A spatiotemporal evaluation method and terminal for flexibility opportunities based on geographic information system

Abstract

The present invention discloses a spatiotemporal evaluation method and terminal for flexibility opportunities based on a geographic information system, including the following steps: Step S1, using a K-NN classification algorithm based on a geographic information system in an incomplete information state to determine the main substation to which each power load is connected; Step S2, predicting the number of various types of electrical appliances in use based on census data, and estimating the future additional power demand based on the average power demand of each electrical appliance in different months and different time periods; Step S3, evaluating the flexibility opportunities based on the capacity of each substation based on future power demand; Step S4, displaying the results of the flexibility opportunity evaluation on a map based on a geographic information system. The present invention collects low-voltage network data of the power system through a K-NN classification algorithm, integrates time and space factors, meets the needs of power planning staff, and is conducive to the development of flexibility resources.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power planning, and in particular to a spatiotemporal evaluation method and terminal for flexibility opportunities based on a geographic information system. Background Art

[0002] In recent years, global energy shortages and environmental pollution have promoted the large-scale development of renewable energy such as wind power and photovoltaics. However, compared with traditional energy such as thermal power, wind, light and other renewable energy sources can change significantly in a short period of time, with strong intermittency and volatility, and the superposition of the volatility of the original load leads to an increase in the net load fluctuation of the system; at the same time, with the increasing advancement of terminal electrification, the demand for electricity continues to increase, which may lead to network congestion. Therefore, the system needs to have sufficient regulation and response capabilities to deal with power fluctuations, that is, flexibility, otherwise it will bring about problems such as wind abandonment, solar abandonment and load shedding, which will affect the safe and stable operation of the system.

[0003] Current flexibility assessment models are mostly concentrated at the power system level, evaluating flexibility resources and power system flexibility, such as the probability of insufficient system flexibility, the expectation of insufficient flexibility, etc., while constraints and congestion issues include issues at the geographic system level. If power system staff want to effectively and efficiently formulate flexibility strategies and flexibility implementation methods, they need to understand the geographic spatial relationship of various flexibility opportunities, clarify which areas will have congestion problems, which areas can provide ancillary services, and which areas face the possibility of wind power dispatch management.

[0004] However, the existing flexibility assessment methods only consider the flexibility resources and flexibility of the power system level, but do not consider the influencing factors of time and space. This makes it difficult to meet the needs of power planning staff and is not conducive to the development of flexibility resources. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a spatiotemporal evaluation method and terminal for flexibility opportunities based on a geographic information system, which evaluates the flexibility of a power system by integrating time and space factors.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A spatiotemporal assessment method for flexibility opportunities based on geographic information system, comprising the steps of:

[0008] Step S1, using a K-NN classification algorithm based on a geographic information system in an incomplete information state to determine the main substation to which each power load is connected;

[0009] Step S2: predicting the usage of various electrical appliances based on the census data, and estimating the future additional electricity demand by aggregating the average electricity demand of each electrical appliance in different months and different time periods;

[0010] Step S3: Evaluate the flexibility opportunities based on the capacity of each substation according to future electricity demand;

[0011] Step S4: Based on the geographic information system, the results of the flexibility opportunity assessment are displayed on a map.

[0012] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0013] A spatiotemporal evaluation terminal for flexibility opportunities based on a geographic information system comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0014] Step S1, using a K-NN classification algorithm based on a geographic information system in an incomplete information state to determine the main substation to which each power load is connected;

[0015] Step S2: predicting the usage of various electrical appliances based on the census data, and estimating the future additional electricity demand by aggregating the average electricity demand of each electrical appliance in different months and different time periods;

[0016] Step S3: Evaluate the flexibility opportunities based on the capacity of each substation according to future electricity demand;

[0017] Step S4: Based on the geographic information system, the results of the flexibility opportunity assessment are displayed on a map.

[0018] The beneficial effects of the present invention are: a spatiotemporal evaluation method and terminal for flexibility opportunities based on a geographic information system, which collects low-voltage network data of the power system under incomplete information conditions through a K-NN classification algorithm, and then connects the geographic system with the power system to evaluate flexibility opportunities and display them on a map based on consideration of time influencing factors, integrating time and space factors, meeting the needs of power planning staff, and facilitating the development of flexibility resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of a flow chart of a method for spatiotemporal evaluation of flexibility opportunities based on a geographic information system according to an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of a process of checking evaluation results according to an embodiment of the present invention;

[0021] Figure 3The present invention is a schematic diagram of the structure of a flexibility opportunity spatiotemporal evaluation terminal based on a geographic information system according to an embodiment of the present invention.

[0022] Description of labels:

[0023] 1. A flexibility opportunity spatiotemporal assessment terminal based on geographic information system; 2. Processor; 3. Memory. DETAILED DESCRIPTION

[0024] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0025] Please refer to Figure 1-2 , a spatiotemporal assessment method for flexibility opportunities based on geographic information systems, comprising the steps of:

[0026] Step S1, using a K-NN classification algorithm based on a geographic information system in an incomplete information state to determine the main substation to which each power load is connected;

[0027] Step S2: predicting the usage of various electrical appliances based on the census data, and estimating the future additional electricity demand by aggregating the average electricity demand of each electrical appliance in different months and different time periods;

[0028] Step S3: Evaluate the flexibility opportunities based on the capacity of each substation according to future electricity demand;

[0029] Step S4: Based on the geographic information system, the results of the flexibility opportunity assessment are displayed on a map.

[0030] From the above description, it can be seen that the beneficial effects of the present invention are: it collects low-voltage network data of the power system under incomplete information state through the K-NN classification algorithm, and then connects the geographical system with the power system on the basis of considering the time influencing factor to evaluate the flexibility opportunities and display them on the map, integrating the factors of time and space, meeting the needs of power planning staff, and facilitating the development of flexibility resources.

[0031] Furthermore, the step S1 specifically includes:

[0032] The center of each small area is determined according to the following formula (C x , C y ):

[0033]

[0034]

[0035]

[0036] Where n is the number of vertices of the small area polygon, (x i ,y i ) are the coordinates of the power load point in the small area, and A is the area of ​​the polygon;

[0037] Calculate the center of each small area (C x , C y ) to all main substations (x p ,y p ), and finally select the nearest substation.

[0038] From the above description, it can be seen that by calculating the distance between each power load and the main substation, the geographical correspondence between the main substation and the flexibility resources can be established, and the geographical system is connected to the power system.

[0039] Furthermore, the various electrical appliances include heat pumps and electric vehicles, and the step S2 specifically includes:

[0040] Predictions of heat pump usage based on census data and heat pump and electric vehicle share and the number of electric vehicles used The ADMD method is used to estimate the future demand for heat pumps and electric vehicle equipment, and the future electricity demand D is obtained. F (j, t):

[0041]

[0042]

[0043]

[0044] In the formula, j represents the month, t represents 24 hours in a day, and N HP and N EV For the existing number of heat pumps and electric vehicles, and For heat pump users HP and users of electric vehicles EV The maximum power demand at time (j, t).

[0045] From the above description, it can be seen that the future heat pump and electric vehicle data are estimated based on the census data, so as to predict the future electricity demand and then establish its spatiotemporal flexibility resource assessment method.

[0046] Furthermore, the flexibility opportunities include congestion management, ancillary services and wind power dispatch management.

[0047] From the above description, we can see that the three flexibility opportunities of congestion management, ancillary services and wind power dispatch are evaluated to clarify which areas will have congestion problems, which areas can provide ancillary services, and which areas face the possibility of wind power dispatch management, so as to meet the needs of power planning staff and facilitate the development of flexibility resources.

[0048] Furthermore, the step S3 specifically includes:

[0049] The chance of blocking management F is calculated according to the following formula CM (j, t), the opportunity for auxiliary services F AS (j, t), the dispatch management opportunity F of wind curtailment due to reduced load demand CU (j, t) and the dispatch management opportunity F for wind curtailment due to failure to meet safety constraints CO (j, t):

[0050]

[0051]

[0052]

[0053]

[0054] In the formula, C u (j, t) and C G (j, t) is the average wind curtailment value due to load demand reduction and failure to meet safety constraints at time (j, t), Hr is the margin of substation connection in small area s, and k is the number of small areas.

[0055] From the above description, it can be seen that a formula is provided to evaluate three flexibility opportunities, namely, congestion management, ancillary services and wind power dispatch, which takes into account the impact of substation congestion and improves the accuracy and efficiency of the evaluation.

[0056] Please refer to Figure 3 , a flexibility opportunity spatiotemporal evaluation terminal based on a geographic information system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0057] Step S1, using a K-NN classification algorithm based on a geographic information system in an incomplete information state to determine the main substation to which each power load is connected;

[0058] Step S2: predicting the usage of various electrical appliances based on the census data, and estimating the future additional electricity demand by aggregating the average electricity demand of each electrical appliance in different months and different time periods;

[0059] Step S3: Evaluate the flexibility opportunities based on the capacity of each substation according to future electricity demand;

[0060] Step S4: Based on the geographic information system, the results of the flexibility opportunity assessment are displayed on a map.

[0061] From the above description, it can be seen that the beneficial effects of the present invention are: it collects low-voltage network data of the power system under incomplete information state through the K-NN classification algorithm, and then connects the geographical system with the power system on the basis of considering the time influencing factor to evaluate the flexibility opportunities and display them on the map, integrating the factors of time and space, meeting the needs of power planning staff, and facilitating the development of flexibility resources.

[0062] Furthermore, the step S1 specifically includes:

[0063] The center of each small area is determined according to the following formula (C x , C y ):

[0064]

[0065]

[0066]

[0067] Where n is the number of vertices of the small area polygon, (x i ,y i ) are the coordinates of the power load point in the small area, and A is the area of ​​the polygon;

[0068] Calculate the center of each small area (C x , C y ) to all main substations (x p ,y p ), and finally select the nearest substation.

[0069] From the above description, it can be seen that by calculating the distance between each power load and the main substation, the geographical correspondence between the main substation and the flexibility resources can be established, and the geographical system is connected to the power system.

[0070] Furthermore, the various electrical appliances include heat pumps and electric vehicles, and the step S2 specifically includes:

[0071] Predictions of heat pump usage based on census data and heat pump and electric vehicle share and the number of electric vehicles used The ADMD method is used to estimate the future demand for heat pumps and electric vehicle equipment, and the future electricity demand D is obtained. F(j, t):

[0072]

[0073]

[0074]

[0075] In the formula, j represents the month, t represents 24 hours in a day, and N HP and N EV For the existing number of heat pumps and electric vehicles, and For heat pump users HP and users of electric vehicles EV The maximum power demand at time (j, t).

[0076] From the above description, it can be seen that the future heat pump and electric vehicle data are estimated based on the census data, so as to predict the future electricity demand and then establish its spatiotemporal flexibility resource assessment method.

[0077] Furthermore, the flexibility opportunities include congestion management, ancillary services and wind power dispatch management.

[0078] From the above description, we can see that the three flexibility opportunities of congestion management, ancillary services and wind power dispatch are evaluated to clarify which areas will have congestion problems, which areas can provide ancillary services, and which areas face the possibility of wind power dispatch management, so as to meet the needs of power planning staff and facilitate the development of flexibility resources.

[0079] Furthermore, the step S3 specifically includes:

[0080] The chance of blocking management F is calculated according to the following formula CM (j, t), the opportunity for auxiliary services F AS (j, t), the dispatch management opportunity F of wind curtailment due to reduced load demand CU (j, t) and the dispatch management opportunity F for wind curtailment due to failure to meet safety constraints CO (j, t):

[0081]

[0082]

[0083]

[0084]

[0085] In the formula, C u (j, t) and CG (j, t) is the average wind curtailment value due to load demand reduction and failure to meet safety constraints at time (j, t), Hr is the margin of substation connection in small area s, and k is the number of small areas.

[0086] From the above description, it can be seen that a formula is provided to evaluate three flexibility opportunities, namely, congestion management, ancillary services and wind power dispatch, which takes into account the impact of substation congestion and improves the accuracy and efficiency of the evaluation.

[0087] The present invention discloses a spatiotemporal flexibility opportunity evaluation method and terminal based on a geographic information system, which are applied in the technical field of electric power planning to predict the additional demand for electric power.

[0088] Please refer to Figure 1 , Embodiment 1 of the present invention is:

[0089] A spatiotemporal assessment method for flexibility opportunities based on geographic information systems comprises the following steps:

[0090] Step S1: Based on the geographic information system, K-NN classification algorithm is used to perform regional division under incomplete information state.

[0091] Based on the statistical information of the geographical location of the power load in the Geographic Information System (GIS), when the information is incomplete, the k-nearest neighbor (k-NN) classification algorithm is used to determine the main substation connected to each power load, and visualized through the map application programming interface (API);

[0092] Specifically, first determine the center of each small area (C x , C y ):

[0093]

[0094]

[0095] Where n is the number of vertices of the small area polygon, (x i ,y i ) is the coordinate of the power load point in the small area, and A is the area of ​​the polygon, which is determined by the following formula:

[0096]

[0097] When each small area is connected to the nearest main substation, the center of each small area (C x , Cy ) to all main substations (x p ,y p ), and finally select the nearest substation, as shown in the following formula:

[0098]

[0099] Where m is the number of main substations, D sp is the distance between the small area and the nearest selected main substation.

[0100] Step 2: Predict the usage of various electrical appliances based on the census data, and estimate the future additional electricity demand by aggregating the average electricity demand of each appliance in different months and time periods.

[0101] For future scenarios, taking heat pumps and electric vehicles as examples, the census data is used to predict the number of heat pumps and electric vehicles used, respectively. and The ADMD (After Diversity Maximum Demand) method is used to estimate the future demand for heat pumps and electric vehicle equipment. Since flexibility opportunities vary with seasons and days, an ADMD matrix with j rows (representing 12 months in a year) and t columns (representing 24 hours in a day) is generated. For the ADMD of heat pumps and electric vehicles, the maximum demand for each hour is determined by aggregating the maximum load demands of N heat pumps or electric vehicles, and then divided by N to obtain the average maximum load demand of each device. The current ADMD is used to estimate the future additional demand, and D F (j, t). As shown in the following formula:

[0102]

[0103]

[0104]

[0105] Where N HP and N EV For the existing number of heat pumps and electric vehicles, and For heat pump users HP and users of electric vehicles EV The maximum power demand at time (j, t).

[0106] Step S3: According to the future electricity demand, three flexibility opportunities are evaluated, namely, future congestion management based on the capacity of each substation, auxiliary services, and current wind power dispatch management.

[0107] A spatiotemporal evaluation method for flexibility opportunities is established to evaluate three flexibility opportunities: future congestion management, ancillary services, and current wind power dispatch management based on the capacity of each substation. This method reflects the future or current flexibility of each region and displays it visually on a map.

[0108] Specifically, the flexibility opportunity assessment includes future congestion management, ancillary services and wind power dispatch management. With the increasing electrification of terminals, especially the increase in heat pumps and electric vehicles, the substation margin is insufficient and congestion problems are prone to occur. For the future scenario, the congestion management opportunity F occurs in each small area (s) at time (j, t) CM (j, t) is given by:

[0109]

[0110] Where Hr is the margin of connecting substations in small area s, k is the number of small areas, and the blocking management opportunity F is CM (j, t) represents the congestion problem caused by insufficient flexibility due to insufficient substation margin, F CM The larger the (j, t) value, the more severe the blockage and the less flexible it is.

[0111] The uncertainty of renewable energy generation such as wind and solar requires the use of ancillary services to ensure the security of the power system and the quality of its power. For resources that can provide ancillary services at a given location, the substations they are connected to must have sufficient margin, otherwise, it will aggravate the congestion of the substation. For the future scenario, each small area provides ancillary service opportunities F at time (j, t) AS (j, t) is given by:

[0112]

[0113] Auxiliary Service OpportunitiesF AS (j, t) indicates that the regional substation has sufficient margin and flexibility to provide ancillary services, F AS The larger the (j, t) value is, the more auxiliary services can be provided and the greater the flexibility is.

[0114] Due to the decrease in load demand or failure to meet safety constraints, dispatchers may abandon wind power to maintain stable operation of the power system. The dispatch management opportunity F of abandoning wind power due to the decrease in load demand in each small area at time (j, t) CU (j, t) is given by the formula: the dispatch management opportunity F of wind power abandoned due to failure to meet safety constraints CO (j, t) is given by:

[0115]

[0116]

[0117] In the formula, C u (j, t) and C G (j, t) is the average wind curtailment due to load demand reduction and failure to meet safety constraints at time (j, t). The increase in load is affected by the substation margin H. r The impact of (j, t), the dispatch management opportunity F of wind curtailment due to reduced load demand CU (j, t) and the dispatch management opportunity F for wind curtailment due to failure to meet safety constraints CO The value of (j, t) represents the amount of wind curtailment that the dispatcher can reduce through dispatch management.

[0118] Step S4: Based on the geographic information system, the results of the flexibility opportunity assessment are displayed on a map.

[0119] Combining the power system with the geographic information system and using the map API through JavaScript (or other languages) is to embed the map into the web page. The API provides a large number of practical tools for processing maps and adding content to the map through various services, so that a full-featured map application can be created on the website. The geospatial relationship is considered in the spatiotemporal evaluation method of flexibility opportunities, and the three flexibility opportunities of congestion management, ancillary services and wind power dispatch management are displayed on the map, which facilitates the power system staff to formulate flexibility strategies and flexibility implementation methods.

[0120] After completion, Figure 3 As shown, power system staff can select specific time and area to view the corresponding estimated congestion management and ancillary services, as well as the current wind power dispatch management possibilities, thereby facilitating the formulation of flexibility strategies and flexibility implementation methods.

[0121] Please refer to Figure 3 , Embodiment 2 of the present invention is:

[0122] A flexibility opportunity spatiotemporal assessment terminal 1 based on a geographic information system comprises a memory 3, a processor 2 and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps of the above-mentioned embodiment 1 are implemented.

[0123] In summary, the present invention provides a spatiotemporal evaluation method and terminal for flexibility opportunities based on a geographic information system. A corresponding relationship is established between regions and nodes through a K-NN algorithm, and future electricity consumption is estimated based on census data and electrical appliance electricity consumption data. Thus, the flexibility of the power system is evaluated based on future electricity consumption, and flexibility opportunities are displayed on a map, providing convenience for power system staff to formulate flexibility strategies and flexibility implementation methods.

[0124] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A spatiotemporal assessment method for flexibility opportunities based on geographic information systems, characterized in that: Includes steps: Step S1, using a K-NN classification algorithm based on a geographic information system in an incomplete information state to determine the main substation to which each power load is connected; Step S2: predicting the usage of various electrical appliances based on the census data, and estimating the future additional electricity demand by aggregating the average electricity demand of each electrical appliance in different months and different time periods; Step S3: Evaluate the flexibility opportunities based on the capacity of each substation according to future electricity demand; Step S4: Based on the geographic information system, the results of the flexibility opportunity assessment are displayed on a map; The various electrical appliances include heat pumps and electric vehicles, and the step S2 specifically includes: Heat pump usage forecast based on census data and heat pump and electric vehicle share Census data forecast of heat pump usage and the number of electric vehicles used , using the ADMD method to estimate the future demand for heat pumps and electric vehicle equipment, and to obtain the future electricity demand : ； ； ； In the formula, j represents the month, t represents 24 hours in a day, and For the existing number of heat pumps and electric vehicles, and For heat pump users and electric vehicle users exist The maximum power demand at any time.

2. The method for spatiotemporal evaluation of flexibility opportunities based on geographic information system according to claim 1, characterized in that: The step S1 specifically includes: Determine the center of each small area divided according to the following formula : ； ； ； Where n is the number of vertices of the small area polygon, is the coordinate of the power load point in the small area, and A is the area of ​​the polygon; Calculate the center of each small area Distance to all main substations , and finally select the nearest substation.

3. The spatiotemporal evaluation method of flexibility opportunities based on geographic information system according to claim 1, characterized in that: The flexibility opportunities include congestion management, ancillary services and wind power dispatch management.

4. The method for spatiotemporal evaluation of flexibility opportunities based on geographic information system according to claim 3, characterized in that: The step S3 specifically includes: The chance of blocking management is calculated according to the following formula , opportunities for auxiliary services , Dispatch management opportunities for wind curtailment due to reduced load demand and dispatch management opportunities for wind curtailment due to failure to meet safety constraints : ； ； ； ； In the formula, and is the average value of wind curtailment due to load demand reduction and failure to meet safety constraints at time (j, t), Hr is the margin of substation connection in small area s, and k is the number of small areas.

5. A spatiotemporal evaluation terminal for flexibility opportunities based on a geographic information system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: Step S1, using a K-NN classification algorithm based on a geographic information system in an incomplete information state to determine the main substation to which each power load is connected; Step S2: predicting the usage of various electrical appliances based on the census data, and estimating the future additional electricity demand by aggregating the average electricity demand of each electrical appliance in different months and different time periods; Step S3: Evaluate the flexibility opportunities based on the capacity of each substation according to future electricity demand; Step S4: Based on the geographic information system, the results of the flexibility opportunity assessment are displayed on a map; The various electrical appliances include heat pumps and electric vehicles, and the step S2 specifically includes: Heat pump usage forecast based on census data and heat pump and electric vehicle share Census data forecast of heat pump usage and the number of electric vehicles used , using the ADMD method to estimate the future demand for heat pumps and electric vehicle equipment, and to obtain the future electricity demand : ； ； ； In the formula, j represents the month, t represents 24 hours in a day, and For the existing number of heat pumps and electric vehicles, and For heat pump users and electric vehicle users exist The maximum power demand at any time.

6. The flexibility opportunity spatiotemporal evaluation terminal based on geographic information system according to claim 5, characterized in that: The step S1 specifically includes: Determine the center of each small area divided according to the following formula : ； ； ； Where n is the number of vertices of the small area polygon, is the coordinate of the power load point in the small area, and A is the area of ​​the polygon; Calculate the center of each small area Distance to all main substations , and finally select the nearest substation.

7. The flexibility opportunity spatiotemporal evaluation terminal based on geographic information system according to claim 5, characterized in that: The flexibility opportunities include congestion management, ancillary services and wind power dispatch management.

8. The flexibility opportunity spatiotemporal evaluation terminal based on geographic information system according to claim 7, characterized in that: The step S3 specifically includes: The chance of blocking management is calculated according to the following formula , opportunities for auxiliary services , Dispatch management opportunities for wind curtailment due to reduced load demand and dispatch management opportunities for wind curtailment due to failure to meet safety constraints : ； ； ； ； In the formula, and is the average value of wind curtailment due to load demand reduction and failure to meet safety constraints at time (j, t), Hr is the margin of substation connection in small area s, and k is the number of small areas.

Images