CN-121461283-B - Traffic energy load demand analysis method and system

CN121461283BCN 121461283 BCN121461283 BCN 121461283BCN-121461283-B

Abstract

The application provides a traffic energy load demand analysis method and a traffic energy load demand analysis system, which are applied to the field of energy load analysis, and dynamically acquire multidimensional data such as the use state of an energy supplementing station, a people flow density index, commercial activity heat, the electric quantity state of a high-power vehicle and the like in a prediction area, and comprehensively judge whether a high-power energy supplementing cluster is formed or not based on the data. Once the high-power energy supplementing cluster is identified, further acquiring a predicted power load and a real-time power load, simulating superposition to calculate the instantaneous influence, and timely sending out risk early warning information and giving a scheduling suggestion by judging whether the instantaneous influence exceeds a preset risk threshold. The method and the system remarkably improve the accuracy and the instantaneity of urban traffic energy load analysis, provide powerful technical support for fine scheduling of an energy distribution network, dynamic optimization configuration of an energy supplementing infrastructure and early warning of potential risks, and effectively guarantee the stability and the reliability of urban energy supply.

Inventors

LEI YUANHUA
YU ZIHUA
LIU HONGCHAO
XIE BIN
SUN ZHIYUN
LU XIAOLONG
CUI ZHIHENG

Assignees

中国能源建设集团湖南省电力设计院有限公司

Dates

Publication Date: 20260505
Application Date: 20251031

Claims (10)

1. The traffic energy load demand analysis method is applied to the field of energy load analysis and is characterized by comprising the following steps of: in the prediction area, acquiring the use state of the energy supplementing station according to a preset data transmission protocol, and respectively acquiring a people stream density index, commercial activity heat and a high-power vehicle electric quantity state; when the using state of the energy supplementing station, the people stream density index, the commercial activity heat and the high-power vehicle electric quantity state in the prediction area all meet preset conditions, defining the prediction area to form a high-power energy supplementing cluster in a preset time period; Obtaining a predicted power load in the high-power energy supplementing cluster according to a preset mode, obtaining a real-time power load in a predicted area, simulating and superposing the predicted power load and the real-time power load, and calculating to obtain instantaneous influence; Judging whether the instantaneous influence exceeds a preset risk threshold, if so, sending risk early warning information and giving a scheduling suggestion; wherein after the predicted power load is obtained, further comprising the steps of: acquiring actual movement tracks of electric vehicles with low electric quantity states of the high-power vehicles around each prediction area, and respectively constructing an expected movement path based on initial intention for each electric vehicle; Comparing the actual moving track and the expected moving path of each electric vehicle in real time, detecting and identifying the behavior deviation of each electric vehicle caused by environmental discomfort, and classifying the behavior deviation into different behavior deviation types; Acquiring the current environmental discomfort intensity and duration, and generating a behavior response correction factor according to the behavior deviation type; and correcting the number of vehicles with high-power energy supplement at the same time and the predicted power load according to the behavior response correction factor, and updating the risk early warning information according to the corrected number of vehicles with high-power energy supplement at the same time and the corrected predicted power load.
2. The traffic energy load demand analysis method is applied to the field of energy load analysis and is characterized by comprising the following steps of: in the prediction area, acquiring the use state of the energy supplementing station according to a preset data transmission protocol, and respectively acquiring a people stream density index, commercial activity heat and a high-power vehicle electric quantity state; when the using state of the energy supplementing station, the people stream density index, the commercial activity heat and the high-power vehicle electric quantity state in the prediction area all meet preset conditions, defining the prediction area to form a high-power energy supplementing cluster in a preset time period; Obtaining a predicted power load in the high-power energy supplementing cluster according to a preset mode, obtaining a real-time power load in a predicted area, simulating and superposing the predicted power load and the real-time power load, and calculating to obtain instantaneous influence; Judging whether the instantaneous influence exceeds a preset risk threshold, if so, sending risk early warning information and giving a scheduling suggestion; wherein after the predicted power load is obtained, further comprising the steps of: when the electric vehicles with lower electric quantity states of the high-power vehicles around each prediction area access the energy supplementing station in the prediction area, acquiring real-time communication data of a battery management system of each electric vehicle; Analyzing the real-time communication data of the battery management system to obtain the actual temperature and the actual maximum energy supplementing power of the battery of each electric vehicle; Acquiring local ambient temperature of an energy replenishment station in a preset area, correlating the local ambient temperature with the actual temperature of the battery of each electric vehicle, and generating an energy replenishment power limiting reason; And correcting the predicted power load according to the energy supplementing power limiting reasons and the actual maximum energy supplementing power of each electric vehicle, and updating the risk early warning information according to the corrected predicted power load.
3. The traffic energy load demand analysis method according to claim 1 or 2, wherein the people stream density index is obtained by: Acquiring the number of active users and the number of wireless network connection devices in a preset area; And obtaining the people stream density index in the predicted area according to the sum of the number of the active users and the number of the wireless network connection devices and the area of the predicted area.
4. The traffic energy load demand analysis method according to claim 1 or 2, wherein the commercial activity heat is obtained by: Acquiring real-time check-in quantity related to a predicted area on a social platform; acquiring real-time transaction quantity and transaction amount change data in a preset area; and respectively adding weights to the real-time check-in quantity, the real-time transaction quantity and the transaction amount change data, summing, and calculating to obtain the commercial activity heat.
5. The traffic energy load demand analysis method according to claim 1 or 2, wherein the defining the prediction area to form a high-power energy replenishment cluster within a preset time period when the energy replenishment station usage status, the people stream density index, the commercial activity heat and the high-power vehicle state of charge within the prediction area all satisfy preset conditions includes the steps of: Judging whether the predicted area is a traditional energy supplementing hot spot area according to the using state of the energy supplementing station in the predicted area, and recording the predicted area as a first judging result; if the first judgment result is negative, judging whether the people stream density index of the prediction area exceeds a people stream density index threshold value in a preset time period, and recording the judgment result as a second judgment result; judging whether electric vehicles with the electric quantity states lower than the preset quantity exist around the prediction area or not, and recording the electric vehicles as a third judgment result; judging that the commercial activity heat in the preset area exceeds a preset heat threshold, and recording the commercial activity heat as a fourth judgment result; And when the second judging result, the third judging result and the fourth judging result are all yes, defining a prediction area to form a high-power energy supplementing cluster in a preset time period.
6. The traffic energy load demand analysis method according to claim 5, wherein the obtaining the predicted power load in the high-power energy supplement cluster according to the preset manner includes the steps of: Acquiring the quantity of electric vehicles with lower electric quantity states of the high-power vehicles around a prediction area, theoretical maximum energy supplement power of each electric vehicle and preference intensity factors of high-power energy supplement; Acquiring the number of vehicles with high-power energy supplement at the same time according to the number of electric vehicles with low electric quantity states of the high-power vehicles around the prediction area, the people stream density index and the commercial activity heat; and acquiring the predicted power load according to the number of vehicles with high-power energy supplement at the same time, the theoretical maximum energy supplement power of each electric vehicle and the preference intensity factor of the high-power energy supplement.
7. The traffic energy load demand analysis method according to claim 1 or 2, wherein the acquiring the predicted power load and the real-time power load in the predicted area in the high-power energy replenishment cluster, the simulated superposition of the predicted power load and the real-time power load, and the calculating the instantaneous influence, comprises the steps of: acquiring real-time operation data and design bearing data of an end energy distribution network in a prediction area; superposing the predicted power load to the terminal energy distribution network in a predicted area, and obtaining the sum of the predicted power load and the real-time power load; acquiring a voltage drop percentage according to the predicted power load, the line impedance and the rated voltage; Acquiring a predicted temperature rise maximum temperature according to the sum of the predicted power load and the real-time environment temperature; Obtaining a predicted line current according to the sum of the predicted power load and the real-time line voltage; taking the voltage drop percentage, the predicted temperature rise maximum temperature and the predicted line current as the transient effects; The real-time operation data comprise the real-time power load, the real-time environment temperature and the real-time line voltage; The design load data includes the nominal voltage and the line impedance.
8. The traffic energy load demand analysis method according to claim 7, wherein the determining whether the transient impact exceeds a preset risk threshold, if so, sending risk early warning information and giving a scheduling suggestion, includes the steps of: Judging whether the voltage drop percentage exceeds a preset voltage stability risk threshold value or not, and recording the voltage drop percentage as a fifth judgment result; judging whether the maximum temperature of the predicted temperature rise exceeds a preset temperature risk threshold value or not, and recording the maximum temperature of the predicted temperature rise as a sixth judgment result; Judging whether the predicted line current exceeds the rated current or not, and recording the predicted line current as a seventh judgment result; according to the fifth judgment result, the sixth judgment result and the seventh judgment result, risk early warning information is sent out and corresponding scheduling suggestions are given out; wherein the design bearing data further includes the rated current.
9. The utility model provides a traffic energy load demand analysis system, is applied to energy load analysis field, characterized in that includes: The prediction area parameter acquisition module is used for acquiring the use state of the energy supplementing station according to a preset data transmission protocol in the prediction area and respectively acquiring the people flow density index, the commercial activity heat and the high-power vehicle electric quantity state; The high-power energy supplementing cluster forming module is used for defining a prediction area to form a high-power energy supplementing cluster in a preset time period when the use state of the energy supplementing station, the people flow density index, the commercial activity heat and the high-power vehicle electric quantity state in the prediction area all meet preset conditions; the instantaneous influence acquisition module is used for acquiring the predicted power load in the high-power energy supplementing cluster according to a preset mode, acquiring the real-time power load in a predicted area, simulating and superposing the predicted power load and the real-time power load, and calculating and acquiring the instantaneous influence; The risk early warning and scheduling module is used for judging whether the instantaneous influence exceeds a preset risk threshold, if so, sending risk early warning information and giving scheduling suggestions; The behavior modification module is used for acquiring actual movement tracks of the electric vehicles with low electric quantity states of the high-power vehicles around each prediction area after acquiring the predicted power load, respectively constructing an expected movement path based on initial intention for each electric vehicle, comparing the actual movement tracks of each electric vehicle with the expected movement path in real time, detecting and identifying behavior deviation of each electric vehicle caused by environmental discomfort, classifying the behavior deviation into different behavior deviation types, acquiring current environmental discomfort intensity and duration, generating a behavior response correction factor according to the behavior deviation types, modifying the number of vehicles with high-power energy supplement at the same time and the predicted power load according to the behavior response correction factor, and updating the risk early warning information according to the number of vehicles with high-power energy supplement at the same time after modification and the predicted power load after modification.
10. The utility model provides a traffic energy load demand analysis system, is applied to energy load analysis field, characterized in that includes: The prediction area parameter acquisition module is used for acquiring the use state of the energy supplementing station according to a preset data transmission protocol in the prediction area and respectively acquiring the people flow density index, the commercial activity heat and the high-power vehicle electric quantity state; The high-power energy supplementing cluster forming module is used for defining a prediction area to form a high-power energy supplementing cluster in a preset time period when the use state of the energy supplementing station, the people flow density index, the commercial activity heat and the high-power vehicle electric quantity state in the prediction area all meet preset conditions; the instantaneous influence acquisition module is used for acquiring the predicted power load in the high-power energy supplementing cluster according to a preset mode, acquiring the real-time power load in a predicted area, simulating and superposing the predicted power load and the real-time power load, and calculating and acquiring the instantaneous influence; The risk early warning and scheduling module is used for judging whether the instantaneous influence exceeds a preset risk threshold, if so, sending risk early warning information and giving scheduling suggestions; The battery correction module is used for obtaining real-time communication data of a battery management system of each electric vehicle after the electric vehicle with lower electric quantity state of each electric vehicle around each prediction area is accessed to the energy supplement station in the prediction area after obtaining the predicted power load, analyzing the real-time communication data of the battery management system, obtaining the actual battery temperature and the actual maximum energy supplement power of each electric vehicle, obtaining the local environment temperature of the energy supplement station in the prediction area, correlating the local environment temperature with the actual battery temperature of each electric vehicle to generate an energy supplement power limit reason, correcting the predicted power load according to the energy supplement power limit reason and the actual maximum energy supplement power of each electric vehicle, and updating the risk early warning information according to the corrected predicted power load.

Description

Traffic energy load demand analysis method and system Technical Field The application relates to the field of energy load analysis, in particular to a traffic energy load demand analysis method and system. Background In urban traffic energy load analysis, the traditional method predicts the energy demand of an area or a time period mainly by aggregating vehicle overall energy consumption data, such as average energy consumption and energy replenishment habits of electric vehicles. This approach has had instructive implications in the past for planning the long-term development of energy distribution networks and the location of energy replenishment stations. However, with rapid progress in electric automobile technology, particularly, a great increase in battery energy storage capacity and an increase in energy replenishment speed, the energy replenishment behavior pattern of the user has changed significantly. For example, users tend to power up when the amount of power is lower, and more frequently utilize the fragmentation time for short, high power, rapid power up. These variations make it difficult for conventional analysis methods to accurately capture localized, transient, high intensity peaks of energy replenishment. For example, in non-traditional energy replenishment hot spot areas and off-peak hours, situations may suddenly arise where a large number of high-capacity electric vehicles are simultaneously being subjected to high-power energy replenishment, forming an "opportunistic" high-power energy replenishment cluster. The presence of such clusters places unprecedented stress on the local energy distribution network, which can lead to overload of the transformer, line voltage dips, and even regional power outages. The traditional load analysis method cannot predict the concentrated local, transient and high-intensity energy supplement demands due to the logic based on the past average data and the typical commute mode, thereby covering the real risk and the potential service bottleneck faced by the energy distribution network. Therefore, there is a need in the art for improvement to establish an analysis mechanism capable of dynamically sensing and predicting the impact of the technical evolution and the user behavior change of the electric vehicle on the traffic energy load, so as to provide prospective data support for the fine scheduling of the energy distribution network, the dynamic optimization configuration of the energy supplementing infrastructure and the early warning of potential risks. Therefore, providing a method and a system for analyzing traffic energy load requirements is a problem to be solved by those skilled in the art. Disclosure of Invention The application discloses a traffic energy load demand analysis method and a traffic energy load demand analysis system, and aims to solve the problems that the traditional traffic energy load analysis method is difficult to accurately capture local, instantaneous and high-strength energy supplement peaks, so that the real risk and potential service bottleneck faced by an energy distribution network are covered. The technical scheme of the application is as follows: in a first aspect, the application discloses a traffic energy load demand analysis method, which is applied to the field of energy load analysis and comprises the following steps: in the prediction area, acquiring the use state of the energy supplementing station according to a preset data transmission protocol, and respectively acquiring a people stream density index, commercial activity heat and a high-power vehicle electric quantity state; when the use state of the energy supplementing station, the people flow density index, the commercial activity heat and the high-power vehicle electric quantity state in the prediction area all meet preset conditions, defining the prediction area to form a high-power energy supplementing cluster in a preset time period; the method comprises the steps of obtaining a predicted power load and a real-time power load in a predicted area in a high-power energy supplementing cluster, simulating and superposing the predicted power load and the real-time power load, and calculating to obtain instantaneous influence; Judging whether the instantaneous influence exceeds a preset risk threshold, if so, sending out risk early warning information and giving out scheduling suggestions. According to the technical scheme, the influence of the technical evolution of the electric automobile and the behavior change of the user on the traffic energy load can be dynamically perceived and predicted, so that prospective data support is provided for the fine scheduling of the energy distribution network, the dynamic optimization configuration of the energy supplementing infrastructure and the early warning of potential risks, and the problem that the traditional method cannot accurately capture local, transient and high-intensity energy supplementing peaks is e