A method and system for intelligent shore power supply of a ship

Abstract

The application discloses a kind of ship intelligent shore power supply method and system, it is related to ship power supply technical field, the specific steps of the method are S100, data acquisition;S200, data preprocessing;S300, energy efficiency label generation;S400, energy efficiency optimization analysis;S500, optimization effect verification, the application is integrated by shore power side, ship side and power grid side data depth, combined with four core basic indexes such as power supply efficiency, load matching rate, standardization calculation is constructed Comprehensive energy efficiency evaluation system covering equipment operation, power consumption behavior and power grid dispatching;Through dynamic weight energy efficiency evaluation formula, incorporate operation target correction and equipment aging correction, generate star energy efficiency label, enable energy efficiency evaluation to turn from static result to dynamic process management, to improve the objectivity and adaptability of energy efficiency evaluation, provide quantitative basis for port energy saving and consumption reduction, help to realize green shipping target.

Description

Technical Field

[0001] This invention relates to the field of ship power supply technology, specifically to an intelligent shore power supply method and system for ships. Background Technology

[0002] With the rapid development of the global shipping industry, the demand for electricity supply during ship berthing is increasing. Traditionally, ships rely on fuel-fired generators for power when docked, which not only leads to high fuel costs but also generates significant emissions of carbon dioxide, nitrogen oxides, and particulate matter, exacerbating environmental pollution in ports and surrounding areas. Simultaneously, the "peak-valley difference" problem on the power grid side is prominent, with power resources idle during off-peak hours, while ship electricity loads often overlap with peak grid demand, further intensifying power supply pressure. Against this backdrop, shore power technology (i.e., providing grid power to berthed ships from ports) has become a key solution to these problems. Replacing fuel-fired generators with shore power can significantly reduce pollutant emissions during ship berthing, while also optimizing electricity costs by taking advantage of off-peak electricity prices. However, existing shore power systems mostly focus on power transmission and basic metering, lacking the ability to dynamically assess and optimize energy efficiency. This makes it difficult to adapt to the complex needs of different ship and equipment conditions and grid dispatching, hindering the large-scale application of shore power technology and the full realization of its energy-saving benefits.

[0003] Traditional shore power systems suffer from three main shortcomings: First, data collection is limited to a single dimension, focusing only on shore power output or ship power consumption without integrating key information such as grid peak and off-peak periods and equipment operating status, leading to a one-sided energy efficiency assessment. Second, the assessment methods are static, using fixed weights or single indicators (such as power supply efficiency) to evaluate energy efficiency, neglecting operational goals (such as emission reduction priorities) and the impact of equipment aging on energy efficiency. For example, the energy consumption coefficient of older equipment increases but is not reflected in the assessment, resulting in distorted evaluation results. Third, there is a lack of closed-loop optimization mechanisms. Energy efficiency analysis is limited to problem identification, without providing matching optimization measures or gain prediction functions. Optimization effectiveness relies on manual experience, resulting in high trial-and-error costs and long cycles. Furthermore, traditional systems cannot verify the actual effects of optimization measures, making it difficult to implement energy efficiency improvement measures. These deficiencies make it difficult for shore power systems to achieve full-process management of "precise energy supply - dynamic optimization - continuous improvement" in actual operation, limiting the release of their energy-saving and consumption-reducing potential. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent shore power supply method and system for ships. By integrating data from the shore power side, the ship side, and the power grid side, a comprehensive energy efficiency evaluation system covering equipment operation, electricity consumption behavior, and power grid dispatch is constructed. A dynamic weighted energy efficiency evaluation formula is adopted, and star-level energy efficiency labels are generated by combining operational goals and equipment aging status to achieve dynamic energy efficiency management. The system has an energy efficiency optimization analysis and closed-loop verification mechanism, which can automatically identify inefficient indicators, match optimization measures, and predict the effects, ensuring that optimization measures are effectively implemented and helping ports achieve energy conservation, emission reduction, and green shipping goals.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On one hand, a method for intelligent shore power supply to ships, the specific steps of which are as follows:

[0006] S100, Data Acquisition: Acquire shore power side data through voltage sensors, current sensors, power sensors, and energy consumption sensors; acquire ship side data through the ship power distribution system communication interface; and acquire grid side data through the power grid distribution network SCADA system data docking unit.

[0007] S200, Data Preprocessing: The collected data is cleaned and processed, the basic indicators required for energy efficiency assessment are calculated, and then the basic indicators are standardized using a multi-dimensional data standardization formula to obtain standardized scores.

[0008] S300, Energy Efficiency Label Generation: Combining preset basic weights, operational target correction parameters, and equipment status correction parameters, the standardized score is weighted and calculated using a dynamic weighted energy efficiency evaluation formula to obtain a comprehensive energy efficiency score, and a star-rated energy efficiency label is generated based on the comprehensive energy efficiency score.

[0009] S400, Energy Efficiency Optimization Analysis: By comparing the standardized score with the preset target score, inefficient indicators are identified and their causes are analyzed. Corresponding optimization measures are matched, and the energy efficiency gain rate after the implementation of the optimization measures is calculated using the energy efficiency optimization gain prediction formula. Then, the comprehensive energy efficiency score is predicted based on the energy efficiency gain rate.

[0010] S500, Optimization Effect Verification: After implementing optimization measures, energy efficiency data is collected again and the actual comprehensive energy efficiency score is calculated. The actual comprehensive energy efficiency score is compared with the predicted comprehensive energy efficiency score using the optimization effect verification formula to determine whether the optimization effect meets the standard. If it does not meet the standard, the optimization measures are readjusted.

[0011] Further, in step S100, the data acquisition step includes shore power side data such as shore power output voltage, output current, output power, total equipment energy consumption, and equipment operating years; wherein, the output voltage is acquired through a voltage sensor, the output current through a current sensor, the output power through a power sensor, the total equipment energy consumption through an energy consumption sensor, and the equipment operating years are obtained through equipment ledger data records; ship side data includes the ship's actual electricity consumption, real-time electricity load, and shore power set load, acquired through the Modbus or Profinet communication interface of the ship's power distribution system; grid side data includes the grid peak and valley time division and the ship's electricity consumption during the low valley time, acquired through a data docking unit that interfaces with the grid distribution network SCADA system.

[0012] Furthermore, in the S200 data preprocessing step, the basic indicators include power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient; wherein, power supply efficiency is the percentage of the ship's actual electricity consumption to the total shore power output, and the total shore power output is the product of the shore power output power and the power supply duration; the load matching rate is the percentage of the ship's average actual load to the shore power's average set load; the peak-valley electricity consumption ratio is the percentage of the ship's electricity consumption during off-peak hours to the ship's total actual electricity consumption; and the equipment energy consumption coefficient is the percentage of the total energy consumption of the shore power equipment to the total shore power output.

[0013] Furthermore, in step S200, the multi-dimensional data standardization calculation formula in the data preprocessing step is as follows: ,in, For the first The standardized scores of the basic indicators, For the first The actual values ​​of the basic indicators For the first The optimal target value of each basic indicator. For the first The reasonable upper limit of the values ​​for each basic indicator. For the first The reasonable lower limit of the basic indicators, For the first Scenario correction coefficients for basic indicators. =1, 2, 3, 4 correspond to power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient, respectively.

[0014] Furthermore, in step S300, the energy efficiency label generation step, the operation target correction parameter is the target-oriented weight correction amount, and the equipment status correction parameter is the equipment aging weight correction amount. The equipment aging weight correction amount is calculated as follows: based on the operating years of the shore power equipment, every 5 years is a calculation cycle, and each cycle corresponds to a correction amount of 0.05. It only applies to the weight corresponding to the equipment energy consumption coefficient. When calculating the correction amount, the equipment operating years are processed by rounding down.

[0015] Furthermore, in step S300, the dynamic weighted energy efficiency assessment calculation formula in the energy efficiency label generation step is as follows: ,in, For comprehensive energy efficiency rating, For the first The basic weights of the basic indicators For the first The target-oriented weight adjustment amount of the basic indicators. For the first The adjustment amount of the equipment aging weight for the basic indicators. For the first The standardized scores of the basic indicators, =1, 2, 3, 4 correspond to power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient, respectively.

[0016] Furthermore, in step S400, the formula for predicting the energy efficiency optimization gain in the energy efficiency optimization analysis step is as follows: , For the first Energy efficiency gain rate of the basic indicators For the first The optimal target value of each basic indicator. To optimize the first The actual values ​​of the basic indicators For the first The feasibility coefficient of the optimization measures corresponding to each basic indicator. For the first The scenario adaptability coefficient of each basic indicator. =1, 2, 3, 4 correspond to power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient, respectively.

[0017] Furthermore, in step S400, the calculation method for predicting the comprehensive energy efficiency score in the energy efficiency optimization analysis step is as follows: based on the comprehensive energy efficiency score before optimization, add the sum of the adjusted weights of each basic indicator, the corresponding energy efficiency gain rate, and 100; wherein, the adjusted weight is the product of the basic weight and the sum of 1 plus the target-oriented weight correction amount and the equipment aging weight correction amount, and the energy efficiency gain rate is calculated through the energy efficiency optimization gain prediction formula.

[0018] Furthermore, in step S500, the calculation formula for the optimization effect verification step is as follows: ,in, To optimize the effect verification coefficient, The optimized actual overall energy efficiency score, The optimized predicted comprehensive energy efficiency score, To verify the accuracy coefficients: when the predicted comprehensive energy efficiency score is not lower than 80, the verification accuracy coefficient is 1.2; when the predicted comprehensive energy efficiency score is not lower than 60 but lower than 80, the verification accuracy coefficient is 1.0; when the predicted comprehensive energy efficiency score is lower than 60, the verification accuracy coefficient is 0.8.

[0019] On the other hand, a ship intelligent shore power supply system includes:

[0020] Data acquisition module: includes voltage sensor, current sensor, power sensor, energy consumption sensor, ship communication interface and power grid SCADA docking unit, which are used to collect shore power side data, ship side data and power grid side data respectively;

[0021] Data preprocessing module: Used to clean the collected data, calculate basic indicators, and perform standardization processing through multi-dimensional data standardization formulas, including data cleaning unit, basic indicator calculation unit, and standardization processing unit;

[0022] Energy efficiency label generation module: used to calculate the comprehensive energy efficiency score and generate star-rated energy efficiency labels through the dynamic weighted energy efficiency evaluation formula, including a weight correction unit, a comprehensive score calculation unit and a label generation unit;

[0023] Energy efficiency optimization analysis module: used to identify inefficiency indicators, match optimization measures, and calculate energy efficiency gain rate through energy efficiency optimization gain prediction formula, including inefficiency indicator diagnosis unit, optimization measure matching unit and gain prediction unit;

[0024] The optimization effect verification module is used to compare the actual and predicted energy efficiency scores and judge the optimization effect by using the optimization effect verification formula. It includes an actual score calculation unit, a verification coefficient calculation unit, and a measure adjustment unit.

[0025] Compared with existing technologies, this intelligent shore power supply method and system for ships has the following advantages:

[0026] I. This invention deeply integrates data from shore power, ship power, and power grid, and combines standardized calculations of four core basic indicators, including power supply efficiency and load matching rate, to construct a comprehensive energy efficiency assessment system covering equipment operation, electricity consumption behavior, and power grid dispatch. Through a dynamic weighted energy efficiency assessment formula, it incorporates operational target correction and equipment aging correction to generate star-rated energy efficiency labels, enabling energy efficiency evaluation to shift from static results to dynamic process management. This improves the objectivity and adaptability of energy efficiency assessment, provides a quantitative basis for energy conservation and consumption reduction in ports, and helps achieve green shipping goals.

[0027] Second, this invention utilizes intelligent diagnostic technology for inefficient indicators. The system can automatically identify the causes of deviations in indicators such as power supply efficiency and peak-valley electricity consumption ratio, and match targeted optimization measures. The energy efficiency optimization gain prediction formula combines the feasibility coefficient of the measures with the scenario adaptation coefficient to quantify the optimization potential and avoid blind transformation. The optimization effect verification module ensures the effectiveness of optimization measures through dynamic comparison between actual scores and predicted scores, forming a continuous improvement cycle of "prediction-implementation-feedback". This mechanism not only shortens the energy efficiency improvement cycle but also reduces trial and error costs, providing a replicable technical path for the intelligent operation and maintenance of port shore power systems and promoting the industry's upgrade towards high efficiency and low carbon.

[0028] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0030] Figure 1 A schematic diagram of the overall process for intelligent shore power supply methods for ships;

[0031] Figure 2 This is a schematic diagram of the data preprocessing and energy efficiency assessment process.

[0032] Figure 3 This is a schematic diagram of the energy efficiency optimization and effect verification process. Detailed Implementation

[0033] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0034] Example 1: Shore power supply scenario for container ship loading and unloading at the port

[0035] During container loading and unloading operations on the container ship "Ocean XX" at a coastal port, the intelligent shore power supply method and system of this invention were applied to replace the traditional fixed parameter power supply mode. The specific process is as follows: Figure 1 As shown:

[0036] S100, Data Acquisition: Voltage, current, and power data of the shore power output are collected in real time through voltage, current, and power sensors installed at the output end of the shore power cabinet. This data directly reflects the real-time output status of the shore power equipment, providing a basis for subsequent judgment on whether the power supply meets the needs of the ship. Energy consumption sensors on key components (transformers, inverters) inside the shore power equipment record the total energy consumption during equipment operation. This data helps to assess the energy loss of the shore power equipment itself. The ledger data of the shore power equipment is retrieved from the port equipment management system to obtain information on the equipment's six years of operation, providing support for subsequent consideration of the impact of equipment aging on energy efficiency. The system connects to the ship's power distribution system via the Modbus communication interface to collect data on the ship's actual electricity consumption and real-time load during loading and unloading, as well as the shore power load data pre-set by the port. The real-time load data dynamically reflects load fluctuations caused by the start and stop of loading and unloading equipment, ensuring that subsequent power supply parameters can adapt to these fluctuations. Through a data interface unit that connects to the port's power grid distribution network SCADA system, the system obtains the peak and valley time periods of the power grid and the container ship's electricity consumption data during off-peak hours. Peak and valley time period information can assist in optimizing electricity usage periods and reducing port electricity costs.

[0037] S200, Data Preprocessing: The collected data is cleaned to remove outliers caused by momentary sensor malfunctions (such as sudden over-range data from voltage sensors) and to supplement data missing due to short-term communication interruptions, preventing abnormal or missing data from affecting the accuracy of subsequent energy efficiency assessments. Based on the cleaned data, four basic indicators are calculated: power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient. Power supply efficiency is the percentage of actual ship power consumption to total shore power output; total shore power output is the product of shore power output power and power supply duration. The load matching rate is the percentage of average actual ship load to average shore power set load. The peak-valley electricity consumption ratio is the percentage of ship power consumption during off-peak hours to the ship's total actual power consumption. The equipment energy consumption coefficient is the percentage of total shore power equipment energy consumption to total shore power output. These four indicators comprehensively reflect the energy efficiency level of shore power supply from different dimensions, providing core assessment objects for subsequent standardization processing. Subsequently, a multi-dimensional data standardization formula is used. The multi-dimensional data standardization calculation formula is as follows: ,in, For the first The standardized scores of the basic indicators, For the first The actual values ​​of the basic indicators For the first The optimal target value of each basic indicator. For the first The reasonable upper limit of the values ​​for each basic indicator. For the first The reasonable lower limit of the basic indicators, For the first Scenario correction coefficients for basic indicators. =1, 2, 3, and 4 correspond to power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient, respectively. Considering the large load fluctuations in container ship loading and unloading scenarios, scenario correction coefficients are determined. The four basic indicators are then standardized to obtain standardized scores for each indicator. These scores eliminate the differences in the dimensions of different indicators, allowing previously incomparable indicators (such as power supply efficiency and peak-valley electricity consumption ratio) to be included in a unified evaluation system. This lays the foundation for subsequent comprehensive energy efficiency scoring. Figure 2 As shown.

[0038] S300, Energy Efficiency Label Generation: Based on the port's current "energy conservation first" operational goal (the port is focusing on energy consumption reduction this quarter), a target-oriented weight adjustment is determined, appropriately increasing the weights of power supply efficiency and peak-valley electricity consumption ratio to guide energy efficiency assessment towards energy conservation. Considering the shore power equipment has been in operation for 6 years, an equipment aging weight adjustment is calculated, and this adjustment only applies to the weight corresponding to the equipment's energy consumption coefficient, as older equipment typically has higher energy consumption and its energy consumption changes need to be closely monitored. A dynamic weighted energy efficiency assessment formula is adopted; the dynamic weighted energy efficiency assessment calculation formula is as follows: ,in, For comprehensive energy efficiency rating, For the first The basic weights of the basic indicators For the first The target-oriented weight adjustment amount of the basic indicators. For the first The adjustment amount of the equipment aging weight for the basic indicators. For the first The standardized scores of the basic indicators, =1, 2, 3, 4 correspond to power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient, respectively. The preset basic weights, target-oriented weight corrections, equipment aging weight corrections, and standardized scores are weighted and calculated to obtain the comprehensive energy efficiency score of this shore power supply. This score takes into account both operational needs and equipment status, and is more in line with actual operational scenarios than the traditional fixed-weight score. Based on the comprehensive energy efficiency score, a corresponding star-level energy efficiency label is generated. The label clearly indicates the comprehensive score, the scores of each basic indicator, and the scoring basis. Operators can quickly identify the energy efficiency shortcomings of this power supply (such as a low peak-valley electricity consumption ratio) through the label, providing a clear direction for subsequent optimization.

[0039] S400, Energy Efficiency Optimization Analysis: Comparing the standardized scores of various basic indicators with the preset target scores, it was found that the standardized scores of load matching rate and peak-valley electricity consumption ratio were lower than the target values. Further analysis revealed that: the low load matching rate was due to the shore power load being set according to the ship's maximum load, while the actual load during loading and unloading was only 75% of the set load, resulting in a "powered load on a small scale" phenomenon; the low peak-valley electricity consumption ratio was due to the ship scheduling non-emergency electricity use such as deck cleaning and equipment maintenance during peak hours. To address these two issues, the matching optimization measures are: adjusting the shore power load setting to a value more closely aligned with the ship's actual needs to avoid energy waste; and coordinating with ships to schedule non-emergency electricity use such as deck cleaning during off-peak hours to reduce the load pressure on the power grid during peak hours. The energy efficiency optimization gain prediction formula is used, and the calculation formula is as follows: , For the first Energy efficiency gain rate of the basic indicators For the first The optimal target value of each basic indicator. To optimize the first The actual values ​​of the basic indicators For the first The feasibility coefficient of the optimization measures corresponding to each basic indicator. For the first The scenario adaptability coefficient of each basic indicator. =1, 2, 3, 4 correspond to power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient, respectively. Combining the feasibility coefficients of two types of measures—adjusting the set load (no additional hardware investment, high feasibility) and coordinating ship power consumption periods (requires ship cooperation, moderate feasibility)—as well as the adaptability coefficient of container ship loading and unloading scenarios, the energy efficiency gain rate after the implementation of optimization measures is calculated. Based on this gain rate, a predicted comprehensive energy efficiency score is calculated. Operators can determine from the predicted score that the optimization measures can improve the comprehensive energy efficiency from 3 stars to 4 stars, and are therefore worth implementing.

[0040] S500, Optimization Effect Verification: Adjustments were implemented according to the determined optimization measures: the shore power output load was reset, and after communication with the ship's crew, the deck cleaning power usage was adjusted to 02:00-04:00 daily (off-peak hours). After the optimization measures were implemented, energy efficiency data from the shore power side, ship side, and grid side were re-collected, and the actual comprehensive energy efficiency score was calculated. This score directly reflects the actual effect of the optimization measures. Using the optimization effect verification formula, the actual comprehensive energy efficiency score was compared with the previously calculated predicted comprehensive energy efficiency score to calculate the optimization effect verification coefficient. The optimization effect verification calculation formula is as follows: ,in, To optimize the effect verification coefficient, The optimized actual overall energy efficiency score, The optimized predicted comprehensive energy efficiency score, To verify the accuracy coefficient, the coefficient was set as follows: 1.2 when the predicted comprehensive energy efficiency score was not lower than 80; 1.0 when the predicted comprehensive energy efficiency score was not lower than 60 but lower than 80; and 0.8 when the predicted comprehensive energy efficiency score was lower than 60. The results showed that the accuracy coefficient was greater than 0.8, indicating that the optimization effect met the standard. After this optimization, the load matching rate increased to 90%, and the peak-valley electricity consumption ratio increased to 35%, effectively reducing energy waste and peak load pressure on the power grid.

[0041] In summary, in the scenario of shore power supply for container ship loading and unloading at ports, multi-type sensors and communication interfaces are used to achieve full-dimensional data collection of "shore power-ship-grid," providing a precise data source for energy efficiency assessment. Data preprocessing eliminates interference, and multi-dimensional data standardization formulas ensure comparability of indicators. Combining operational goals and equipment aging status, a scenario-appropriate star-level energy efficiency label is generated based on a dynamic weighted energy efficiency assessment formula. Subsequently, energy efficiency optimization analysis identifies load matching and electricity usage time issues, and an energy efficiency optimization gain prediction formula predicts the effectiveness of measures. Finally, an optimization effect verification formula confirms that optimization has met standards. The entire process requires no reliance on human experience, solving the poor adaptability problem of traditional fixed-parameter power supply and achieving closed-loop management of energy efficiency from assessment to optimization. This effectively reduces energy waste and peak grid load pressure, fully demonstrating the technical value of this invention in precise and scenario-based energy efficiency management for container ship loading and unloading.

[0042] Example 2: Shore power supply scenario for passenger boarding and departure at cruise ports

[0043] During passenger boarding and disembarkation operations on the luxury cruise ship "Ocean YY" at an international cruise homeport, the intelligent shore power supply method and system of this invention were applied to replace the traditional manually controlled shore power supply mode, effectively adapting to the high load and multi-device collaborative power needs of the cruise ship. The specific implementation process is as follows:

[0044] S100, Data Acquisition: Utilizing voltage, current, and power sensors deployed at the shore power cabinet output terminals, the system monitors and collects real-time voltage, current, and power data from the shore power output. This data provides real-time feedback on the power supply stability of the shore power equipment, preventing abnormal output parameters from affecting the operation of critical electrical systems such as cabin air conditioning and restaurant equipment on cruise ships. Energy consumption sensors installed on the core components (inverters, transformers) of the shore power equipment collect the total energy consumption during equipment operation. This data accurately captures the energy consumption of the shore power equipment itself, providing a basis for subsequent assessment of the equipment's energy consumption rationality. The system also queries the commissioning time of the shore power equipment from the port equipment ledger management system, confirming that it has been in operation for 3 years. This information helps determine the degree of equipment aging and provides support for subsequent adjustments to energy consumption monitoring weights. Through the Profinet communication interface of the cruise ship's power distribution system, the actual electricity consumption and real-time power load (including loads of cabin air conditioning, restaurant and kitchen equipment, passenger corridor lighting, baggage handling equipment, etc.) during passenger boarding and departure are collected, as well as the set load data of the shore power system. The real-time power load data can dynamically reflect the power fluctuations caused by changes in passenger flow (such as a sudden increase in air conditioning load during peak boarding periods), ensuring that subsequent power supply adjustments can accurately match such dynamic demands. Through the data docking unit that interfaces with the power grid distribution network SCADA system, peak and off-peak time information of the power grid and the electricity consumption data of the cruise ship during off-peak times are obtained. Peak and off-peak time information can assist in optimizing subsequent power usage arrangements, reducing port electricity costs while ensuring passenger experience.

[0045] S200, Data Preprocessing: Cleans various data such as voltage, current, power consumption, and load collected, removes transient fluctuations and abnormal values ​​caused by equipment start-up and shutdown (such as batch start-up of equipment in cruise ship restaurants), and supplements data in a reasonable way for data disconnection periods caused by communication interruptions, so as to avoid abnormal or missing data interfering with the accuracy of subsequent energy efficiency assessments and ensure that all data used for analysis are reliable. Based on the cleaned data, four basic indicators were calculated: power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient. Power supply efficiency is the percentage of actual ship power consumption to total shore power output, where total shore power output is the product of shore power output power and power supply duration. Load matching rate is the percentage of average actual ship load to average set shore power load. Peak-valley electricity consumption ratio is the percentage of ship power consumption during off-peak hours to total actual ship power consumption. Equipment energy consumption coefficient is the percentage of total shore power equipment energy consumption to total shore power output. These four indicators comprehensively outline the energy efficiency profile of shore power supply from four core dimensions: energy utilization efficiency, supply-demand matching degree, rationality of electricity consumption periods, and equipment energy consumption level, providing a clear evaluation object for subsequent standardization processing. Then, a multi-dimensional data standardization formula was adopted. The multi-dimensional data standardization calculation formula is as follows: Taking into full account the characteristics of large fluctuations in electricity load and diverse equipment types during the boarding and departure of cruise passengers, corresponding scenario correction coefficients were set, and the four basic indicators were standardized to obtain standardized scores. These scores eliminated the incomparability caused by differences in the dimensions of different indicators (such as power supply efficiency as a percentage and equipment energy consumption coefficient as a ratio), enabling the four indicators to be included in a unified energy efficiency assessment framework and providing a fair and unified input basis for subsequent comprehensive scoring.

[0046] S300, Energy Efficiency Label Generation: Based on the port's current operational goal of "balancing energy conservation and passenger experience" (reducing energy consumption while ensuring passenger comfort during boarding and departure, and avoiding equipment downtime due to energy saving), the target-oriented weight adjustment is determined. The weights of power supply efficiency and peak-valley electricity consumption ratio are appropriately increased to strengthen the energy-saving orientation, while retaining a reasonable weight for load matching rate to ensure power supply stability. Considering that the shore power equipment has been in operation for 3 years, the equipment aging weight adjustment is calculated. This adjustment only applies to the weight of the equipment's energy consumption coefficient, because although the equipment has not aged severely in 3 years, its energy consumption may have slightly increased, requiring close monitoring of its energy consumption trend through weight adjustments. A dynamic weighted energy efficiency assessment formula is adopted. The dynamic weighted energy efficiency assessment calculation formula is as follows: The system calculates the comprehensive energy efficiency score for the cruise ship shore power supply by weighting the preset base weights, target-oriented weight corrections, equipment aging weight corrections, and standardized scores. This score balances the port's energy-saving goals with passenger experience and equipment condition, making it more aligned with the actual operational needs of cruise ports than traditional fixed-weight scores. Based on the comprehensive energy efficiency score, a star-rating energy efficiency label is generated. The label details the comprehensive score, the specific scores of each basic indicator, and the scoring logic (e.g., "The peak-valley electricity consumption ratio score is low because the electricity consumption ratio during the boarding peak period is too high"). Operations personnel can quickly identify energy efficiency shortcomings and clarify the key areas for future optimization through the label, eliminating the need to rely on manual experience analysis.

[0047] S400, Energy Efficiency Optimization Analysis: Comparing the standardized scores of various basic indicators with the preset target scores revealed that the scores for power supply efficiency and equipment energy consumption coefficient did not meet the target values. Further analysis of the root causes revealed that the low power supply efficiency was due to minor contact defects in the shore power supply lines, resulting in additional energy loss during transmission. The high equipment energy consumption coefficient was due to the performance degradation of some filter components in the shore power equipment after three years of operation, increasing the equipment's own energy consumption. To address these two issues, targeted optimization measures were implemented: A comprehensive overhaul of the power supply lines was organized by professionals to identify and address contact defects to reduce transmission losses; aging filter components were replaced to restore equipment energy efficiency, while ensuring that the optimization measures did not affect the cruise ship's normal power supply (e.g., conducting line maintenance during passenger boarding breaks). The energy efficiency optimization gain prediction formula was used, and the calculation formula for the energy efficiency optimization gain prediction is as follows: Combining the feasibility coefficients of two types of measures—line maintenance (simple operation, no long-term downtime, high feasibility) and component replacement (requires professional personnel but is short-term and relatively feasible)—and the adaptability coefficient for the high power supply continuity requirements of cruise passenger boarding and departure scenarios, the energy efficiency gain rate after the implementation of optimization measures is calculated, thereby obtaining a predicted comprehensive energy efficiency score. Operators can use this predicted score to determine whether the optimization measures can improve the comprehensive energy efficiency from 4 stars to 5 stars without affecting the passenger experience, thus demonstrating clear implementation value. Figure 3 As shown.

[0048] S500, Optimization Effect Verification: The optimized measures were implemented systematically. During passenger boarding breaks, personnel conducted a comprehensive overhaul of the shore power supply lines, replacing aging filter components. The entire process was strictly controlled in duration to ensure uninterrupted power supply to the cruise ship. After the optimization measures were implemented, energy efficiency data were re-collected from the shore power side (output parameters, equipment energy consumption), the ship side (actual electricity consumption, load), and the grid side (peak and off-peak electricity distribution). Based on this data, an actual comprehensive energy efficiency score was calculated. This score directly reflects the true effect of the optimization measures, avoiding the problem of "theoretically effective but practically ineffective." The optimization effect verification formula was used, and the calculation formula for the optimization effect verification is as follows: The actual comprehensive energy efficiency score was compared with the previously calculated predicted comprehensive energy efficiency score, and the optimization effect verification coefficient was calculated. The results showed that the verification coefficient was greater than 0.8, indicating that the optimization effect met the standard. After optimization, the power supply efficiency was significantly improved, and the equipment energy consumption coefficient was reduced to a reasonable range. At the same time, during passenger boarding and departure, all kinds of electrical equipment on the cruise ship operated stably, and there were no cases of poor passenger experience due to power supply problems.

[0049] In summary, in the scenario of shore power supply for cruise ship passengers boarding and disembarking, this invention addresses the challenges of fluctuating power loads and the need to balance energy conservation with passenger experience. It utilizes multi-dimensional data collection to capture real-time power supply and consumption dynamics, ensuring the data reflects equipment operation and load changes. Data preprocessing and multi-dimensional data standardization formulas provide reliable and unified indicators for energy efficiency assessment. Combined with operational objectives, a comprehensive energy efficiency score and star rating are generated, balancing energy conservation and passenger experience. Energy efficiency optimization analysis identifies line losses and equipment aging issues, and the feasibility of measures is assessed using energy efficiency optimization gain prediction formulas. Finally, the optimization effect verification formula confirms the effectiveness of the optimization. The entire implementation process ensures stable power supply and passenger experience during boarding and disembarking while simultaneously improving energy efficiency and controlling equipment energy consumption. This verifies the adaptability and practicality of this invention in high-load, high-demand cruise ship scenarios, providing a replicable technical solution for shore power energy efficiency management in similar scenarios.

[0050] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for intelligent shore power supply to ships, characterized in that, The specific steps of this method are as follows: S100, Data Acquisition: Acquire shore power side data through voltage sensors, current sensors, power sensors, and energy consumption sensors; acquire ship side data through the ship power distribution system communication interface; and acquire grid side data through the power grid distribution network SCADA system data docking unit. S200, Data Preprocessing: The collected data is cleaned and processed, the basic indicators required for energy efficiency assessment are calculated, and then the basic indicators are standardized using a multi-dimensional data standardization formula to obtain a standardized score. The multi-dimensional data standardization calculation formula is as follows: ,in, For the first The standardized scores of the basic indicators, For the first The actual values ​​of the basic indicators For the first The optimal target value of each basic indicator. For the first The reasonable upper limit of the values ​​for each basic indicator. For the first The reasonable lower limit of the basic indicators, For the first Scenario correction coefficients for basic indicators. =1, 2, 3, 4 correspond to power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient, respectively. S300, Energy Efficiency Label Generation: Combining preset basic weights, operational target correction parameters, and equipment status correction parameters, the standardized score is weighted and calculated using a dynamic weighted energy efficiency evaluation formula to obtain a comprehensive energy efficiency score. A star-rated energy efficiency label is then generated based on this comprehensive energy efficiency score. The dynamic weighted energy efficiency evaluation calculation formula is as follows: ,in, For comprehensive energy efficiency rating, For the first The basic weights of the basic indicators For the first The target-oriented weight adjustment amount of the basic indicators. For the first The adjustment amount of equipment aging weight for the basic indicators. For the first The standardized scores of the basic indicators, =1, 2, 3, 4 correspond to power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient, respectively. S400, Energy Efficiency Optimization Analysis: By comparing the standardized score with the preset target score, inefficient indicators are identified and their causes analyzed. Corresponding optimization measures are matched, and the energy efficiency gain rate after the implementation of the optimization measures is calculated using the energy efficiency optimization gain prediction formula. Then, based on the energy efficiency gain rate, the predicted comprehensive energy efficiency score is calculated. The energy efficiency optimization gain prediction calculation formula is as follows: , For the first Energy efficiency gain rate of the basic indicators For the first The optimal target value of each basic indicator. To optimize the first The actual values ​​of the basic indicators For the first The feasibility coefficient of the optimization measures corresponding to each basic indicator. For the first The scenario adaptability coefficient of each basic indicator. =1, 2, 3, 4 correspond to power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient, respectively. S500, Optimization Effect Verification: After implementing the optimization measures, energy efficiency data is re-collected and the actual comprehensive energy efficiency score is calculated. The actual comprehensive energy efficiency score is compared with the predicted comprehensive energy efficiency score using the optimization effect verification formula to determine whether the optimization effect meets the standard. If it does not meet the standard, the optimization measures are readjusted. The optimization effect verification formula is as follows: ,in, To optimize the effect verification coefficient, The optimized actual overall energy efficiency score, The optimized predicted comprehensive energy efficiency score, To verify the accuracy coefficients: when the predicted comprehensive energy efficiency score is not lower than 80, the verification accuracy coefficient is 1.2; when the predicted comprehensive energy efficiency score is not lower than 60 but lower than 80, the verification accuracy coefficient is 1.0; when the predicted comprehensive energy efficiency score is lower than 60, the verification accuracy coefficient is 0.

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2. The intelligent shore power supply method for ships according to claim 1, characterized in that, In step S100, the data acquisition process includes shore power side data such as shore power output voltage, output current, output power, total equipment energy consumption, and equipment operating years. Output voltage is acquired via a voltage sensor, output current via a current sensor, output power via a power sensor, total equipment energy consumption via an energy consumption sensor, and equipment operating years via equipment ledger data records. Ship side data includes actual ship power consumption, real-time power load, and shore power set load, acquired via the Modbus or Profinet communication interface of the ship's power distribution system. Grid side data includes grid peak-valley time periods and ship power consumption during off-peak hours, acquired via a data interface unit connected to the grid distribution network SCADA system.

3. The intelligent shore power supply method for ships according to claim 1, characterized in that, In step S200, the basic indicators in the data preprocessing step include power supply efficiency, load matching rate, peak-valley electricity consumption ratio, and equipment energy consumption coefficient. Among them, power supply efficiency is the percentage of the ship's actual electricity consumption to the total shore power output, and the total shore power output is the product of the shore power output power and the power supply duration. The load matching rate is the percentage of the ship's average actual load to the shore power's average set load. The peak-valley electricity consumption ratio is the percentage of the ship's electricity consumption during off-peak hours to the ship's total actual electricity consumption. The equipment energy consumption coefficient is the percentage of the total energy consumption of shore power equipment to the total shore power output.

4. The intelligent shore power supply method for ships according to claim 1, characterized in that, In step S300, the energy efficiency label generation step uses the target correction parameter as the target-oriented weight correction amount and the equipment status correction parameter as the equipment aging weight correction amount. The equipment aging weight correction amount is calculated as follows: based on the operating years of the shore power equipment, each full year is a calculation cycle, and each cycle corresponds to a correction amount of 0.

05. It only applies to the weight corresponding to the equipment energy consumption coefficient. The correction amount is calculated by rounding down the operating years of the equipment.

5. The intelligent shore power supply method for ships according to claim 1, characterized in that, In the S400 energy efficiency optimization analysis step, the calculation method for predicting the comprehensive energy efficiency score is as follows: based on the comprehensive energy efficiency score before optimization, add the sum of the adjusted weights of each basic indicator, the corresponding energy efficiency gain rate, and 100; wherein, the adjusted weight is the product of the basic weight and the sum of 1 plus the target-oriented weight correction amount and the equipment aging weight correction amount, and the energy efficiency gain rate is calculated through the energy efficiency optimization gain prediction formula.

6. A ship intelligent shore power supply system, characterized in that, This system is applicable to the intelligent shore power supply method for ships according to any one of claims 1-5, characterized in that the system comprises: Data acquisition module: includes voltage sensor, current sensor, power sensor, energy consumption sensor, ship communication interface and power grid SCADA docking unit, which are used to collect shore power side data, ship side data and power grid side data respectively; Data preprocessing module: Used to clean the collected data, calculate basic indicators, and perform standardization processing through multi-dimensional data standardization formulas, including data cleaning unit, basic indicator calculation unit, and standardization processing unit; Energy efficiency label generation module: used to calculate the comprehensive energy efficiency score and generate star-rated energy efficiency labels through the dynamic weighted energy efficiency evaluation formula, including a weight correction unit, a comprehensive score calculation unit and a label generation unit; Energy efficiency optimization analysis module: used to identify inefficiency indicators, match optimization measures, and calculate energy efficiency gain rate through energy efficiency optimization gain prediction formula, including inefficiency indicator diagnosis unit, optimization measure matching unit and gain prediction unit; The optimization effect verification module is used to compare the actual and predicted energy efficiency scores and judge the optimization effect by using the optimization effect verification formula. It includes an actual score calculation unit, a verification coefficient calculation unit, and a measure adjustment unit.

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