy storage emergency site selection and path planning only by knowing the upper and lower bounds of uncertain parameters, thereby having more practical value.
The result obtained by the method has better anti-interference capability, and effectively avoids the risk of uncertain factor fluctuation on facility site selection. In addition, the method has the advantages of time robustness of the robust address setting optimization solution, response timeliness of the road network remote emergency demand points, and excellent overall service performance of the road network demand points, and can better consider the problems of network centers and mediates.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic diagram of a coupled electric power transportation natural gas network structure according to an embodiment of the present invention;
FIG. 3 is a graph of a daily forecast of electrical load and wind power for an embodiment of the present invention;
FIG. 4 is a graph showing the power output and consumption of each unit of the power system under scenario 1 according to the embodiment of the present invention;
FIG. 5 is a graph showing the output and consumption of electric energy by each unit of the power system under scenario 2 according to the embodiment of the present invention;
FIG. 6 is a diagram of the spatiotemporal characteristics of mobile energy storage under scenario 1 in an embodiment of the invention;
fig. 7 is a diagram showing the space-time characteristics of mobile energy storage under scenario 2 in an embodiment of the present invention.
Detailed Description
Embodiments of the invention are further described below with reference to the accompanying drawings:
as shown in fig. 1, the mobile energy storage robust site selection and path planning method considering emergency time reliability comprises the following steps:
s1, setting an electric power node as an interval point in a traffic network, constructing a topological structure of the traffic network, and calculating the weight of each node according to the known information of each node;
s2, defining the reliability of rescue emergency time as: after the extreme event occurs, the mobile energy storage reaches a demand point within the maximum rescue time range, so that an emergency time non-probability reliability model is established;
s3, establishing a maximum path of the non-probability reliability of the emergency time and a path weight matrix model thereof according to the non-probability reliability model of the emergency time;
s4, constructing a star network based on nodes by taking the power network as an absolute center in the traffic network, and calculating a maximum reliability path and a weight matrix thereof;
and S5, establishing an emergency facility deviation robust site selection model based on the minimum maximum remorse value, and solving to obtain a deviation robust optimal solution.
In this embodiment, a system in which an IEEE39 node power system, a belgium 20 node natural gas system, and a 16 node traffic system are coupled is selected for data verification. The coupled electro-pneumatic-transportation network system is shown in fig. 2.
In fig. 2, the pure numbers and Node numbers represent nodes, wherein the natural gas network (natural gas system) is shown on the left side of fig. 2, the nodes of the Node numbers are natural gas nodes, the traffic network consisting of traffic lines (traffic system, wherein the light-colored transverse wide bars are original traffic nodes) and an electric power network (electric power system) is shown on the right side of fig. 2, and the pure numbers are electric power nodes (black transverse wide bars).
The electric load data are shown in fig. 3, the nodes 31 and 11 of the electric power network are respectively connected with two distributed fans (fan 1 and fan 2 respectively), and the fan output is shown in fig. 2; node 25, node 26, node 28, node 21, node 24, node 12 are coupled as charging stations with nodes 1, 2, 3, 8, 11, 15 of the traffic network as emergency charge demand points of the power network. And data simulation is adopted for the interval length of the 6 nodes, the conditions of the line and surrounding environment risk, the historic emergency frequency, the disaster level, the effective degree of information acquisition, the status in network communication and the demand intensity of rescue, and the weight coefficient of each factor for other factors except the factors is calculated as follows:
。
in this embodiment, 2 emergency energy storage facilities (namely, mobile energy storage 1 and mobile energy storage 2, respectively MES 1 and MES 2 in fig. 4-7) are established for 6 emergency demand interval points of the traffic network and the power network, and when an emergency occurs at any one emergency demand interval point, the emergency demand interval points are ensured to existAt least one emergency service facility point can arrive at the accident site for emergency rescue within a limited rescue time, and the emergency rescue time is limited toThe running speed of the vehicle isThe time interval was 15 minutes.
In order to verify the improvement effect of the mobile energy storage emergency site selection planning method on the toughness of the power distribution network when facing an emergency, the following 2 situations are considered.
Scenario 1: when the mobile energy storage emergency power supply vehicle starts from the node 1 of the traffic network, rescue is carried out;
scenario 2: the mobile energy storage emergency power supply vehicle starts from the node obtained by the method for rescuing.
In the 2 situations, when line faults with different severity occur in the power distribution network, the invention takes the line damage of the power network connection node 11 and the node 12 as an example for analysis.
The power output and consumption of each unit of the power systems in scenario 1 and scenario 2 to electric energy are shown in fig. 4 and 5. The spatiotemporal characteristics of the mobile energy storage in scenario 1 and scenario 2 are shown in fig. 6 and 7. As can be seen from fig. 4-7, when the power network connects node 11 to node 12 and the line breaks down (i.e. when the emergency demand point 6 requires power), the mobile energy store immediately draws power from each node and then issues to and provides power for that point.
1) Impact of site selection on total cost of system operation
As shown in table 1, in the case of scenario 2, the total cost of system operation is reduced by 1670698 yuan compared to scenario 1, and therefore, the total cost of system operation can be reduced by rationally planning the site selection position.
2) Influence of site selection on emergency rescue
As shown in table 2, the time taken for the mobile energy storage to start at the node selected by the method of the invention is significantly reduced compared with the time taken for the node selected by the ordinary method to start, and the total operation time of one day of the mobile energy storage is not influenced.
Claims (7)
1. A mobile energy storage robust site selection and path planning method considering emergency time reliability is characterized by comprising the following steps:
s1, setting an electric power node as an interval point in a traffic network, constructing a topological structure of the traffic network, and calculating the weight of each node according to the known information of each node;
s2, defining the reliability of rescue emergency time as: after the extreme event occurs, the mobile energy storage reaches a demand point within the maximum rescue time range, so that an emergency time non-probability reliability model is established;
s3, establishing a maximum path of the non-probability reliability of the emergency time and a path weight matrix model thereof according to the non-probability reliability model of the emergency time;
s4, constructing a star network based on nodes by taking the power network as an absolute center in the traffic network, and calculating a maximum reliability path and a weight matrix thereof;
and S5, establishing an emergency facility deviation robust site selection model based on the minimum maximum remorse value, and solving to obtain a deviation robust optimal solution.
2. The mobile energy storage robust site selection and path planning method considering emergency time reliability according to claim 1, wherein: in the step S1, the method for constructing the topological structure of the traffic network comprises the following steps:
the topological relation of the traffic network is thatWherein->For a set of power nodes in the traffic network C, +.>Is an arc segment set for connecting each node, namely a traffic network road, and is provided with any node +.>The weight of (2) is +.>，/>、/>The lower bound and the upper bound of the weight of the node i are respectively +.>The value can be arbitrarily taken in the corresponding interval, and the formula is as follows:
（1）；
（2）；
（3）；
in the formula (1)The method comprises the steps of respectively obtaining the interval length of each node, the risk conditions of the line and the surrounding environment, the frequency of historical emergencies, the grade of disasters, the effective degree of information acquisition, the status in network communication,Weight coefficient of 7 factors relative to other factors for demand intensity of rescue, +.>The quantized values of 7 factors are respectively adopted, and the minimum value and the maximum value of the node weight interval are respectively adopted as 1 and 2.
3. The mobile energy storage robust site selection and path planning method considering emergency time reliability according to claim 2, wherein: in the step S2, the process of establishing the emergency time non-probability reliability model is as follows:
s21, any path between any two traffic network nodes i and j is as follows，/>To connect all possible path sets between the two nodes i, j, the energy storage is moved through +.>Is +.>Determining whether the mobile energy storage can pass the route +.>At maximum emergency time->The success of rescue in the range of (a) is:
（4）；
（5）；
wherein,for moving energy storage under ideal state>Time of (2)>Time spent for mobile energy storage due to road congestion; when->If the emergency rescue fails, the emergency rescue is not successful, otherwise, < +.>Then the rescue is successful;
s22, as can be seen from the formula (4), U is an interval value, and the center point thereof is:
the radius is as follows:
thus, the emergency time non-probability reliability is defined as:
（6）；
when (when)When in use, then->I.e. rescue success, when->Then->I.e. rescue fails, whenWhen it is, there is +.>、/>Two possibilities, in which case the rescue is not necessarily successful, but can be determined +.>The larger the value, the higher the likelihood of success of rescue;
s23, setting the maximum emergency response timeThe formula (5) is simplified as:
（7）；
the emergency time non-probability reliability between the nodes i and j is obtained.
4. The mobile energy storage robust site selection and path planning method considering emergency time reliability according to claim 3, wherein: in the step S3, the step of establishing the maximum path of the non-probability reliability of the emergency time and the path weight matrix model thereof is as follows:
s31, let l be the compositionIf the maximum emergency time non-probability reliability between the nodes i, j can be calculated as:
（8）；
s32, introducing an independent variable as to find a path with optimal time reliabilityFunction of->The time reliability parameter of the path between any two nodes i and j in the emergency rescue network is used as a new path weight;
s33, when the emergency time is abundant, the allowable time in the rescue process takes an upper limit value, if the time spent in the actual operation is less than the upper limit value, the result is very reliable, otherwise, if the emergency time is insufficient, each link in the rescue process must be more compact, and the time spent in the rescue process is as far as possible deviated from a lower limit value, so that the emergency time is very shortThe calculation formula is as follows:
（9）；
in the middle of、/>、/>Respectively an upper limit value, a central value and a lower limit value of a time-consuming interval of the kth road from the i node to the j node;as can be seen from formula (8), +.>The value of (2) depends on->The smaller the value of the interval parameter of the value interval and the time spent by the rescue route, the more reliable the rescue is through the route; at the same time +.>Consider a non-probabilistic reliability path between nodes i, j; with independent variable->When a certain rescue path has a minimum +.>I.e. +.>=When the emergency time is in the emergency time, the path is the path corresponding to the non-probability reliability with the maximum emergency time between the nodes i and jSimply referred to as the maximum reliability path;
s34, obtaining a path with maximum reliability between any two nodes i and j by using Floyd algorithmAnd its path weight +.>The maximum reliability path matrix between the nodes in the emergency rescue network is +.>And its corresponding path weight matrix +.>Expressed as:
（10）；
（11）。
5. the mobile energy storage robust site selection and path planning method considering emergency time reliability according to claim 4, wherein: in the step S4, the absolute center of the electric power network in the traffic network has the smallest maximum distance from the electric power network to each power demand point, and the shortest distance from the absolute center to each farthest point is called the absolute radius of the traffic network; for any node i, i and other nodes are connected according to the shortest path to form a star network, namely, the star network generated by i, a point exists in the star network, the maximum distance from the point to the farthest node is minimum, the point is called a local center, the distance value is called the local radius of the local center, and the method for calculating the maximum reliability path and the weight moment thereof comprises the following steps:
s41, constructing a star network formed by nodes, and finding out corresponding local center points；
S42, orderCalculate and derive the initial maximum reliability path matrix +.>And the corresponding path weightMatrix array；
S43, slaveFind the maximum element +.>And (2) and (4) at->The corresponding maximum reliability path；
S44, slaveFind the next largest element in row k>And is not in the way->The procedure is repeated until the n+1th element +.>Calculating local radius +.>：
；
；
……；
；
S45, ifS46, if not, directly transferring to S47;
s46, from the local center pointEdge->And->Direction (S)>Anddirection … …, < >>And->Direction is moved respectively +.>，/>，……，/>Units, record newly formed dots +.>；
S47, ifS43 is carried out, otherwise S48 is carried out;
s48, selecting n groups of newly formed points according to the local radius;
s49, new nodes are added to the original traffic network C, and the maximum reliability path and the path weight matrix thereof between the new nodes and the original traffic network nodes are recalculated.
6. The mobile energy storage robust site selection and path planning method considering emergency time reliability according to claim 5, wherein: in the step S5, the emergency facility deviation robust model is solved by the following steps:
s51, as known, a point x is required to be found on any arc in the traffic network C, so that the mobile energy storage is in an emergency response time limit periodIn the implementation of emergency facilities in various possible scenarios +.>The maximum deviation from the optimal solution to the sum of weighted path weights of all nodes in the network is minimized, wherein the scenario set O is interval +.>；
S52, defining a scenarioThe lower mobile energy storage emergency site selection is carried out at a local central point +.>The calculation formula of the cost function of (2) is as follows:
（12）；
s53, defining and using local center pointsAlternative node +.>And paying out a function of the maximum regret value of the cost:
（13）；
s54, obtaining the condition meeting the constraint condition by using the distance matrix method in the formula (13)Is +.>，/>The maximum element in the z-th row of the distance matrix is moved in the two directions of the second maximum element which is not on the path>A series of newly marked nodes, new nodes and the whole of the original nodes contained on the connection line of the new nodes, which are obtained by the difference between the local radius values, are recorded as an alternative node set->；
S55, setting:
（14）；
s56, establishing a minimum maximum remorse emergency facility deviation robust site selection model based on the formulas (11), (12) and (13):
（15）；
and S57, solving the model in the S56 to obtain a bias robust optimal solution.
7. The mobile energy storage robust site selection and path planning method considering emergency time reliability according to any one of claims 1-6, wherein: in the S1-S5, MATLAB modeling and solving are used.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311523042.9A CN117236672A (en) | 2023-11-16 | 2023-11-16 | Mobile energy storage robust site selection and path planning method considering emergency time reliability |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311523042.9A CN117236672A (en) | 2023-11-16 | 2023-11-16 | Mobile energy storage robust site selection and path planning method considering emergency time reliability |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117236672A true CN117236672A (en) | 2023-12-15 |
Family
ID=89086540
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311523042.9A Pending CN117236672A (en) | 2023-11-16 | 2023-11-16 | Mobile energy storage robust site selection and path planning method considering emergency time reliability |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117236672A (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20080019401A (en) * | 2006-08-28 | 2008-03-04 | (주)플러스빅 | Real-time accident rescue support system |
| CN109211244A (en) * | 2018-11-12 | 2019-01-15 | 重庆交通大学 | Paths planning method based on UTMD algorithm |
| CN111242544A (en) * | 2020-01-10 | 2020-06-05 | 东南大学 | Site selection method for rescue material storage points aiming at dangerous goods transportation accidents |
| CN114117738A (en) * | 2021-10-28 | 2022-03-01 | 国网浙江省电力有限公司杭州供电公司 | Dual-target optimization scheduling method for mobile emergency power supply |
| CN114614482A (en) * | 2022-05-12 | 2022-06-10 | 山东理工大学 | Method for improving toughness of power distribution system through virtual energy storage based on continuous time scale |
| WO2022142392A1 (en) * | 2020-12-28 | 2022-07-07 | 国网天津市电力公司电力科学研究院 | Method for formulating spatio-temporal combined optimization scheduling policy for mobile energy storage |
| CN114723258A (en) * | 2022-03-29 | 2022-07-08 | 华北电力大学 | Two-stage planning method and system for power emergency resources |
| CN115718981A (en) * | 2022-11-17 | 2023-02-28 | 中国地质大学（武汉） | Emergency facility site selection and path optimization method and system based on second-order cone optimization |
| CN116862149A (en) * | 2023-06-15 | 2023-10-10 | 华南理工大学 | Power distribution network mobile emergency resource pre-configuration method considering extreme weather influence |
-
2023
- 2023-11-16 CN CN202311523042.9A patent/CN117236672A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20080019401A (en) * | 2006-08-28 | 2008-03-04 | (주)플러스빅 | Real-time accident rescue support system |
| CN109211244A (en) * | 2018-11-12 | 2019-01-15 | 重庆交通大学 | Paths planning method based on UTMD algorithm |
| CN111242544A (en) * | 2020-01-10 | 2020-06-05 | 东南大学 | Site selection method for rescue material storage points aiming at dangerous goods transportation accidents |
| WO2022142392A1 (en) * | 2020-12-28 | 2022-07-07 | 国网天津市电力公司电力科学研究院 | Method for formulating spatio-temporal combined optimization scheduling policy for mobile energy storage |
| CN114117738A (en) * | 2021-10-28 | 2022-03-01 | 国网浙江省电力有限公司杭州供电公司 | Dual-target optimization scheduling method for mobile emergency power supply |
| CN114723258A (en) * | 2022-03-29 | 2022-07-08 | 华北电力大学 | Two-stage planning method and system for power emergency resources |
| CN114614482A (en) * | 2022-05-12 | 2022-06-10 | 山东理工大学 | Method for improving toughness of power distribution system through virtual energy storage based on continuous time scale |
| CN115718981A (en) * | 2022-11-17 | 2023-02-28 | 中国地质大学（武汉） | Emergency facility site selection and path optimization method and system based on second-order cone optimization |
| CN116862149A (en) * | 2023-06-15 | 2023-10-10 | 华南理工大学 | Power distribution network mobile emergency resource pre-configuration method considering extreme weather influence |
Non-Patent Citations (1)
| Title |
|---|
| 汤兆平等: "基于非概率可靠性的铁路应急设施选址-路径鲁棒优化", 中国管理科学, vol. 30, no. 9, pages 207 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10886736B2 (en) | Post-disaster topology detection and energy flow recovery in power distribution network | |
| CN111697566B (en) | Reliability assessment method for active power distribution network information physical system considering information failure | |
| CN109118098A (en) | The cascading failure methods of risk assessment and system of high proportion wind-electricity integration | |
| CN105376156A (en) | Multi-attribute decision-making based power backbone transmission network route planning method | |
| CN110222889B (en) | Power distribution network feeder automation terminal configuration method based on multiple intelligent algorithms | |
| CN110971525B (en) | Service routing and addressing method for service operation of power communication network | |
| CN111475953B (en) | Energy supply reliability influence analysis method, device equipment and storage medium | |
| CN115310378A (en) | Power grid toughness evaluation and differentiation planning method under extreme typhoon disaster | |
| CN109919398A (en) | The zonal reserve Optimal Configuration Method of electric system containing wind-powered electricity generation based on figure partitioning algorithm | |
| CN109558990B (en) | Power distribution network disaster prevention backbone network frame planning method based on Steiner tree model | |
| CN110412417B (en) | Micro-grid data fault diagnosis method based on intelligent power monitoring instrument | |
| Garau et al. | ICT reliability modelling in co-simulation of smart distribution networks | |
| CN114117730A (en) | Elasticity evaluation method for power distribution network under typhoon disaster | |
| CN106850253A (en) | A kind of method of the transmission time reliability measurement based on multimode network | |
| CN117424210A (en) | Method, system and equipment for evaluating vulnerability of power CPS under typhoon disaster | |
| CN117236672A (en) | Mobile energy storage robust site selection and path planning method considering emergency time reliability | |
| CN117236030A (en) | Power system toughness evaluation modeling method considering cascading overload fault occurrence under typhoon disaster | |
| CN116862149A (en) | Power distribution network mobile emergency resource pre-configuration method considering extreme weather influence | |
| CN115906610A (en) | Distributed power supply site selection planning method considering line faults and power grid toughness | |
| CN111027855A (en) | Power system risk control method considering power transmission line meteorological disaster fault probability | |
| CN114221901B (en) | Energy Internet CPS toughness scheduling method, system and storage medium thereof | |
| CN115065166A (en) | Low-voltage distribution network state sensing and abnormal alarming method | |
| CN103166317A (en) | Reliable detection method and device containing distinguished encoding rule (DER) power distribution communication manner | |
| CN114465351A (en) | Method and system for generating topological structure of low-voltage distribution network | |
| CN112036740A (en) | Node power failure risk invariant node reserve capacity rapid assessment method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |