A port power distribution network power supply capacity evaluation method fusing source-load characteristics and potential energy feedback

Abstract

The present application provides a kind of fusion source load characteristic and potential energy feedback's port distribution network power supply capacity evaluation method, it is related to wisdom port distribution system analysis field, including the following steps: step 1 according to the collaborative work characteristics of shore bridge, rail bridge, charging pile and shore power load in port area, and the potential energy feedback behavior of lifting type equipment, combined with dynamic load, potential energy feedback and distributed renewable power output data, construct the power supply capacity evaluation model of port distribution network that fuses source load characteristic and potential energy feedback;Step 2 is based on the power supply capacity evaluation model of port distribution network to evaluate the maximum load access capacity of port substation, adopt the solution method of scene constraint optimization and scene probability weighting, obtain the maximum power supply capacity of port substation that fuses source load characteristic and potential energy feedback.The method can effectively solve the problem of insufficient precision of port power supply capacity evaluation, improve the accuracy and engineering applicability of maximum load access capacity evaluation of port distribution network.

Description

Technical Field

[0001] This invention relates to the field of port power distribution network operation and power supply capacity assessment technology, and in particular to a method for assessing the power supply capacity of port power distribution networks that integrates source-load characteristics and potential energy feedback. Background Technology

[0002] As a core node in the global trade and logistics network, ports are accelerating their electrification and automation transformation. With the implementation of technologies such as 5G remote control and unmanned container terminals, the power load structure of port areas has undergone significant changes: the single-unit power of large lifting equipment such as quay cranes and rail-mounted cranes has increased, the scale of electric truck charging pile clusters has grown significantly, the shore power system's single-berth access capacity can reach several megawatts, and the penetration rate of distributed wind power, photovoltaic and other new energy equipment is increasing year by year. This high-load and high-penetration power consumption pattern has transformed the port area's power distribution network from a traditional single power consumption end to a multi-source and load interactive system, greatly increasing the complexity of power supply capacity assessment.

[0003] Traditional methods for assessing the power supply capacity of distribution networks have significant limitations in port scenarios. On the one hand, traditional methods use static load simultaneity rates to describe load characteristics, failing to consider dynamic collaborative behaviors such as the timing coupling of quay cranes and electric trucks, and the constraints of logistics paths on equipment start-up and shutdown. This makes it difficult to accurately reflect the actual operating patterns of the port load, leading to a disconnect between the load demand data input for power supply capacity assessment and reality. On the other hand, traditional methods ignore the potential energy feedback characteristics of port lifting equipment during the descent phase and fail to include it in the power balance constraints, resulting in redundant capacity assessments of the distribution network.

[0004] Currently, although some studies have attempted to introduce dynamic load models or energy feedback analysis into the optimized operation models of distribution networks, these studies only build models for single equipment types and lack the ability to collaboratively model multiple equipment; or they only use feedback energy as an energy-saving indicator for statistics, without establishing its coupling relationship with the power flow of the distribution network. Meanwhile, the superposition of the power output fluctuations of distributed generation sources and the pulse-like nature of port loads further exacerbates the difficulty of balancing source and load power. Therefore, it is urgent to construct a power supply capacity assessment method that integrates the dynamic characteristics of source and load and potential energy feedback, providing a scientific basis for the safe operation and expansion planning of port distribution networks. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for evaluating the power supply capacity of port distribution networks that integrates source-load characteristics and potential energy feedback.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for assessing the power supply capacity of a port distribution network that integrates source-load characteristics and potential energy feedback includes the following steps: Step 1: Based on the collaborative operation characteristics of quay cranes, rail cranes, charging piles and shore power loads in the port area, as well as the potential energy feedback behavior of lifting equipment, and combined with dynamic load, potential energy feedback and distributed renewable power output data, construct a port power distribution network power supply capacity assessment model that integrates source load characteristics and potential energy feedback. The port distribution network power supply capacity assessment model that integrates source and load characteristics and potential energy feedback includes: dynamic load simultaneity rate model, lifting equipment potential energy feedback reverse injection power model, clustered reduced multi-source and multi-load power injection scenario set, and multi-scenario port distribution network power supply capacity assessment model with the maximum load access capacity of port substation as the target and voltage and capacity as constraints. Step 2 evaluates the maximum load access capacity of the port substation based on the port distribution network power supply capacity assessment model that integrates source-load characteristics and potential energy feedback. The maximum power supply capacity of the port substation that integrates source-load characteristics and potential energy feedback is obtained by using the solution method of intra-scenario constraint optimization and inter-scenario probability weighting.

[0007] Furthermore, the dynamic load simultaneity rate model, based on the operation scheduling logic of typical load types in the port area, collects load operation data from quay cranes, rail-mounted cranes, electric truck charging piles, and shore power. The load operation data includes the hoisting mechanism power and operation plan of the quay crane, the traveling speed and positioning information of the rail bridge, the charging start and stop time and charging power of the electric truck charging pile, and the access capacity and power consumption duration of the shore power. By combining equipment timing, operation rhythm and logistics path dependence, and analyzing the coupling relationship of operation timing between quay cranes and electric trucks, and the matching relationship between rail crane travel path and yard logistics path, the characteristics of load logistics collaborative behavior are identified. Based on load operation data and load-logistics coordination behavior characteristics, a dynamic load simultaneity rate model is constructed as follows: 1) Equipment parameter matrix representation:

[0008] Define the port area equipment load matrix P ∈R n×T row index i For load type, i =1 quay bridge, i =2 track bridges, i =3 charging stations i =4 shore power; column index t , representing step size, element P ( i,t ) is the first i Class of devices in the t Total load at any given time n This represents the total number of equipment types. T For statistical periods; Define the rated capacity vectorS ∈R n×1 ,element S i For the first i Rated capacity of the load class; Define the equipment operation association matrix A ∈R m×n row index j For collaborative work groups, m The column index represents the total number of collaborative work groups. i For load type; element A j,i For the work group j Medium type i The number of equipment; 2) Calculation of base concurrent rate:

[0009] Define the basic simultaneous rate vector c base ∈R n×T , ; Define the normalized load matrix P norm ∈R n×T , ; Define the device timing coupling matrix C ∈R m×T ; (1) In the formula, △ t The time window is represented by 1, which represents the quay crane, and 3 represents the charging station. 3) Calculation of the corrected dynamic load simultaneity rate:

[0010] Define the final dynamic load simultaneity rate vector as c ∈R n×T , The proposed method is scalable; when a new device type is added, the number of matrix rows can be increased.

[0011] Furthermore, the aforementioned potential energy feedback reverse injection power model for lifting equipment is based on the logistics characteristics and operating conditions of quay cranes and rail-mounted cranes. A matrix-based potential energy feedback model is constructed. Specifically, considering the potential energy conversion phenomenon during the loading and unloading operations of quay cranes and rail-mounted cranes in the cargo descent and empty descent phases, operational data of the quay cranes and rail-mounted cranes are collected, including the empty grab bucket mass, container mass, descent height, operating efficiency, and working status. Based on the principle of energy conservation and equipment characteristics, a potential energy feedback power model is constructed. The real-time feedback power is calculated and incorporated as a negative load into the power balance calculation for power supply capacity assessment. The specific model is as follows: 1) Matrix representation of equipment parameters:

[0012] Define device parameter matrix I ∈R Ns×6 row index k This represents the number of lifting equipment. k =1,2,…, N s , N s The total number of lifting equipment is represented by the following column index: I k,1 This is the rated lifting capacity. I k,2 The rated lifting speed, I k,3 For mechanical efficiency, I k,4 For electrical efficiency, I k,5 Lifting height I k,6 This refers to the job cycle time. Define the job status matrix I ∈R Ns×T ,element I k,t For equipment k exist t The status of the work at any time. I k,t = 0 indicates standby mode. I k,t = 1 indicates lifting. I k,t = 2 represents a decrease. I k,t = 3 represents translation, achieved through the device parameter matrix. I Clearly separating equipment physical parameters from operating status facilitates model expansion; 2) Define the single-device potential energy feedback power matrix P re,unit ∈RNs×T , (2) In the formula, g = 9.8 m / s 2 Let gravitational acceleration be the acceleration due to gravity; define the total feedback power matrix as follows: P re,all ∈R 1×T , Define the device coordination matrix C load ∈R n×Ns ,element C load ( i,k ) is the first i The type of load and the first k The coordination coefficient of each type of lifting equipment; 3) Define the load offset matrix P net ∈R n×T , (3).

[0013] Furthermore, the clustered and reduced multi-source, multi-load power injection scenario set, based on the dynamic load matrix and potential energy feedback matrix, constructs a matrix-based multi-scenario generation model. Through structured matrix operations, it integrates source-load uncertainties to generate a scenario set covering various typical operating states, as detailed below: 1) Define the new energy power output scenario matrix. P DG_sc ∈R Ng×T×S , (4) In the formula, S For the number of scenes, N g For the number of distributed power sources connected, element P DG_sc ( g,t,s ) is the first s In the scenario, the first g The first new energy in t The output at any given moment is calculated using a matrix that is fitted with historical output data of distributed power sources in the port area. d DG The random fluctuation coefficient of power output from new energy sources; 2) Define the scene probability vector. l ∈R 1×S ,element l s For the first s The probability of each scenario occurring. Define the net load scenario matrix P net_sc∈R n×T×S , (5) In the formula, d load The random fluctuation coefficient of net load; Furthermore, a clustering algorithm is used to generate representative scenarios, and a probability vector is defined after clustering. l m ∈R 1×M , M The number of scenarios after clustering; the dynamic load concurrency rate vector generated after clustering. c final ∈R 1×M Total potential energy feedback power matrix P re,final ∈R 1×T×M Load power matrix P load,final ∈R n×T×M New energy power output matrix P DG,final ∈R Ng×T×M .

[0014] Furthermore, the aforementioned multi-scenario port distribution network power supply capacity assessment model, based on the dynamic load simultaneity rate matrix, potential energy feedback power matrix, and source load power injection scenario set and scenario probability vector, evaluates the maximum load access capacity of the port substation main transformer under various typical operating conditions through multi-scenario power flow simulation analysis of the load-bearing characteristics of each feeder, while meeting the operational constraints of distribution network voltage, current, and feeder capacity. Specifically, the model is as follows: Considering the differences in source load characteristics among multiple feeders under the main transformer, a feeder correlation matrix is ​​defined. L ∈R f×n×Ng+1 In the formula, f Number of feeders, column 1~ n For connection between load and feeder, L ( l , i )=1 indicates the first i If the load is connected to feeder l, then it is 0; otherwise, it is 0. n +1~ n + N g For connecting new energy sources to feeders, L ( l , n + g )=1 indicates the first g New energy access feeder l Otherwise, it is 0; column n + N g +1 is for feedback power connection to the feeder. L( l , n + N g +1)=1 indicates the feeder l If the feeder can absorb the feedback power, then the value is 0; define the feeder net power matrix. P feeder,net ∈R f×T×M This reflects the actual power requirements of each feeder; (6) In the formula, the first addition term is the feeder. l The actual load including the simultaneity rate, the second addition term is for the feeder. l The feedback power absorbed, the third addition term is the feeder. l The injected new energy output; Matrix operations are used to achieve power cancellation of load, power feedback, and new energy at the feeder level, thereby reducing actual power supply demand; The objective function is to maximize the load access capacity of a substation under multiple scenarios, specifically: (7) In the formula, S m For the scene m The maximum load capacity that can be safely connected to the substation in this scenario, expressed in kW. l m For the first m The probability of each scenario occurring; The constraints are power balance constraints, feeder-level operation constraints, and main transformer capacity constraints, as detailed below: Power balance constraints are based on the following scenario. m Down t The load, feedback power, and renewable energy output at any given time satisfy the power balance: (8) In the formula, the left side of the equation represents the scenario. m Down t The actual load at any given time P grid ( t,m ) for scene m Down t The power that the substation purchases from the grid at any given time; The feeder-level operational constraints must satisfy both feeder capacity constraints and voltage constraints. The feeder capacity constraint is defined by the scenario. m and time t Down feeder l The carrying capacity is not greater than the rated capacity, specifically: The feeder voltage is: (9) In the formula, U (sub, t,m () represents the voltage of the upstream substation. R l For feeder l The resistance, X l For feeder l Reactance, Q feed,in This refers to the reactive power output of the feeder. The feeder voltage must meet both voltage constraints and main transformer capacity constraints: the voltage constraint is... , U N The reference voltage is used; the main transformer capacity constraint is used in the scenario. m and time t The sum of the power of the multiple feeders should not exceed the rated capacity of the main transformer, as follows: , S sub The rated capacity of the main transformer.

[0015] Furthermore, the solution method for constraint optimization within a scenario and probability weighting between scenarios involves determining the maximum load access capacity of a single scenario through load scaling iteration and constraint verification within the scenario, and performing weighted summation between scenarios based on the source load power injection scenario probability vector and the results of each scenario to output the maximum load access assessment result of the port substation.

[0016] Furthermore, the method for solving the intra-scene constraint optimization and inter-scene probability weighting problems achieves efficient solutions through matrix iteration and numerical optimization. Specifically, the method is as follows: Iterative solution within a scenario: Calculation of maximum load access capacity in a single scenario. For the scenario set of various typical operating states, calculate the maximum load capacity that the substation can safely access in each scenario. The dynamic load simultaneity rate model and the potential energy feedback reverse injection power model of lifting equipment are associated to ensure that the solution fits the actual operating logic. Specifically, this includes: (1) parameter initialization, using the dynamic load simultaneity rate, potential energy feedback power, load power under the scenario and new energy output data as boundary conditions, and setting the first... m Initial load scaling factor in each scenario k m (0), used to amplify or reduce load power; (2) power flow constraint verification, substitute load power, potential energy feedback power, and new energy output data into the evaluation model, calculate the power flow, node voltage and total power of each feeder, and verify whether the following operating constraints are met: the actual carrying power of all feeders does not exceed their rated capacity, the node voltage is within the safe operating range, and the total input power of the main transformer does not exceed its rated capacity; (3) iterative optimization of load access, if the above constraints are met, the current load has not reached the access limit, and the load scaling factor needs to be increased. k m (n+1) Let the step size be... d , k m (n+1) =k m (n) +d n represents the iteration number. After scaling up the load power proportionally, the power flow calculation and constraint verification are performed again. If any constraint is not satisfied, the load scaling factor needs to be reduced. k m (n+1) =k m (n) -d Repeat the power flow calculation and verification; (4) Determine the maximum capacity of a single scenario and continue to iterate until the adjustment range of the load scaling factor is less than the preset threshold. If the adjustment amount is less than the minimum threshold, the load power at this time is the maximum safe access load in this scenario. Inter-scenario probability integration: After calculating the maximum load access capacity of each scenario for all scenarios, the maximum load access capacity of each scenario is multiplied by its corresponding probability by the scenario set covering various typical operating states, and then all products are summed to obtain the maximum load access capacity of the port substation.

[0017] An electronic device includes a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the port power distribution network power supply capacity assessment method that integrates source-load characteristics and potential energy feedback as described above.

[0018] A computer-readable storage medium storing computer instructions thereon, which, when executed, perform the aforementioned method for assessing the power supply capacity of a port distribution network that integrates source-load characteristics and potential energy feedback.

[0019] The advantages and positive effects of this invention are: This method addresses the challenges in quantifying the collaborative characteristics of various load types in automated terminals due to operational scheduling, the insufficient consideration of the impact of potential energy feedback from hoisting equipment on the power distribution network, and the inadequate accuracy of power supply capacity assessment under source-load fluctuation matching differences. Through a systematic modeling and solution process, it achieves accurate assessment of the maximum load access capacity of the port area's power distribution network. Attached Figure Description

[0020] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that these drawings are designed for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, these drawings are only intended to conceptually illustrate the processes and illustrations described herein and are not necessarily drawn to scale.

[0021] Figure 1 This is a framework diagram of the evaluation model for the evaluation scheme provided by the present invention.

[0022] Figure 2 This is a flowchart of the solution process for the evaluation method of this invention. Detailed Implementation

[0023] The structure of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0024] The following is a detailed description of the port power distribution network power supply capacity assessment method that integrates source-load characteristics and potential energy feedback, provided by the present invention, with reference to the accompanying drawings. The steps are as follows: Step 1: Taking the power distribution network of an automated container terminal as the specific implementation object, collect initial information. The terminal's power distribution network includes the following equipment and parameters: Load equipment: 2 quay cranes, 3 rail cranes, 10 electric truck charging piles, 2 sets of shore power equipment, and 1 4.5MW distributed wind power unit; Power distribution network parameters: main transformer rated capacity 20MW, 2 feeders each rated capacity 800kW, rated voltage 10kV, voltage constraint range 9.5kV-10.5kV, time sequence operation data of distributed wind power, quay cranes, rail cranes, electric trucks, and shore power; Logistics operation data: quay crane lifting mechanism power, operation plan, empty grab bucket weight, full load weight, descent height, and operation efficiency; rail crane traveling speed and positioning information; electric truck charging pile charging start and stop time and charging power; shore power access capacity and power consumption duration.

[0025] Based on the aforementioned load operation data and load-logistics coordination behavior characteristics, a dynamic load simultaneity rate model is constructed (see Appendix). Figure 1 The evaluation model framework diagram of the evaluation scheme shown is as follows: 1) Equipment parameter matrix expression: Define the port area equipment load matrix P ∈R n×T row indexi For load type, i =1 quay bridge, i =2 track bridges, i =3 charging stations i =4 shore power; column index t , representing step size, element P ( i,t ) is the first i Class of devices in the t Total load at any given time (kW) n This represents the total number of equipment types. T Define the statistical period; define the rated capacity vector. S ∈R n×1 ,element S i For the first i Rated capacity (kW) of the load class; define the equipment operation association matrix. A ∈R m×n row index j For collaborative operations, such as quay cranes and electric container trucks forming one group, m The column index represents the total number of collaborative work groups. i For load type; element A j,i For the work group j Medium type i The number of devices, such as A j,i =1 indicates a work group j It includes one quay crane.

[0026] 2) Calculation of base simultaneous rate: Define the base simultaneous rate vector c base ∈R n×T , Define the normalized load matrix. P norm ∈R n×T , Define the device timing coupling matrix. C ∈R m×T , In the formula, △ t The time window is represented by 1, which represents the quay crane, and 3 represents the charging pile.

[0027] 3) Calculation of the corrected dynamic load simultaneity rate: Define the final dynamic load simultaneity rate vector as... c ∈R n×T , The proposed method is scalable; when new device types are added, the number of matrix rows can be increased. PThis data is directly used as the base load data for scenario generation in step 3, and multiple scenario load curves are randomly generated through matrix operations and overlay. (Vector) c It is directly used in the maximum load access assessment model in step 4 to represent the load simultaneity rate under multiple scenarios.

[0028] Step 2: 1) Matrix representation of equipment parameters, defining the equipment parameter matrix. I ∈R Ns×6 row index k This represents the number of lifting equipment. k =1,2,…, N s , N s The total number of lifting equipment is represented by the following column index: I k,1 Rated lifting capacity (tons). I k,2 Rated lifting speed (m / s). I k,3 Mechanical efficiency (%) I k,4 Electrical efficiency (%) I k,5 The lifting height is in meters (m). I k,6 Define the job cycle time (seconds); define the job state matrix. I ∈R Ns×T ,element I k,t For equipment k exist t The status of the work at any time. I k,t = 0 indicates standby mode. I k,t = 1 indicates lifting. I k,t = 2 represents a decrease. I k,t = 3 represents translation. 2) Define the single-device potential energy feedback power matrix. P re,unit ∈R Ns×T , In the formula, g = 9.8 m / s 2 Let gravitational acceleration be the acceleration due to gravity; define the total feedback power matrix as follows: P re,all ∈R 1×T , Define the device coordination matrix C load ∈R n×Ns ,elementC load ( i, k ) is the first i The type of load and the first k Coordination coefficients for various lifting equipment, such as the coordination coefficient between quay cranes and electric container trucks; defining load offset matrices. P net ∈R n×T , In the formula, P ( i,t () represents the load matrix element from step 1. Through the matrix... P re,all and P net It can be directly used as the basic data for the scenario generation in step 3, and multi-scenario power feedback curves can be generated through matrix operations.

[0029] Step 3: Based on the dynamic load matrix from Step 1 and the potential energy feedback matrix from Step 2, construct a matrix-based multi-scenario generation model. Integrate source-load uncertainties through structured matrix operations to generate a set of scenarios covering various typical operating states, specifically as follows: Define the new energy output scenario matrix. P DG_sc ∈R Ng×T×S , In the formula, S For the number of scenes, N g For the number of distributed power sources connected, element P DG_sc ( g,t,s ) is the first s In the scenario, the first g The first new energy in t The output (kW) at any given time is calculated using a matrix that is fitted with historical output data of distributed power sources in the port area. d DG The random fluctuation coefficient for new energy output; defining the scenario probability vector. l ∈R 1×S ,element l s For the first s The probability of each scenario occurring. Define the net load scenario matrix. P net_sc ∈R n×T×S In the formula, , d load Let be the random fluctuation coefficient of the net load; further, a clustering algorithm is used to generate representative scenarios, and the probability vector after clustering is defined. l m ∈R 1×M , M The number of scenarios after clustering is given. The dynamic load simultaneous rate vector, total potential energy feedback power matrix, load power matrix, and new energy output matrix generated after clustering are as follows: c final ∈R 1×M , P re,final ∈R 1×T×M , P load,final ∈R n×T×M , P DG,final ∈R Ng×T×M This is used for the probability weighting calculation in step 4.

[0030] Step 4: Based on the dynamic load simultaneity rate matrix generated in Step 1, the potential energy feedback power matrix constructed in Step 2, and the source load power injection scenario set and scenario probability vector formed in Step 3, under the condition of meeting the operating constraints such as distribution network voltage, current, and feeder capacity, the load-bearing characteristics of each feeder are analyzed through multi-scenario power flow simulation to evaluate the maximum load access capacity, i.e., the maximum power supply capacity, of the main transformer of the port substation under various typical operating states. Specifically, considering the differences in source load characteristics of multiple feeders under the main transformer, a feeder correlation matrix is ​​defined. L ∈R f×n×Ng+1 In the formula, f Number of feeders, column 1~ n For connection between load and feeder, L ( l , i )=1 indicates the first i If the load is connected to feeder l, then it is 0; otherwise, it is 0. n +1~ n + N g For connecting new energy sources to feeders, L ( l , n + g )=1 indicates the first g New energy access feeder l Otherwise, it is 0; column n + N g +1 is for feedback power connection to the feeder. L ( l , n + N g +1)=1 indicates the feeder l The net power of the feeder is 0 if it can absorb the feedback power. P feeder,net ∈R f×T×MThis reflects the actual power requirements of each feeder: In the formula, the first addition term is the feeder. l The actual load including the simultaneity rate, the second addition term is for the feeder. l The feedback power absorbed, the third addition term is the feeder. l The injected new energy output is used to achieve load, power feedback, and power cancellation of new energy at the feeder level through matrix operations.

[0031] The objective function is to maximize the load access capacity of a substation under multiple scenarios, specifically: In the formula, S m For the scene m The maximum load capacity that can be safely connected to the substation in this scenario, expressed in kW. l m For step 3 m The probability of each scenario occurring.

[0032] The constraints are power balance constraints, feeder-level operation constraints, and main transformer capacity constraints, as detailed below: Power balance constraints are based on the following scenario. m Down t The load, feedback power, and renewable energy output at any given time satisfy the power balance: In the formula, the left side of the equation represents the scene. m Down t The actual load at any given time P grid ( t,m ) for scene m Down t The power purchased by the substation from the grid at any given time; the feeder-level operational constraints must meet feeder capacity constraints and voltage constraints, with the feeder capacity constraint defined in the scenario. m and time t Down feeder l The carrying capacity is not greater than the rated capacity, specifically: The feeder voltage is In the formula, U (sub, t,m () represents the voltage of the upstream substation. R l For feeder l The resistance, X l For feeder l Reactance, Q feed,in For the reactive power output of the feeder, the feeder voltage must meet the operating constraints: , U N The reference voltage is used; the main transformer capacity constraint is used in the scenario. m and timet The sum of the power of the multiple feeders shall not exceed the rated capacity of the main transformer, specifically: , S sub The rated capacity of the main transformer.

[0033] Step 5: Based on the evaluation objective of the maximum load access capacity of the port area substation under multiple scenarios in Step 4, and combining the matrix results from Steps 1-3, a solution method for intra-scenario constraint optimization and inter-scenario probability weighting is proposed. (See Appendix) Figure 2 The flowchart of the evaluation method is shown. It is divided into two steps. The first step is the iterative solution within the scenario: the calculation of the maximum load access capacity of a single scenario. For each typical scenario generated in step 3, the maximum load capacity that the substation can safely access under that scenario is calculated separately. This process needs to strictly associate the dynamic load characteristics of step 1 with the potential energy feedback characteristics of step 2 to ensure that the solution fits the actual operation logic. Specifically, it includes: (1) parameter initialization. The dynamic load simultaneity rate, potential energy feedback power, load power and new energy output data output in steps 1-3 are used as boundary conditions to set the first step. m Initial load scaling factor in each scenario k m (0) = 1. Used to amplify or reduce load power. Calculate the actual load power in the scenario according to step 1; (2) Power flow constraint verification: Substitute the initialized load power, potential energy feedback power, and new energy output data into the power flow model in step 4, calculate the power flow, node voltage, and total power of the main transformer for each feeder, and verify whether the following operating constraints are met: the actual carrying power of all feeders does not exceed their rated capacity, the node voltage is within the safe operating range, and the total input power of the main transformer does not exceed its rated capacity; (3) Iterative optimization of load access: if the above constraints are met, it indicates that the current load has not reached the access limit, and the load scaling factor needs to be increased. k m Let the step size be increased. d , k m (n+1) =k m (n) +d , n The iteration count is [number], and the load power is scaled up proportionally before recalculating and verifying constraints. If any constraint is not satisfied, the load scaling factor needs to be reduced. Let the reduction step size be [number]. d , k m (n+1) =k m (n) -dRepeat the power flow calculation and verification; (4) Determine the maximum capacity of a single scenario and continue iterating until the adjustment range of the load scaling factor is less than the preset threshold. If the adjustment amount is less than the minimum threshold, the load power at this time is the maximum safe access load in the scenario; The second step is the probability integration between scenarios: After completing the calculation of the maximum load access capacity of a single scenario for all scenarios, combine the scenario probability output in step 3, multiply the maximum load access capacity of each scenario by its corresponding probability, and then sum all the products to obtain the comprehensive maximum load access capacity.

[0034] The present invention also provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform any of the methods described above.

[0035] The present invention also provides a computer storage medium having computer instructions stored thereon, the computer instructions being used to cause a computer to perform any of the methods described above.

[0036] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0037] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for evaluating the power supply capacity of a port distribution network that integrates source-load characteristics and potential energy feedback, characterized in that, Includes the following steps: Step 1: Based on the collaborative operation characteristics of quay cranes, rail cranes, charging piles and shore power loads in the port area, as well as the potential energy feedback behavior of lifting equipment, and combined with dynamic load, potential energy feedback and distributed renewable power output data, construct a port power distribution network power supply capacity assessment model that integrates source load characteristics and potential energy feedback. The port distribution network power supply capacity assessment model that integrates source and load characteristics and potential energy feedback includes: dynamic load simultaneity rate model, lifting equipment potential energy feedback reverse injection power model, clustered reduced multi-source and multi-load power injection scenario set, and multi-scenario port distribution network power supply capacity assessment model with the maximum load access capacity of port substation as the target and voltage and capacity as constraints. The dynamic load simultaneity rate model, based on the operation scheduling logic of typical load types in the port area, collects load operation data from quay cranes, rail-mounted cranes, electric truck charging stations, and shore power. The load operation data includes the hoisting mechanism power and operation plan of the quay crane, the traveling speed and positioning information of the rail bridge, the charging start and stop time and charging power of the electric truck charging pile, and the access capacity and power consumption duration of the shore power. By combining equipment timing, operation rhythm and logistics path dependence, and analyzing the coupling relationship of operation timing between quay cranes and electric trucks, and the matching relationship between rail crane travel path and yard logistics path, the characteristics of load logistics collaborative behavior are identified. Based on load operation data and load-logistics coordination behavior characteristics, a dynamic load simultaneity rate model is constructed as follows: 1) Equipment parameter matrix representation: Define the port area equipment load matrix P ∈R n×T row index i For load type, i =1 quay bridge, i =2 track bridges, i =3 charging stations i =4 shore power; column index t , representing step size, element P ( i,t ) is the first i Class of devices in the t Total load at any given time n This represents the total number of equipment types. T For statistical periods; Define the rated capacity vector S ∈R n×1 ,element S i For the first i Rated capacity of the load class; Define the equipment operation association matrix A ∈R m×n row index j For collaborative work groups, m The column index represents the total number of collaborative work groups. i For load type; element A j,i For the work group j Medium type i The number of equipment; 2) Calculation of base concurrent rate: Define the basic simultaneous rate vector γ base ∈R n×T , ； Define the normalized load matrix P norm ∈R n×T , ; Define the device timing coupling matrix C ∈R m×T ; (1) In the formula, △ t The time window is represented by 1, which represents the quay crane, and 3 represents the charging station. 3) Calculation of the corrected dynamic load simultaneity rate: Define the final dynamic load simultaneity rate vector as γ ∈R n×T , The proposed method is scalable; when a new device type is added, the number of matrix rows can be increased. Step 2 evaluates the maximum load access capacity of the port substation based on the port distribution network power supply capacity assessment model that integrates source-load characteristics and potential energy feedback. The maximum power supply capacity of the port substation that integrates source-load characteristics and potential energy feedback is obtained by using the solution method of intra-scenario constraint optimization and inter-scenario probability weighting.

2. The method for evaluating the power supply capacity of a port distribution network by integrating source-load characteristics and potential energy feedback as described in claim 1, characterized in that, The aforementioned potential energy feedback reverse injection power model for lifting equipment is based on the logistics characteristics and operating conditions of quay cranes and rail-mounted cranes. A matrix-based potential energy feedback model is constructed. Specifically, it addresses the potential energy conversion phenomenon during the loading and unloading operations of quay cranes and rail-mounted cranes, specifically by collecting operational data from the quay cranes and rail-mounted cranes, including the empty grab bucket mass, container mass, descent height, operating efficiency, and working status. Based on the principle of energy conservation and equipment characteristics, a potential energy feedback power model is constructed. The real-time feedback power is calculated and incorporated as a negative load into the power balance calculation for power supply capacity assessment. The specific model is as follows: 1) Matrix representation of equipment parameters: Define device parameter matrix Θ ∈R Ns×6 row index k This represents the number of lifting equipment. k =1,2,…, N s , N s The total number of lifting equipment is represented by the following column index: Θ k,1 This is the rated lifting capacity. Θ k,2 The rated lifting speed, Θ k,3 For mechanical efficiency, Θ k,4 For electrical efficiency, Θ k,5 Lifting height Θ k,6 This refers to the job cycle time. Define the job status matrix I ∈R Ns×T ,element I k,t For equipment k exist t The status of the work at any time I k,t = 0 indicates standby mode. I k,t = 1 indicates lifting. I k,t = 2 represents a decrease. I k,t = 3 represents translation, achieved through the device parameter matrix. Θ Clearly separating equipment physical parameters from operating status facilitates model expansion; 2) Define the potential energy feedback power matrix for a single device. P re,unit ∈R Ns×T , (2) In the formula, g = 9.8 m / s 2 Let gravitational acceleration be the acceleration due to gravity; define the total feedback power matrix as follows: P re,all ∈R 1×T , Define the device coordination matrix C load ∈R n×Ns ,element C load ( i,k ) is the first i The type of load and the first k The coordination coefficient of each type of lifting equipment; 3) Define the load offset matrix P net ∈R n×T , (3)。 3. The method for evaluating the power supply capacity of a port distribution network by integrating source-load characteristics and potential energy feedback as described in claim 2, characterized in that, The clustered and reduced multi-source, multi-load power injection scenario set is based on a dynamic load matrix and a potential energy feedback matrix. A matrix-based multi-scenario generation model is constructed, integrating source-load uncertainties through structured matrix operations to generate a scenario set covering various typical operating states, as detailed below: 1) Define the new energy power output scenario matrix. P DG_sc ∈R Ng×T×S , (4) In the formula, S For the number of scenes, N g For the number of distributed power sources connected, element P DG_sc ( g,t,s ) is the first s In the scenario, the first g The first new energy in t The output at any given moment is calculated using a matrix that is fitted with historical output data of distributed power sources in the port area. δ DG The random fluctuation coefficient of power output from new energy sources; 2) Define the scene probability vector. λ ∈R 1×S ,element λ s For the first s The probability of each scenario occurring. Define the net load scenario matrix P net_sc ∈R n×T×S , (5) In the formula, δ load The random fluctuation coefficient of net load; A clustering algorithm is used to generate representative scenes, and the probability vectors after clustering are defined. λ m ∈R 1×M , M The number of scenarios after clustering; the dynamic load concurrency rate vector generated after clustering. γ final ∈R 1×M Total potential energy feedback power matrix P re,final ∈R 1 ×T×M Load power matrix P load,final ∈R n×T×M New energy power output matrix P DG,final ∈R Ng×T×M .

4. The method for evaluating the power supply capacity of a port distribution network by integrating source-load characteristics and potential energy feedback as described in claim 3, characterized in that, The aforementioned multi-scenario port distribution network power supply capacity assessment model, based on the dynamic load simultaneity rate matrix, potential energy feedback power matrix, and source load power injection scenario set and scenario probability vector, evaluates the maximum load access capacity of the main transformer of the port substation under various typical operating conditions through multi-scenario power flow simulation analysis of the load-bearing characteristics of each feeder, while meeting the operating limitations of distribution network voltage, current, and feeder capacity. Specifically, the model is as follows: Considering the differences in source load characteristics among multiple feeders under the main transformer, a feeder correlation matrix is ​​defined. L ∈R f×n×Ng+1 In the formula, f Number of feeders, column 1~ n For connection between load and feeder, L ( l , i )=1 indicates the first i If the load is connected to feeder l, then it is 0; otherwise, it is 0. n +1~ n + N g For connecting new energy sources to feeders, L ( l , n + g )=1 indicates the first g New energy access feeder l Otherwise, it is 0; column n + N g +1 is for feedback power connection to the feeder. L ( l , n + N g +1)=1 indicates the feeder l If the feeder can absorb the feedback power, then the value is 0; define the feeder net power matrix. P feeder,net ∈R f×T×M This reflects the actual power requirements of each feeder; (6) In the formula, the first addition term is the feeder. l The actual load including the simultaneity rate, the second addition term is for the feeder. l The feedback power absorbed, the third addition term is the feeder. l The injected new energy output; Matrix operations are used to achieve power cancellation of load, power feedback, and new energy at the feeder level, thereby reducing actual power supply demand; The objective function is to maximize the load access capacity of a substation under multiple scenarios, specifically: (7) In the formula, S m For the scene m The maximum load capacity that can be safely connected to the substation in this scenario, expressed in kW. λ m For the first m The probability of each scenario occurring; The constraints are power balance constraints, feeder-level operation constraints, and main transformer capacity constraints, as detailed below: Power balance constraints are based on the following scenario. m Down t The load, feedback power, and renewable energy output at any given time satisfy the power balance: (8) In the formula, the left side of the equation represents the scenario. m Down t The actual load at any given time P grid ( t,m ) for scene m Down t The power that the substation purchases from the grid at any given time; The feeder-level operational constraints must satisfy both feeder capacity constraints and voltage constraints. The feeder capacity constraint is defined by the scenario. m and time t Down feeder l The carrying capacity is not greater than the rated capacity, specifically: The feeder voltage is: (9) In the formula, U (sub, t,m () represents the voltage of the upstream substation. R l For feeder l The resistance, X l For feeder l Reactance, Q feed,in This refers to the reactive power output of the feeder. The feeder voltage must meet both voltage constraints and main transformer capacity constraints: the voltage constraint is... , U N The reference voltage is used; the main transformer capacity constraint is used in the scenario. m and time t The sum of the power of the multiple feeders should not exceed the rated capacity of the main transformer, as follows: , S sub The rated capacity of the main transformer.

5. The method for evaluating the power supply capacity of a port distribution network by integrating source-load characteristics and potential energy feedback as described in claim 4, characterized in that, The proposed method for solving the constraint optimization within a scenario and the probability weighting between scenarios determines the maximum load access capacity of a single scenario through load scaling iteration and constraint verification within the scenario. Between scenarios, the method performs a weighted summation based on the source load power injection scenario probability vector and the results of each scenario, and outputs the maximum load access assessment result of the port substation.

6. The method for evaluating the power supply capacity of a port distribution network by integrating source-load characteristics and potential energy feedback as described in claim 5, characterized in that, The method for solving the intra-scene constraint optimization and inter-scene probability weighting problems achieves efficient solutions through matrix iteration and numerical optimization. The specific method is as follows: Iterative solution within a scenario: Calculation of maximum load access capacity in a single scenario. For the scenario set of various typical operating states, calculate the maximum load capacity that the substation can safely access in each scenario. The dynamic load simultaneity rate model and the potential energy feedback reverse injection power model of lifting equipment are associated to ensure that the solution fits the actual operating logic. Specifically, this includes: (1) parameter initialization, using the dynamic load simultaneity rate, potential energy feedback power, load power under the scenario and new energy output data as boundary conditions, and setting the first... m Initial load scaling factor in each scenario k m (0) , used to amplify or reduce load power; (2) power flow constraint verification, substitute load power, potential energy feedback power, and new energy output data into the evaluation model, calculate the power flow, node voltage and total power of each feeder, and verify whether the following operating constraints are met: the actual carrying power of all feeders does not exceed their rated capacity, the node voltage is within the safe operating range, and the total input power of the main transformer does not exceed its rated capacity; (3) iterative optimization of load access, if the above constraints are met, the current load has not reached the access limit, and the load scaling factor needs to be increased. k m (n+1) Let the step size be... δ , k m (n+1) =k m (n) +δ n represents the iteration number. After scaling up the load power proportionally, the power flow calculation and constraint verification are performed again. If any constraint is not satisfied, the load scaling factor needs to be reduced. k m (n+1) =k m (n) - δ Repeat the power flow calculation and verification; (4) Determine the maximum capacity of a single scenario and continue to iterate until the adjustment range of the load scaling factor is less than the preset threshold. If the adjustment amount is less than the minimum threshold, the load power at this time is the maximum safe access load in this scenario. Inter-scenario probability integration: After calculating the maximum load access capacity of each scenario, the maximum load access capacity of each scenario is multiplied by its corresponding probability by the scenario set covering various typical operating states, and then all products are summed to obtain the maximum load access capacity of the port substation.

7. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform any of the port power distribution network power supply capacity assessment methods according to any one of claims 1-6.

8. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed, they perform the port power supply capacity assessment method that integrates source-load characteristics and potential energy feedback as described in any one of claims 1-6.

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