Method and device for constructing and quantifying shore power virtual power plant under air-river combined transportation

Abstract

The invention relates to a shore power virtual power plant construction and quantification method and device under air-river combined transportation, and the method comprises the steps: obtaining an intra-day flight cargo information list, and the energy storage initial state and multi-source environment information of an adjustable ship at a port in a day-ahead stage, predicting a peak regulation demand window and a spot market price fluctuation interval of the power system of the next day based on the historical operation data; constructing a ship charging-departure time sequence coupling model fusing energy flow-logistics-environment data; for uncertain disturbance factors in actual operation, constructing a disturbance scene set and introducing a probability-robustness hybrid modeling method to correct a time sequence model; a shore power virtual power plant response strategy optimization model comprehensively considering marketization income and new energy consumption capability is constructed, and the economical efficiency and environmental protection of a shore power virtual power plant construction scheme are improved by fully utilizing a logistics full-link energy consumption space-time adjustable characteristic in a harbor district. And the adjustable capability quantification and response strategy generation of the shore power virtual power plant under the air-river combined transportation background are realized.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method and device for constructing and quantifying a shore power virtual power plant under air-river combined transport. Background Art

[0002] As key nodes of energy consumption and transportation hubs, ports are accelerating their transformation toward intelligent and low-carbon development. Air-river intermodal transport, a new model for port logistics organization, integrates the advantages of aviation and water transport to effectively improve the efficiency and economy of cargo transportation. A shore-based virtual power plant is a typical example of a deeply coupled energy flow and logistics system. The energy flow system provides energy supply to ships through the shore power interface, while the logistics system arranges ship dispatch based on flight and cargo demand. The two work closely together in time and space, ensuring that the shore-based virtual power plant can respond to grid control commands in an orderly manner while meeting shipping needs, achieving both economic and environmental benefits.

[0003] Currently, shore-based virtual power plants (VPPs) primarily operate on the principle of "low charge, high discharge" arbitrage, failing to fully consider the rigid logistics constraints and complex time-varying nature of ship scheduling. In the context of air-river intermodal transport, logistics information exhibits strong temporal dependencies. The matching degree of cargo arrival time, tonnage, transport distance, and meteorological conditions is strongly coupled with the ship's battery capacity. Ignoring these complex, nonlinear relationships will severely impact the VPP's ability to adjust and respond accurately in the electricity market.

[0004] In summary, existing technologies fail to fully consider the rigid logistics constraints and complex time-varying nature of ship scheduling, which greatly affects the adjustable ability and response accuracy of shore power virtual power plants participating in the power market, and urgently need to be resolved. Summary of the Invention

[0005] This application provides a method and device for constructing and quantifying a shore power virtual power plant under air-river intermodal transport, so as to solve the problems that the existing technology fails to fully consider the rigid constraints and complex time-varying nature of logistics in ship scheduling.

[0006] The first embodiment of the present application provides a method for constructing and quantifying a shore power virtual power plant under air-river intermodal transport, comprising the following steps: obtaining a list of intraday flight cargo information of the target port in the day-ahead stage, the initial state of energy storage of adjustable ships at the port, and multi-source environmental information, and based on the intraday flight cargo information list, the initial state of energy storage and the multi-source environmental information, predicting the peak demand window of the target power system and the spot market price fluctuation range of the next day; based on the peak demand window of the target power system and the spot market price fluctuation range of the next day, constructing a ship timing coupling model to obtain the peak demand window of the target power system and the spot market price fluctuation range of the next day through the ship timing coupling model. The coupling model characterizes the deep coupling characteristics of power configuration and transportation scheduling on the time scale; obtains the uncertain disturbance factors in actual operation, and constructs a disturbance scenario set based on the deep coupling characteristics and the uncertain disturbance factors, and corrects the ship timing coupling model according to the disturbance scenario set and the preset probability-robust hybrid modeling strategy; based on the corrected ship timing coupling model, constructs a shore power virtual power plant response strategy optimization model to construct a shore power virtual power plant through the shore power virtual power plant response strategy optimization model, and generates the adjustable capacity quantification and response strategy of the shore power virtual power plant.

[0007] Optionally, in one embodiment of the present application, the obtaining of the intraday flight cargo information list of the target port in the day-ahead stage, the energy storage initial state of the adjustable ships at the port and the multi-source environmental information, and based on the intraday flight cargo information list, the energy storage initial state and the multi-source environmental information, predicting the peak demand window of the target power system and the spot market price fluctuation range of the next day, includes: obtaining the cargo tonnage, cargo arrival time, cargo planned departure time, cargo transportation mileage and cargo quantity of the target port in the day-ahead stage, and determining the intraday flight cargo information list by the cargo tonnage, the cargo arrival time, the cargo planned departure time, the cargo transportation mileage and the cargo quantity; obtaining the target The energy storage battery capacity, maximum shipping mileage and number of available ships of the power system are used, and the initial energy storage state of the adjustable ships in the port is determined based on the energy storage battery capacity, the maximum shipping mileage and the number of available ships; the wind speed data, wind direction data, water flow speed data and water level data corresponding to the target port are obtained to construct the multi-source environmental information through the wind speed data, the wind direction data, the water flow speed data and the water level data; the historical operation data of the target power market are collected, and based on the intraday flight cargo information list, the initial energy storage state, the multi-source environmental information and the historical operation data, the peak-shaving demand window of the target power system for the next day and the price fluctuation range of the spot market are predicted.

[0008] Optionally, in one embodiment of the present application, the ship timing coupling model is constructed based on the peak-shaving demand window of the next day target power system and the spot market price fluctuation range, including: constructing a nonlinear ship energy consumption model based on the intraday flight cargo information list, the energy storage initial state and the multi-source environmental information; obtaining the ship energy storage charge and discharge amount and the actual departure time of the ship in the target port, and determining the ship energy storage dynamic equation based on the ship energy storage charge and discharge amount, the actual departure time of the ship and the nonlinear ship energy consumption model; determining the safety redundancy coefficient of the target port, and obtaining the ship type and the energy storage capacity corresponding to different types of ships in the target port, so as to construct corresponding energy storage capacity constraints and charge and discharge power constraints corresponding to the different types of ships according to the ship energy storage charge and discharge amount, the energy storage capacity, the safety redundancy coefficient and the nonlinear ship energy consumption model, and establish the ship timing coupling model based on the energy storage capacity constraints and the charge and discharge power constraints.

[0009] Optionally, in one embodiment of the present application, the uncertain disturbance factors in actual operation are obtained, and a disturbance scenario set is constructed based on the deep coupling characteristics and the uncertain disturbance factors, and the ship timing coupling model is corrected according to the disturbance scenario set and a preset probability-robust hybrid modeling strategy, including: determining the arrival time deviation parameters and wind speed and direction deviation parameters corresponding to the target port, so as to construct a disturbance parameter set according to the arrival time deviation parameters and the wind speed and direction deviation parameters; based on a preset quantile strategy, generating multiple representative scenarios corresponding to the disturbance parameter set, so as to construct the disturbance scenario set through the multiple representative scenarios; calculating the generation probability corresponding to each representative scenario in the disturbance scenario set, so as to construct a corresponding probability scenario set through the generation probability of each representative scenario; based on the probability scenario set, performing marginal relaxation operation on the uncertain disturbance factors to correct the ship timing coupling model.

[0010] Optionally, in one embodiment of the present application, the mathematical expression of the shore power virtual power plant response strategy optimization model is:

[0011]

[0012] Among them, C cost Charging cost for ship energy storage; B peak The profit of shore power virtual power plant participating in peak load regulation market; B spot The profit of shore power virtual power plant participating in spot market; C delay Penalty cost for logistics delay; A newenergy is the amount of new energy consumed; μ is the basic penalty per unit tonnage; τ is the time sensitivity coefficient.

[0013] The second embodiment of the present application provides a device for constructing and quantifying a shore power virtual power plant under air-river intermodal transport, including: a prediction module for obtaining a list of intraday flight cargo information of a target port in the day-ahead stage, an initial state of energy storage of adjustable ships at the port, and multi-source environmental information, and based on the intraday flight cargo information list, the initial state of energy storage, and the multi-source environmental information, predicting the peak-shaving demand window of the target power system and the price fluctuation range of the spot market on the next day; a modeling module for constructing a ship timing coupling model based on the peak-shaving demand window of the target power system and the price fluctuation range of the spot market on the next day, so as to predict the peak-shaving demand window of the target power system and the price fluctuation range of the spot market on the next day through the ship timing coupling. The combined model characterizes the deep coupling characteristics of power configuration and transportation scheduling on the time scale; the correction module is used to obtain the uncertain disturbance factors in actual operation, and based on the deep coupling characteristics and the uncertain disturbance factors, construct a disturbance scenario set, and correct the ship timing coupling model according to the disturbance scenario set and the preset probability-robust hybrid modeling strategy; the generation module is used to construct a shore power virtual power plant response strategy optimization model based on the corrected ship timing coupling model, so as to construct a shore power virtual power plant through the shore power virtual power plant response strategy optimization model, and generate the adjustable capacity quantification and response strategy of the shore power virtual power plant.

[0014] Optionally, in one embodiment of the present application, the prediction module includes: a first acquisition unit for acquiring the cargo tonnage, cargo arrival time, cargo planned departure time, cargo transportation mileage and cargo quantity of the target port in the previous day period, and determining the intraday flight cargo information list through the cargo tonnage, cargo arrival time, cargo planned departure time, cargo transportation mileage and cargo quantity; a second acquisition unit for acquiring the energy storage battery capacity, maximum shipping mileage and available number of ships of the target power system, and determining the intraday flight cargo information list based on the energy storage battery capacity, maximum shipping mileage and available number of ships. The number of ships is used to determine the initial state of energy storage of the adjustable ships in the port; a third acquisition unit is used to obtain wind speed data, wind direction data, water flow speed data and water level data corresponding to the target port, so as to construct the multi-source environmental information through the wind speed data, the wind direction data, the water flow speed data and the water level data; an acquisition unit is used to collect historical operation data of the target power market, and based on the intraday flight cargo information list, the initial state of energy storage, the multi-source environmental information and the historical operation data, predict the peak demand window of the target power system for the next day and the price fluctuation range of the spot market.

[0015] Optionally, in one embodiment of the present application, the modeling module includes: a first construction unit, used to construct a nonlinear ship energy consumption model based on the intraday flight cargo information list, the energy storage initial state and the multi-source environmental information; a determination unit, used to obtain the ship energy storage charge and discharge amount and the actual departure time of the ship in the target port, and determine the ship energy storage dynamic equation based on the ship energy storage charge and discharge amount, the actual departure time of the ship and the nonlinear ship energy consumption model; an establishment unit, used to determine the safety redundancy coefficient of the target port, and obtain the ship type and the energy storage capacity corresponding to different types of ships in the target port, so as to construct corresponding energy storage capacity constraints and charge and discharge power constraints corresponding to the different types of ships according to the ship energy storage charge and discharge amount, the energy storage capacity, the safety redundancy coefficient and the nonlinear ship energy consumption model, and establish the ship time series coupling model based on the energy storage capacity constraints and the charge and discharge power constraints.

[0016] Optionally, in one embodiment of the present application, the correction module includes: a second construction unit, used to determine the arrival time deviation parameter and wind speed and direction deviation parameter corresponding to the target port, so as to construct a disturbance parameter set based on the arrival time deviation parameter and the wind speed and direction deviation parameter; a third construction unit, used to generate a plurality of representative scenarios corresponding to the disturbance parameter set based on a preset quantile strategy, so as to construct the disturbance scenario set through the plurality of representative scenarios; a calculation unit, used to calculate the generation probability corresponding to each representative scenario in the disturbance scenario set, so as to construct a corresponding probability scenario set through the generation probability of each representative scenario; a marginal relaxation unit, used to perform a marginal relaxation operation on the uncertainty disturbance factor based on the probability scenario set, so as to correct the ship timing coupling model.

[0017] Optionally, in one embodiment of the present application, the mathematical expression of the shore power virtual power plant response strategy optimization model is:

[0018]

[0019] Among them, C cost Charging cost for ship energy storage; B peak The profit of shore power virtual power plant participating in peak load regulation market; B spot The profit of shore power virtual power plant participating in spot market; C delay Penalty cost for logistics delay; A newenergy is the amount of new energy consumed; μ is the basic penalty per unit tonnage; τ is the time sensitivity coefficient.

[0020] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method for constructing and quantifying a virtual power plant for shore power supply under air-river intermodal transport as described in the above embodiment.

[0021] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned method for constructing and quantifying a shore power virtual power plant under air-river intermodal transport.

[0022] The fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned method for constructing and quantifying a shore power virtual power plant under air-river intermodal transport.

[0023] Therefore, the embodiments of the present application have the following beneficial effects:

[0024] The embodiments of the present application can obtain the intraday flight cargo information list of the target port in the day-ahead stage, the initial state of energy storage of adjustable ships in the port, and multi-source environmental information, and based on the intraday flight cargo information list, the initial state of energy storage and multi-source environmental information, predict the peak-shaving demand window of the target power system and the spot market price fluctuation range of the next day; based on the peak-shaving demand window of the target power system and the spot market price fluctuation range of the next day, construct a ship timing coupling model to characterize the deep coupling characteristics of power configuration and transportation scheduling on the time scale through the ship timing coupling model; obtain the uncertainty disturbance factors in actual operation, and based on the deep coupling characteristics and the uncertainty disturbance factors, construct a disturbance scenario set, and correct the ship timing coupling model according to the disturbance scenario set and the preset probability-robust hybrid modeling strategy; based on the corrected ship timing coupling model, construct a shore power virtual power plant response strategy optimization model to construct a shore power virtual power plant through the shore power virtual power plant response strategy optimization model, and generate the adjustable capacity quantification and response strategy of the shore power virtual power plant. This application leverages the spatiotemporal scalability of energy consumption across all aspects of port logistics to improve the economic and environmental efficiency of shore-based virtual power plant construction. It also quantifies the scalability of shore-based virtual power plants and generates response strategies for air-river intermodal transport. This addresses the challenges of existing technologies that fail to fully account for the rigid logistics constraints and complex time-varying nature of ship scheduling.

[0025] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0027] Figure 1 This is a flow chart of a method for constructing and quantifying a shore power virtual power plant under air-river intermodal transport according to an embodiment of the present application;

[0028] Figure 2 This is an example diagram of a device for constructing and quantifying a shore power virtual power plant for air-river combined transport according to an embodiment of the present application;

[0029] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0030] Among them, 10-air-river combined transport shore power virtual power plant construction and quantification device; 100-prediction module, 200-modeling module, 300-correction module, 400-generation module; 301-memory, 302-processor, 303-communication interface. DETAILED DESCRIPTION

[0031] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0032] The following describes the method and device for constructing and quantifying a virtual power plant for shore power supply under air-river intermodal transport according to an embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a method for constructing and quantifying a virtual power plant for shore power supply under air-river intermodal transport. In this method, by obtaining the intraday flight cargo information list of the target port in the day-ahead stage, the initial state of energy storage of the adjustable ships at the port and multi-source environmental information, and based on the intraday flight cargo information list, the initial state of energy storage and multi-source environmental information, the peak-shaving demand window of the target power system for the next day and the price fluctuation range of the spot market are predicted; based on the peak-shaving demand window of the target power system for the next day and the price fluctuation range of the spot market, a ship time series coupling model is constructed to The deep coupling characteristics of power configuration and transportation scheduling on the time scale are characterized by the ship timing coupling model; the uncertainty disturbance factors in actual operation are obtained, and based on the deep coupling characteristics and the uncertainty disturbance factors, a disturbance scenario set is constructed, and the ship timing coupling model is corrected according to the disturbance scenario set and the preset probability-robust hybrid modeling strategy; based on the corrected ship timing coupling model, a shore power virtual power plant response strategy optimization model is constructed to construct a shore power virtual power plant through the shore power virtual power plant response strategy optimization model, and generate the adjustable capacity quantification and response strategy of the shore power virtual power plant. This application can make full use of the spatiotemporal adjustable energy consumption characteristics of all links in the port logistics to improve the economy and environmental protection of the shore power virtual power plant construction plan, and realize the adjustable capacity quantification and response strategy generation of the shore power virtual power plant in the context of air-river transport. Therefore, it solves the problem that the existing technology fails to fully consider the logistics rigid constraints and complex time-varying nature of ship scheduling.

[0033] Specifically, Figure 1 This is a flow chart of a method for constructing and quantifying a shore power virtual power plant under air-river combined transport provided in an embodiment of the present application.

[0034] like Figure 1 As shown in FIG, the construction and quantification method of the shore power virtual power plant under the air-river intermodal transport includes the following steps:

[0035] In step S101, the intraday flight cargo information list of the target port in the day-ahead phase, the initial energy storage status of the adjustable ships at the port, and the multi-source environmental information are obtained. Based on the intraday flight cargo information list, the initial energy storage status, and the multi-source environmental information, the peak demand window of the target power system and the spot market price fluctuation range for the next day are predicted.

[0036] The embodiment of the present application can first obtain a list of intraday flight cargo information, the initial energy storage status of adjustable ships in port, and multi-source environmental information (including meteorological and hydrological variables such as wind speed, wind direction, and water flow) in the day-ahead stage, and combine it with historical operating data to predict the peak demand window of the power system and the spot market price fluctuation range for the next day.

[0037] Optionally, in one embodiment of the present application, a list of intraday flight cargo information of the target port in the day-ahead phase, the initial state of energy storage of adjustable ships at the port, and multi-source environmental information are obtained, and based on the list of intraday flight cargo information, the initial state of energy storage, and the multi-source environmental information, the peak-shaving demand window of the target power system and the spot market price fluctuation range of the next day are predicted, including: obtaining the cargo tonnage, cargo arrival time, planned cargo departure time, cargo transportation mileage, and cargo quantity of the target port in the day-ahead phase, and determining the list of intraday flight cargo information according to the cargo tonnage, cargo arrival time, planned cargo departure time, cargo transportation mileage, and cargo quantity; Obtain the energy storage battery capacity, maximum shipping mileage and number of available ships of the target power system, and determine the initial energy storage state of the adjustable ships in the port based on the energy storage battery capacity, maximum shipping mileage and number of available ships; obtain the wind speed data, wind direction data, water flow speed data and water level data corresponding to the target port, and construct multi-source environmental information through wind speed data, wind direction data, water flow speed data and water level data; collect historical operation data of the target power market, and based on the intraday flight cargo information list, the initial energy storage state, multi-source environmental information and historical operation data, predict the peak demand window of the target power system and the spot market price fluctuation range for the next day.

[0038] Specifically, the process of multi-source data collection and market price signal prediction in the embodiment of the present application is as follows:

[0039] 1. Get logistics data (flight cargo list), the mathematical expression is as follows:

[0040]

[0041] Among them, W m is the cargo tonnage; The time when the goods arrive at the port; Plan the departure time for the goods; D m is the cargo transportation mileage; M is the cargo quantity.

[0042] 2. Obtain energy flow data (data of ships in port), the mathematical expression of which is as follows:

[0043]

[0044] in, is the energy storage battery capacity; is the maximum shipping mileage; k is the number of available ships.

[0045] 3. Afterwards, the embodiment of the present application needs to collect environmental data (meteorological and hydrological data), the mathematical expression of which is:

[0046] ε={(v wind ,θ wind,v water ,h water )}

[0047] Among them, v wind is the wind speed; θ wind is the wind direction; v water is the water flow velocity; h water For the water level.

[0048] 4. The embodiments of the present application are based on artificial intelligence methods such as data-driven and combined with historical information of the electricity market to predict peak demand periods, peak demand and spot market price signals, as shown in the following formula:

[0049]

[0050] in, For peak demand period; is the peak-shaving market price; is the spot market price.

[0051] Therefore, the embodiment of the present application obtains the intraday flight information list, meteorological data and the initial battery capacity of the ships available in the port to predict the price signals of power peak regulation and spot market, thereby providing reliable data support for the construction of the ship timing coupling model.

[0052] In step S102, based on the peak demand window of the target power system for the next day and the price fluctuation range of the spot market, a ship timing coupling model is constructed to characterize the deep coupling characteristics of power configuration and transportation scheduling on a time scale through the ship timing coupling model.

[0053] Furthermore, the embodiments of the present application also need to construct a ship charging-departure timing coupling model that integrates energy flow, logistics, and environmental data based on the peak demand window of the next day's target power system and the price fluctuation range of the spot market, so as to characterize the deep coupling characteristics of power configuration and transportation scheduling on a time scale.

[0054] Optionally, in one embodiment of the present application, a ship timing coupling model is constructed based on the peak-shaving demand window of the target power system for the next day and the price fluctuation range of the spot market, including: constructing a nonlinear ship energy consumption model based on the intraday flight cargo information list, the initial state of the energy storage and the multi-source environmental information; obtaining the ship energy storage charge and discharge amount and the actual departure time of the ship in the target port, and determining the ship energy storage dynamic equation based on the ship energy storage charge and discharge amount, the actual departure time of the ship and the nonlinear ship energy consumption model; determining the safety redundancy coefficient of the target port, and obtaining the ship type and the energy storage capacity corresponding to different types of ships in the target port, so as to construct corresponding energy storage capacity constraints and charge and discharge power constraints corresponding to different types of ships according to the ship energy storage charge and discharge amount, energy storage capacity, safety redundancy coefficient and nonlinear ship energy consumption model, and establishing a ship timing coupling model based on the energy storage capacity constraint and the charge and discharge power constraint.

[0055] In the actual implementation process, the process of constructing the ship timing coupling model in the embodiment of the present application is as follows:

[0056] 1. Construct a nonlinear energy consumption model:

[0057] The embodiment of the present application can construct a nonlinear ship energy consumption model based on energy flow-logistics-environmental data, as shown in the following formula:

[0058]

[0059] Where σ represents the nonlinear energy consumption under heavy load; ρ air is the air density; C d,m is the drag coefficient; α, β, γ are energy consumption coefficients, which can be obtained by fitting historical data.

[0060] (2) Establish the dynamic equation of ship energy storage:

[0061] In the embodiment of the present application, a ship energy storage dynamic equation can be constructed to describe the state of the ship energy storage to ensure that the ship energy storage has sufficient power to smoothly transport the adapted cargo to the destination when the ship leaves the port. The ship energy storage dynamic equation is shown as follows:

[0062]

[0063] in, and Indicates the charge and discharge amount of the ship's energy storage at time t; is an indicator function, indicating that the ship starts to consume energy storage power after leaving the port; S represents a set of data scenarios, and each scenario s corresponds to a set of environmental data; Indicates the actual departure time of the ship. Here, the planned departure time needs to be relaxed, as shown in the following formula:

[0064]

[0065] in, The maximum permissible delay time for a ship.

[0066] (3) Safety redundancy and matching constraints:

[0067] In addition, the embodiment of the present application can determine the safety redundancy coefficient ξ k The safety redundancy factor can usually be set to 10%. The specific value can be adjusted up or down based on the port's historical risk data. It means that when a ship leaves the port, the energy storage capacity must cover the total energy consumption of cargo transportation (including safety redundancy) to avoid power shortages caused by sudden environmental disturbances, thereby improving the robustness of the model.

[0068] In order to better describe the cargo and ship matching process, the embodiment of the present application may introduce an auxiliary variable x k,m , thus, we can get the following formula:

[0069]

[0070] Among them, x k,m =1 Cargo m selects ship type k; the energy storage capacity of ship k is at this time, The following constraints must be met:

[0071]

[0072] The charging and discharging power constraints of different ship types of energy storage are:

[0073]

[0074] Therefore, the embodiments of the present application can construct a ship time-series coupling model that takes into account the fusion of multi-source data of energy flow, logistics and environment, so as to fully utilize the temporal and spatial adjustable characteristics of energy consumption in all links of logistics in the port area to improve the economy and environmental protection of the shore power virtual power plant construction plan.

[0075] In step S103, the uncertainty disturbance factors in actual operation are obtained, and based on the deep coupling characteristics and the uncertainty disturbance factors, a disturbance scenario set is constructed, and the ship time series coupling model is corrected according to the disturbance scenario set and the preset probability-robust hybrid modeling strategy.

[0076] Afterwards, the embodiments of the present application also need to construct a disturbance scenario set for the uncertain disturbance factors in actual operation, such as the ship arrival punctuality rate, meteorological parameter prediction error, etc., and introduce a probability-robust hybrid modeling strategy to correct the ship timing coupling model.

[0077] Optionally, in one embodiment of the present application, uncertain disturbance factors in actual operation are obtained, and based on the deep coupling characteristics and the uncertain disturbance factors, a disturbance scenario set is constructed, and the ship timing coupling model is corrected according to the disturbance scenario set and the preset probability-robust hybrid modeling strategy, including: determining the arrival time deviation parameters and wind speed and direction deviation parameters corresponding to the target port, so as to construct a disturbance parameter set according to the arrival time deviation parameters and the wind speed and direction deviation parameters; based on the preset quantile strategy, generating multiple representative scenarios corresponding to the disturbance parameter set, so as to construct a disturbance scenario set through multiple representative scenarios; calculating the generation probability corresponding to each representative scenario in the disturbance scenario set, so as to construct a corresponding probability scenario set through the generation probability of each representative scenario; based on the probability scenario set, performing marginal relaxation operations on the uncertain disturbance factors to correct the ship timing coupling model.

[0078] It should be noted that the process of generating disturbance scenarios and performing probability-robust correction in the embodiment of the present application is as follows:

[0079] (1) The embodiment of the present application may first define the following set of disturbance parameters:

[0080]

[0081] (2) Based on the above set of disturbance parameters, the embodiment of the present application can generate several representative scenarios through the quantile method and calculate their probabilities:

[0082]

[0083] in, represents the delay deviation of ship k in scenario s (deviation of departure time caused by late arrival); Represents the energy consumption deviation, and the generation probability of each scenario is:

[0084]

[0085] (3) Based on the above set of probability scenarios, “marginal relaxation” is performed on the uncertainty variables to modify the ship time series model to ensure the robustness of the model:

[0086]

[0087] Therefore, the embodiments of the present application construct a set of disturbance scenarios and introduce a probabilistic-robust hybrid modeling strategy to correct the timing model, which not only improves the robustness of the model, but also provides solid data guidance and basis for the construction of the subsequent shore power virtual power plant response strategy optimization model.

[0088] In step S104, based on the modified ship timing coupling model, a shore power virtual power plant response strategy optimization model is constructed to construct a shore power virtual power plant through the shore power virtual power plant response strategy optimization model, and generate the adjustable capacity quantification and response strategy of the shore power virtual power plant.

[0089] Furthermore, the embodiments of the present application also need to construct a shore power virtual power plant response strategy optimization model that comprehensively considers market benefits and new energy absorption capacity, so as to fully utilize the spatiotemporal and temporal adjustable characteristics of energy consumption in all links of logistics within the port area to improve the economy and environmental protection of the shore power virtual power plant construction plan, thereby realizing the quantification of the flexible adjustable capacity and response strategy generation of the shore power virtual power plant in the context of air-river transport.

[0090] Optionally, in one embodiment of the present application, the mathematical expression of the shore power virtual power plant response strategy optimization model is:

[0091]

[0092] Among them, C cost Charging cost for ship energy storage; B peak The profit of shore power virtual power plant participating in peak load regulation market; B spot The profit of shore power virtual power plant participating in spot market; C delay Penalty cost for logistics delay; A newenergy is the amount of new energy consumed; μ is the basic penalty per unit tonnage; τ is the time sensitivity coefficient.

[0093] In the specific implementation process, the embodiment of the present application can construct a shore power virtual power plant response strategy optimization model based on the above-mentioned constraint model (i.e., the modified ship timing coupling model) based on the market benefits and new energy absorption capacity of the shore power virtual power plant, as shown in the following formula:

[0094]

[0095] Among them, C cost Charging cost for ship energy storage; B peak The profit of shore power virtual power plant participating in peak load regulation market; B spot The profit of shore power virtual power plant participating in spot market; C delay Penalty cost for logistics delay; A newenergy is the amount of new energy consumed; μ is the basic penalty per unit tonnage; τ is the time sensitivity coefficient.

[0096] Therefore, the embodiments of the present application fully consider the logistics rigid constraints and complex time-varying nature of ship scheduling, and construct the above-mentioned shore power virtual power plant response strategy optimization model, which greatly improves the adjustable ability and response accuracy of the shore power virtual power plant in participating in the power market.

[0097] According to the method for constructing and quantifying a shore power virtual power plant under air-river intermodal transport proposed in an embodiment of the present application, by obtaining the intraday flight cargo information list of the target port in the day-ahead stage, the initial state of energy storage of adjustable ships in the port, and multi-source environmental information, and based on the intraday flight cargo information list, the initial state of energy storage, and multi-source environmental information, the peak-shaving demand window of the target power system the next day and the spot market price fluctuation range are predicted; based on the peak-shaving demand window of the target power system the next day and the spot market price fluctuation range, a ship timing coupling model is constructed to characterize the deep coupling characteristics of power configuration and transportation scheduling on the time scale through the ship timing coupling model; uncertain disturbance factors in actual operation are obtained, and based on the deep coupling characteristics and uncertain disturbance factors, a disturbance scenario set is constructed, and the ship timing coupling model is corrected according to the disturbance scenario set and the preset probability-robust hybrid modeling strategy; based on the corrected ship timing coupling model, a shore power virtual power plant response strategy optimization model is constructed to construct a shore power virtual power plant through the shore power virtual power plant response strategy optimization model, and generate the adjustable capacity quantification and response strategy of the shore power virtual power plant. This application can make full use of the spatiotemporal adjustable characteristics of energy consumption in all aspects of logistics within the port area to improve the economy and environmental protection of the shore power virtual power plant construction plan, and realize the quantification of the adjustable capacity and response strategy generation of the shore power virtual power plant in the context of air-river intermodal transport.

[0098] Secondly, the construction and quantification device of the shore power virtual power plant under air-river combined transport proposed in the embodiment of the present application is described with reference to the accompanying drawings.

[0099] Figure 2 It is a block diagram of the shore power virtual power plant construction and quantification device under the air-river combined transport embodiment of the present application.

[0100] like Figure 2 As shown, the air-river combined transport shore power virtual power plant construction and quantification device 10 includes: a prediction module 100, a modeling module 200, a correction module 300 and a generation module 400.

[0101] Among them, the prediction module 100 is used to obtain the daily flight cargo information list of the target port in the day-ahead stage, the initial energy storage status of the adjustable ships in the port, and multi-source environmental information, and based on the daily flight cargo information list, the initial energy storage status and multi-source environmental information, predict the peak demand window of the target power system and the spot market price fluctuation range for the next day.

[0102] The modeling module 200 is used to construct a ship timing coupling model based on the peak demand window of the next day's target power system and the price fluctuation range of the spot market, so as to characterize the deep coupling characteristics of power configuration and transportation scheduling on the time scale through the ship timing coupling model.

[0103] The correction module 300 is used to obtain the uncertainty disturbance factors in actual operation, and construct a disturbance scenario set based on the deep coupling characteristics and the uncertainty disturbance factors, and correct the ship time series coupling model according to the disturbance scenario set and the preset probability-robust hybrid modeling strategy.

[0104] The generation module 400 is used to construct a shore power virtual power plant response strategy optimization model based on the modified ship timing coupling model, so as to construct a shore power virtual power plant through the shore power virtual power plant response strategy optimization model and generate the adjustable capacity quantification and response strategy of the shore power virtual power plant.

[0105] Optionally, in one embodiment of the present application, the prediction module 100 includes: a first acquisition unit, a second acquisition unit, a third acquisition unit and a collection unit.

[0106] Among them, the first acquisition unit is used to obtain the cargo tonnage, cargo arrival time, cargo planned departure time, cargo transportation mileage and cargo quantity of the target port in the previous day, and determine the cargo information list of the intraday flight based on the cargo tonnage, cargo arrival time, cargo planned departure time, cargo transportation mileage and cargo quantity.

[0107] The second acquisition unit is used to obtain the energy storage battery capacity, maximum shipping mileage and number of available ships of the target power system, and determine the initial energy storage state of the adjustable ships in the port based on the energy storage battery capacity, maximum shipping mileage and number of available ships.

[0108] The third acquisition unit is used to acquire wind speed data, wind direction data, water flow speed data and water level data corresponding to the target port, so as to construct multi-source environmental information through wind speed data, wind direction data, water flow speed data and water level data.

[0109] The collection unit is used to collect historical operating data of the target power market and predict the peak demand window of the target power system and the spot market price fluctuation range for the next day based on the intraday flight cargo information list, energy storage initial status, multi-source environmental information and historical operating data.

[0110] Optionally, in one embodiment of the present application, the modeling module 200 includes: a first construction unit, a determination unit, and an establishment unit.

[0111] Among them, the first construction unit is used to construct a nonlinear ship energy consumption model based on the daily flight cargo information list, the initial state of energy storage and multi-source environmental information.

[0112] The determination unit is used to obtain the ship energy storage charge and discharge amount and the actual ship departure time in the target port, and determine the ship energy storage dynamic equation based on the ship energy storage charge and discharge amount, the actual ship departure time and the nonlinear ship energy consumption model.

[0113] A unit is established to determine the safety redundancy coefficient of the target port and obtain the ship types and energy storage capacities corresponding to different types of ships in the target port, so as to construct corresponding energy storage capacity constraints and charge and discharge power constraints corresponding to different types of ships according to the ship energy storage charge and discharge amount, energy storage capacity, safety redundancy coefficient and nonlinear ship energy consumption model, and establish a ship time series coupling model based on the energy storage capacity constraints and charge and discharge power constraints.

[0114] Optionally, in one embodiment of the present application, the correction module 300 includes: a second construction unit, a third construction unit, a calculation unit, and a margin relaxation unit.

[0115] The second construction unit is configured to determine an arrival time deviation parameter and a wind speed and direction deviation parameter corresponding to the target port, so as to construct a disturbance parameter set according to the arrival time deviation parameter and the wind speed and direction deviation parameter.

[0116] The third construction unit is configured to generate a plurality of representative scenarios corresponding to the disturbance parameter set based on a preset quantile strategy, so as to construct a disturbance scenario set through the plurality of representative scenarios.

[0117] The calculation unit is used to calculate the generation probability corresponding to each representative scenario in the disturbance scenario set, so as to construct a corresponding probability scenario set through the generation probability of each representative scenario.

[0118] The marginal relaxation unit is used to perform marginal relaxation operations on uncertain disturbance factors based on a set of probability scenarios to correct the ship time series coupling model.

[0119] Optionally, in one embodiment of the present application, the mathematical expression of the shore power virtual power plant response strategy optimization model is:

[0120]

[0121] Among them, C cost Charging cost for ship energy storage; B peak The profit of shore power virtual power plant participating in peak load regulation market; B spot The profit of shore power virtual power plant participating in spot market; C delay Penalty cost for logistics delay; A newenergy is the amount of new energy consumed; μ is the basic penalty per unit tonnage; τ is the time sensitivity coefficient.

[0122] It should be noted that the above explanation of the embodiment of the method for constructing and quantifying a virtual power plant for shore power under air-river intermodal transport is also applicable to the device for constructing and quantifying a virtual power plant for shore power under air-river intermodal transport in this embodiment, and will not be repeated here.

[0123] The device for constructing and quantifying a virtual power plant for shore power under air-river combined transport proposed in an embodiment of the present application includes a prediction module 100 for obtaining a list of intraday flight cargo information of a target port in the day-ahead phase, an initial state of energy storage of adjustable ships at the port, and multi-source environmental information, and based on the list of intraday flight cargo information, the initial state of energy storage, and multi-source environmental information, predicts the peak-shaving demand window of the target power system and the price fluctuation range of the spot market on the next day; a modeling module 200 for constructing a ship timing coupling model based on the peak-shaving demand window of the target power system and the price fluctuation range of the spot market on the next day, so as to obtain the peak-shaving demand window of the target power system and the price fluctuation range of the spot market on the next day through the ship timing coupling model. The model characterizes the deep coupling characteristics of power configuration and transportation scheduling on the time scale; the correction module 300 is used to obtain the uncertainty disturbance factors in actual operation, and based on the deep coupling characteristics and the uncertainty disturbance factors, construct a disturbance scenario set, and correct the ship timing coupling model according to the disturbance scenario set and the preset probability-robust hybrid modeling strategy; the generation module 400 is used to construct a shore power virtual power plant response strategy optimization model based on the corrected ship timing coupling model, so as to construct a shore power virtual power plant through the shore power virtual power plant response strategy optimization model, and generate the adjustable capacity quantification and response strategy of the shore power virtual power plant. This application can make full use of the spatiotemporal adjustable characteristics of energy consumption in all links of logistics in the port area to improve the economy and environmental protection of the shore power virtual power plant construction plan, and realize the adjustable capacity quantification and response strategy generation of the shore power virtual power plant in the context of air-river transport.

[0124] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0125] Memory 301 , processor 302 , and computer programs stored in the memory 301 and executable on the processor 302 .

[0126] When the processor 302 executes the program, the shore power virtual power plant construction and quantification method under air-river combined operation provided in the above embodiment is implemented.

[0127] Furthermore, the electronic device further includes:

[0128] The communication interface 303 is used for communication between the memory 301 and the processor 302 .

[0129] The memory 301 is used to store computer programs that can be run on the processor 302 .

[0130] The memory 301 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0131] If the memory 301, processor 302, and communication interface 303 are implemented independently, the communication interface 303, memory 301, and processor 302 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0132] Optionally, in a specific implementation, if the memory 301 , the processor 302 and the communication interface 303 are integrated on a chip, the memory 301 , the processor 302 and the communication interface 303 can communicate with each other through an internal interface.

[0133] The processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0134] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for constructing and quantifying a shore power virtual power plant under air-river intermodal transport.

[0135] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for constructing and quantifying a shore power virtual power plant under air-river intermodal transport.

[0136] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0137] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0138] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0139] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0140] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0141] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0142] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0143] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for constructing and quantifying a shore power virtual power plant under air-river intermodal transport, characterized in that: The following steps are involved: Obtaining a list of intraday cargo flight information, the initial energy storage status of available vessels at the target port, and multi-source environmental information for the day-ahead phase, and predicting the peak demand window and spot market price fluctuation range of the target power system for the next day based on the list of intraday cargo flight information, the initial energy storage status, and the multi-source environmental information; Based on the peak-shaving demand window of the next-day target power system and the price fluctuation range of the spot market, a ship time series coupling model is constructed to characterize the deep coupling characteristics of power configuration and transportation scheduling on a time scale through the ship time series coupling model; Acquire uncertain disturbance factors in actual operation, and construct a disturbance scenario set based on the deep coupling characteristics and the uncertain disturbance factors, and modify the ship time series coupling model according to the disturbance scenario set and a preset probabilistic-robust hybrid modeling strategy; Based on the modified ship timing coupling model, a shore power virtual power plant response strategy optimization model is constructed to construct a shore power virtual power plant through the shore power virtual power plant response strategy optimization model, and generate the adjustable capacity quantification and economic response strategy of the shore power virtual power plant.

2. The method according to claim 1, characterized in that The obtaining of a list of intraday cargo flight information, an initial energy storage status of adjustable vessels at the target port, and multi-source environmental information in the day-ahead phase, and predicting a peak-shaving demand window and a spot market price fluctuation range of the target power system for the next day based on the list of intraday cargo flight information, the initial energy storage status, and the multi-source environmental information, includes: Obtaining the cargo tonnage, cargo arrival time, cargo planned departure time, cargo transportation mileage, and cargo quantity of the target port in the previous day, and determining a cargo information list of flights within the day based on the cargo tonnage, cargo arrival time, cargo planned departure time, cargo transportation mileage, and cargo quantity; Obtaining the energy storage battery capacity, maximum shipping mileage, and number of available ships of the target power system, and determining the initial energy storage state of the adjustable ship in the port based on the energy storage battery capacity, the maximum shipping mileage, and the number of available ships; Acquiring wind speed data, wind direction data, water flow speed data, and water level data corresponding to the target port, so as to construct the multi-source environmental information through the wind speed data, the wind direction data, the water flow speed data, and the water level data; Collect historical operating data of the target power market, and based on the intraday flight cargo information list, the energy storage initial state, the multi-source environmental information and the historical operating data, predict the peak demand window of the target power system for the next day and the spot market price fluctuation range.

3. The method according to claim 2, characterized in that The constructing of a ship time series coupling model based on the peak-shaving demand window of the next-day target power system and the spot market price fluctuation range includes: Constructing a nonlinear ship energy consumption model based on the intraday flight cargo information list, the energy storage initial state, and the multi-source environmental information; Obtaining the amount of energy stored in the ship at the target port and the actual time the ship leaves the port, and determining a ship energy storage dynamic equation based on the amount of energy stored in the ship, the actual time the ship leaves the port, and the nonlinear ship energy consumption model; Determine the safety redundancy coefficient of the target port, and obtain the ship types and energy storage capacities corresponding to different types of ships in the target port, so as to construct corresponding energy storage capacity constraints and charge and discharge power constraints corresponding to the different types of ships according to the ship energy storage charge and discharge amount, the energy storage capacity, the safety redundancy coefficient and the nonlinear ship energy consumption model, and establish the ship time series coupling model based on the energy storage capacity constraints and the charge and discharge power constraints.

4. The method according to claim 3, characterized in that The obtaining of uncertainty disturbance factors in actual operation, and constructing a disturbance scenario set based on the deep coupling characteristics and the uncertainty disturbance factors, and correcting the ship time series coupling model according to the disturbance scenario set and a preset probability-robust hybrid modeling strategy, includes: Determining an arrival time deviation parameter and a wind speed and direction deviation parameter corresponding to the target port, and constructing a disturbance parameter set according to the arrival time deviation parameter and the wind speed and direction deviation parameter; Based on a preset quantile strategy, a plurality of representative scenarios corresponding to the disturbance parameter set are generated, so as to construct the disturbance scenario set through the plurality of representative scenarios; Calculating the generation probability corresponding to each representative scenario in the disturbance scenario set, so as to construct a corresponding probability scenario set according to the generation probability of each representative scenario; Based on the set of probability scenarios, a marginal relaxation operation is performed on the uncertainty disturbance factors to correct the ship time series coupling model.

5. The method according to claim 4, characterized in that The mathematical expression of the shore power virtual power plant response strategy optimization model is: Among them, C cost Charging cost for ship energy storage; B peak The profit of shore power virtual power plant participating in peak load regulation market; B spot The profit of shore power virtual power plant participating in spot market; C delay Penalty cost for logistics delay; A newenergy is the amount of new energy consumed; μ is the basic penalty per unit tonnage; τ is the time sensitivity coefficient.

6. A device for constructing and quantifying shore power virtual power plants under air-river combined transport, characterized in that: include: A prediction module is configured to obtain a list of intraday cargo flight information, the initial energy storage status of available vessels at the target port, and multi-source environmental information during the day-ahead phase, and predict the peak demand window for the target power system and the spot market price fluctuation range for the next day based on the list of intraday cargo flight information, the initial energy storage status, and the multi-source environmental information; a modeling module for constructing a ship time series coupling model based on the peak-shaving demand window of the target power system for the next day and the price fluctuation range of the spot market, so as to characterize the deep coupling characteristics of power configuration and transportation scheduling on a time scale through the ship time series coupling model; A correction module is used to obtain uncertain disturbance factors in actual operation, and construct a disturbance scenario set based on the deep coupling characteristics and the uncertain disturbance factors, and correct the ship time series coupling model according to the disturbance scenario set and a preset probabilistic-robust hybrid modeling strategy; A generation module is used to construct a response strategy optimization model of multiple shore power virtual power plants based on the modified ship timing coupling model, so as to construct a shore power virtual power plant through the shore power virtual power plant response strategy optimization model and generate the adjustable capacity quantification and response strategy of the shore power virtual power plant.

7. The device according to claim 6, characterized in that The prediction module includes: a first acquiring unit configured to acquire cargo tonnage, cargo arrival time, cargo planned departure time, cargo transportation mileage, and cargo quantity of the target port in the previous day, and determine a cargo information list of the intra-day flights based on the cargo tonnage, cargo arrival time, cargo planned departure time, cargo transportation mileage, and cargo quantity; a second acquiring unit, configured to acquire the energy storage battery capacity, the maximum shipping mileage, and the number of available ships of the target power system, and determine the initial energy storage state of the adjustable ship in the port based on the energy storage battery capacity, the maximum shipping mileage, and the number of available ships; a third acquisition unit, configured to acquire wind speed data, wind direction data, water flow speed data, and water level data corresponding to the target port, so as to construct the multi-source environmental information through the wind speed data, the wind direction data, the water flow speed data, and the water level data; A collection unit is used to collect historical operating data of the target power market, and based on the intraday flight cargo information list, the initial state of the energy storage, the multi-source environmental information and the historical operating data, predict the peak demand window of the target power system for the next day and the price fluctuation range of the spot market.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for constructing and quantifying a shore power virtual power plant under air-river intermodal transport as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for constructing and quantifying a shore power virtual power plant under air-river combined transport as described in any one of claims 1-5.

10. A computer program product comprising a computer program, characterized in that The computer program is executed to implement the method for constructing and quantifying a shore power virtual power plant under air-river combined transport as described in any one of claims 1 to 5.

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