Port clean energy power supply system capacity collaborative configuration method and system

Abstract

The application discloses a kind of port clean energy power supply system capacity collaborative configuration method and system, obtain the ship side production and operation information of target port, wharf side production and operation information, port planning construction information, clean energy resource information and power operation constraint information, respectively predict ship energy load curve and wharf energy load curve, and generate port year hourly energy load curve on unified time axis superposition;According to clean energy resource information, calculate the clean energy developable capacity and standard output coefficient curve, combined with the port year hourly energy load curve, build source network load storage scale optimization model, and according to power operation constraint information, the clean energy development scale, energy storage configuration scale and external power access scheme are obtained by solving, to improve the matching ability of port clean energy power supply system and hourly energy load.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and more specifically, to a method and system for coordinated capacity configuration of a port clean energy power supply system. Background Technology

[0002] Port energy consumption scenarios include various loads such as shore power for ship berthing, ship charging, operation of terminal loading and unloading equipment, charging of yard operation equipment, and auxiliary production power consumption. Different loads have significant differences in time distribution, peak power, duration, and operational correlation. Port loads also fluctuate with changes in ship arrival schedules, cargo throughput, terminal operation periods, and the electrification rate of equipment.

[0003] The existing port clean energy configuration method usually determines the scale of wind power, photovoltaic, energy storage and external power access based on annual electricity consumption or experience installed capacity ratio. It is difficult to reflect the superposition characteristics of ship-side load and terminal-side load on the hourly time axis, and it is also difficult to combine the output fluctuation of wind and solar resources and power operation constraints to coordinate and match capacity. This easily leads to problems such as mismatch between clean energy output and port energy load, insufficient energy storage configuration or redundant external power access capacity.

[0004] Therefore, a technical solution is needed that can coordinate the power supply system capacity configuration according to the hourly load and clean energy output characteristics of the port. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for coordinated capacity configuration of a port clean energy power supply system.

[0006] In a first aspect, embodiments of the present invention provide a method for coordinated capacity configuration of a port clean energy power supply system, the method comprising:

[0007] Obtain the basic port planning data corresponding to the target port. The basic port planning data includes ship-side production and operation information, terminal-side production and operation information, port planning and construction information, clean energy resource information, and power operation constraint information.

[0008] Based on the ship-side production and operation information and the port planning and construction information, the ship energy load corresponding to the target port is predicted to obtain the ship energy load curve;

[0009] Based on the terminal-side production and operation information and the port planning and construction information, the terminal energy load corresponding to the target port is predicted to obtain the terminal energy load curve.

[0010] The port's annual hourly energy load curve is obtained by superimposing the ship energy load curve and the wharf energy load curve on a time axis.

[0011] Calculate the exploitable clean energy capacity and standard clean energy output coefficient curve corresponding to the target port based on the clean energy resource information;

[0012] Based on the exploitable capacity of clean energy, the standard output coefficient curve of clean energy, and the annual hourly energy load curve of the port, an optimization model for the scale of source, grid, load, and storage is constructed.

[0013] The source-grid-load-storage scale optimization model is solved based on the power operation constraint information to obtain the clean energy development scale, energy storage configuration scale, and external power access scheme corresponding to the target port.

[0014] Secondly, embodiments of the present invention provide a port clean energy power supply system capacity coordination configuration system, including at least one service node;

[0015] The service node includes a storage unit and a computing unit; the storage unit is used to store program code; the computing unit is used to run the program code to execute the port clean energy power supply system capacity collaborative configuration method described in the first aspect.

[0016] Compared with existing technologies, the beneficial effects provided by this invention include: by predicting the energy load on the ship side and the dock side separately and superimposing it hourly, it can more accurately reflect the actual energy consumption fluctuation characteristics of the port; by combining the standard output coefficient curve of clean energy and the power operation constraints to solve the source-grid-load-storage scale optimization model, it can improve the matching of wind power, photovoltaic, energy storage and external power access capacity, reduce the risk of clean energy curtailment and power supply capacity redundancy, and improve the operational reliability of the port clean energy power supply system. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as limiting the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the steps of the capacity collaborative configuration method for a port clean energy power supply system provided in an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of the annual hourly energy load synthesis of a port, provided as an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram illustrating the calculation of the exploitable capacity of clean energy in an embodiment of the present invention.

[0021] Figure 4A schematic block diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0022] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0023] In order to solve the technical problems mentioned in the background art Figure 1 This is a flowchart illustrating the capacity collaborative configuration method for a port clean energy power supply system provided in this embodiment. The following is a detailed description of the capacity collaborative configuration method for a port clean energy power supply system.

[0024] Step S201: Obtain the port planning basic data corresponding to the target port. The port planning basic data includes ship-side production and operation information, terminal-side production and operation information, port planning and construction information, clean energy resource information, and power operation constraint information.

[0025] Step S202: Based on the ship-side production and operation information and the port planning and construction information, predict the ship energy load corresponding to the target port to obtain the ship energy load curve;

[0026] Step S203: Based on the production and operation information of the terminal and the port planning and construction information, predict the terminal energy load corresponding to the target port to obtain the terminal energy load curve;

[0027] Step S204: Perform time axis superposition processing on the ship energy load curve and the wharf energy load curve to obtain the port's annual hourly energy load curve;

[0028] Step S205: Calculate the exploitable clean energy capacity and standard clean energy output coefficient curve corresponding to the target port based on the clean energy resource information;

[0029] Step S206: Based on the exploitable capacity of clean energy, the standard output coefficient curve of clean energy, and the annual hourly energy load curve of the port, construct a source-grid-load-storage scale optimization model.

[0030] Step S207: Solve the source-grid-load-storage scale optimization model based on the power operation constraint information to obtain the clean energy development scale, energy storage configuration scale, and external power access scheme corresponding to the target port.

[0031] In this embodiment of the invention, for example, the server first obtains the port planning basic data corresponding to the target port. When the target port is a newly built or under-construction port, the server collects data from ship AIS data, shore power information system, terminal production statistics system, port feasibility study report, preliminary design report, meteorological resource database, and power grid access data. The ship-side production and operation information includes ship type, length, beam, tonnage, arrival time, berthing time, departure time, shore power usage time, shore power consumption, shore power power, and shore power capacity. The terminal-side production and operation information includes terminal cargo types, throughput by cargo type, and the quantity, power, drive method, and energy consumption of equipment such as quay cranes, gantry cranes, stacker cranes, forklifts, belt conveyors, stacker-reclaimers, ship loaders, ship unloaders, horizontal transport vehicles, and collection and distribution trucks. The port planning and construction information includes planned berth grades, design throughput, planned ship type distribution, expected ship traffic, operation organization methods, and equipment configuration schemes. The clean energy resource information includes wind speed, air density, solar irradiance, ambient temperature, available wind turbine area, and available photovoltaic installation area within the port's red line and surrounding usable area. The power operation constraint information includes time-of-use pricing, grid capacity cost standards, clean energy penetration requirements, curtailment rate limits, energy storage construction costs, and external power access capacity constraints.

[0032] The server predicts the energy load of ships at the target port based on the ship-side production and operation information and the port planning and construction information. First, the server extracts historical "ship berthing-shore power usage" events from operating terminals of similar cargo types and sizes. Each complete shore power supply process is used as an analysis unit to construct a shore power event feature vector containing ship tonnage, ship type, length, beam, berthing duration, berth level, and throughput by cargo type. The corresponding shore power active power time series, maximum power, and actual power consumption are extracted. The server normalizes each shore power power time series by maximum power to obtain a normalized shore power load shape curve. Then, a large number of normalized curves are clustered to form typical shore power load patterns such as continuous high load for container ships and intermittent fluctuation for bulk carriers. The server then uses the historical event feature vectors to train a maximum power prediction model and a total power consumption prediction model. The planned berthing ship events are input into the models to obtain predicted maximum shore power and predicted shore power consumption. Finally, the server matches the closest typical normalized load shape curve based on a classifier. The server multiplies the predicted maximum shore power by the typical normalized load shape curve to form an initial shore power load curve. It then performs a proportional correction based on the difference between the integrated power of the initial shore power load curve and the predicted shore power consumption, ensuring that the curve power of a single shore power event matches the predicted power. The server also generates charging events based on the electric vessel's battery capacity, charging pile output power, berthing operation duration, and departure time, representing each charging event as a rectangular power pulse with constant power over its duration. The server superimposes the annual ship shore power load curve and the electric vessel charging power pulses onto a unified 8760-hour time axis to obtain the ship energy consumption load curve.

[0033] The server predicts the energy load of the target port's terminals based on the terminal's production and operation information and the port's planning and construction information. The server categorizes terminal operating equipment into basic stable load equipment and intermittent charging load equipment. For basic stable load equipment such as quay cranes, gantry cranes, rail-mounted cranes, belt conveyors, ship loaders, and ship unloaders, the server calculates the actual electricity consumption of similar operating terminals. For non-electric drive equipment such as diesel or hydrogen-powered equipment, the server converts it into equivalent electricity consumption based on fuel consumption, lower heating value, and comprehensive efficiency conversion factor. The server establishes a regression relationship with throughput by cargo type as the independent variable and the equivalent electricity consumption of basic stable equipment as the dependent variable. The daily throughput by cargo type for the target port's planned year is then input into the regression relationship to obtain the daily basic stable electricity consumption, which is then converted into an hourly basic stable load curve according to loading and unloading operation periods. For intermittently charging load equipment such as tire yard axles, straddle carriers, forklifts, forklifts, and electric trucks, the server converts the daily energy demand of mobile machinery into total daily charging power, establishes a regression model between total daily charging power and throughput by cargo type, and extracts a normalized daily charging power distribution shape curve based on the historical power curves of charging piles at similar terminals. The server distributes the total daily charging power for the planned year to various time periods according to the normalized daily charging power distribution shape curve to obtain the intermittent charging load curve. The server superimposes the basic stable load curve and the intermittent charging load curve on the same time axis to obtain the terminal energy load curve.

[0034] The server performs time-axis overlay processing based on the ship energy load curve and the terminal energy load curve. Using a 1-hour calculation resolution, the server maps ship shore power load, electric ship charging load, terminal foundation stable load, and terminal intermittent charging load to 8760 time points throughout the year. It then sums the various loads within the same time point to form the port's annual hourly energy load curve. This annual hourly energy load curve reflects the changes in electricity demand at the target port in different months, during different operational peaks, and at different times of concentrated ship arrivals, providing load-side input for subsequent calculations of clean energy installed capacity, energy storage capacity, and external power access capacity.

[0035] The server calculates the exploitable clean energy capacity and standard output coefficient curve for the target port based on the clean energy resource information. The server selects wind turbine types based on wind speed, air density, and available area around the port, and arranges turbine locations within the deployable area according to the required spacing between turbines in the prevailing wind direction and perpendicular to the prevailing wind direction. It then counts the number of turbines that can be installed to obtain the exploitable wind power capacity. The server calculates the standard output coefficient curve based on the turbine wind speed-power curve and hourly wind speed data. The server calculates the exploitable photovoltaic capacity based on the net installable photovoltaic area, unit area module capacity, and system installability coefficient of the port roof, storage yard roof, parking area, and vacant areas. It also calculates the standard output coefficient curve based on hourly total horizontal irradiance, ambient temperature, nominal module operating temperature, module peak power temperature coefficient, and inverter efficiency.

[0036] The server constructs a source-grid-load-storage scale optimization model based on the exploitable clean energy capacity, the standard output coefficient curve of clean energy, and the annual hourly energy load curve of the port. The server sets the actual installed capacity of wind power, the actual installed capacity of photovoltaic power, energy storage power, and energy storage capacity as variables to be optimized, and sets the external power purchase curve, the curtailment curve, and the total cost of port electricity consumption as output targets. Within the exploitable capacity range of wind and photovoltaic power, the server generates multiple sets of clean energy installation candidate schemes, calculates the actual output curve of clean energy for each candidate scheme, and subtracts it from the annual hourly energy load curve of the port to obtain the net load curve. For periods with negative net load, the server schedules energy storage charging and calculates the curtailed electricity; for periods with positive net load, it schedules energy storage discharging and calculates the power still requiring supplementation from the external grid. The server eliminates schemes that do not meet the constraints of clean energy penetration rate, curtailment rate, energy storage charging and discharging power, energy storage capacity, and external power access based on the power operation constraint information. Then, using the total port electricity cost (comprising clean energy development cost, energy storage cost, external power purchase cost, and grid capacity cost) as the evaluation objective, it solves for the lowest-cost clean energy development scale, energy storage configuration scale, and external power access scheme. Consequently, the server outputs the target port's wind power installed capacity, photovoltaic installed capacity, energy storage power, energy storage capacity, annual external power purchase power curve, and maximum demand, serving as the planning and design results for the port's near-zero carbon clean energy development and utilization system.

[0037] In this embodiment of the invention, the step of predicting the ship energy load corresponding to the target port based on the ship-side production and operation information and the port planning and construction information to obtain the ship energy load curve can be implemented through the following example.

[0038] Historical ship berthing shore power usage events are extracted from the ship-side production and operation information, and each historical ship berthing shore power usage event is used as a shore power load analysis unit.

[0039] Based on the ship tonnage, ship type, ship length, ship width, berthing duration, berth design tonnage, and cargo throughput of each shore power load analysis unit, a shore power event feature vector is constructed.

[0040] Extract the shore power active power time series, maximum shore power, and actual shore power consumption corresponding to each shore power load analysis unit;

[0041] The shore power active power time series is normalized based on the maximum shore power to obtain a normalized shore power load shape curve.

[0042] Clustering is performed on multiple normalized shore power load shape curves to obtain a set of typical shore power load shape curves.

[0043] A shore power and electricity consumption prediction model is established based on the shore power event feature vector, the maximum power of the shore power, and the actual electricity consumption of the shore power.

[0044] Based on the port planning and construction information, the set of typical shore power load shape curves, and the shore power consumption prediction model, the ship shore power load curve in the ship energy consumption load curve is generated.

[0045] In an exemplary embodiment of the invention, when predicting the energy load of ships at a target port, the server first extracts historical ship berthing shore power usage events from the ship-side production and operation information. This ship-side production and operation information originates from operating terminals of similar cargo type and scale. The server obtains ship type, length, beam, arrival time, berthing time, and departure time through ship AIS data, and shore power connection time, disconnection time, real-time power, power consumption, and capacity through the shore power information system. The server defines the complete power supply process of the shore power system from connection to disconnection during a single ship berthing as a historical ship berthing shore power usage event, and uses this historical ship berthing shore power usage event as a shore power load analysis unit. For example, if the server identifies a 50,000-ton container ship berthing at an operating container terminal for 8 hours, with the shore power system continuously supplying power for 7.5 hours, and the shore power power fluctuating at different loading and unloading stages, the server considers this complete shore power supply process as a shore power load analysis unit, rather than using only a single average power value for load prediction.

[0046] The server then constructs shore power event feature vectors based on the ship tonnage, ship type, ship length, ship width, berthing duration, berth design tonnage, and daily cargo throughput for each shore power load analysis unit. Specifically, the server encodes information such as "50,000-ton class, container ship, ship length 280 meters, ship width 40 meters, berthing duration 8 hours, berth design level 50,000-ton class, daily container throughput 3000 TEUs" into a unified format shore power event feature vector, enabling comparison and learning of different ship berthing events within the same model. The server simultaneously extracts the shore power active power time series corresponding to the shore power load analysis unit, recording shore power power changes at a sampling granularity of 15 minutes or 1 hour, and determining the maximum shore power and actual shore power consumption from the shore power active power time series. For berthing events involving peak loading / unloading times, refrigerated container access, or auxiliary equipment startup, the server retains the fluctuation pattern of the power time series, thus avoiding the loss of time-series load characteristics due to relying solely on total power consumption.

[0047] The server further normalizes the shore power active power time series based on the maximum shore power power to obtain a normalized shore power load shape curve. That is, the server divides the shore power power at each sampling moment by the maximum shore power power, converting shore power events of different tonnages and power scales into load shape curves within the range of 0 to 1. The server then clusters multiple normalized shore power load shape curves to obtain a set of typical shore power load shape curves. For example, the server categorizes container ship berthing events with continuous high load as "continuous high load type," bulk carrier berthing events with power fluctuating multiple times according to loading and unloading rhythm as "intermittent fluctuation type," and events with high power at the beginning of berthing and gradually decreasing later as "high at the beginning and low at the end." This set of typical shore power load shape curves represents the common patterns of shore power load changes over time under different berthing scenarios.

[0048] The server establishes a shore power and electricity consumption prediction model based on the shore power event feature vector, the maximum shore power, and the actual shore power consumption. Specifically, the server uses the shore power event feature vector as the model input and the maximum shore power and actual shore power consumption as the model output to train a model capable of predicting shore power capacity demand and single-time electricity consumption based on planned ship berthing conditions. When the target port is a newly built terminal, the server reads the planned berth class, expected ship type, expected berthing duration, and planned throughput from the port planning and construction information, inputs the planning information into the shore power and electricity consumption prediction model, and obtains the predicted maximum shore power and predicted shore power consumption corresponding to the planned berthing event. The server then combines the typical shore power load shape curve set, selects the normalized shore power load shape curve that best matches the planned berthing event, and uses the predicted maximum shore power to reconstruct the normalized shore power load shape curve to generate the single-time ship berthing shore power load curve. The server performs the above processing on each planned berthing event throughout the year, and maps all individual ship berthing shore power load curves onto a unified annual time axis to form the ship shore power load curve in the ship energy load curve, providing ship-side load input for subsequent port annual hourly energy load calculation and source-grid-load-storage scale optimization.

[0049] In this embodiment of the invention, the step of generating the ship shore power load curve in the ship energy load curve based on the port planning and construction information, the set of typical shore power load shape curves, and the shore power power consumption prediction model can be implemented through the following example.

[0050] The planned berthing events are determined based on the port planning and construction information. The planned berthing events include the estimated arrival time, berthing time, departure time, vessel type, and vessel tonnage.

[0051] The planned event features corresponding to the planned berthing events are input into the shore power load shape classifier, and the matching target normalized shore power load shape curve is determined from the set of typical shore power load shape curves.

[0052] The planning event characteristics are input into the shore power power consumption prediction model to obtain the predicted maximum shore power and the predicted shore power consumption.

[0053] An initial shore power load curve is generated based on the predicted maximum shore power and the target normalized shore power load shape curve.

[0054] The shore power ratio correction factor is determined based on the difference between the integral power of the initial shore power load curve and the predicted shore power consumption.

[0055] The initial shore power load curve is proportionally corrected according to the shore power ratio correction coefficient to obtain the load curve for a single shore power event.

[0056] The shore power load curves of each individual shore power event corresponding to multiple planned berthing events throughout the year are superimposed on a unified time axis to obtain the ship shore power load curve.

[0057] In an embodiment of the invention, for example, when generating the ship shore power load curve, the server first determines the planned berthing events for the entire year based on the feasibility study report, preliminary design report, and planned ship schedule data of the target port. The server reads the berth class, design throughput, planned ship type structure, and expected ship arrival frequency from the target port's planning and construction information to form multiple planned berthing events. Taking a newly built container berth at the target port as an example, the server determines a planned berthing event as "a 50,000-ton container ship arrives at the port at 8:00 AM on March 12th, berths for 8 hours, and departs at 4:00 PM," and records the ship type, ship tonnage, expected arrival time, berthing time, and departure time corresponding to the planned berthing event. The server further converts the planned berthing event into planned event features, which include ship tonnage code, ship type code, berthing duration, berth design tonnage, planned daily throughput, and cargo type information.

[0058] The server inputs the planned event features into a shore power load shape classifier, which is trained based on historical shore power usage events of operating terminals of similar cargo type and scale. The server retrieves the load pattern closest to the planned event features from a set of typical shore power load shape curves, identifying one of the following as the target normalized shore power load shape curve: continuous high load, intermittent fluctuation, or high at the beginning and low at the end. For the aforementioned berthing event of a 50,000-ton container ship, the server identified that the planned event features had the highest match with the historical continuous high load power consumption pattern during container ship loading and unloading, and therefore selected the continuous high load normalized curve as the target normalized shore power load shape curve.

[0059] The server simultaneously inputs the planned event characteristics into the shore power and electricity consumption prediction model to obtain the predicted maximum shore power and predicted shore power consumption. The shore power and electricity consumption prediction model is established based on historical shore power event feature vectors, maximum shore power, and actual shore power consumption, and can output shore power capacity requirements and single-time power supply amounts that match the planned berthing conditions. The server multiplies the predicted maximum shore power by the normalized values ​​at each moment in the target normalized shore power load shape curve to generate an initial shore power load curve, enabling the initial shore power load curve to reflect the temporal distribution of shore power changes with loading and unloading operation intensity during berthing.

[0060] The server then calculates the integral electricity of the initial shore power load curve during the berthing duration and compares the integral electricity with the predicted shore power consumption to determine the shore power ratio correction factor. The server uses this correction factor to scale the initial shore power load curve proportionally, ensuring that the corrected curve retains the temporal shape of the target normalized shore power load curve while maintaining consistency between the integral electricity and the predicted shore power consumption. The server identifies the corrected curve as the single shore power event load curve and maps it to an 8760-hour time axis throughout the year, based on the expected arrival and departure times of the planned berthing vessel events. The server repeats this process for multiple planned berthing vessel events throughout the year and superimposes the single shore power event load curves corresponding to different berths and vessels at the same time to obtain the target port's vessel shore power load curve. This vessel shore power load curve, as an important component of the vessel energy load curve, is used for subsequent annual hourly energy load calculations for the port and for optimizing the scale of clean energy, energy storage, and external power access.

[0061] In this embodiment of the invention, the step of predicting the ship energy load corresponding to the target port based on the ship-side production and operation information and the port planning and construction information to obtain the ship energy load curve can also be implemented through the following examples.

[0062] The electric vessel charging parameters are determined based on the ship-side production and operation information and the port planning and construction information. The electric vessel charging parameters include the rated capacity of the ship's power battery, the ship's berthing operation time, the output power of the charging equipment, and the duration of a single charge.

[0063] Based on the port planning and construction information, an electric vessel arrival event flow is generated. The electric vessel arrival event flow includes multiple electric vessel arrival events, and each electric vessel arrival event includes vessel identification, arrival time, berthing time and planned departure time.

[0064] Based on the duration of a single charge and the planned departure time, determine the charging start time, charging end time, and actual charging amount for each electric vessel arrival event;

[0065] Based on the charging start time, the charging end time, and the output power of the charging device, a rectangular power pulse is generated corresponding to each electric vessel arrival event.

[0066] By superimposing the rectangular power pulses corresponding to multiple electric ship arrival events throughout the year on a unified time axis, the electric ship charging load curve in the ship energy load curve is obtained.

[0067] The ship's energy load curve is obtained based on the ship's shore power load curve and the electric ship's charging load curve.

[0068] In this embodiment of the invention, for example, the server, based on the completed prediction of shore power load for ships, further predicts the charging load of electric ships. The server first determines the charging parameters of the electric ships based on the ship-side production and operation information and the port planning and construction information. Specifically, the server reads the rated capacity of the ship's power battery, the ship's berthing operation time, the output power of the charging equipment, and the charging strategy from the port area's electric ship operation data, the planned ship type configuration table, and the charging facility configuration plan. Taking the planned access of electric port service ships, short-haul electric cargo ships, and electric passenger roll-on / roll-off ships to the target port as an example, the server records the battery capacity, standard charging power, and berthing operation time corresponding to each type of ship, and calculates the duration of a single charging cycle according to the rule of "charging begins after berthing and ends before departure." The server processes the single charging process as a constant power charging process, enabling the electric ship charging load to be included in the annual load curve calculation in the form of a stable power segment.

[0069] The server then generates an electric vessel arrival event stream based on the port planning and construction information. The server reads the target port's planned ship schedule, berth utilization plan, expected vessel arrival frequency, and vessel type distribution to generate multiple electric vessel arrival events for the entire year. Each electric vessel arrival event includes a vessel identifier, arrival time, berthing time, and planned departure time. For example, the server records that electric vessel E-Ship-01 arrived at 9:00 AM on June 10th, berthed for 5 hours, and is scheduled to depart at 2:00 PM, treating this event as a calculable charging scheduling unit. For scenarios where multiple berths operate concurrently throughout the year, the server arranges all electric vessel arrival events according to a uniform time scale, ensuring accurate reflection of the cumulative impact of multiple vessels charging simultaneously on the port's power grid load.

[0070] The server further determines the charging start time, charging end time, and actual charging amount for each electric vessel arrival event based on the single charging duration and the planned departure time. The server uses the arrival time or berthing start time as the charging start time and calculates the theoretical charging completion time based on the battery's remaining charge and the charging equipment's output power. If the theoretical charging completion time is earlier than the planned departure time, the server determines the theoretical charging completion time as the charging end time and calculates the complete charging amount; if the theoretical charging completion time is later than the planned departure time, the server determines the planned departure time as the charging end time and calculates the actual charging amount based on the actual available charging time. Through this process, the server ensures that each charging event meets both battery capacity and charging power constraints, as well as vessel berthing window constraints.

[0071] The server generates a rectangular power pulse corresponding to each electric vessel arrival event based on the charging start time, charging end time, and charging equipment output power. This rectangular power pulse remains equal to the charging equipment output power during the charging start and end times, and is zero during other time periods. The server maps all rectangular power pulses corresponding to electric vessel arrival events throughout the year onto a unified 8760-hour time axis, and algebraically superimposes the charging power generated by different berths, charging piles, and electric vessels at the same time to obtain the electric vessel charging load curve within the ship energy load curve. Finally, the server adds the electric vessel charging load curve to the aforementioned ship shore power load curve on the same annual time axis to obtain the complete ship energy load curve. This ship energy load curve simultaneously reflects the shore power demand of berthing vessels and the charging demand of electric vessels, providing ship-side input for calculating the port's annual hourly energy load, estimating clean energy installed capacity, and optimizing energy storage capacity.

[0072] In this embodiment of the invention, the step of predicting the terminal energy load corresponding to the target port based on the terminal-side production and operation information and the port planning and construction information to obtain the terminal energy load curve can be implemented through the following example.

[0073] Based on the production and operation information at the wharf, the wharf operation equipment is divided into basic stable load equipment and intermittent charging load equipment.

[0074] Obtain the equipment type, quantity, rated power of a single unit, actual power consumption, and fuel consumption of non-electric drive equipment corresponding to the basic stable load equipment;

[0075] The equivalent power consumption of the basic stable load equipment is determined based on the fuel consumption of the non-electrically driven equipment, the lower heating value of the fuel, and the comprehensive efficiency conversion factor.

[0076] A basic stable load regression model is established based on the equivalent electricity consumption and throughput by cargo type.

[0077] Based on the port planning and construction information, the daily throughput of cargo types in the planning year is determined, and the daily throughput of cargo types in the planning year is input into the basic stable load regression model to obtain the daily basic stable electricity consumption in the planning year.

[0078] Based on the planned annual daily basic stable electricity consumption and planned operating periods, a daily basic stable power curve is generated;

[0079] By superimposing multiple daily basic stable power curves on a unified time axis, the basic stable load curve in the terminal energy load curve is obtained.

[0080] In this embodiment of the invention, for example, when predicting the energy load of a target port's terminal, the server first classifies the terminal's operating equipment into load types based on the terminal's production and operation information. The server reads the equipment ledgers, production statistics records, and energy consumption records of operating terminals of the same cargo type and similar scale, classifying quay cranes, gantry cranes, rail yard cranes, belt conveyors, continuous ship loaders, continuous ship unloaders, and stacker-reclaimers that are directly connected to the power grid and operate continuously during loading and unloading operations as basic stable load equipment; and classifying straddle carriers, forklifts, forklifts, electric trucks, etc., that rely on power batteries and are recharged through charging piles or battery swapping stations as intermittent charging load equipment. In this embodiment, when processing the basic stable load equipment, the server focuses on extracting the energy consumption characteristics of the basic stable load equipment that are relatively continuous and have small power fluctuations during the operating period, so that the basic stable load curve can reflect the stable power demand of the main loading and unloading process of the terminal.

[0081] The server then obtains the equipment type, quantity, rated power per unit, actual electricity consumption, and fuel consumption of non-electrically driven equipment corresponding to the basic stable load equipment. For example, the server reads the quantity and rated power of ship unloaders, belt conveyors, stacker-reclaimers, and ship loaders from the production system of an operational bulk cargo terminal; it reads the daily electricity consumption of electric equipment from the electricity meter system; and it reads the daily diesel consumption of diesel-driven equipment from the fuel consumption statistics table. For equipment that has already been electrified, the server directly includes the actual electricity consumption in the basic stable load sample; for diesel, hydrogen, or other non-electrically driven equipment, the server determines the equivalent electricity consumption corresponding to the basic stable load equipment based on the fuel consumption, lower heating value of the fuel, and comprehensive efficiency conversion factor of the non-electrically driven equipment. For example, the server multiplies the fuel consumption of diesel equipment by the lower heating value of diesel, and then multiplies it by the comprehensive efficiency conversion factor of diesel equipment from fuel to effective work output to obtain the equivalent electricity consumption corresponding to the diesel equipment completing the same amount of work, thereby unifying equipment with different drive methods under the electricity load caliber.

[0082] The server establishes a basic stable load regression model based on the equivalent electricity consumption and throughput by cargo type. Specifically, the server uses the daily or monthly throughput by cargo type of the operating terminal as the input variable and the total equivalent electricity consumption of the basic stable load equipment within the same statistical period as the output variable to establish a regression relationship between the throughput by cargo type and the basic stable electricity consumption. For container terminals, the server uses the number of TEUs as the main throughput variable; for dry bulk terminals, the server uses the tonnage of coal, ore, grain, etc., as throughput variables. Through the basic stable load regression model, the server can estimate the basic stable electricity demand of a newly built terminal on different dates based on the planned production scale.

[0083] The server further determines the daily throughput by cargo type for the planned year based on the port planning and construction information. The server reads the target port's designed annual throughput, cargo type ratio, monthly production plan, and daily allocation coefficient, decomposing the annual throughput into daily throughput by cargo type for 365 days. This planned year's daily throughput by cargo type is then input into the basic stable load regression model to obtain the planned year's daily basic stable electricity consumption. Subsequently, the server generates daily basic stable power curves based on the planned year's daily basic stable electricity consumption and planned operating periods. The server allocates the basic stable electricity consumption for a given day to the operating periods of unloading, loading, yard transfer, or continuous conveyor belt transport, converting them into power values ​​for the corresponding periods according to the start time, end time, and duration of the operations. Finally, the server maps multiple daily basic stable power curves to a unified 8760-hour time axis for the entire year and superimposes the basic stable power generated by different berths and different continuous operating lines at the same time to obtain the basic stable load curve in the terminal energy load curve. This basic stable load curve is used together with subsequent intermittent charging load curves to form the terminal-side total energy load curve.

[0084] In this embodiment of the invention, the step of predicting the terminal energy load corresponding to the target port based on the terminal-side production and operation information and the port planning and construction information to obtain the terminal energy load curve can also be implemented through the following examples.

[0085] Obtain the equipment type, number of equipment, single-unit battery capacity, charging power, and mobile machinery fuel consumption corresponding to the intermittent charging load equipment;

[0086] The equivalent charging capacity corresponding to the intermittent charging load equipment is determined based on the mobile machinery fuel consumption, fuel lower heating value, and comprehensive efficiency conversion factor.

[0087] An intermittent charging capacity regression model is established based on the equivalent charging capacity and the throughput by cargo type.

[0088] Based on the port planning and construction information, the daily throughput by cargo type for the planning year is determined, and the daily throughput by cargo type for the planning year is input into the intermittent charging power regression model to obtain the total daily charging power for the planning year.

[0089] Based on the historical power curves of charging equipment at similar terminals, determine the normalized daily charging power distribution shape curve.

[0090] Based on the total daily charging capacity of the planned year and the normalized daily charging power distribution shape curve, determine the actual daily charging power curve;

[0091] By superimposing multiple daily actual charging power curves on a unified time axis, the intermittent charging load curve in the terminal energy load curve is obtained.

[0092] The terminal energy load curve is obtained based on the basic stable load curve and the intermittent charging load curve.

[0093] In this embodiment of the invention, for example, after obtaining the basic stable load curve, the server continues to predict the intermittent charging load at the terminal. The server first obtains the equipment type, quantity, single-unit battery capacity, charging power, and mobile machinery fuel consumption corresponding to the intermittent charging load equipment from the terminal's production and operation information. The intermittent charging load equipment includes tire yard axles, straddle carriers, forklifts, electric trucks, and other mobile operating machinery that relies on power batteries and is recharged through charging piles or battery swapping stations. The server reads the equipment ledgers and energy statistics tables of similar operating terminals, recording the number of various types of mobile machinery, rated battery capacity, standard charging power, daily operating shifts, and daily fuel consumption. For example, the server uses electric trucks, forklifts, and tire yard axles in operating container terminals as samples of mobile machinery, and includes the charging needs of these mobile machinery on peak production days and ordinary operating days in the sample data.

[0094] The server then determines the equivalent charging capacity corresponding to the intermittent charging load equipment based on the mobile machinery's fuel consumption, the lower heating value of the fuel, and the overall efficiency conversion factor. For diesel trucks, diesel stackers, and diesel forklifts that have not yet completed electrification, the server multiplies the daily diesel consumption by the lower heating value of the diesel fuel, and then combines this with the diesel equipment's overall efficiency conversion factor to obtain the equivalent battery charging capacity required to complete the same transportation, storage, or loading / unloading auxiliary operations. Through this processing, the server uniformly converts mobile machinery with different drive modes into a charging capacity caliber, enabling the operating data of diesel equipment at existing terminals to support the prediction of charging loads after the electrification of new terminals.

[0095] The server establishes an intermittent charging capacity regression model based on the equivalent charging capacity and cargo throughput. Specifically, the server uses the daily cargo throughput of the operating terminal as the input variable and the total equivalent charging capacity of mobile machinery on that day as the output variable to establish the correspondence between throughput and mobile machinery charging demand. For container terminals, the server uses the daily TEU quantity, the proportion of loaded containers, and the intensity of horizontal transport operations as important variables affecting charging demand; for dry bulk terminals, the server uses the tonnage of coal, ore, and other cargo types, as well as the yard transfer distance, as variables affecting charging demand. The server then determines the daily cargo throughput for the planning year based on the port planning and construction information of the target port, and inputs the daily cargo throughput for the planning year into the intermittent charging capacity regression model to obtain the total daily charging capacity for the planning year.

[0096] The server further determines the normalized daily charging power distribution shape curve based on the historical power curves of charging equipment at similar terminals. The server statistically analyzes the hourly total power curves of all charging piles at all operating terminals of the same type over a month or year, and normalizes the daily curves to the maximum power, obtaining a typical 24-hour charging power distribution shape varying within the range of 0 to 1. This normalized daily charging power distribution shape reflects the typical charging patterns of mobile machinery during shift changes, midday refueling, and concentrated nighttime charging. Based on the planned annual daily total charging volume and the normalized daily charging power distribution shape curve, the server calculates a power scaling factor and multiplies it by the normalized daily charging power distribution shape curve to obtain the daily actual charging power curve. The server maps multiple daily actual charging power curves for the entire year onto a unified 8760-hour time axis and superimposes the charging power generated by different charging piles, battery swapping stations, and mobile machinery at the same time to obtain the intermittent charging load curve in the terminal's energy load curve. Finally, the server adds the basic stable load curve and the intermittent charging load curve on the same time axis to obtain the terminal energy load curve, which serves as the terminal-side input for subsequent annual hourly energy load calculations and source-grid-load-storage scale optimization for the port.

[0097] In this embodiment of the invention, the step of calculating the exploitable clean energy capacity and the standard output coefficient curve of the target port based on the clean energy resource information can be implemented through the following example.

[0098] Based on the clean energy resource information, obtain wind resource data, photovoltaic resource data, and deployable area data of the available area around the target port;

[0099] Based on the wind resource data, wind turbine selection information is determined, and based on the wind turbine selection information, wind turbine spacing requirements, and the available deployment area data, the exploitable wind power capacity is determined.

[0100] Based on the wind resource data and the wind turbine selection information, calculate the wind power standard output coefficient curve;

[0101] Based on the photovoltaic resource data, the net area of ​​installable photovoltaics, the module capacity per unit area, and the system installability coefficient, the exploitable photovoltaic capacity is determined.

[0102] Based on the photovoltaic resource data, module temperature parameters, module peak power temperature coefficient, and inverter efficiency, calculate the photovoltaic standard output coefficient curve;

[0103] Based on the exploitable wind power capacity, the exploitable photovoltaic capacity, the standard output coefficient curve of wind power, and the standard output coefficient curve of photovoltaic power, the exploitable clean energy capacity and the standard output coefficient curve of clean energy are obtained.

[0104] In this embodiment of the invention, for example, after obtaining the annual hourly energy load curve of the port, the server further calculates the exploitable clean energy capacity and the standard output coefficient curve of the clean energy corresponding to the target port based on clean energy resource information. The server first acquires wind resource data, photovoltaic resource data, and deployable area data for the usable area surrounding the target port. The wind resource data includes hourly wind speed, annual average wind speed, air density, and prevailing wind direction at different heights within and around the port's boundary. The photovoltaic resource data includes meteorological parameters such as total horizontal irradiance, ambient temperature, wind speed, and solar altitude angle throughout the year. The deployable area data includes the area of ​​storage yard roofs, warehouse roofs, parking area roofs, office building roofs, open spaces along the breakwater, and other usable areas that do not affect port production safety. The server maps the wind resource data, photovoltaic resource data, and deployable area data to the target port's geographic information model, ensuring that the clean energy capacity calculation corresponds to the actual spatial boundary of the port.

[0105] The server determines wind turbine selection information based on the wind resource data, and determines the exploitable wind power capacity based on the wind turbine selection information, turbine spacing requirements, and the available deployment area data. Specifically, the server selects suitable wind turbine models based on the average annual wind speed and wind speed distribution characteristics of the area surrounding the target port. When the average annual wind speed in the coastal port area is at a high level, the server prioritizes matching wind turbines with large rotors, tall towers, and larger single-unit capacities. The server further determines the turbine arrangement direction based on the prevailing wind direction, and generates candidate turbine locations within the deployable area according to the rule of maintaining a preset multiple of the rotor diameter spacing between adjacent turbines in the prevailing wind direction and a preset multiple of the rotor diameter spacing between adjacent turbines perpendicular to the prevailing wind direction. The server eliminates locations that conflict with waterways, loading and unloading areas, hazardous materials storage yards, and safety clearance zones, counts the remaining number of installable turbines, and multiplies the number of installable turbines by the rated capacity of a single unit to obtain the exploitable wind power capacity corresponding to the target port.

[0106] The server calculates the standard output coefficient curve of wind power based on the wind resource data and the wind turbine selection information. The server reads the wind speed-power curve of the selected wind turbine, inputs the hourly wind speed of the target port throughout the year into the wind speed-power curve, and obtains the theoretical output power of the wind turbine for each hour. The server then divides the theoretical output power by the rated power of the wind turbine to obtain the standard output coefficient of wind power, which varies within the range of 0 to 1. The standard output coefficient curve represents the output ratio of each unit of installed wind power capacity over 8760 hours throughout the year, and is separated from the final actual installed capacity, facilitating the subsequent source-grid-load-storage scale optimization model to quickly calculate the actual output of clean energy among different wind power installed capacity candidate schemes.

[0107] The server determines the exploitable photovoltaic (PV) capacity based on the PV resource data, the net installable PV area, the module capacity per unit area, and the system installability coefficient. The server extracts the net installable PV area from the port's geographic information model, deducting maintenance access roads, equipment shading areas, fire safety clearance areas, and areas with structural load-bearing limitations. The server calculates the theoretical installed capacity based on the unit area capacity of the selected PV modules, and then determines the system installability coefficient by combining the module tilt angle, front-to-back row spacing, inverter layout, and actual installable ratio, ultimately obtaining the exploitable PV capacity corresponding to the target port. For large, continuous areas such as warehouse roofs and parking shed roofs, the server prioritizes module placement based on area; for scattered building roofs, the server performs zoned statistics based on available area and shading conditions to ensure that the exploitable PV capacity aligns with the actual construction conditions of the port.

[0108] The server calculates the standard photovoltaic output coefficient curve based on the photovoltaic resource data, component temperature parameters, component peak power temperature coefficient, and inverter efficiency. The server calculates the photovoltaic component operating temperature based on the total hourly horizontal irradiance and ambient temperature throughout the year, and corrects the component output capacity based on the deviation of the component operating temperature from the standard test temperature. The server further combines the irradiance output ratio, temperature correction coefficient, and inverter efficiency to obtain the photovoltaic standard output coefficient for each hour. The photovoltaic standard output coefficient curve reflects the output ratio of each unit of photovoltaic installed capacity under different seasons, weather conditions, and sunshine periods. Finally, the server encapsulates the exploitable wind power capacity, the exploitable photovoltaic capacity, the wind power standard output coefficient curve, and the photovoltaic standard output coefficient curve into a unified clean energy resource calculation result, obtaining the exploitable clean energy capacity and the clean energy standard output coefficient curve. This provides input for subsequent selection of actual clean energy installed capacity based on port load, optimization of energy storage scale, and calculation of external power access capacity.

[0109] In this embodiment of the invention, the step of constructing a source-grid-load-storage scale optimization model based on the exploitable capacity of clean energy, the standard output coefficient curve of clean energy, and the annual hourly energy load curve of the port can be implemented through the following examples.

[0110] Within the defined limits of the exploitable capacity of clean energy, the actual installed capacity of wind power and the actual installed capacity of photovoltaic power are set as optimization variables for clean energy installation.

[0111] Calculate the actual power output curve of clean energy based on the actual installed capacity of wind power, the actual installed capacity of photovoltaic power, and the standard power output coefficient curve of clean energy.

[0112] Calculate the net load curve based on the port's annual hourly energy load curve and the actual output curve of the clean energy source.

[0113] The search range for energy storage power and energy storage capacity is determined based on the net load curve, and energy storage power and energy storage capacity are set as energy storage optimization variables.

[0114] Based on the clean energy installed capacity optimization variables, the energy storage optimization variables, the external power purchase curve, the power curtailment curve, and the total cost of port electricity consumption, the source-grid-load-storage scale optimization model is constructed.

[0115] In this embodiment of the invention, for example, after obtaining the exploitable clean energy capacity, the clean energy standard output coefficient curve, and the port's annual hourly energy load curve, the server constructs a source-grid-load-storage scale optimization model. The server first sets the actual installed capacity of wind power and actual installed capacity of photovoltaics as clean energy installation optimization variables within the defined range of the exploitable clean energy capacity. Taking a target port with a calculated exploitable wind power capacity of 40MW and an exploitable photovoltaic capacity of 25MW as an example, the server generates multiple actual installed wind power capacity values ​​within the range of 0 to 40MW according to a preset installation step size, and generates multiple actual installed photovoltaic capacity values ​​within the range of 0 to 25MW, so that each set of actual installed wind power capacity and actual installed photovoltaic capacity forms a clean energy installation candidate scheme.

[0116] The server calculates the actual clean energy output curve based on the actual installed wind power capacity, the actual installed photovoltaic capacity, and the clean energy standard output coefficient curve. Specifically, the server multiplies the actual installed wind power capacity by the wind power standard output coefficient curve hourly to obtain the annual 8760-hour actual wind power output curve; the server multiplies the actual installed photovoltaic capacity by the photovoltaic standard output coefficient curve hourly to obtain the annual 8760-hour actual photovoltaic output curve; the server then adds the actual wind power output and actual photovoltaic output at the same moment to obtain the clean energy actual output curve. The clean energy actual output curve reflects the clean electricity that the clean energy installation candidate scheme can supply to the port in each hour throughout the year.

[0117] The server calculates the net load curve based on the port's annual hourly energy load curve and the actual clean energy output curve. Each hour, the server subtracts the actual clean energy output from the port's energy load to obtain the corresponding net load value. When the net load value is positive, the server identifies it as a period of insufficient clean energy output; when the net load value is negative, the server identifies it as a period of excessive clean energy output. The server determines the energy storage power search range and energy storage capacity search range based on the net load curve. The server extracts the maximum excess power from negative net load periods as the reference upper limit for energy storage charging power, and extracts the maximum shortfall power from positive net load periods as the reference upper limit for energy storage discharging power, and determines the energy storage power search range accordingly. The server combines the continuous discharge hours of energy storage, the port load fluctuation amplitude, and the duration of clean energy surplus to determine the energy storage capacity search range, and sets energy storage power and energy storage capacity as energy storage optimization variables.

[0118] The server constructs a source-grid-load-storage scale optimization model based on the clean energy installed capacity optimization variables, the energy storage optimization variables, the external power purchase curve, the curtailment curve, and the total port electricity cost. The server sets energy storage operation rules in the model: during periods of negative net load, priority is given to charging energy storage; the portion exceeding the energy storage charging power and capacity constraints forms a curtailment curve. During periods of positive net load, priority is given to discharging energy storage to fill the load gap; the portion still unmet after energy storage discharge forms an external power purchase curve. The server further incorporates clean energy development costs, energy storage construction costs, energy storage operation costs, external power purchase costs, and grid capacity costs into the total port electricity cost, using the total port electricity cost as the optimization objective. Therefore, the source-grid-load-storage scale optimization model can compare different combinations of actual wind power installed capacity, actual photovoltaic installed capacity, energy storage power, and energy storage capacity, providing a model foundation for subsequently solving for the clean energy development scale, energy storage configuration scale, and external power access scheme that minimizes costs and meets power operation constraints.

[0119] In this embodiment of the invention, the step of solving the source-grid-load-storage scale optimization model based on the power operation constraint information to obtain the clean energy development scale, energy storage configuration scale, and external power access scheme corresponding to the target port can be implemented through the following example.

[0120] Based on the power operation constraint information, constraints on clean energy penetration rate, curtailment rate, energy storage charging and discharging power, energy storage capacity, and external power access are determined.

[0121] A two-layer cyclic search is performed within the range of the actual installed capacity of wind power and the actual installed capacity of photovoltaic power to obtain multiple sets of clean energy installation candidate schemes;

[0122] For each set of clean energy installation candidate schemes, energy storage scale search is performed based on the net load curve within the energy storage power search range and the energy storage capacity search range to obtain multiple sets of energy storage candidate schemes;

[0123] For each group of energy storage candidate schemes, a year-round hourly charge-discharge simulation was performed to obtain the corresponding external power purchase curve, power curtailment curve, and total port electricity cost.

[0124] Candidate schemes that do not meet the constraints of clean energy penetration rate, curtailment rate, energy storage charging and discharging power, energy storage capacity, and external power access are eliminated.

[0125] The candidate scheme with the lowest total cost of electricity consumption at the port is selected from the remaining candidate schemes to obtain the clean energy development scale, the energy storage configuration scale, and the external power access scheme.

[0126] In this embodiment of the invention, for example, after constructing a source-grid-load-storage scale optimization model, the server solves the model based on power operation constraint information. The server first reads the time-of-use electricity price, grid capacity fee standard, clean energy penetration rate requirement, curtailment rate policy limit, energy storage system power configuration requirement, energy storage capacity configuration requirement, and external power access capacity conditions for the target port location, and accordingly determines the clean energy penetration rate constraint, curtailment rate constraint, energy storage charging and discharging power constraint, energy storage capacity constraint, and external power access constraint. The server uses the clean energy penetration rate constraint to limit the proportion of clean energy supplied by wind power, photovoltaics, and other clean energy sources in the port's annual electricity consumption; the curtailment rate constraint to limit the proportion of excess clean energy output that cannot be absorbed; the energy storage charging and discharging power constraint and energy storage capacity constraint to limit the operating boundaries of the energy storage system during charging, discharging, and energy storage processes; and the external power access constraint to limit the maximum power and access capacity that the port can purchase from the external grid.

[0127] The server then performs a two-level loop search within the range of actual installed wind power and photovoltaic (PV) capacity to obtain multiple clean energy installation candidate schemes. Specifically, within the exploitable wind power capacity, the server sequentially selects multiple actual installed wind power capacities according to a preset step size. For each actual wind power capacity, within the exploitable PV capacity, the server sequentially selects multiple actual PV capacities according to a preset step size, so that each combination of actual wind power and actual PV capacity forms a clean energy installation candidate scheme. For example, the server combines 20MW wind power with 10MW PV, 20MW wind power with 15MW PV, and 25MW wind power with 10MW PV as different candidate schemes, and calculates the corresponding annual clean energy actual output curve and net load curve for each.

[0128] For each set of clean energy installation candidate schemes, the server searches for energy storage scale within the search ranges of energy storage power and energy storage capacity based on the net load curve, resulting in multiple sets of energy storage candidate schemes. The server identifies periods of excess and insufficient clean energy output from the net load curve, using the peak value of excess power as the reference upper limit for energy storage charging power configuration and the peak value of insufficient power as the reference upper limit for energy storage discharging power configuration, and determines the range of energy storage capacity values ​​by combining this with the continuous output time of energy storage. The server sequentially selects energy storage power within the energy storage power search range and sequentially selects energy storage capacity within the energy storage capacity search range, so that each set of energy storage power and capacity forms an energy storage candidate scheme.

[0129] The server performs hourly charge-discharge simulations for each energy storage candidate scheme throughout the year, obtaining the corresponding external power purchase curve, curtailment curve, and total port electricity cost. During periods of net load less than zero, the server controls the energy storage system to charge. The charging power is limited by both the energy storage charging power and the remaining energy storage capacity. Excess clean energy power exceeding the energy storage's absorption capacity is recorded as curtailment, forming a curtailment curve. During periods of net load greater than zero, the server controls the energy storage system to discharge. The discharge power is limited by both the energy storage discharge power and the current energy storage capacity. Any remaining load gap after energy storage discharge is purchased from the external grid, forming an external power purchase curve. The server calculates the total port electricity cost based on clean energy development costs, energy storage construction costs, energy storage operation costs, external power purchase costs, and grid capacity costs.

[0130] The server further eliminates candidate schemes that do not meet the constraints of clean energy penetration rate, curtailment rate, energy storage charging and discharging power, energy storage capacity, and external power access. For schemes with a clean energy penetration rate lower than the planning requirements, the server determines that the clean energy development is insufficient; for schemes with a curtailment rate higher than the policy limit, the server determines that the clean energy configuration is mismatched with the energy storage absorption capacity; for schemes with external power purchase peak exceeding the grid access capacity, the server determines that external power access is infeasible. The server sorts the remaining candidate schemes from low to high according to the total port electricity cost and selects the candidate scheme with the lowest total port electricity cost as the optimal scheme. The server outputs the actual installed capacity of wind power, actual installed capacity of photovoltaic power, energy storage power, energy storage capacity, annual external power purchase curve, curtailment curve, and maximum external power demand corresponding to the optimal scheme, thereby obtaining the clean energy development scale, energy storage configuration scale, and external power access scheme corresponding to the target port.

[0131] In one optional implementation, after obtaining the clean energy installed capacity, energy storage power, energy storage capacity and external power access capacity scheme corresponding to the target port, the server further performs robust verification processing on the clean power supply capacity configuration scheme under port operation disturbances and clean energy output fluctuations, so as to improve the adaptability of the clean power supply capacity configuration scheme in actual operation scenarios.

[0132] The server first acquires historical operational disturbance data and historical meteorological disturbance data for the target port and similar operational ports. The historical operational disturbance data includes the offset of actual vessel arrival time relative to planned arrival time, the offset of actual berthing duration relative to planned berthing duration, the offset of daily throughput by cargo type relative to planned throughput, the offset of shore power access time, the offset of electric vessel charging time, the offset of concentrated charging periods for mobile machinery, and the offset of start-up and shutdown periods for continuous terminal equipment. The historical meteorological disturbance data includes the deviation between predicted and actual wind speeds, the deviation between predicted and actual solar irradiance, ambient temperature deviations, and periods of reduced wind or solar power output due to extreme weather. The server performs statistical distribution analysis on the historical operational disturbance data and the historical meteorological disturbance data to obtain the distributions of vessel arrival disturbances, berthing duration disturbances, throughput disturbances, concentrated charging disturbances for mobile machinery, wind power output disturbances, and solar power output disturbances.

[0133] The server reconstructs the port's annual hourly energy load curve based on the ship arrival disturbance distribution, berthing duration disturbance distribution, and throughput disturbance distribution. Specifically, the server shifts the estimated arrival time in the original planned berthing ship event flow according to the ship arrival disturbance distribution, extends or shortens the berthing duration according to the berthing duration disturbance distribution, and corrects the daily throughput by cargo type according to the throughput disturbance distribution. Based on the corrected ship berthing event flow, the server regenerates the ship shore power load curve and the electric ship charging load curve, and regenerates the basic stable load curve and the intermittent charging load curve based on the corrected throughput by cargo type. The server overlays the regenerated ship shore power load curve, electric ship charging load curve, basic stable load curve, and intermittent charging load curve on a time axis to obtain the disturbed load curve. The server repeats this disturbance reconstruction multiple times to form a set of disturbed load curves.

[0134] The server reconstructs the standard output coefficient curves of clean energy based on the perturbation distributions of wind power and photovoltaic power output. The server corrects the original wind power standard output coefficient curve according to wind speed deviation, turbine power curve changes, and periods of extreme low wind speed, resulting in a perturbed wind power standard output coefficient curve. Similarly, the server corrects the original photovoltaic standard output coefficient curve according to irradiance deviation, module temperature deviation, and periods of continuous cloudy / rainy weather, resulting in a perturbed photovoltaic standard output coefficient curve. Based on the perturbed wind power standard output coefficient curves, the perturbed photovoltaic standard output coefficient curves, the actual installed capacity of wind power, and the actual installed capacity of photovoltaic power, the server obtains a perturbed clean energy output curve. The server repeats this clean energy output perturbation reconstruction multiple times, forming a set of perturbed clean energy output curves.

[0135] The server combines the set of disturbance load curves with the set of disturbance clean energy output curves to generate multiple operating pressure scenarios. Each operating pressure scenario includes one disturbance load curve and one disturbance clean energy output curve. The server calculates the disturbance net load curve for each operating pressure scenario and identifies continuous power shortage periods and continuous surplus periods within the disturbance net load curve. The continuous power shortage period represents a period where the disturbance load is consistently higher than the disturbance clean energy output, and the continuous surplus period represents a period where the disturbance clean energy output is consistently higher than the disturbance load. Based on the maximum power shortfall and cumulative power shortfall during the continuous power shortage period, the server determines the energy storage discharge power margin and energy storage capacity margin; based on the maximum excess power and cumulative excess power during the continuous surplus period, the server determines the energy storage charging power margin and clean energy consumption margin.

[0136] The server further constructs capacity robustness verification indicators. These indicators include the disturbance power supply gap rate, disturbance curtailment rate, number of energy storage energy overruns, number of external power access overruns, and critical period power supply margin. The disturbance power supply gap rate characterizes the proportion of port loads that cannot be met by energy storage and external power access under various operational pressure scenarios; the disturbance curtailment rate characterizes the proportion of clean energy output that cannot be absorbed by the load and energy storage under various operational pressure scenarios; the number of energy storage energy overruns characterizes the number of times energy storage capacity exceeds the upper limit or falls below the lower limit; the number of external power access overruns characterizes the number of times purchased power exceeds the external power access capacity; and the critical period power supply margin characterizes the power margin that the port power system still possesses when ships are concentrated berthing, mobile machinery is concentrated charging, and low wind and low solar power occur simultaneously.

[0137] The server compares the capacity robustness verification index with a preset robust operating threshold. When the capacity robustness verification index meets the preset robust operating threshold, the server determines the current clean power supply capacity configuration scheme as a robust capacity configuration scheme. When the capacity robustness verification index does not meet the preset robust operating threshold, the server determines the capacity correction direction based on the type of index that does not meet the threshold. When the disturbance power supply gap rate or the number of external power access violations exceeds the threshold, the server increases the energy storage discharge power, energy storage capacity, or external power access capacity; when the disturbance curtailment rate exceeds the threshold, the server increases the energy storage charging power, energy storage capacity, or reduces the clean energy installed capacity; when the power supply margin during critical periods is lower than the threshold, the server jointly corrects the actual wind power installed capacity, actual photovoltaic installed capacity, energy storage power, energy storage capacity, and external power access capacity. The server re-inputs the corrected capacity configuration scheme into the disturbance operating pressure scenario for verification until the capacity robustness verification index meets the preset robust operating threshold.

[0138] Through the above processing, the server can not only determine the initial capacity configuration scheme based on the annual hourly energy load curve and the clean energy standard output coefficient curve, but also robustly verify and correct the clean energy installed capacity, energy storage power, energy storage capacity and external power access capacity under actual disturbance scenarios such as ship arrival deviation, berthing time changes, throughput fluctuations, centralized charging of mobile machinery, wind power output decline and photovoltaic power output decline. This improves the power supply reliability and clean energy absorption stability of the port clean power supply system under complex operating conditions.

[0139] To more clearly describe the solutions provided in the embodiments of the present invention, a more detailed implementation method is provided below.

[0140] In one specific implementation, the port clean energy power supply system capacity collaborative configuration method is applied to the planning scenario of near-zero carbon clean energy development and utilization system for newly built or under-construction ports. The server acts as the execution entity, performing unified calculations on the target port's ship energy load, wharf energy load, clean energy development potential, energy storage configuration scale, and external power access scheme.

[0141] The server first acquires the port planning basic data corresponding to the target port. This port planning basic data includes ship-side production and operation information, terminal-side production and operation information, port planning and construction information, clean energy resource information, and power operation constraint information. The ship-side production and operation information includes ship type, length, beam, berthing terminal, arrival time, berthing time, and departure time from the ship's AIS data, as well as shore power usage time, shore power consumption, shore power power, and shore power capacity from the shore power information system. The terminal-side production and operation information includes terminal type, throughput by cargo type, port machinery, and the type, quantity, power, drive method, and energy consumption of horizontal transport vehicles and cargo trucks. The port machinery includes cranes, forklifts, straddle carriers, container gantry cranes, forklifts, belt conveyors, stacker-reclaimers, ship loaders, and ship unloaders. The port planning and construction information includes the target port's feasibility study report, preliminary design report, design throughput, berth class, planned ship type distribution, estimated ship traffic, estimated berthing time, planned operation organization method, and equipment configuration scheme. The clean energy resource information includes wind speed, air density, prevailing wind direction, solar irradiance, ambient temperature, wind speed, available wind power deployment area, and available photovoltaic installation area within and around the target port's red line. The power operation constraint information includes time-of-use pricing, grid capacity cost standards, clean energy penetration requirements, curtailment rate limits, energy storage construction costs, energy storage operation costs, and external power access capacity constraints.

[0142] The server predicts the shore power load of ships at the target port. For newly built or under-construction terminals, the server extracts each "ship berthing-shore power usage" record from historical data of operating terminals of similar cargo type and scale, treating a complete process of continuous shore power supply during ship berthing as a historical shore power usage event. The server constructs a shore power event feature vector X for each historical shore power usage event. This feature vector X includes ship tonnage, ship type code, ship length, ship width, berthing duration, unloading status, berth design tonnage, and daily cargo throughput by type. The server further extracts the active power time series from the shore power connection to the shore power disconnection period of the historical shore power usage event. And record the active power time series. maximum shore power Actual electricity consumption of shore power The server will store the active power time series of each historical shore power usage event. Based on the maximum power of shore power After normalization, the normalized shore load shape curve with values ​​ranging from 0 to 1 is obtained: .

[0143] The server analyzes multiple normalized shore power load shape curves. Clustering was performed to obtain a set of typical shore power load shape curves. This set includes load patterns such as continuous high load during container ship unloading, fluctuating load during intermittent bulk carrier operations, and high load at the beginning of berthing followed by a decrease. Each cluster center represents a typical normalized load curve. .

[0144] The server uses the shore power event feature vector The maximum power of the shore power and the actual electricity consumption of the shore power A shore power consumption prediction model is established. The server takes the shore power event feature vector X as input and the maximum shore power as input. and the actual electricity consumption of the shore power For the output, establish the maximum power prediction function. And the function for predicting total electricity consumption in a single event The server generates planned berthing vessel events based on the target port's planned sailing schedule, planned berth class, expected vessel type, expected berthing duration, and planned throughput. The server inputs the planned event features corresponding to each planned berthing vessel event into a shore power load shape classifier, and determines the matching target normalized shore power load shape curve from the set of typical shore power load shape curves. The planned event characteristics are then input into the shore power consumption prediction model to obtain the predicted maximum shore power. and forecast shore power consumption The server will predict the maximum shore power. The target normalized shore load shape curve Multiplying these together yields the initial shore power load curve: .

[0145] The server calculates the initial shore power load curve. Integrated battery charge during berthing duration and the integrated power The data is compared with the predicted shore power consumption E. The server then calculates the integrated power consumption. The difference between the predicted shore power consumption E and the shore power ratio correction factor is determined: .

[0146] The server uses the shore power ratio correction factor α to adjust the initial shore power load curve. By scaling the curve proportionally, the final load curve for a single shore power event is obtained: .

[0147] The server generates a yearly planned berthing event flow based on the expected vessel traffic and vessel type distribution at the target port, and also generates the load curves for all individual shore power events throughout the year. By aligning and overlaying the curves on a unified 8760-hour time axis, the shore power load curve for the target port can be obtained. .

[0148] The server further predicts the charging load of electric vessels at the target port. Based on port area electric vessel operation data and target port planning and construction information, the server determines the electric vessel charging parameters, including the rated capacity of the vessel's power battery. , ship berthing operation time, charging equipment output power and duration of a single charge The server approximates a single electric ship charging process as a constant power charging process, and calculates the charging time based on the rated capacity of the ship's power battery. and charging device output power Determine the theoretical charging duration: .

[0149] The server generates an electric vessel arrival event stream based on the target port's planned shipping schedule or on the vessel arrival frequency and type distribution of similar operating terminals. The electric vessel arrival event stream includes multiple electric vessel arrival events, each of which includes a vessel identifier and arrival time. berthing time and planned departure time The server will use the arrival time as the charging start time. .

[0150] The server is based on the theoretical charging duration. and planned departure time Determine the charging end time. At that time, the server will use the theoretical completion time of charging as the charging end time: . At that time, the server will use the scheduled departure time as the charging end time: .

[0151] The server determines the charging start time. Charging end time and charging device output power A rectangular power pulse is generated. The rectangular power pulse is... The power within the interval is The power is 0 at other times. The server superimposes the rectangular power pulses corresponding to multiple electric ship arrival events throughout the year onto a unified 8760-hour time axis to obtain the electric ship charging load curve. The server will display the ship's shore power load curve. and the electric ship charging load curve Adding them together, we obtain the ship energy load curve for the target port.

[0152] The server predicts the energy load on the terminal side of the target port. Based on the energy consumption patterns of the terminal operating equipment and the characteristics of grid interaction, the server classifies the terminal operating equipment into basic stable load equipment and intermittent charging load equipment. The basic stable load equipment includes stationary or semi-stationary equipment such as quay cranes, gantry cranes, rail-mounted cranes, belt conveyors, continuous ship loaders, continuous ship unloaders, and stacker-reclaimers, which are directly connected to the grid and operate continuously during operation. The intermittent charging load equipment includes mobile operating machinery such as tire yard cranes, straddle carriers, forklifts, forklifts, and electric trucks, which are equipped with power batteries and replenished through charging piles or battery swapping stations.

[0153] For basic stable load equipment, the server collects at least one full year's worth of production and energy consumption data from operating terminals of similar cargo type and scale. This includes daily or monthly throughput by cargo type, a list of basic stable equipment, equipment drive method, number of units, rated power per unit, and actual energy consumption records for the corresponding equipment. For electric equipment, the server directly reads the actual electricity consumption; for diesel, hydrogen-powered, or other non-electric driven equipment, the server calculates based on fuel consumption. Lower heating value of fuel and comprehensive efficiency conversion factor The fuel consumption of the non-electrically driven equipment is converted into equivalent electricity consumption. : .

[0154] in, This refers to the physical quantity of fuel consumption for a certain type of non-electrical equipment. The lower heating value of the fuel is... This is the overall efficiency conversion factor from fuel to effective operational output. The server sums the actual and equivalent power consumption of all operational terminal infrastructure stabilization equipment to obtain the total equivalent power consumption of the infrastructure stabilization equipment. The server uses throughput by cargo type as the independent variable and the total equivalent power consumption of basic stable equipment as the reference value. Establish a basic stable load regression model with the load as the dependent variable:

[0155] ,in, , , and For regression coefficients, , and For the corresponding cargo throughput, the server determines the daily cargo throughput for the planned year based on the target port's designed annual throughput and the daily average allocation coefficient for each cargo type. This daily cargo throughput is then input into the basic stable load regression model to obtain the daily basic stable electricity consumption E_base,day for the planned year. Based on the target port's planned operating hours, the server allocates the daily basic stable electricity consumption to the daily loading, unloading, yard transshipment, or continuous transport operating periods, and converts this into a daily basic stable power curve.

[0156] ,in, This represents the start time of the assignment. This represents the end time of the assignment. This represents a single workload duration. The server will display multiple daily baseline stable power curves throughout the year. By overlaying these curves onto a unified 8760-hour time axis, the basic stability load curve of the target port is obtained. .

[0157] For equipment with intermittent charging loads, the server collects information from similar operating terminals regarding mobile machinery type, drive mode, number of units, single-unit battery capacity, charging power, daily fuel consumption, and daily throughput by cargo type. The server converts the fuel consumption of diesel or other non-electrically driven mobile machinery into equivalent charging capacity at the battery end using a conversion factor based on the fuel's lower heating value and overall efficiency, and calculates the total daily equivalent charging capacity of mobile machinery at operating terminals. The server uses daily throughput by cargo type as the independent variable and the total daily equivalent charging capacity of mobile machinery as the statistical variable. Using [the variable] as the dependent variable, establish a regression model for intermittent charging power: ,in , , and is the regression coefficient.

[0158] The server calculates the total daily charging power for the planned year based on the daily throughput of different cargo types at the target port. The server collects historical power curves of charging piles from similar operational docks, calculates the total power of all charging piles every 15 minutes or every hour, and normalizes the typical daily charging power curve according to the maximum value to obtain the normalized daily charging power distribution shape curve. The server calculates the normalized daily charging power distribution shape curve. Intraday integral area : .

[0159] The server's daily total charging capacity is based on the planned year. The power scaling factor is determined by the intraday integral area A. : The server modifies the power scaling factor SC with the normalized daily charging power distribution shape curve. Multiply by each product to obtain the daily actual charging power curve: .

[0160] The server will display multiple daily actual charging power curves throughout the year. By overlaying the curves on a unified 8760-hour time axis, an intermittent charging load curve is obtained. The server will display the aforementioned basic stable load curve. and the intermittent charging load curve Adding them together, we obtain the energy load curve of the target port's terminal.

[0161] Please refer to the following: Figure 2 , Figure 2 This is a schematic diagram of the annual hourly energy load synthesis for a port, provided in an embodiment of the present invention. The server performs time-axis overlay processing on the ship energy load curve and the terminal energy load curve to obtain the annual hourly energy load curve for the port. The server uses 1 hour as the calculation resolution to include ship shore power load. Electric ship charging load Stable load of wharf foundation and intermittent charging load at the dock The load is uniformly mapped to 8760 time points throughout the year, and various loads at the same time point are summed to obtain the hourly power load demand of the target port throughout the year. : .

[0162] The port's annual hourly energy load curve It is used for subsequent clean energy output superposition, net load calculation, energy storage scale configuration and external power access capacity design.

[0163] Please refer to the following: Figure 3 , Figure 3This is a schematic diagram illustrating the spatial calculation of exploitable clean energy capacity provided in this embodiment of the invention. The server calculates the exploitable clean energy capacity and the standard output coefficient curve of clean energy corresponding to the target port based on clean energy resource information. For wind power resources, the server obtains wind speed, air density, and prevailing wind direction at different heights around the port based on global wind energy maps, historical data from nearby meteorological stations, and wind measurement data from the target port. The server determines wind turbine selection information based on the annual average wind speed and wind speed distribution characteristics, and deploys candidate wind turbine locations within the usable area around the port based on the wind turbine selection information, the prevailing wind direction turbine spacing requirements, the vertical prevailing wind direction turbine spacing requirements, and the available wind power deployment area. The server eliminates candidate wind turbine locations that conflict with waterways, loading and unloading areas, hazardous materials storage yards, safety control areas, and production operation areas, and counts the remaining number of installable wind turbines. and the number of installable fans With single-machine capacity Multiply by each other to obtain the exploitable wind power capacity. : .

[0164] The server determines the wind speed-power curve of the selected wind turbine. and hourly wind speed data throughout the year Calculate the theoretical output power of the wind turbine per hour, and divide the theoretical output power by the rated power of the wind turbine unit. The standard output coefficient curve of wind power was obtained. : .

[0165] For photovoltaic resources, the server extracts the net area suitable for photovoltaic installation based on the geographic information model of the target port. The net area available for photovoltaic installation This includes warehouse roofs, storage yard roofs, parking area roofs, office building roofs, and other usable areas that do not affect port production safety. The server calculates the exploitable photovoltaic capacity based on the unit area component capacity ρ_panel and the system installability factor η. : .

[0166] in, The capacity of components that can be installed per unit area. This is the system installability factor. The system installability factor reflects the impact of component tilt angle, front-to-back row spacing, maintenance access, obstructed areas, and structural load-bearing limitations on the actual installable capacity. The server is based on the hourly total horizontal irradiance data from NASA POWER, ERA5, or a local weather station throughout the year. Ambient temperature Calculate the operating temperature of photovoltaic modules using wind speed data. : .

[0167] in, The nominal operating temperature of the components. The server uses the component's peak power temperature coefficient. Standard test radiation Illuminance, standard test temperature and inverter efficiency Calculate the standard output factor curve of photovoltaics : .

[0168] The server will utilize the exploitable capacity of wind power. Installed capacity and exploitable photovoltaic capacity Wind power standard output coefficient curve and photovoltaic standard output factor curve As a result of clean energy resource calculations, the exploitable wind power capacity... Installed capacity and the aforementioned exploitable photovoltaic capacity Select the actual installed capacity of wind power within the range and actual installed capacity of photovoltaic At that time, take Server calculates actual clean energy output curve : .

[0169] The server constructs a source-grid-load-storage scale optimization model based on the exploitable clean energy capacity, the clean energy standard output coefficient curve, and the port's annual hourly energy load curve. The server then calculates the actual installed wind power capacity. Actual installed capacity of photovoltaic power Energy storage capacity and energy storage capacity Set the external power purchase curve as the variable to be optimized. Currency curtailment curve and total cost of electricity for ports Set as model output. Server in wind power exploitable capacity. Installed capacity and exploitable photovoltaic capacity Within the specified range, multiple sets of actual installed wind power capacity are generated according to a preset step size. and actual installed capacity of photovoltaic Combinations, in which: , For any given actual installed wind power capacity and actual installed capacity of photovoltaic The server calculated the actual output curve of clean energy over 8760 hours. The net load curve is obtained by subtracting the actual output curve of the clean energy source from the port's annual hourly energy load curve. The net load curve is generally expressed as follows: .

[0170] When the value is greater than 0, the server determines that the current output of clean energy is insufficient to meet the load demand, and it needs to be supplemented by energy storage discharge or power purchase from the external power grid. When the value is less than 0, the server determines that the current output of clean energy is excessive and needs to be absorbed by energy storage charging. The portion not absorbed by energy storage becomes abandoned electricity.

[0171] Server based on net load curve Determine energy storage capacity With energy storage capacity The search space. The server analyzes all negative periods in the net load curve and determines the maximum absolute value of excess power as the upper limit of energy storage charging power. : .

[0172] The server analyzes all positive periods in the net load curve and determines the maximum power deficit as the upper limit of energy storage discharge power. : .

[0173] The server will store energy power The search scope is set as follows: .

[0174] The server sets the energy storage capacity according to a preset number of hours of continuous power output from the energy storage system. The search range is defined, and multiple sets of energy storage candidate schemes are generated within the energy storage power search range and the energy storage capacity search range.

[0175] The server performs hourly charge-discharge simulations for each energy storage candidate scheme throughout the year. This is for periods of clean energy power surplus. The server controls the charging of the energy storage system, with a charging power of: .

[0176] For excess power that cannot be stored, the server records it as abandoned power. The server calculates the power abandonment rate. : .

[0177] The total power generation from clean energy is obtained through the actual output curve of clean energy. Calculated. Power curtailment rate. When policy limits are exceeded, the server will directly remove the corresponding operating condition. This applies during periods of insufficient clean energy power. The server controls the energy storage system to discharge, and the discharge power is: .

[0178] The energy gap that remains unfilled after the energy storage discharges is filled by purchasing electricity from the external power grid, which is recorded by the server as purchased electricity. And form an annual external power purchase curve. .

[0179] The server solves the source-grid-load-storage scale optimization model based on power operation constraints. The server determines constraints such as clean energy penetration rate, curtailment rate, energy storage charging and discharging power, energy storage capacity, and external power access. The server considers the actual installed capacity of wind power... and actual installed capacity of photovoltaic A double-layer loop search is performed within the range of values ​​to obtain multiple sets of clean energy installation candidate schemes. For each set of clean energy installation candidate schemes, the energy storage capacity is determined. and energy storage capacity Within the search scope, an energy storage scale search is performed to obtain multiple sets of energy storage candidate schemes. The server calculates the annual external electricity purchase cost for each set of energy storage candidate schemes. : .in, The price of electricity purchased from external sources. Peak power purchased from external power sources The standard is based on grid capacity costs. The server further calculates the total cost of electricity used at the port. : .

[0180] in, Represents the cost of developing clean energy, and the actual installed capacity of wind power. and actual installed capacity of photovoltaic Related; Represents the cost of purchasing external electricity; Represents energy storage cost, and energy storage capacity and energy storage capacity Related. The server eliminates candidate solutions that do not meet the constraints of clean energy penetration rate, curtailment rate, energy storage charging and discharging power, energy storage capacity, and external power access. For the remaining candidate solutions, the server calculates based on the total port electricity cost. Sort by cost from lowest to highest, and select the total cost of electricity used at the port. The candidate with the lowest value is selected as the optimal solution. The server outputs the actual installed wind power capacity corresponding to the optimal solution. Actual installed capacity of photovoltaic power Energy storage capacity Energy storage capacity Annual external power purchase curve The curtailment curve, maximum external power demand, and external power access capacity are used as the basis for determining the target port's clean energy development scale, energy storage configuration scale, and external power access scheme.

[0181] This invention provides a computer device 100, which includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device 100 executes the aforementioned capacity coordination configuration method for a port clean energy power supply system. Figure 4 As shown, Figure 4 This is a structural block diagram of a computer device 100 provided in an embodiment of the present invention. The computer device 100 includes a memory 111, a processor 112, and a communication unit 113. To enable data transmission or interaction, the memory 111, processor 112, and communication unit 113 are electrically connected to each other directly or indirectly. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. For illustrative purposes, the foregoing description has been made with reference to specific embodiments. However, the foregoing illustrative discussion is not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed. Numerous modifications and variations are possible in accordance with the foregoing teachings. These embodiments were chosen and described to best illustrate the principles of the present disclosure and its practical application, thereby enabling those skilled in the art to best utilize the present disclosure and to utilize various embodiments with different modifications to suit a particular intended application.

Claims

1. A method for coordinated capacity configuration of a port clean energy power supply system, characterized in that, The method includes: Obtain the basic port planning data corresponding to the target port. The basic port planning data includes ship-side production and operation information, terminal-side production and operation information, port planning and construction information, clean energy resource information, and power operation constraint information. Based on the ship-side production and operation information and the port planning and construction information, the ship energy load corresponding to the target port is predicted to obtain the ship energy load curve; Based on the terminal-side production and operation information and the port planning and construction information, the terminal energy load corresponding to the target port is predicted to obtain the terminal energy load curve. The port's annual hourly energy load curve is obtained by superimposing the ship energy load curve and the wharf energy load curve on a time axis. Calculate the exploitable clean energy capacity and standard clean energy output coefficient curve corresponding to the target port based on the clean energy resource information; Based on the exploitable capacity of clean energy, the standard output coefficient curve of clean energy, and the annual hourly energy load curve of the port, an optimization model for the scale of source, grid, load, and storage is constructed. The source-grid-load-storage scale optimization model is solved based on the power operation constraint information to obtain the clean energy development scale, energy storage configuration scale, and external power access scheme corresponding to the target port.

2. The method according to claim 1, characterized in that, The process of predicting the ship energy load corresponding to the target port based on the ship-side production and operation information and the port planning and construction information to obtain the ship energy load curve includes: Historical ship berthing shore power usage events are extracted from the ship-side production and operation information, and each historical ship berthing shore power usage event is used as a shore power load analysis unit. Based on the ship tonnage, ship type, ship length, ship width, berthing duration, berth design tonnage, and cargo throughput of each shore power load analysis unit, a shore power event feature vector is constructed. Extract the shore power active power time series, maximum shore power, and actual shore power consumption corresponding to each shore power load analysis unit; The shore power active power time series is normalized based on the maximum shore power to obtain a normalized shore power load shape curve. Clustering is performed on multiple normalized shore power load shape curves to obtain a set of typical shore power load shape curves. A shore power and electricity consumption prediction model is established based on the shore power event feature vector, the maximum power of the shore power, and the actual electricity consumption of the shore power. Based on the port planning and construction information, the set of typical shore power load shape curves, and the shore power consumption prediction model, the ship shore power load curve in the ship energy consumption load curve is generated.

3. The method according to claim 2, characterized in that, The step of generating the ship-to-shore power load curve from the ship energy load curve based on the port planning and construction information, the set of typical shore power load shape curves, and the shore power consumption prediction model includes: The planned berthing events are determined based on the port planning and construction information. The planned berthing events include the estimated arrival time, berthing time, departure time, vessel type, and vessel tonnage. The planned event features corresponding to the planned berthing events are input into the shore power load shape classifier, and the matching target normalized shore power load shape curve is determined from the set of typical shore power load shape curves. The planning event characteristics are input into the shore power power consumption prediction model to obtain the predicted maximum shore power and the predicted shore power consumption. An initial shore power load curve is generated based on the predicted maximum shore power and the target normalized shore power load shape curve. The shore power ratio correction factor is determined based on the difference between the integral power of the initial shore power load curve and the predicted shore power consumption. The initial shore power load curve is proportionally corrected according to the shore power ratio correction coefficient to obtain the load curve for a single shore power event. The shore power load curves of each individual shore power event corresponding to multiple planned berthing events throughout the year are superimposed on a unified time axis to obtain the ship shore power load curve.

4. The method according to claim 2, characterized in that, The step of predicting the ship energy load corresponding to the target port based on the ship-side production and operation information and the port planning and construction information to obtain the ship energy load curve also includes: The electric vessel charging parameters are determined based on the ship-side production and operation information and the port planning and construction information. The electric vessel charging parameters include the rated capacity of the ship's power battery, the ship's berthing operation time, the output power of the charging equipment, and the duration of a single charge. Based on the port planning and construction information, an electric vessel arrival event flow is generated. The electric vessel arrival event flow includes multiple electric vessel arrival events, and each electric vessel arrival event includes vessel identification, arrival time, berthing time and planned departure time. Based on the duration of a single charge and the planned departure time, determine the charging start time, charging end time, and actual charging amount for each electric vessel arrival event; Based on the charging start time, the charging end time, and the output power of the charging device, a rectangular power pulse is generated corresponding to each electric vessel arrival event. By superimposing the rectangular power pulses corresponding to multiple electric ship arrival events throughout the year on a unified time axis, the electric ship charging load curve in the ship energy load curve is obtained. The ship's energy load curve is obtained based on the ship's shore power load curve and the electric ship's charging load curve.

5. The method according to claim 1, characterized in that, The process of predicting the terminal energy load corresponding to the target port based on the terminal-side production and operation information and the port planning and construction information, to obtain the terminal energy load curve, includes: Based on the production and operation information at the wharf, the wharf operation equipment is divided into basic stable load equipment and intermittent charging load equipment. Obtain the equipment type, quantity, rated power of a single unit, actual power consumption, and fuel consumption of non-electric drive equipment corresponding to the basic stable load equipment; The equivalent power consumption of the basic stable load equipment is determined based on the fuel consumption of the non-electrically driven equipment, the lower heating value of the fuel, and the comprehensive efficiency conversion factor. A basic stable load regression model is established based on the equivalent electricity consumption and throughput by cargo type. Based on the port planning and construction information, the daily throughput of cargo types in the planning year is determined, and the daily throughput of cargo types in the planning year is input into the basic stable load regression model to obtain the daily basic stable electricity consumption in the planning year. Based on the planned annual daily basic stable electricity consumption and planned operating periods, a daily basic stable power curve is generated; By superimposing multiple daily basic stable power curves on a unified time axis, the basic stable load curve in the terminal energy load curve is obtained.

6. The method according to claim 5, characterized in that, The step of predicting the terminal energy load corresponding to the target port based on the terminal-side production and operation information and the port planning and construction information, and obtaining the terminal energy load curve, further includes: Obtain the equipment type, number of equipment, single-unit battery capacity, charging power, and mobile machinery fuel consumption corresponding to the intermittent charging load equipment; The equivalent charging capacity corresponding to the intermittent charging load equipment is determined based on the mobile machinery fuel consumption, fuel lower heating value, and comprehensive efficiency conversion factor. An intermittent charging capacity regression model is established based on the equivalent charging capacity and the throughput by cargo type. Based on the port planning and construction information, the daily throughput by cargo type for the planning year is determined, and the daily throughput by cargo type for the planning year is input into the intermittent charging power regression model to obtain the total daily charging power for the planning year. Based on the historical power curves of charging equipment at similar terminals, determine the normalized daily charging power distribution shape curve. Based on the total daily charging capacity of the planned year and the normalized daily charging power distribution shape curve, determine the actual daily charging power curve; By superimposing multiple daily actual charging power curves on a unified time axis, the intermittent charging load curve in the terminal energy load curve is obtained. The terminal energy load curve is obtained based on the basic stable load curve and the intermittent charging load curve.

7. The method according to claim 1, characterized in that, The calculation of the exploitable clean energy capacity and standard output coefficient curve of the target port based on the clean energy resource information includes: Based on the clean energy resource information, obtain wind resource data, photovoltaic resource data, and deployable area data of the available area around the target port; Based on the wind resource data, wind turbine selection information is determined, and based on the wind turbine selection information, wind turbine spacing requirements, and the deployable area data, the exploitable wind power capacity is determined. Based on the wind resource data and the wind turbine selection information, calculate the wind power standard output coefficient curve; Based on the photovoltaic resource data, the net area of ​​installable photovoltaics, the module capacity per unit area, and the system installability coefficient, the exploitable photovoltaic capacity is determined. Based on the photovoltaic resource data, module temperature parameters, module peak power temperature coefficient, and inverter efficiency, calculate the photovoltaic standard output coefficient curve; Based on the exploitable wind power capacity, the exploitable photovoltaic capacity, the standard output coefficient curve of wind power, and the standard output coefficient curve of photovoltaic power, the exploitable clean energy capacity and the standard output coefficient curve of clean energy are obtained.

8. The method according to claim 1, characterized in that, The step of constructing a source-grid-load-storage scale optimization model based on the exploitable capacity of clean energy, the standard output coefficient curve of clean energy, and the annual hourly energy load curve of the port includes: Within the defined limits of the exploitable capacity of clean energy, the actual installed capacity of wind power and the actual installed capacity of photovoltaic power are set as optimization variables for clean energy installation. Calculate the actual power output curve of clean energy based on the actual installed capacity of wind power, the actual installed capacity of photovoltaic power, and the standard power output coefficient curve of clean energy. Calculate the net load curve based on the port's annual hourly energy load curve and the actual output curve of the clean energy source. The search range for energy storage power and energy storage capacity is determined based on the net load curve, and energy storage power and energy storage capacity are set as energy storage optimization variables. Based on the clean energy installed capacity optimization variables, the energy storage optimization variables, the external power purchase curve, the power curtailment curve, and the total cost of port electricity consumption, the source-grid-load-storage scale optimization model is constructed.

9. The method according to claim 8, characterized in that, The step of solving the source-grid-load-storage scale optimization model based on the power operation constraint information to obtain the clean energy development scale, energy storage configuration scale, and external power access scheme corresponding to the target port includes: Based on the power operation constraint information, constraints on clean energy penetration rate, curtailment rate, energy storage charging and discharging power, energy storage capacity, and external power access are determined. A two-layer cyclic search is performed within the range of the actual installed capacity of wind power and the actual installed capacity of photovoltaic power to obtain multiple sets of clean energy installation candidate schemes; For each set of clean energy installation candidate schemes, energy storage scale search is performed based on the net load curve within the energy storage power search range and the energy storage capacity search range to obtain multiple sets of energy storage candidate schemes; For each group of energy storage candidate schemes, a year-round hourly charge-discharge simulation was performed to obtain the corresponding external power purchase curve, power curtailment curve, and total port electricity cost. Candidate schemes that do not meet the constraints of clean energy penetration rate, curtailment rate, energy storage charging and discharging power, energy storage capacity, and external power access are eliminated. The candidate scheme with the lowest total cost of electricity consumption at the port is selected from the remaining candidate schemes to obtain the clean energy development scale, the energy storage configuration scale, and the external power access scheme.

10. A capacity coordination configuration system for a port clean energy power supply system, characterized in that, Includes at least one service node; The service node includes a storage unit and a computing unit; the storage unit is used to store program code; the computing unit is used to run the program code to execute the port clean energy power supply system capacity collaborative configuration method according to any one of claims 1 to 9.

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