A method for visualizing power spot market clearing prices and dynamic power flow

Abstract

This invention relates to a method for generating dynamic electricity prices and power flow diagrams for spot market transactions based on nodal price clearing results and adapted to different grid node distributions. The method first acquires grid node information, network model data, and Geo map data; then, it performs topology analysis based on the acquired data and system parameters, automatically generating a physical wiring distribution diagram based on the connection relationships of each node; it accesses the electricity spot market clearing results and calculates line loads according to set constraints; then, it automatically matches the clearing results with the node physical wiring diagrams; it renders nodal prices, power flow direction (p-direction), and line load information; finally, it generates a dynamic electricity price and power flow diagram, clearly displaying the nodal prices, branch power flow direction, and power flow load information of the grid to the grid dispatching agency; this greatly improves the work efficiency of dispatching personnel, enhances the power grid's reliability, and reduces the power control risks brought about by the electricity spot market.

Description

Technical Field

[0001] This invention relates to the fields of power grid wiring diagram drawing and power monitoring incorporated into spot market transactions, and particularly to a method for generating dynamic clearing prices and power flow diagrams in power spot transactions. Background Technology

[0002] Currently, with the continuous advancement of power market reform, my country has basically established a power market architecture of "unified market, two-tier operation." The spot market construction work carried out in the eight pilot regions nationwide has undergone multiple rounds of long-term settlement trial operations, and the spot market construction within other provincial power grid companies is also becoming increasingly sophisticated. In the power market environment, traditional planned power generation has incorporated market factors, posing significant challenges to power reliability management. Furthermore, with the continuous increase in installed capacity of new energy sources, the high proportion of new energy output is characterized by randomness and large fluctuations, resulting in a common situation of intertwined new energy curtailment and insufficient power supply. Against the backdrop of carbon reduction, the reliability management of the entire power grid system becomes increasingly difficult. Currently, spot trading by major provincial grid companies mainly emphasizes price matching between buyers and sellers during the clearing process to achieve supply and demand balance. Often, only a pre-balancing is performed before the clearing calculation, and all constraints and boundary settings are automatically met during the clearing calculation. The clearing process cannot be manually intervened, and the safety verification mechanism is not yet sound. The clearing results must be iterated and re-verified in the power generation plan, which cannot meet the timeliness requirements of the transaction. Furthermore, the clearing data obtained is mostly presented in the form of 96-point tabular data, which makes it difficult for the control department to clearly and intuitively understand the distribution of the clearing results in various regions. It is necessary to combine the grid dispatch control platform to understand the load status of the lines in each region. The frequent switching between the control platform and the spot trading system by dispatchers increases the complexity of their work and makes it impossible to monitor the line load problems caused by the spot market in real time.

[0003] Combining real-time clearing results from spot trading with actual control measures, and visually displaying the provincial power grid node wiring diagram within a single system, along with real-time data such as spot trading clearing results and line load status, would significantly reduce the workload of dispatching and further improve power reliability management. Currently, industry research on power grid wiring diagram drawing primarily relies on exporting data models, manually drawing them using third-party graphics platforms, and then using XML and SVG files as data carriers to achieve graphical data interconnection before integrating this data into the corresponding system. This approach lacks flexibility and versatility, cannot adapt to real-time changes in data unit additions or deletions, and there are currently no precedents for using and automatically generating power grid wiring diagrams in spot trading systems. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a method for generating dynamic electricity prices and power flow diagrams based on different grid node distributions and spot market clearing results. This method assists in spot market trading management and power generation monitoring management, effectively reducing the workload of dispatchers, improving power reliability, and mitigating power control risks associated with the electricity spot market.

[0005] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0006] A method for generating dynamic electricity prices and power flow diagrams for spot trading in the electricity market based on nodal price clearing results and adapted to different grid node distributions includes the following steps:

[0007] S1. Obtain the node information, network model data, and geographic geo map data of the province or region where the power grid is located;

[0008] S2. Perform topology analysis based on the obtained data and the vector parameters set by the system to obtain a tiled node distribution state diagram;

[0009] S3. Automatically generate a physical wiring diagram of nodes adapted to the power grid based on the connection relationship of each node;

[0010] S4. Access the clearing results of the electricity spot market transaction, including node clearing results and branch clearing results data;

[0011] S5. Automatically match the clearing data of each clearing node and branch on the physical wiring diagram;

[0012] S6. Based on the clearing data and model coordinates, use CAVAS rendering technology to render the node electricity price, power flow direction, and line load.

[0013] S7. Generate dynamic electricity price diagrams and power flow diagrams.

[0014] In the above scheme, the following steps are taken: First, the node information, network model data, and geographic geomap data of the province or region where the power grid is located are acquired. Then, topology analysis is performed based on the acquired data and the vector parameters set by the system to obtain a tiled node distribution diagram. Next, a physical wiring diagram of the nodes adapted to the power grid is automatically generated based on the connection relationship of each node. Then, the power spot market clearing results, including node clearing results and branch clearing results, are accessed, and the line load is calculated based on the branch clearing results. The clearing data of each clearing node and branch is automatically matched on the physical wiring diagram. Then, based on the clearing data and model coordinates, CAVAS rendering technology is used to render the node electricity price, power flow direction, and line load. Finally, a dynamic electricity price diagram and power flow diagram are generated. Through this technical solution, the cleared power grid wiring diagram of each trading period within the trading day can be generated in real time and dynamically, clearly displaying the current spot market clearing volume and price information, line power flow direction, and line load, thereby improving the efficiency of dispatching work.

[0015] In step S1, acquiring data includes:

[0016] Power grid node information: basic information of nodes participating in the market, node geographic coordinates, and graphic symbols;

[0017] Power grid node network model data: connection relationships and basic parameters between nodes;

[0018] Geographic geomap data of the area where the power grid is located: administrative region description, latitude and longitude geographical location information;

[0019] Node clearing result data: transaction date, transaction period, node name, node number, node load, node price, whether the node belongs to a generating unit node, generating capacity of the generating unit, etc.

[0020] Branch clearing result data: transaction date, transaction period, branch name, branch number, starting node name, starting node number, ending node name, ending node number, line power;

[0021] In step S2, the vector parameters are:

[0022] Vector 1: (d x1 d y1 ), (d lon1 d lat1 );

[0023] Vector 2: (d x2 d y2 ), (d lon2 d lat2 );

[0024] The vector calculation function is:

[0025]

[0026] in:

[0027] x1 represents the x-coordinate of the reference point 1 in pixels, which is usually 0;

[0028] y1 represents the pixel ordinate of reference point 1, which is usually 0;

[0029] lon1 represents the geographic x-coordinate of reference point 1, which is generally the minimum longitude on a geo map;

[0030] lat1 represents the geographic vertical coordinate of reference point 1, which is generally the maximum latitude on the geo map;

[0031] x2 represents the pixel horizontal coordinate of reference point 2, which is generally the maximum width of the screen container;

[0032] y2 represents the pixel ordinate of reference point 2, which is generally the maximum height of the screen container;

[0033] lon2 represents the geographic x-coordinate of reference point 2, which is generally the maximum longitude on the geo map;

[0034] lat2 represents the geographic ordinate of reference point 2, which is generally the minimum latitude on a Geo map;

[0035] x represents the x-coordinate of the node in pixels;

[0036] y represents the pixel ordinate of the node;

[0037] lon indicates that the geographic x-coordinate of the node is required and is obtained from the power grid node information;

[0038] lat indicates that the geographic ordinate of the node is required and is obtained from the power grid node information;

[0039] In this scheme, the objective function for converting node geographic coordinates into screen pixel coordinates is:

[0040]

[0041] in,

[0042] k1 = norm(d x1 d y1 );

[0043] k2 = norm(d lon1 d lat1 );

[0044] k3 = norm(d x2 d y2 );

[0045] k4 = norm(d lon2 d lat2 );

[0046] The norm(V) function is expressed as the Euclidean norm of vector V, and its calculation formula is as follows:

[0047]

[0048] In this technical solution, the power grid node wiring diagram has three layers:

[0049] First layer: Map of the area where the power grid is located;

[0050] Second layer: Node distribution status diagram;

[0051] Third layer: Node connection diagram;

[0052] In this scheme, the line load rate of each branch line must be calculated, as described below:

[0053] L r =L p ÷L l ×100%

[0054] Where: L r For line load factor, L p For line power, L l This represents the power flow limit value of the line;

[0055] The line load status is displayed using different colors based on the line load rate value, and the judgment criteria are as follows:

[0056]

[0057] Compared with existing technologies, the beneficial effects of this invention are that it combines the results of electricity market clearing with daily power dispatch and control, and innovatively introduces a power grid connection diagram into the electricity market trading system, which clearly displays the clearing results of nodes, the flow direction of power lines and the load factor. This provides dispatchers with power operation data after being included in spot trading, avoids the need to switch between the spot trading system and the control platform, reduces the complexity of the work, and improves the efficiency of work. Attached Figure Description

[0058] Figure 1 This is a flowchart of a method for generating dynamic electricity prices and power flow diagrams based on a power grid model and clearing results, according to the present invention.

[0059] Figure 2 This is an example of a schematic diagram illustrating an implementation case of the supporting system design of this invention. Detailed Implementation Plan

[0060] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described herein are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] A method for generating dynamic electricity prices and power flow diagrams based on a power grid model and clearing results is illustrated in the flowchart below. Figure 1 As shown, the specific implementation is as follows:

[0062] First, obtain the node information, network model data, and geographic geo map data of the province or region where the power grid is located;

[0063] The node information includes all market participants in the electricity trading market, including thermal power units, hydropower units, wind power units, and photovoltaic power plants, etc., and its characteristics are as follows:

[0064] Since nodes include information from both the generation side and the user side, and the equipment topology model at the plant end is complex, the node concept is used to represent them uniformly, but different identifiers and graphic elements can be used to distinguish between generating units and users.

[0065] The node information includes basic information and geographical location information of the market entity, namely accurate latitude and longitude coordinates.

[0066] The node information includes the graphical symbol data of each node. These graphical symbols include elements, labels, colors, and wiring. This data must comply with the automated graphical interface specifications or related requirements issued by the relevant power company. Figure 2 As shown;

[0067] The network model data includes the connection relationships and basic parameters between nodes, and the data conforms to the CIM model specification of IEC 61970 standard;

[0068] The geographic geo map data can be carried using js files and json files;

[0069] The aforementioned geo map data file contains descriptions of all administrative regions and their latitude and longitude coordinates for that area;

[0070] The map is rendered to the first layer of the visualization window screen canvas layer in the transaction clearing result display module;

[0071] Then, topology analysis is performed based on the node network model data and the vector parameters set by the system. The vector parameters mentioned are system settings, which are two known reference points set based on screen pixel coordinates and geographic coordinates.

[0072] Define the reference point as the origin of pixel coordinates (0, 0) and the maximum pixel coordinate point (Xmax, Ymax), which are the top left and bottom right vertices of the visualization window;

[0073] In this embodiment, Xmax = 504px, Ymax = 466px;

[0074] The X and Y values ​​of the maximum pixel coordinate point are the width and height of the visualization window;

[0075] Two sets of vectors are formed using these two known reference points and the required node coordinate parameters;

[0076] Vector 1 is (d x1 d y1 ), (d lon1 d lat1 );

[0077] Vector 2 is (d x2 d y2 ), (d lon2 d lat2 );

[0078] in,

[0079] The pixel coordinates of reference point 1 are (x1, y1), and the geographic coordinates are (lon1, lat1).

[0080] The pixel coordinates of reference point 2 are (x2, y2), and the geographic coordinates are (lon2, lat2).

[0081] The required node has pixel coordinates (x, y) and geographic coordinates (lon, lat).

[0082] The calculation function for the two vectors is:

[0083]

[0084] The nodes required are the nodes in the node network model corresponding to the spot trading market participants, including unit nodes and load nodes;

[0085] The goal of the vector is to convert the geographic coordinates of all nodes into screen pixel coordinates and calculate the distribution status of each node.

[0086] The objective function for converting node geographic coordinates to screen pixel coordinates is:

[0087]

[0088] in,

[0089] k1 = norm(d x1 d y1 );

[0090] k2 = norm(d lon1 d lat1 );

[0091] k3 = norm(d x2 d y2 );

[0092] k4 = norm(d lon2 d lat2 );

[0093] The norm(V) function is the Euclidean norm of vector V, also known as the L2 norm or Euclidean length. It is defined as the square root of the sum of the squares of all elements of the vector, and its calculation formula is as follows:

[0094]

[0095] The longitude and latitude represented by each pixel on the screen are calculated using the Euclidean norm method to obtain the coordinate transformation scaling factor. Then, the geographical coordinates of all nodes participating in the spot market are converted into screen pixel coordinates.

[0096] After converting the geographic coordinates of all nodes into pixel coordinates in the above manner, the distribution status diagram of each node on the visualization window is automatically drawn, and different graphic elements are used to distinguish between unit nodes and load nodes.

[0097] The node distribution state diagram is located on the second layer of the visualization window screen canvas layer, that is, above the map;

[0098] Next, the connection relationships between the nodes in the node network model data are obtained;

[0099] Based on the pixel distribution of each node on the screen, and combined with the connection relationship of each node, the connection is automatically drawn to obtain the node physical wiring diagram adapted to the power grid.

[0100] When generating a connection, the inflection point (turn point) of the connection is automatically calculated according to the principle of minimum intersection. The endpoints of the connection are the coordinates of the two nodes involved in the connection relationship.

[0101] The node physical wiring diagram is located on the third layer of the visualization window screen canvas layer, that is, above the node distribution state diagram.

[0102] The drawn node wiring diagram conforms to the model specifications of IEC 61970 standard;

[0103] Then, the electricity spot market clearing results are accessed, including node clearing results and branch clearing results data.

[0104] The node clearing result data includes: transaction date, transaction period, node name, node number, node load, node electricity price, whether the node belongs to a generating unit node, generating unit power, etc.

[0105] The branch clearing result data includes: transaction date, transaction period, branch name, branch number, starting node name, starting node number, ending node name, ending node number, and line power;

[0106] Then, based on the line power flow limit in the network model data and the line power in the branch clearing results, the line load rate is calculated, and the objective function is:

[0107] L r =L p ÷L l ×100%

[0108] Where: L r For line load factor, L p For line power, L l This represents the power flow limit value of the line;

[0109] Then, the clearing data of each clearing node and branch is automatically matched on the physical wiring diagram;

[0110] The physical wiring diagram of the nodes includes the identification information of each node and the screen pixel coordinate information;

[0111] The physical wiring diagram of the nodes includes the identification information of each branch and the screen pixel coordinate information;

[0112] The automatic matching of clearing data for each clearing node and branch is achieved by traversing and matching the node identification code and the node number in the clearing result data of each node, assigning the transaction information such as the name, load, and electricity price of the node to the node element, and identifying whether the node belongs to the unit node or the load node according to the node attributes.

[0113] The automatic matching of clearing data for each clearing node and branch is based on the identification code of each branch and the branch number in the branch clearing result data. The transaction information such as the name of the branch, line power, and line load rate are assigned to the line element.

[0114] Then, based on the clearing data and model coordinates, CAVAS rendering technology is used to render the nodal electricity price, power flow direction, and line load. The rendering steps are as follows:

[0115] (1) Traverse all element information in the node distribution state diagram and obtain the unit node attributes and load node attributes respectively. The attributes include the distinguishing identifier and pixel coordinates.

[0116] (2) Render different clearing data for different attribute element identifiers. Add data such as unit name, unit power generation, and clearing price to unit elements, and add data such as load name, node load and clearing price to load elements.

[0117] (3) Create different node tooltip styles for graphic element identifiers with different attributes. When the mouse moves over the graphic element, distinguish between displaying unit clearing information and load clearing information.

[0118] (4) Traverse all branch diagram elements in the node physical wiring diagram;

[0119] (5) Obtain the pixel coordinates of all endpoints and vertices of each branch;

[0120] (6) Based on the starting node name, starting node number, ending node name, and ending node number information in the branch clearing result data, arrange the endpoints and inflection point coordinates of the branch in the order from start to end;

[0121] (7) Draw the dynamic flow diagram sequentially using CAVAS rendering technology according to the coordinate order of the arrangement to realize the dynamic flow of power flow;

[0122] (8) Render the transaction information such as the name, line power, and line load rate of the branch based on the branch element identifier and the branch clearing result data;

[0123] (9) Determine the line load rate of all branches and distinguish them with different colors. The judgment criteria are as follows:

[0124]

[0125] (10) Create tooltip styles for all branch elements, so that when the mouse moves over the element, the specific clearing status of the branch is displayed.

[0126] Finally, dynamic electricity price diagrams and power flow diagrams are automatically generated based on the above scheme and method.

[0127] The distribution of each node in the diagram conforms to the actual distribution of each node in the power grid area;

[0128] The connection relationships between the nodes in the diagram conform to the connection relationships in the network model data;

[0129] Each element in the graph dynamically contains detailed transaction information for the current clearing period of the transaction date, including node data and branch data;

[0130] The branches between the nodes in the diagram can dynamically display the direction of power flow;

[0131] The diagram shows the line load rate for each branch line during the current clearing period.

[0132] Although the present invention has been described in detail through the foregoing examples, it should be understood that the above description should not be considered as a limitation of the invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the foregoing. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for generating dynamic electricity price and power flow map of electricity market spot transaction based on nodal price clearing result and adaptive to different distribution of nodes in power grid, characterized in that: It comprises the following steps: S1. Obtain node information, network model data and geographic map data of the province or region where the power grid is located; S2. Perform topological analysis according to the obtained data and vector parameters set by the system to obtain a tiled node distribution state diagram; S3. Automatically generate a node physical wiring diagram suitable for the power grid according to the connection relationship of each node; S4. Access the power spot transaction clearing result, including node clearing result and branch clearing result data; S5. Automatically match the clearing data of each clearing node and branch on the physical wiring diagram; S6. Render the node electricity price, power flow direction and line load condition using cavas rendering technology according to the clearing data and model coordinates; S7. Form a dynamic electricity price diagram and a power flow diagram.

2. The method for generating dynamic electricity market spot transaction prices and power flow diagrams based on nodal price clearing results and adapted to different grid node distributions, as described in claim 1, is characterized in that: The node information, network model data and geographic map data of the province or region where the power grid is located comprise: 1) The node information includes all market subject nodes participating in the power trading market, including thermal power units, hydropower units, wind power units and photovoltaic power stations. Since the nodes include both generation side and user side information, and the equipment topology model of the station end is complex, a unified node concept is used to represent it, and different symbols and primitives are used to distinguish between units and users; 2) The node information includes the basic information and geographic location information of the market subject, i.e. the accurate longitude and latitude coordinate information; 3) The node information includes the graphical symbols of each node, including primitives, annotations, colors and wiring. This data should comply with the automatic graphical interface specifications or related requirements issued by the power company; 4) The network model data includes the connection relationship and basic parameters between nodes. This data complies with the CIM model specification of IEC61970 standard; 5) The geographic map data file can use a file with js extension written in javascript script language, or a lightweight data exchange format file with json extension; 6) The geographic map data file contains all administrative area descriptions and longitude and latitude geographic location information in the region; 7) The map data is located in the first layer of the screen canvas layer.

3. The method for generating dynamic electricity market spot transaction prices and power flow diagrams based on nodal price clearing results and adapted to different grid node distributions, as described in claim 1, is characterized in that: The topological analysis according to the obtained data and vector parameters set by the system to obtain a tiled node distribution state diagram comprises the following features: 1) The vector parameters are system setting parameters, i.e. two known reference points are set according to the screen pixel coordinates and geographic coordinates, and two sets of vectors are composed of these two known reference points and the required node coordinate parameters; Vector 1 is (d x1 , d y1 ) and (d lon1 , d lat1 ). Vector 2 is (d x2 , d y2 ) and (d lon2 , d lat2 ). Wherein, d x1 is the difference between the pixel x coordinates of the two reference points; d y1 is the difference in pixel y coordinates between the two reference points; d lon1 is the difference in longitude between the geographical coordinates of the two reference points; d lat1 is the difference in geographical coordinate latitude between the two reference points; d x2 the difference between the pixel x coordinate of the required node and one of the reference points; d y2 the difference between the pixel y coordinate of the required node and one of the reference points; d lon2 the difference in geographic coordinate longitude between the required node and one of the reference points; d lat2 the difference in geographical coordinate latitude between the required node and one of the reference points; In order to simplify the calculation, the origin and the maximum point are taken as the two reference points, wherein the origin is the point at the top left corner of the screen and the map, and the maximum point is the point at the bottom right corner of the screen and the map; 2) Calculate the longitude and latitude represented by each pixel in the screen by the Euclidean norm calculation method to obtain the coordinate conversion scale factor, and then convert the geographic coordinates of all nodes participating in the spot market into screen pixel coordinates; 3) Automatically draw the distribution state diagram of each node on the screen using the above calculated screen pixel coordinate list, and distinguish between unit nodes and load nodes using different primitives; 4) the drawn node distribution state diagram is located on the second layer of the screen canvas layer.

4. The method for generating dynamic electricity price and power flow map of electricity market spot transaction based on nodal price clearing result and adaptive to different distribution of electricity grid nodes according to claim 1, characterized in that: A node physical wiring diagram suitable for the power grid is automatically generated through the connection relationship of each node, and the calculation steps include: 1) obtaining the connection relationship between each node in the network model data; 2) automatically drawing a connection line according to the pixel distribution position of each node in the screen and combining the connection relationship of each node to obtain a node physical wiring diagram suitable for the power grid; 3) the node physical wiring diagram is located on the third layer of the screen canvas layer.

5. The method for generating dynamic electricity price and power flow map of electricity market spot transaction based on nodal price clearing result and adaptive to different distribution of electricity grid nodes according to claim 1, characterized in that: Accessing the power spot transaction clearing result, including node clearing result and branch clearing result data, the data contains: 1) the node clearing result data includes: transaction date, transaction period, node name, node number, node load, node price, whether the node belongs to a unit node, and unit power generation; 2) the branch clearing result data includes: transaction date, transaction period, branch name, branch number, starting node name, starting node number, ending node name, ending node number, and line power; 3) calculating the line load rate according to the line flow limit in the network model data and the line power in the branch clearing result.

6. The method for generating dynamic electricity price and power flow map of electricity market spot transaction based on nodal price clearing result and adaptive to different distribution of electricity grid nodes according to claim 1, characterized in that Automatically matching the clearing data of each clearing node and branch on the physical wiring diagram, including: 1) the node physical wiring diagram contains the identification information and screen pixel coordinate information of each node; 2) the node physical wiring diagram contains the identification information and screen pixel coordinate information of each branch; 3) the node part of the automatically matched clearing data of each clearing node and branch is matched according to the identification code of each node and the node number in the node clearing result data, and the name, load, and price of the node are assigned to the node element, and the node is identified according to the node attribute as a unit node or a load node; 4) the branch part of the automatically matched clearing data of each clearing node and branch is automatically matched according to the identification code of each branch and the branch number in the branch clearing result data, and the name, line power, and line load rate of the branch are assigned to the line element.

7. The method for generating dynamic electricity market spot transaction prices and power flow diagrams based on nodal price clearing results and adapted to different grid node distributions, as described in claim 1, is characterized in that: According to the clearing data and model coordinates, the node price, power flow direction, and line load condition are rendered using the cavas rendering technology, and the rendering steps are: 1) traversing all the element information in the node distribution state diagram, respectively obtaining the unit node attribute and the load node attribute, which contains the distinguishing identification and pixel coordinates; 2) rendering different clearing data for different attribute element identifications, adding unit name, unit power generation, and clearing price data to the unit element, and adding load name, node load, and clearing price data to the load element; 3) creating different node prompt styles for different attribute element identifications, and displaying the unit clearing information and the load clearing information when the mouse moves to the element; 4) traversing all the branch element information in the node physical wiring diagram; 5) obtaining the pixel coordinates of all endpoints and turning points of each branch; 6) According to the starting node name, starting node number, end node name, end node number information in the branch clearing result data, arrange the end point and turning point coordinates of the branch in order from the starting to the end; 7) Use cavas rendering technology to draw the dynamic flow chart in order according to the arranged coordinate order, and realize the dynamic flow of power flow; 8) According to the branch graph element identification and the branch clearing result data, render the name, line power and line load rate of the branch; 9) Judge the line load rate of all branches, and distinguish them by different colors; 10) Create prompt style for all branch graph elements, and display the specific clearing situation of the branch when the mouse moves to the graph element.

8. The method for generating dynamic electricity market spot transaction prices and power flow diagrams based on nodal price clearing results and adapted to different grid node distributions, as described in claim 1, is characterized in that: According to the method, the dynamic electricity price chart and the power flow chart are automatically generated, which contains the following content characteristics: 1) The distribution state of each node conforms to the actual distribution form of each node in the power grid area; 2) The connection relationship of each node conforms to the connection relationship in the network model data; 3) Each graph element in the chart dynamically contains the detailed transaction information of the current clearing period, including node data and branch data; 4) The branch between each node can dynamically show the power flow direction; 5) Each branch graph element distinguishes and displays the line load rate of the current clearing period.

Images