Inland river port area load priority ranking method, system, equipment and medium

Abstract

The invention discloses an inland river port area load priority ranking method, system and device and a medium, and relates to the technical field of load power control, and the method comprises the steps: constructing a load response priority ranking index based on the historical data of an inland river port area demand response parameter; obtaining a priority ranking index data value at the current moment according to the priority ranking index and the data participating in the response last time; according to the sorting index data value at the current moment, a subjective weight is calculated through an AHP algorithm, and an objective weight is calculated through a CRITIC algorithm; calculating a response priority ranking index comprehensive weight according to a subjective weight and the objective weight; and according to the load response priority ranking index comprehensive weight, through an improved TOPSIS algorithm, carrying out priority ranking on the load participating in the demand response of the inland river harbor district. According to the method, the load response priority ranking index is put forward firstly, then the inland river port area load priority ranking method is put forward, and rapid load power control of the inland river port area is achieved.

Description

Technical Field

[0001] This invention relates to the field of load power control technology in inland river port areas, and in particular to a method, system, equipment and medium for prioritizing loads in inland river port areas. Background Technology

[0002] With the rapid development of shore power technology and the widespread adoption of electric heavy-duty trucks, their dynamic load coordination capability has become crucial for supporting the low-carbon transformation of inland river ports. Against this backdrop, accurately quantifying the response characteristics of port loads and establishing a scientific scheduling priority mechanism are of great significance for achieving coordinated optimization of the port energy system and distribution network. However, current research faces challenges such as the lack of a port load priority ranking index system and insufficient adaptability of priority ranking methods, making it difficult to achieve optimized scheduling of diversified load resources in port areas.

[0003] Existing research lacks specific indicators for the load response characteristics of port areas. Current methods mostly use traditional industrial load assessment indicators, failing to take into account key factors in actual port operation scenarios, such as the flexibility of charging time for electric heavy trucks, the rigid constraints of planned shore power supply for ships, and the dynamic interaction characteristics between load and power grid. This results in insufficient accuracy in load response potential assessment, making it difficult to quantify the actual response capacity and dispatchable space of port loads when participating in demand response.

[0004] Existing priority ranking methods mostly adopt the classic TOPSIS method based on Euclidean distance, but it has significant shortcomings: First, Euclidean distance only reflects the absolute difference between load characteristics and the ideal solution, ignoring the complementarity between features (for example, when a load performs well in the power elasticity dimension but has poor time-series adjustability, it cannot balance the differences in multiple dimensions); Second, it is difficult to quantify the degree of matching between load response characteristics and grid regulation requirements, resulting in the ranking results being out of touch with the actual scenario.

[0005] To address the aforementioned issues, this invention proposes a load priority ranking method for inland river port areas. Based on a system of load response characteristic indicators for port areas, this method calculates the combined subjective and objective weights of each indicator and evaluates the dynamic response performance of various load types using an improved TOPSIS method, thereby achieving rapid power control of loads such as electric heavy trucks and shore power for ships in inland river port areas. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the problem that this invention aims to solve is that current methods mostly use traditional industrial load assessment indicators, which fail to take into account key factors such as the flexibility of charging time for electric heavy trucks in actual port operation scenarios, the rigid constraints of ship shore power supply plans, and the dynamic interaction characteristics between load and power grid, resulting in insufficient accuracy in load response potential assessment.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for prioritizing the load of inland river ports, comprising: collecting historical data of demand response parameters of inland river ports; constructing a load response priority ranking index for inland river ports based on the collected historical data; obtaining the current load response priority ranking index data value based on the response priority ranking index and the data from the previous response; calculating the subjective weight value of the load response priority ranking index using the AHP algorithm (Analytic Hierarchy Process) based on the current ranking index data value, and calculating the objective weight value of the load response priority ranking index using the CRITIC algorithm (Combined method of Interactive weighting and ICriteria Importance based on Correlation Information); calculating the comprehensive weight value of the load response priority ranking index based on the subjective weight value and the objective weight value, using a comprehensive weighting model combining subjective and objective ranking indices; and calculating the comprehensive weight value of the load response priority ranking index using an improved TOPSIS algorithm (Technique for Order Preference by Similarity to...) based on the comprehensive weight value of the load response priority ranking index. IdealSolution (approximate ideal solution ranking method) prioritizes the loads participating in the demand response of inland river ports; based on the priority ranking results, it determines the order of participation in the demand response of inland river ports.

[0009] As a preferred embodiment of the inland port area load priority ranking method described in this invention, the inland port area demand response parameters include the operating power of electric heavy trucks, electric-driven port machinery, and ship shore power load; the historical data includes the time when the inland port area load receives the response command, the time when the load begins to execute the response command, the time when the load terminates its participation in the inland port area response and returns to its original power consumption mode, the baseline power of the load before receiving the command, the minimum operating power that the load can reduce to during the response, the amount of power reduction required by the response command, and the actual amount of power reduction during the response process.

[0010] As a preferred embodiment of the inland port area load priority ranking method described in this invention, the construction of inland port area load response priority ranking indicators includes: analyzing the response characteristics of inland port area loads through collected historical response data, initially establishing ranking indicators, and forming inland port area load response priority ranking indicators through empirical methods; specifically, it includes: determining response speed indicators and responsive duration indicators based on the time when the load receives the response command, the time when the load begins to execute the response command, and the time when the load terminates participation in the response and returns to its original power consumption mode; determining responsive capacity indicators based on the baseline power of the load before receiving the command and the minimum operating power that can be reduced during the response; and determining response command utilization rate indicators, actual response utilization rate indicators, and response power accuracy indicators based on the amount of power reduction required by the response command and the actual amount of power reduction during the response process.

[0011] As a preferred embodiment of the inland port area load priority ranking method of the present invention, the step of obtaining the current inland port area load response priority ranking index data value includes substituting the data of the previous electric heavy truck, electric-driven port machinery operating power and ship shore power load participating in the response into the defined formula of the six inland port area load response priority ranking indices to obtain the true value of the inland port area load response priority ranking index, which is used as the original input data for priority ranking when the load participates in the response this time.

[0012] As a preferred embodiment of the inland river port area load priority ranking method described in this invention, the formula for calculating the subjective weight value of the load response priority ranking index using the AHP algorithm is expressed as follows:

[0013]

[0014] Where, ω AHPj The subjective weight of the j-th load response priority ranking index; u i The square root vector is obtained by processing the judgment matrix of the load response priority ranking index using the square root method, where n represents the number of evaluation indexes; the formula for calculating the objective weights of the load response priority ranking index using the CRITIC algorithm is expressed as follows:

[0015]

[0016] Among them, C cj ω represents the information carrying capacity of the j-th ranking index. CRITICj This represents the objective weight of the j-th load response priority ranking index.

[0017] As a preferred embodiment of the inland river port area load priority ranking method described in this invention, the formula for calculating the comprehensive weight value of the load response priority ranking index is expressed as follows:

[0018] ω j =α×ω AHPj +(1-α)×ω CRTICj

[0019] Where α represents the introduced preference coefficient; ω j This represents the comprehensive weight of the j-th load response priority ranking index.

[0020] As a preferred embodiment of the inland port area load priority ranking method described in this invention, the prioritization of loads participating in the demand response of inland port areas using the improved TOPSIS algorithm includes: performing positive standardization on the data in the ranking data matrix composed of the true values ​​of m loads and n indicators, and constructing a weighted normalization matrix by combining the comprehensive weights of the ranking indicators; determining the positive ideal solution and negative ideal solution corresponding to each ranking indicator in the weighted normalization matrix, calculated using the following formula:

[0021]

[0022] in, and Let V represent the maximum positive ideal solution and the minimum negative ideal solution for each ranking index in the weighted normalization matrix V. The distance from each scheme to the positive and negative ideal solutions in the weighted normalization matrix is ​​calculated using the Tanimoto coefficients, as shown in the formula.

[0023]

[0024] in, and The values ​​of the j-th index in the weighted normalization matrix are respectively... Positive similarity and The negative similarity is calculated; the relative proximity of the loads participating in the response of each inland port area is calculated, expressed by the formula,

[0025]

[0026] Among them, C i This indicates the relative proximity of the i-th participating inland port area demand response load.

[0027] To address the aforementioned technical problems, this invention provides the following technical solution: an inland port area load priority ranking system, comprising: a data acquisition module, an index construction module, an index weighting module, and a priority ranking module; the data acquisition module collects historical response data of electric heavy trucks, electric-driven port machinery operating power, and ship shore power load participating in the inland port area demand response through a charging pile management system, a data acquisition and monitoring control system, and an energy management system in the inland port area; the index construction module constructs inland port area load response priority ranking indicators and calculates the true values ​​of the ranking indicators from historical response data as input; the index weighting module obtains the comprehensive weight value of the ranking indicators by combining subjective and objective weights; the priority ranking module prioritizes the loads participating in the inland port area demand response using an improved TOPSIS method, determining the order of electric heavy trucks, electric-driven port machinery operating power, and ship shore power load participating in the inland port area demand response.

[0028] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the inland river port area load priority sorting method as described above.

[0029] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the inland river port area load priority sorting method as described above.

[0030] The beneficial effects of this invention are as follows: This invention first proposes a load response priority ranking index, and then proposes a load priority ranking method for inland river port areas, enabling rapid power control of loads in inland river port areas. Compared with previous methods, this invention establishes a set of quantitative indicators for ranking port area load response characteristics. By integrating multi-dimensional parameters such as load dynamic response time, it overcomes the limitations of traditional single-indicator ranking, significantly improving the accuracy and adaptability of load characteristic analysis under complex operating conditions. Furthermore, it proposes a priority ranking method suitable for inland river port area loads, achieving dynamic optimization of load grading, greatly improving the response speed of critical loads, and simultaneously enhancing the accuracy of priority ranking results. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1This is a flowchart of the load response priority power ranking strategy for an inland port area in Example 1.

[0033] Figure 2 This is a diagram showing the load response priority ranking index of an inland port area in Example 1, which is a load priority ranking method for inland port areas.

[0034] Figure 3 This is a diagram showing the priority ranking results of electric heavy trucks, ship shore power loads, and electrically driven port machinery in an inland port area load priority ranking method according to Example 2.

[0035] Figure 4 This is a comparison chart showing the sorting completion time of three priority sorting algorithms for an inland port area load priority sorting method in Example 2. Detailed Implementation

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0037] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0038] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a method for prioritizing the load of inland river ports, including, as follows: Figure 1 As shown:

[0039] First, collect historical data on demand response parameters of inland river port areas, and construct a priority ranking index for load response of inland river port areas based on the collected historical data.

[0040] The demand response parameters for inland port areas include the operating power of electric heavy trucks, electric-driven port machinery, and shore power load of ships.

[0041] The specific data collection methods include collecting data through the charging pile management system, data acquisition and monitoring control system, and energy management system in the inland port area. This data includes the time when the load in the inland port area receives the response command, the time when the load begins to execute the response command, the time when the load terminates its participation in the inland port area response and returns to its original power consumption mode, the baseline power of the load before receiving the command, the minimum operating power that the load can reduce during the response, the amount of power reduction required by the response command, and the actual amount of power reduction during the response process.

[0042] By collecting historical response data, the response characteristics of inland port area loads are analyzed, preliminary ranking indicators are established, and an empirical method is used to form a priority ranking index for inland port area load responses.

[0043] Specifically, including, such as Figure 2 As shown, the response speed index and the response duration index are determined based on the time when the load receives the response command, the time when the load starts to execute the response command, and the time when the load terminates its participation in the response and returns to its original power consumption mode.

[0044] The responsive capacity metric is determined based on the baseline power collected before the load receives instructions and the minimum operating power that can be reduced to during response.

[0045] Based on the amount of power reduction required by the response command and the actual amount of power reduction during the response process, determine the response command utilization rate index, the actual response utilization rate index, and the response power accuracy index.

[0046] To calculate the true values ​​of the above indicators using the collected historical response data, and to use these values ​​as the original input data for prioritizing load participation in the current response, this embodiment defines the indicators as follows:

[0047] (1) Response speed index: refers to the time interval between receiving a response command and the actual execution of power reduction actions by load resources such as electric heavy trucks. The formula is expressed as follows:

[0048] T latency =t activation -t signal

[0049] Among them, T latency The total response time of the inland port area is represented by t. signal t represents the moment when the inland port area load receives the response command. activation This indicates the starting point at which the load power of the inland river port area actually begins to decrease.

[0050] (2) Response Time Index: This refers to the effective participation time of loads such as electric heavy trucks from the time they actually reduce their power consumption to the time they cease participating in the inland port area response and return to their original power consumption mode. The formula is as follows:

[0051] T duration =t termination -t activation

[0052] Among them, T duration t represents the duration of the response. termination This indicates the moment when the response command terminates and the load power returns to the baseline value.

[0053] (3) Response capacity index: refers to the maximum adjustment capability of loads such as electric heavy trucks from the baseline power consumption to the actual power reduction within a single response cycle. The formula is expressed as follows:

[0054] C response =P baseline -P min

[0055] Among them, C response P represents the responsive capacity. baseline P represents the baseline power consumption when not affected by the response command. min This indicates the minimum operating power that the load resource can achieve within the response period.

[0056] (4) Response Command Utilization Rate Index: This is an important parameter referring to the degree of matching between the target value of the load response command and the maximum power response capability of electric heavy trucks, etc. It is defined as the percentage of the power required by the response command relative to its maximum responsive capability, expressed by the formula:

[0057]

[0058] Where, η DR P represents the response command utilization rate (dimensionless, with a value between 0 and 1). instruction This indicates the target value for ideal power reduction required by the response command for the inland port area.

[0059] (5) Actual Response Utilization Rate Index: Defined as the ratio of the reduced power actually requested to its maximum responsive capacity, it characterizes the effective mobilization level of load response capacity, expressed by the formula:

[0060]

[0061] Where, η actual P represents the actual response utilization rate (dimensionless, with a value of 0-1). actual This indicates the reduction in power capacity actually utilized by inland port area load resources.

[0062] (6) Response Power Accuracy Index: This refers to the percentage deviation between the actual power reduction and the target power reduction during the response process of loads such as electric heavy trucks in inland port areas. The formula is as follows:

[0063]

[0064] Among them, P AI This indicates the accuracy of the response power (dimensionless, with a value of 0-1).

[0065] To further explain, this embodiment requires substituting the collected historical response data of the previous inland port area load participation in demand response into the definition of the response priority ranking index constructed above, so as to obtain the true value of the inland port area load response priority ranking index, which serves as the original input data for priority ranking when the load participates in the response this time.

[0066] Second, based on the response priority ranking index and the data from the previous response, the current value of the inland port area load response priority ranking index is obtained.

[0067] Substituting the data from the previous response involving electric heavy trucks, electric-driven port machinery, and ship shore power loads into the defined formulas of the six inland port area load response priority ranking indicators, we obtain the true values ​​of the inland port area load response priority ranking indicators, which serve as the original input data for prioritizing loads during this response.

[0068] Third, based on the current ranking index data values, calculate the subjective weight of the load response priority ranking index using the AHP algorithm, and calculate the objective weight of the load response priority ranking index using the CRITIC algorithm.

[0069] The pairwise importance of the six load priority ranking indicators is compared to construct a judgment matrix, which is represented as follows:

[0070] A = (a ij ) n×n

[0071] Where A represents the judgment matrix, and n represents the number of priority ranking indicators; a ij This indicates the relative importance of priority ranking indicator i to indicator j.

[0072] The judgment matrix composed of priority ranking indicators is processed using the square root method to finally obtain the subjective weight vector of the ranking indicators. The calculation formula is as follows:

[0073]

[0074] Where, ω AHPj The subjective weight of the j-th load response priority ranking index; u i This is the root vector obtained after processing the judgment matrix of the load response priority ranking index using the root method.

[0075] To further explain, the subjective weight value of the load priority ranking index calculated in this embodiment is used as the input value for subsequently determining the comprehensive weight.

[0076] This embodiment uses a calculation example with six ranking indicators for electric heavy trucks. The judgment matrix A obtained by comparing the importance of each pair of indicators is as follows:

[0077]

[0078] The calculation result of u1 is: And so on, u2 = 0.637, u3 = 1.570, u4 = 0.637, u5 = 1.570, u6 = 3.412. The determined subjective weights are... And so on, ω AHP2 =0.078, ω AHP3 =0.193, ω AHP4 =0.078, ω AHP5 =0.193, ω AHP6 =0.420.

[0079] The objective weights of load response priority ranking indicators are calculated using the CRITIC algorithm. Specifically, taking electric heavy-duty trucks as an example, assuming the actual values ​​of 3 electric heavy-duty trucks and 6 ranking indicators constitute a ranking data matrix X = (x... ij ) 3×6 The elements in the matrix represent the true values ​​of the ranking indicators corresponding to a specific electric heavy-duty truck. The ranking indicator data in the constructed ranking data matrix X are standardized using the range standardization method to obtain the dimensionless standardized matrix Z = (z... ij ) 3×6 .

[0080] Based on the obtained standardized matrix Z, the information carrying capacity of the j-th ranking index is obtained by calculating the conflict and contrast strength of the j-th ranking index. The calculation formula is as follows:

[0081] C cj =σ j ×f j

[0082]

[0083] Among them, C cj σ represents the information carrying capacity of the j-th indicator; j Let be the comparative strength of the j-th indicator; r represents the mean of the j-th indicator; ij f represents the correlation coefficient between the i-th and j-th indicators; j This indicates the conflict between the i-th indicator and the j-th indicator.

[0084] Finally, the objective weight of the ranking index is obtained based on the ratio of the information carrying capacity of the ranking index. The calculation formula is as follows:

[0085]

[0086] Where, ω cjThis represents the objective weight of the j-th ranking index determined according to the CRTIC method.

[0087] To further explain, the objective weight value of the load priority ranking index calculated in this embodiment is used as the input value for subsequently determining the comprehensive weight.

[0088] IV. Based on subjective and objective weights, and using a comprehensive weighting model that combines subjective and objective weights, calculate the comprehensive weight of the load response priority ranking index.

[0089] Introducing a preference coefficient α (0≤α≤1) to allocate subjective and objective weight values, we can obtain the weight vector ω of the AHP-CRITIC comprehensive weighting. j This allows us to determine the combined subjective and objective weights of the indicators, expressed by the formula:

[0090] ω j =α×ω AHPj +(1-α)×ω CRITICj

[0091] Where, ω j This represents the overall weight of the j-th ranking index.

[0092] To further explain, the comprehensive weight value of the load priority ranking index calculated in this embodiment is used as the original input value for prioritizing the loads in step five.

[0093] V. Based on the comprehensive weight of the load response priority ranking index, the loads participating in the demand response of the inland river port area are prioritized using the improved TOPSIS algorithm.

[0094] Given the sorted data matrix X = (x ij ) 3×6 Based on this, the actual data of the ranking indicators in the matrix are positiveized by displaying a positiveization method. Specifically, cost-type ranking indicators in the ranking data matrix are converted into benefit-type indicators.

[0095] The obtained positively processed ranking index data is normalized to obtain the ranking index vector matrix R = (r ij ) 3×6 .

[0096] The combined weights of the six priority ranking indicators are multiplied by the corresponding data in the ranking indicator vector matrix to obtain the weighted normalized matrix V = (v ij ) 3×6 The calculation formula is as follows:

[0097] v ij =ω j ·r ij

[0098] Among them, v ij These are the elements in the weighted normalized matrix V.

[0099] Determine the maximum positive ideal solution and the minimum negative ideal solution for each ranking index in the normalized matrix. The maximum positive ideal solution is the maximum value in the j-th column, and the minimum negative ideal solution is the minimum value in the j-th column. The calculation formula is as follows:

[0100]

[0101] in, and These are the maximum positive ideal solution and the minimum negative ideal solution for each index in the weighted normalized matrix V, respectively.

[0102] By introducing the Tanimoto coefficient to replace the original Euclidean distance, the value of the j-th ranking index in the weighted normalization matrix is ​​calculated to V. + Similarity D + and to V - Similarity D - .

[0103]

[0104] in, and The values ​​of the j-th index in the weighted normalization matrix are respectively... Positive similarity and The negative similarity.

[0105] Calculate the relative proximity of the three electric heavy trucks, and determine the final sorting scheme of the port area load according to the relative proximity. The greater the relative proximity, the higher the priority of the port area electric heavy trucks to participate in the response.

[0106]

[0107] Among them, C i This indicates the relative similarity between the samples.

[0108] It should be further clarified that only the response speed indicator in the sorted data matrix is ​​a cost-based indicator, while the rest are benefit-based indicators. Furthermore, based on the priority ranking results, the order in which electric heavy trucks, electrically driven port machinery, and ship shore power loads participate in the inland port area demand response can be determined, thus completing the load command for this inland port area demand response.

[0109] This embodiment uses a calculation example. Assuming that after data collection, the six ranking index data corresponding to three electric heavy trucks are obtained, the resulting ranking data matrix X is:

[0110]

[0111] Since the first column contains cost-related indicators, it needs to be converted. The conversion result is as follows:

[0112] The ranking index vector matrix R is:

[0113]

[0114] Assume the weight vector is: W = [0.036 0.078 0.193 0.078 0.193 0.420]

[0115] Therefore, the weighted normalization matrix is:

[0116]

[0117] The calculation results for the positive and negative ideal solutions are as follows:

[0118]

[0119] Positive similarity of the first electric heavy-duty truck And so on,

[0120] The relative tracking progress calculation results are as follows: Following this logic, C2 = 0.577 and C3 = 0.412, so the second electric heavy truck has the highest priority.

[0121] Finally, based on the priority ranking results, the order of participation in the demand response of inland river port areas was determined.

[0122] Example 2, refer to Figure 3 and Figure 4 This is the second embodiment of the present invention, which differs from the first embodiment in that: the inland river port area load priority ranking method further includes a verification experiment: to verify the practicality of the method of the present invention, a domestic inland river port area was selected as the research object, with 100 electric heavy trucks, 20 ship shore power load points, and 30 electrically driven port machinery as samples. The port area load priority ranking method based on AHP-CRITIC-improved TOPSIS was used to simulate and analyze the above samples.

[0123] During the experiment, data on the entire process of load receiving demand response commands was collected using sensors in the port's Energy Management System (EMS) and port machinery and transportation equipment. This included the command acceptance time, the actual start time of reducing electricity demand, the response termination time, and the response capacity. Subsequently, the collected historical data was comprehensively analyzed to create a quantitative data table of response priority ranking indicators. Some data is shown in Table 1.

[0124] Table 1 Quantitative Data Table of Response Priority Ranking Indicators

[0125]

[0126] Based on the quantitative data of the response priority ranking indicators in Table 1, this invention first verifies the solution by assigning subjective and objective weights to the six response priority ranking indicators. Using the AHP method, the subjective weights of each indicator for electric heavy-duty trucks are calculated as follows: The subjective weights of each indicator for electrically driven port machinery are: The subjective weights of each indicator of ship shore power load are: The consistency ratios were verified to meet the requirements for consistent indicator determination; the objective weights of each indicator for electric heavy-duty trucks were calculated using the CRITIC method as follows: The objective weights of each indicator for electrically driven port machinery are: The objective weights of each indicator of ship shore power load are: Based on the subjective and objective weight calculation results of the above indicators, this invention obtains the final comprehensive weight of the indicators by setting the preference coefficient α = 0.4, as shown in Table 2.

[0127] Table 2 Comprehensive Weighting Table of Response Priority Ranking Indicators

[0128]

[0129] Then, based on the comprehensive weight data of each ranking index in Table 2, this invention improves the traditional TOPSIS algorithm by introducing the Tanimoto coefficient instead of the original Euclidean distance. This algorithm is then used to prioritize electric heavy trucks, ship shore power loads, and electrically driven port machinery. Simulation verification shows some of the priority ranking results are as follows: Figure 3 As shown.

[0130] To further verify the advantages of the improved TOPSIS method proposed in this invention for priority ranking of port loads, this invention prioritizes electric heavy-duty trucks under the same dataset and experimental conditions. The improved TOPSIS method is compared with the traditional TOPSIS method and the grey relational analysis method, focusing on the priority ranking completion time. Specific comparison results are as follows: Figure 4 As shown.

[0131] from Figure 4As can be seen, the improved TOPSIS achieves the fastest priority ranking completion time, significantly outperforming the traditional TOPSIS and the grey relational analysis method, demonstrating superior performance. The traditional TOPSIS performs moderately well among the three algorithms, while the grey relational analysis method is relatively slower. Overall, the improved TOPSIS demonstrates higher accuracy, more efficient response, and more stable decision-making in priority ranking, making it particularly significant in port area load priority ranking applications. It helps to quickly find the optimal response solution in inland river port resource allocation and load scheduling.

[0132] This invention only validates the proposed improved method using the proposed ranking index. However, when actual inland port loads participate in demand response, other types of indicators can be considered to prioritize different loads more comprehensively and scientifically.

[0133] Example 3, the third embodiment of the present invention, differs from the previous two embodiments in that it provides an inland port area load priority ranking system, comprising a data acquisition module, an index construction module, an index weighting module, and a priority ranking module. The data acquisition module collects historical response data on the participation of electric heavy trucks, electrically driven port machinery operating power, and ship shore power load in the inland port area's demand response through a charging pile management system, a data acquisition and monitoring control system, and an energy management system. The index construction module constructs inland port area load response priority ranking indicators and calculates the true values ​​of the ranking indicators from historical response data as input. The index weighting module combines subjective and objective weights of the ranking indicators to obtain a comprehensive weight value. The priority ranking module prioritizes the loads participating in the inland port area's demand response using an improved TOPSIS method, determining the order of participation of electric heavy trucks, electrically driven port machinery operating power, and ship shore power load in the current inland port area demand response.

[0134] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0136] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0137] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented in combination with any of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0138] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for prioritizing loads in inland river port areas, characterized in that: include, Historical data on demand response parameters of inland river port areas are collected, and based on the collected historical data, a priority ranking index for load response of inland river port areas is constructed. Based on the response priority ranking index and the data from the previous response, the current value of the inland port area load response priority ranking index is obtained. Based on the current ranking index data values, the subjective weight of the load response priority ranking index is calculated using the AHP algorithm, and the objective weight of the load response priority ranking index is calculated using the CRITIC algorithm. Based on the subjective weights and the objective weights, and using a comprehensive weighting model that combines subjective and objective factors, the comprehensive weights of the load response priority ranking indicators are calculated. Based on the comprehensive weight of the load response priority ranking index, the improved TOPSIS algorithm is used to prioritize the loads participating in the demand response of the inland port area. Based on the priority ranking results, a list of participants in the inland river port area demand response order will be determined.

2. The inland river port area load priority ranking method as described in claim 1, characterized in that: The demand response parameters for inland port areas include the operating power of electric heavy trucks, electric-driven port machinery, and ship shore power load. The historical data includes the time when the inland port area load receives the response command, the time when the load begins to execute the response command, the time when the load terminates its participation in the inland port area response and returns to its original power consumption mode, the baseline power of the load before receiving the command, the minimum operating power that the load can reduce to during the response, the amount of power reduction required by the response command, and the actual amount of power reduction during the response process.

3. The inland river port area load priority ranking method as described in claim 2, characterized in that: The construction of the load response priority ranking index for inland river port areas includes: analyzing the response characteristics of inland river port area loads through collected historical response data, initially establishing ranking indexes, and forming the load response priority ranking index for inland river port areas through empirical methods. Specifically, this includes determining the response speed index and the response duration index based on the time when the load receives the response command, the time when the load begins to execute the response command, and the time when the load terminates its participation in the response and returns to its original power consumption mode. Based on the baseline power collected before the load receives instructions and the minimum operating power that can be reduced to during response, the responsive capacity index is determined. Based on the amount of power reduction required by the response command and the actual amount of power reduction during the response process, determine the response command utilization rate index, the actual response utilization rate index, and the response power accuracy index.

4. The inland river port area load priority ranking method as described in claim 3, characterized in that: The process of obtaining the current load response priority ranking index data value of the inland port area includes substituting the data of the previous operation power of electric heavy trucks, electric-driven port machinery and ship shore power loads into the definition formula of the six inland port area load response priority ranking indexes to obtain the true value of the inland port area load response priority ranking index, which serves as the original input data for priority ranking when the loads participate in the response this time.

5. The inland river port area load priority ranking method as described in claim 4, characterized in that: The formula for calculating the subjective weights of the load response priority ranking index using the AHP algorithm is expressed as follows: Where, ω AHPj The subjective weight of the j-th load response priority ranking index; u i The square root vector is obtained by processing the judgment matrix of the load response priority ranking index using the square root method, where n represents the number of evaluation indexes. The formula for calculating the objective weights of the load response priority ranking index using the CRITIC algorithm is expressed as follows: Among them, C cj ω represents the information carrying capacity of the j-th ranking index. CRITICj This represents the objective weight of the j-th load response priority ranking index.

6. The inland river port area load priority ranking method as described in claim 5, characterized in that: The formula for calculating the comprehensive weighting value of the load response priority ranking index is expressed as follows: oh j =α×ω AHPj +(1-a)×ω CRTICj Where α represents the introduced preference coefficient; ω j This represents the comprehensive weight of the j-th load response priority ranking index.

7. The inland river port area load priority ranking method as described in claim 6, characterized in that: The process of prioritizing loads participating in demand response in inland river port areas using the improved TOPSIS algorithm includes: The data in the sorted data matrix consisting of m loads and n index real values ​​are positively standardized, and a weighted normalized matrix is ​​constructed by combining the comprehensive weights of the sorted indexes. The positive and negative ideal solutions corresponding to each ranking index in the weighted normalization matrix are determined by the following formula: in, and These are the maximum positive ideal solution and the minimum negative ideal solution for each ranking index in the weighted normalized matrix V, respectively. The distance to the positive and negative ideal solutions of each scheme in the weighted canonical matrix is ​​calculated using the Tanimoto coefficients. The formula is as follows: in, and The values ​​of the j-th index in the weighted normalization matrix are respectively... Positive similarity and Negative similarity; The relative proximity of the loads participating in the response of each inland port area is calculated using the formula, which is as follows: Among them, C i This indicates the relative proximity of the i-th participating inland port area demand response load.

8. An inland river port area load priority ranking system, employing the inland river port area load priority ranking method as described in any one of claims 1 to 7, characterized in that: It includes a data acquisition module, an indicator construction module, an indicator weighting module, and a priority ranking module; The data acquisition module collects historical response data of electric heavy trucks, electric-driven port machinery operating power, and ship shore power load when participating in the demand response of inland port areas through the charging pile management system, data acquisition and monitoring control system, and energy management system in the inland port area. The indicator construction module is used to construct a priority ranking index for the load response of inland river port areas, and the real value of the ranking index is calculated from historical response data as input. The indicator weighting module is used to obtain the comprehensive weight value of the ranking indicator by combining the subjective weight and objective weight of the ranking indicator. The priority ranking module is used to prioritize the loads participating in the demand response of inland port areas by improving the TOPSIS method, and to determine the order of electric heavy trucks, electric-driven port machinery operating power and ship shore power loads participating in the demand response of inland port areas.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the inland river port area load priority sorting method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the load priority sorting method for inland river port areas as described in any one of claims 1 to 7.

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