CN112257950A - Trade path configuration method applied to power market and computer-readable storage medium - Google Patents
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Abstract
The invention provides a trading path configuration method applied to an electric power market and a computer readable storage medium. Secondly, setting source points and sinks corresponding to power selling and power purchasing in the transaction path through the peer-to-peer network, carrying out breadth-first search accumulation on the maximum feasible flow among the original sinks, and realizing effective and quantitative rapid estimation on the maximum transmission limit of the transaction path.
Description
Technical Field
The present invention relates to the field of power transmission and distribution, and more particularly, to a method for configuring a transaction path applied to an electric power market and a computer-readable storage medium.
Background
The imbalance of the distribution of the Chinese energy sources and the difference of the regional economic development cause the reverse distribution of the supply and demand of the Chinese energy sources. Taking the new energy capacity and consumption in Gansu as an example, the local consumption capacity in the northwest region is limited, and the large-scale wind and light abandoning situation can be effectively improved only by the national resource optimization allocation, namely, the policy support of the electric power spot transaction.
According to the planning of national grid companies, the construction of extra-high voltage networks is continuously promoted, and the transmission network is developed from the existing chain structure to the mesh structure. The 'three vertical three horizontal' ultrahigh rack target network frame is built in 2017, and the 'five vertical five horizontal' main network frame formed by the ultra-high voltage synchronous power grids in North China, China and east China in 2020 is built.
At present, the internal extra-high voltage channel is relatively single, and national power grid companies generally carry out overall organization of cross-region electric quantity transaction according to the year, the quarter or the month. However, as extra-high voltage lines are added, the complexity of the transmission network will continue to increase. The configuration problem of the inter-provincial power transaction path will be highlighted increasingly and embodied in the following three aspects:
1) the number of transaction paths between power selling parties and power purchasing parties is increased, and an effective evaluation means is lacked;
2) under complex network conditions, the configuration of a transaction path is limited by the capacity of a channel;
3) and under the condition of increasing the number of transaction paths, estimating the maximum transmission amount.
From the current research situation at home and abroad, no effective and direct solution is provided for the problem of cross-regional transaction path configuration in a complex network.
Disclosure of Invention
The invention aims to provide a transaction path configuration method and a computer-readable storage medium applied to an electric power market, which can effectively quantify a transaction path and the maximum transmission limit thereof and aims to solve the problems that key nodes are difficult to identify and the maximum transmission limit cannot be quickly solved in the configuration of the transaction path for new energy cross-regional consumption.
The invention discloses a trading path configuration method applied to an electric power market, which comprises the following steps: s1, constructing a network graph G of the electric power market transaction path, wherein the rated power of the electric transmission line between nodes u and v in the network graph G is the capacity c (u, v) of the edge, the power selling party in the electric power transaction is a source point S, and the purchasing party in the electric power transaction is a sink point t; s2, initializing the capacity of all edges in the network, wherein c < u, v > inherits the changed capacity and is initialized to 0, and the edges < v, u > are return edges; initializing a maximum flow to 0; s3, finding an augmentation path p from a source point S to a sink point t in the residual network, and if the augmentation path p can be found, entering the step S4; if the augmentation path p cannot be found, the process proceeds to step S6; s4, finding the edge with the minimum capacity in the path in the augmented path p, marking as a bottleneck edge, accumulating the capacity value X of the bottleneck edge into the maximum stream, and entering the step S5; s5, subtracting X from c < u, v > in the augmented path, adding X to all c < v, u > to form a new residual network, and entering the step S3; and S6, obtaining the maximum network flow.
Preferably, the width is taken as a priority value in the residual network, and an augmentation path p from a source point s to a sink point t is searched; when the point popped up by the line head of the path is the end point, the augmented path can be judged to be found.
Preferably, the number of times of finding the augmented path in step S2 is not more than E × V times, where E is the number of edges in the network graph G and V is the number of points in the network graph G.
Preferably, the time complexity of each search for an augmented path is O (E), and the time complexity of the algorithm for the network maximum flow is O (E)2V)。
Preferably, the augmented path p is a feasible flow in the survivor network; the residual capacity of the augmented path p is the maximum additional flow that can be added along the path:
preferably, the feasible flow f in the survivor networkpIs defined as:
f is a feasible flow in the given network graph G, f + fpIs one possible flow of the network graph G.
Preferably, the survivor network is a composed network with capacity cfThe given network of (a); f is a feasible flow of the network graph G, f1Is a residual network graph GfOne possible flow of (1), and f + f1Is one possible flow of the network graph G.
Preferably, assuming that the flow f is a flow in the network graph G, the residual capacity is defined for each edge < u, v > in the network graph G as: and (4) after the capacity occupied by the feasible flow f is calculated, the allowable extra flow can be obtained on the premise of not exceeding any limit capacity constraint.
Preferably, if a flow in the network flow is a real-valued function f and satisfies the following three properties:
0≤f(e)≤c(e)
f(u,v)＝-f(v,u)
∑f(u,v)＝0(v∈V)
then f (u, v) is called the feasible flow from u to v.
After the technical scheme is adopted, compared with the prior art, the method has the following beneficial effects:
1. the invention introduces the inter-provincial transaction path configuration problem, and solves the cross-regional transaction path evaluation and configuration problem under the complex network condition from the viewpoint of a topological structure.
2. The physical meaning of a residual network in an electric power transmission network is a transmission network consisting of lines in the electric power transmission network that still have residual capacity. The cross-regional new energy consumption is constructed on the basis of the priority of the intra-provincial power transmission requirement, so that the configuration of a new energy consumption transaction path is necessarily constructed on the basis of a residual network after the intra-provincial power transmission amount is counted, the electric energy transmission increment between source sinks is searched and utilized, and the optimal configuration route can be obtained by utilizing the residual network;
3. the introduction of the augmented path will result in a flow with a larger value, and the definition of the augmented path ensures that the tolerance is positive, namely the increment of the flow, has the property of being augmented, and can coordinate and distribute each feasible flow to each type of energy source transmission through the augmented path;
4. the method for searching the augmented path and the efficiency thereof are important criteria of the quality of the maximum flow algorithm, and the method adopts breadth-first search, namely the breadth is taken as a priority value, and is taken as the method for searching the augmented path, so that the accuracy of the augmented path search can be improved;
5. the invention leads out the return edge, changes the search of the augmentation path into a dynamic modification process and also ensures the correctness of the final result.
Drawings
FIG. 1 is a schematic diagram of the Gonesburg bridge problem;
FIG. 2 is a flow chart illustrating a transaction path configuration method in accordance with a preferred embodiment of the present invention;
FIG. 3 is a simplified inter-provincial link diagram according to a preferred embodiment of the present invention;
FIG. 4 is an equivalent simplified network connectivity test chart in accordance with a preferred embodiment of the present invention;
fig. 5 is an algorithm diagram of the gansu-shanghai transaction path maximum network flow according to a preferred embodiment of the present invention.
Detailed Description
The advantages of the invention are further illustrated in the following description of specific embodiments in conjunction with the accompanying drawings.
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in themselves. Thus, "module" and "component" may be used in a mixture.
The following describes the definitions and terminology of the network diagram G of the present invention.
When the connectivity and the interrelation of the system are involved in the engineering mathematical problem, a graph can be defined so as to analyze and solve the problem in a visual way. Taking the Gonesburg bridge problem shown in FIG. 1 as an example, 4 regions connected by 7 bridges can be simplified into a graph traversal problem by a graph theory approach.
A net graph G is a triplet of a set of vertices v (G), a set of edges e (G), and a relationship that associates each edge with two vertices (not necessarily distinct vertices) and refers to the two vertices as the endpoints of the edge. An edge associated with a pair of vertices is referred to as a parallel edge if there is more than one edge. An edge is associated with two identical vertices, then this edge is called a self-loop. Each node in the power transmission network of the power system can be described as a set of vertices in the network graph G, each transmission line can be described as a set of edges in the network graph G, the direction of the current in the transmission line between the nodes u, v is described as the direction of the current f (u, v) in the edge, and the rated power of the transmission line is described as the capacity c (u, v) of the edge. In summary, the power system transmission network includes neither parallel edges nor self-loops, so the power system transmission network can be described as a directed simple network graph G (V, E, C) consisting of a set of node vertices V (G), a set of transmission line edges E (G), and a set of line capacities C (G).
With respect to the connectivity problem of the transaction path, the power market is built on a perfect power transmission network that cannot be easily broken down. The electricity vendor in the electricity transaction, such as the northwest wind farm, is depicted in the figure as source s. The point of purchase parties in the power transaction, such as the Long delta load, are depicted in the figure as a sink t. In a directed simple network graph G describing a power transmission network, all the different sets of possible transmission paths between a source s and a sink T are described as transaction paths T (s, T). It is often desirable to have a graph of possible transaction paths that remain connected in the event that certain nodes or edges need to be removed from the system due to various types of failures. On the other hand, from the viewpoint of economy, the construction cost of the power transmission network is very expensive, and it is desirable to achieve the above object with as few sides as possible. Thus, the connectivity problem of trading paths in the electricity market can be attributed to the balance of system resilience and system economy.
Connectivity of the network graph G is defined as: if the subset of vertices S ∈ V (G) of the net graph G is such that G-S branches more than one, then a separate set or cut of G is referred to. The smallest size of the subset S in graph G that leaves G-S unconnected or has only one vertex, called the connectivity of G, is denoted as k (G). If the connectivity of the network graph G is at least k, it is referred to as k-connected. As in this example, the set S ═ {2, 3, 4} is a 1, 5-cut of size 3, so k (1, 5) ═ 3. I.e., the deletion of 3 vertices in the subset S of vertices in graph G may cause the graph to become unconnected. Thus, graph-theory based transaction path connectivity analysis may provide an estimate of inter-source sink path connectivity vulnerability for power transactions from a pure topological perspective.
Regarding the maximum network flow problem, in the configuration problem of the power transaction path, besides the balance problem of system resilience and economy related to network connectivity, another important problem of evaluating the transaction path is to find a solution to maximize the total transmission amount between the source and the sink. That is, for the directed weighted graph G, each directed edge connects two specific vertices, and has a corresponding weight representing the maximum load per unit time of the path, assuming that the flows cannot be aggregated at the connection points of the directed edges. The maximum traffic between two node sources s, sinks t in a given network is solved. The solution of the problem also gives a reference value of the maximum flow per unit time for the configuration of the transaction path from the pure topology perspective and a visual evaluation of the bottleneck lines that may be generated in the transmission system.
In the solution of the maximum network flow problem, a feasible flow f from a source point to a sink point in the network is not simply the sum of the maximum carrying capacity per unit time of all directed edges on a line. The capacity of a directed edge on a line always becomes a bottleneck, i.e. the bottleneck line first consumes the capacity in the process of increasing the feasible flow, and the directed edge cannot accommodate any forward flow increment any more. The mathematical definition of the network flow is as follows:
a network is a directed graph, each edge e of which has a non-negative capacity c (e) and which also has mutually distinct source s and sink t. Vertices are also referred to as nodes. One stream assigns a value f (e) to each edge e. The total flow on all sides leaving v is recorded as f + (v) and the total flow on all sides entering v is recorded as f- (v). If a flow in a network flow is a real-valued function f and satisfies the following three properties:
(1) capacity constraint:
0≤f(e)≤c(e)；
(2) antisymmetry:
f(u,v)＝-f(v,u)；
(3) conservation of flow:
∑f(u,v)＝0(v∈V)；
then f (u, v) is called the feasible flow from u to v.
After the feasible flows in the network are defined, the basic concepts of residual networks and augmented paths can be introduced, which have corresponding physical meanings in the power transmission network.
Regarding the residual network, for the network graph G (V, E, C), it is assumed that the flow f is a flow in the network graph G. After the feasible flow f is intuitively assigned by the residual network, the network formed by more current edges can be accommodated. For each edge < u, v > residual capacity in the network graph G, the extra traffic that can pass through without exceeding any edge capacity constraint after considering the capacity occupied by the feasible flow f is defined.
cf(u,v)＝c(u,v)-f(u,v)
Given a network graph G ═ V, E, C and a feasible flow f, the residual network is Cf(V, E, f), wherein edge sets
Ef＝{(u,v)∈V×V|cf(u,v)＞0}
The remnant network is a composed network with capacity cfA given network. Let f be a feasible flow of (V, E, C) in the network graph G, f1Is a residual network graph GfSince the residual network is a subset of the original network, f + f1Is also necessarily one possible flow of the network graph G. The theory makes clear the relation between the original network and the residual network and also provides a premise for leading out the next augmentation path.
The physical meaning of a residual network in an electric power transmission network is a transmission network consisting of lines in the electric power transmission network that still have residual capacity. The cross-regional new energy consumption is constructed on the basis of priority of the intra-provincial power transmission requirement, so that the configuration of a new energy consumption transaction path is necessarily constructed on the basis of a residual network after the intra-provincial power transmission amount is counted, and the electric energy transmission increment between source sinks is searched and utilized.
Regarding the augmented path, on the basis of the residual network, the feasible flow is continuously searched to form a new residual network. Thus forming an iterative loop until no new feasible stream can be found. The feasible flows obtained in the survivor network are defined as the augmented paths p, which are the survivor network graph GfA simple path from the source s to the sink t. The residual capacity of the path is the maximum extra traffic that can be added along the path:
the introduction of an augmented path will result in a larger value of flow, the definition of the augmented path ensuring that the tolerance is positive, i.e. the increment of flow, with the property of being augmented.
Feasible flow f in survivor networkpIs defined as:
given a feasible flow f in the network graph G, then f + fpStill a viable flow of network graph G.
The physical meaning of the augmented path in the power transmission network is that in the residual power transmission network, a transaction path capable of providing forward flow increment is arranged between source sinks. The cross-regional consumption of new energy can be realized through a multi-terminal direct current internet or a traditional alternating current internet. If a traditional communication internet is selected, other types of trans-regional energy transmission need to be considered. The individual feasible flows can be coordinated to be allocated to the individual types of energy supply by the concept of an extended path.
Combining the concepts in the above mentioned network flow algorithm, it is possible to give a visual numerical estimate of the system resilience and maximum transport capacity of the transaction path from a topological point of view.
Referring to fig. 2, the present invention discloses a transaction path configuration method applied to an electric power market, including the following steps: s1, constructing a network graph G of the electric power market transaction path, wherein the rated power of the electric transmission line between nodes u and v in the network graph G is the capacity c (u, v) of the edge, the power selling party in the electric power transaction is a source point S, and the purchasing party in the electric power transaction is a sink point t; s2, initializing the capacity of all edges in the network, wherein c < u, v > inherits the changed capacity and is initialized to 0, and the edges < v, u > are return edges; initializing a maximum flow to 0; s3, finding an augmentation path p from a source point S to a sink point t in the residual network, and if the augmentation path p can be found, entering the step S4; if the augmentation path p cannot be found, the process proceeds to step S6; s4, finding the edge with the minimum capacity in the path in the augmented path p, marking as a bottleneck edge, accumulating the capacity value X of the bottleneck edge into the maximum stream, and entering the step S5; s5, subtracting X from c < u, v > in the augmented path, adding X to all c < v, u > to form a new residual network, and entering the step S3; and S6, obtaining the maximum network flow.
The back edge changes the search of the augmentation path into a dynamic modification process, and also ensures the correctness of the final result.
Because the method for searching the augmented path and the efficiency thereof are important criteria for the quality of the maximum flow algorithm, the method preferably searches the augmented path p from the source point s to the sink point t by taking the width as a priority value in the residual network, and judges that the augmented path is found when a point popped up at the head of a queue of the path is a terminal point. In step S2, the number of times of finding the augmented path does not exceed E × V times, where E is the number of edges in the network graph G, and V is the number of points in the network graph G. The time complexity of each search for an augmented path is O (E), and the time complexity of the algorithm of the maximum flow of the network is O (E)2V)。
This is explained in detail below by means of an example.
The embodiment is analysis of a Gansu-Shanghai new energy consumption example, and simplifies and equates to an inter-provincial connecting line channel by taking a domestic existing ultra-high voltage transmission network architecture as reference. The provincial electricity selling party and the electricity purchasing party serve as a vertex set V in a network graph G, the provincial interconnection lines serve as an edge set E in the network graph G, and the power transmission channel capacity serves as an edge capacity set C. Thereby constructing a weight directed network graph G ═ V, E, C. Suppose that the whole country cross-regional electricity trading is organized, wherein 12 provinces participate in electricity selling and 9 provinces participate in electricity purchasing. A simplified network diagram of the interstate links from source to sink from top to bottom is shown in fig. 3, where the unit is GW.
The Gansu province is selected as an electricity selling party, the Gansu province is selected as one of the most serious provinces for abandoning wind and limiting electricity, the local absorption capacity in the northwest region is limited, the double increase of wind power and photoelectric generated energy is promoted by actively participating in the spot market among the provinces and medium-long-term trading, and the double decrease of abandoning wind and abandoning light is realized. Shanghai city is selected as the electricity purchasing party, and occupies more than 50% of electricity purchasing amount in east China. The current transaction route of Gansu to Shanghai is Gansu-Shaanxi-Sichuan-Chongqing-Hubei-Shanghai.
The system performance of the transaction path and the maximum transmission limit of the transaction path are analyzed from the aspects of network connectivity and maximum flow algorithm. The operating environment of the simulation model is Matlab, junction information of line sources of the provincial interconnection is transmitted, upper and lower limits are transmitted, and line network loss is ignored.
The network connectivity test is shown in fig. 4. The network diagram G (V, E, C) is a 1-connected diagram, and the cut points are Hubei, Shaanxi and Henan. The network graph G can be divided into 4 sub-graphs through the cutting points, and the sub-graphs respectively correspond to a 1-northeast China area, a 2-east China area, a 3-China area and a 4-northwest China area in the geographic environment. The Gansu-Shanghai transaction path involves 3 subgraphs, greater than or equal to 2 cutpoints.
The transaction path maximum transmission limit estimation adopts the transaction path configuration method of the invention, and the limit of the transmittable electric quantity within 30 days can be obtained by calculating the limit of the electric transmission limit after considering the conditions of maintenance and the like according to the capacity of the electric transmission line. The path of the amplification is shown in figure 5. The optimization result of the current trade path of Gansu Shanghai is Gansu-Shaanxi-Sichuan-Chongqing-Hubei-Shanghai, and the electric quantity of the successful transaction is 12.4 GWh. The two transaction paths are shown by dotted lines, and in addition to the original transaction path, a Gansu-Shaanxi-Henan-Hubei-Shanghai transaction path is newly added, wherein a bottleneck edge is a 'bottleneck' line and is a 12.4GWh transmission limit of the Shaanxi-Sichuan line, and the maximum transmission limit is 756.4 GWh. Under the condition that other trades in the market are not taken into account, compared with the current trading electric quantity, the 744GWH transmission margin is still displayed, and the new energy consumption potential across the region is huge.
The invention introduces the inter-provincial transaction path configuration problem into the graph theory network flow method, and solves the cross-regional transaction path evaluation and configuration problem under the complex network condition from the viewpoint of a topological structure. The invention models the transaction path in the simplified equivalent graph from two aspects of network connectivity and maximum flow calculation, performs corresponding analysis and calculation, and performs comparative analysis on the result obtained by the model and the result of the current transaction path.
The result shows that the simplified equivalent graph algorithm can visually evaluate the system feedback of the transaction path and predict and estimate the maximum transmission limit of the transaction path under a complex network. The algorithm provides powerful technical support for cross-regional transaction consumption of new energy through inter-provincial links.
It should be noted that the embodiments of the present invention have been described in terms of preferred embodiments, and not by way of limitation, and that those skilled in the art can make modifications and variations of the embodiments described above without departing from the spirit of the invention.
Claims (10)
1. The transaction path configuration method applied to the electric power market is characterized by comprising the following steps of:
s1, constructing a network graph G of the electric power market transaction path, wherein the rated power of the electric transmission line between nodes u and v in the network graph G is the capacity c (u, v) of the edge, the power selling party in the electric power transaction is a source point S, and the purchasing party in the electric power transaction is a sink point t;
s2, initializing the capacity of all edges in the network, wherein c < u, v > inherits the changed capacity and is initialized to 0, and the edges < v, u > are return edges; initializing a maximum flow to 0;
s3, finding the augmentation path p from the source point S to the sink point t in the residual network,
if the augmented path p can be found, the process proceeds to step S4;
if the augmentation path p cannot be found, the process proceeds to step S6;
s4, finding the edge with the minimum capacity in the path in the augmented path p, marking as a bottleneck edge, accumulating the capacity value X of the bottleneck edge into the maximum stream, and entering the step S5;
s5, subtracting X from c < u, v > in the augmented path, adding X to all c < v, u > to form a new residual network, and entering the step S3;
and S6, obtaining the maximum network flow.
2. The transaction path configuration method according to claim 1, wherein an augmented path p from a source point s to a sink point t is searched for in the survivor network with a width as a priority value;
when the point popped up by the line head of the path is the end point, the augmented path can be judged to be found.
3. The transaction path configuration method according to claim 2, wherein the number of times of finding the augmented path in step S2 is not more than E × V, where E is the number of edges in the network graph G and V is the number of points in the network graph G.
4. The transaction path configuration method according to claim 2, wherein the time complexity of each search for an augmented path is O (E), and the time complexity of the algorithm for the network maximum flow is O (E)2V)。
5. The transaction path configuration method according to claim 1, wherein the augmented path p is a feasible flow in the survivor network;
the residual capacity of the augmented path p is the maximum additional flow that can be added along the path:
6. the transaction path configuration method of claim 5, wherein the feasible flow f in the survivor networkpIs defined as:
f is a feasible flow in the given network graph G, f + fpIs one possible flow of the network graph G.
7. The transaction path configuration method of claim 1, wherein the survivor network is a network having a capacity of cfThe given network of (a);
f is a feasible flow of the network graph G, f1Is a residual network graph GfOne possible flow of (1), and f + f1Is one possible flow of the network graph G.
8. The transaction path configuration method according to claim 7, wherein assuming that the flow f is a flow in the network graph G, the residual capacity is defined for each edge < u, v > in the network graph G as:
and (4) after the capacity occupied by the feasible flow f is calculated, the allowable extra flow can be obtained on the premise of not exceeding any limit capacity constraint.
9. The transaction path configuration method of claim 8, wherein if the flow in the network flow is a real-valued function f and satisfies the following three properties:
0≤f(e)≤c(e)
f(u,v)＝-f(v,u)
∑f(u,v)＝0(v∈V)
then f (u, v) is called the feasible flow from u to v.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the transaction path configuration method according to any one of claims 1 to 9.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114066019A (en) * | 2021-10-27 | 2022-02-18 | 国核电力规划设计研究院有限公司 | Energy bidding scheduling method and system based on graph theory |
| CN115600766A (en) * | 2022-11-23 | 2023-01-13 | 中国电力科学研究院有限公司（Cn） | Cross-provincial electric power transaction path searching method, system, equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105303256A (en) * | 2015-10-29 | 2016-02-03 | 西安交通大学 | Power inter-provincial and inter-district trade path analysis method |
| CN109905281A (en) * | 2019-03-24 | 2019-06-18 | 西安电子科技大学 | The group of stars network Telemetry Service transmission method of multipath maximum throughput |
-
2020
- 2020-11-02 CN CN202011202631.3A patent/CN112257950A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105303256A (en) * | 2015-10-29 | 2016-02-03 | 西安交通大学 | Power inter-provincial and inter-district trade path analysis method |
| CN109905281A (en) * | 2019-03-24 | 2019-06-18 | 西安电子科技大学 | The group of stars network Telemetry Service transmission method of multipath maximum throughput |
Non-Patent Citations (3)
| Title |
|---|
| 冯林 等: "《图论及应用》", 31 March 2012 * |
| 程海花等: "基于拓展网络流方法的跨区跨省交易路径优化", 《电力系统自动化》 * |
| 郑亚先等: "计及清洁能源的跨区跨省交易路径优化建模与算法", 《电力系统自动化》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114066019A (en) * | 2021-10-27 | 2022-02-18 | 国核电力规划设计研究院有限公司 | Energy bidding scheduling method and system based on graph theory |
| CN115600766A (en) * | 2022-11-23 | 2023-01-13 | 中国电力科学研究院有限公司（Cn） | Cross-provincial electric power transaction path searching method, system, equipment and storage medium |
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