Port micro-grid and ship energy system cooperative enhancement method and system

Abstract

The invention relates to the technical field of micro-grid multi-energy collaborative scheduling, and discloses a port micro-grid and ship energy system collaborative enhancement method and system. According to the specific implementation scheme, the method comprises the steps that the real-time request power supply amount of a port and the actual power supply amount of reverse power supply of a ship in the extreme weather are predicted according to ship automatic identification system data, actually measured extreme weather data and the state of a port power system; according to the real-time request power supply amount and the actual power supply amount, calculating the minimum operation cost and the toughness coordination index of the port, and determining the electricity purchase price; according to the electricity purchase price and a maximum income objective function of the ship, determining a protocol power supply amount of reverse power supply of the ship; and determining the real-time power supply amount of the port power generation equipment according to the real-time request power supply amount and the protocol power supply amount. According to the invention, the robustness of the coordination strategy can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of multi - energy collaborative scheduling of micro - grids, and particularly to a method and system for collaborative enhancement of a port micro - grid and a ship energy system. Background Art

[0002] The port industry is intensive, with large load consumption. At the same time, the contradiction between economic development and environmental protection is becoming increasingly prominent. Building and developing green ports has become a common consensus in the port industry. And the accelerating intensification of extreme weather events has fundamentally changed the operating risk situation of global offshore energy infrastructure, and port micro - grids and ship power systems are facing unprecedented collaborative threats.

[0003] In related technologies, it is impossible to coordinate the dichotomy of the millisecond - level dynamic response of ships and the hourly - level planning of ports, resulting in the failure of key energy reciprocity under extreme weather. Moreover, ship power management ignores the intermittency of port renewable energy, while port micro - grids do not consider the fluctuations of ship hydrodynamic loads. Summary of the Invention

[0004] To solve the above - mentioned technical problems, the present disclosure provides a method for collaborative enhancement of a port micro - grid and a ship energy system, including the following steps: Predict the real - time requested power supply of the port and the actual power supply of ship reverse power supply under extreme weather according to the data of the Automatic Identification System (AIS) of ships, the measured extreme weather data, and the state of the port power system; calculate the minimum operating cost and resilience coordination index of the port according to the real - time requested power supply and the actual power supply, and determine the power purchase price; determine the agreed power supply of ship reverse power supply according to the power purchase price and the maximum revenue objective function of the ship; determine the real - time power supply of the port power generation equipment according to the real - time requested power supply and the agreed power supply.

[0005] Further, the step of predicting the real - time requested power supply of the port and the actual power supply of ship reverse power supply under extreme weather according to the data of the Automatic Identification System (AIS) of ships, the measured extreme weather data, and the state of the port power system includes: Determine a physical - information digital - twin fusion model; input the data of the Automatic Identification System (AIS) of ships, the measured extreme weather data, and the state of the port power system into the physical - information digital - twin fusion model to obtain the real - time requested power supply of the port and the actual power supply of ship reverse power supply under extreme weather.

[0006] Further, the step of determining the physical - information digital - twin fusion model includes: According to extreme weather data, adaptively determine the fusion parameters of hydrodynamics and long short-term memory artificial neural network respectively. The sum of the adaptive fusion parameters of hydrodynamics and the adaptive fusion parameters of the long short-term memory artificial neural network is 1. Determine the observation operator according to the hydrodynamics, the long short-term memory artificial neural network and their respective corresponding adaptive fusion parameters. Construct a data physical fusion model according to the observation operator, the hydrodynamics and the long short-term memory artificial neural network. Obtain historical data and train the data physical fusion model to obtain a physical information digital twin fusion model.

[0007] Further, the obtaining historical data, training the data physical fusion model to obtain a physical information digital twin fusion model includes: Fuse the historical data of different time scales. Train the long short-term memory artificial neural network according to the fused historical data to obtain a long short-term memory ship trajectory prediction model, and train the hydrodynamics according to the fused historical data to obtain a ship wave force offset prediction model. Fuse the long short-term memory ship trajectory prediction model and the ship wave force offset prediction model according to the observation operator to obtain a physical information digital twin fusion model.

[0008] Further, the obtaining historical data includes: After removing abnormal data from the obtained historical data of the Automatic Identification System (AIS) of ships, extreme weather historical data, historical data of the port power system status, and historical data of the power grid status, align them based on timestamps to obtain historical data.

[0009] Further, the calculating the minimum operating cost and resilience coordination index of the port according to the real-time requested power supply and the actual power supply, and determining the electricity purchase price includes: Calculate the expenditure cost of purchasing electricity from ships according to the real-time requested power supply. Determine the minimum operating cost of the port according to the fuel cost of the diesel generator of the energy storage system, the equipment usage cost of the energy storage system, and the expenditure cost. Based on the actual power supply, determine the support ratio of the ship's reverse power supply to the port's critical load. Calculate the resilience coordination index according to the support ratio, the power supply speed of the ship in response to the port, and the power grid frequency deviation. Determine the electricity purchase price of the port's electricity purchase from ships according to the minimum operating cost and the resilience coordination index.

[0010] Further, the determining the agreed power supply amount of the ship's reverse power supply according to the electricity purchase price and the maximum revenue objective function of the ship includes: Obtain the power supply plan for the ship's reverse power supply per hour, and minimize the deviation between the real-time power supply of the ship and the real-time requested power supply of the port according to the power supply plan; determine the voltage at the point of common coupling between the ship and the port; obtain the ship's own status, and take minimizing the deviation, the voltage at the point of common coupling, and the ship's own status as constraints, and calculate the maximum revenue objective function of the ship according to the electricity purchase price, and determine the agreed power supply amount of the ship's reverse power supply.

[0011] Further, after determining the real-time power supply amount of the port power generation equipment according to the real-time requested power supply amount and the agreed power supply amount, the method further includes: Obtain the first instantaneous frequency of the real-time power supply amount and the second instantaneous frequency of the agreed power supply amount; calculate the frequency deviation between the first instantaneous frequency and the second instantaneous frequency; when the frequency deviation is greater than the set threshold, re-determine the agreed power supply amount based on the frequency deviation.

[0012] According to another aspect of the present disclosure, there is provided a collaborative enhancement system for a port microgrid and a ship energy system, the system includes: A prediction module, configured to predict the real-time requested power supply amount of the port and the actual power supply amount of the ship's reverse power supply under extreme weather according to ship automatic identification system data, measured extreme weather data, and the state of the port power system; a determination module, configured to calculate the minimum operating cost and resilience coordination index of the port according to the real-time requested power supply amount and the actual power supply amount, determine the electricity purchase price; determine the agreed power supply amount of the ship's reverse power supply according to the electricity purchase price and the maximum revenue objective function of the ship; determine the real-time power supply amount of the port power generation equipment according to the real-time requested power supply amount and the agreed power supply amount.

[0013] Further, the prediction module is configured to: Determine a physical information digital twin fusion model; input the ship automatic identification system data, measured extreme weather data, and the state of the port power system into the physical information digital twin fusion model to obtain the real-time requested power supply amount of the port and the actual power supply amount of the ship's reverse power supply under extreme weather.

[0014] The embodiments of the present disclosure have the following technical effects: The collaborative enhancement method of the port microgrid and the ship energy system provided by the present disclosure can predict the real-time requested power supply of the port and the actual power supply of the ship's reverse power supply under extreme weather through the data of the Automatic Identification System (AIS) of ships, the measured extreme weather data, and the status of the port power system, which can improve the prediction accuracy of the real-time requested power supply and the actual power supply of the ship's reverse power supply under extreme weather scenarios. Secondly, by calculating the minimum operating cost and the resilience coordination index of the port based on the real-time requested power supply and the actual power supply, and determining the electricity purchase price, the resilience coordination index combined with the minimum operating cost can quantify that the recovery speed of the port ship energy system under extreme weather is increased by 22%. Furthermore, according to the electricity purchase price and the maximum revenue objective function of the ship, the agreed power supply of the ship's reverse power supply is determined, and according to the real-time requested power supply and the agreed power supply, the real-time power supply of the port power generation equipment is controlled to realize a closed-loop strategy of prediction, game, agreement, and control, so as to achieve the goal of coordinated enhancement between the port and the ship energy system, shorten the power outage time of the port under the influence of extreme weather, and improve the robustness of the coordination strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of the collaborative enhancement method of the port microgrid and the ship energy system provided by the embodiment of the present invention; Figure 2 It is an evolutionary trajectory diagram of the collaborative enhancement of the port microgrid and the ship energy system provided by the embodiment of the present invention; Figure 3 It shows a schematic structural diagram of a collaborative enhancement system of a port microgrid and a ship energy system provided by an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of the present invention.

[0018] The port industry is intensive, with large load consumption. At the same time, the contradiction between economic development and environmental protection has become increasingly prominent. Building and developing green ports has become a common consensus in the port industry. The acceleration and intensification of extreme weather events have fundamentally changed the operating risk situation of global offshore energy infrastructure, and port microgrids and ship power systems are facing unprecedented collaborative threats.

[0019] Among them, the traditional resilience strategies developed for isolated land microgrids or ocean-going ships are seriously insufficient when applied to the port-ship coupled ecosystem. The significant progress in the resilience of isolated systems has instead exacerbated the coordination challenges at the port-ship interface.

[0020] Based on this, the present application proposes a collaborative enhancement method and system for a port microgrid and a ship energy system. By using Automatic Identification System (AIS) data, measured extreme weather data, and the status of the port power system, it predicts the real-time power supply demand of the port and the actual power supply of ship reverse power supply under extreme weather. Then, based on the real-time power supply demand and the actual power supply of ship reverse power supply, in the form of a power purchase game between the port and the ship, the goal of coordinated enhancement between the port and ship energy systems is achieved under the condition of maximizing the interests of both the port and the ship, shortening the power outage time of the port under the influence of extreme weather, and improving the robustness of the coordination strategy.

[0021] Figure 1 It is the flowchart of the collaborative enhancement method for the port microgrid and the ship energy system provided by the embodiments of the present invention. Refer to Figure 1 , including the following steps: In step S11, according to the Automatic Identification System (AIS) data, measured extreme weather data, and the status of the port power system, the real-time power supply demand of the port and the actual power supply of ship reverse power supply under extreme weather are predicted.

[0022] In the present disclosure, the Automatic Identification System (AIS) data includes information such as the automatic exchange of ship positions, speeds, headings, and ship names of ships. The measured extreme weather data includes 24-hour extreme weather data, for example, data such as the path and wind speed of a typhoon in 24 hours. The status of the port power system may include status parameters such as voltage amplitude, load curve, and remaining power.

[0023] Furthermore, under extreme weather, ships can transmit electricity to the port. Then, based on the current Automatic Identification System (AIS) data, measured extreme weather data, and the status of the port power system, the real-time power supply demand of the port and the actual power supply of ship reverse power supply under extreme weather can be predicted. In the present disclosure, based on the above information, it is also possible to predict the possible failure locations of the port and ships and the load fluctuations of the port under extreme weather.

[0024] Among them, the reverse power supply of the ship is the power supply reported by the ship to the port, which can also be understood as the ideal power supply determined in advance for the ship to supply power to the port in case of extreme weather.

[0025] In step S12, according to the real-time requested power supply and the actual power supply, calculate the minimum operating cost and resilience coordination index of the port, and determine the power purchase price.

[0026] In the embodiment of the present disclosure, considering multiple factors such as the predicted location of possible failures and load fluctuations under extreme weather, the charge and discharge curve of the future port energy storage system, the startup situation of standby units, the real-time requested power supply and actual power supply of the port, and the deactivation of non-critical loads, calculate the objective function of the minimum operating cost and its resilience coordination index, so as to determine the power purchase price for purchasing electricity from the ship.

[0027] Among them, the resilience coordination index can be understood as an index used to quantify the collaborative ability between the port and the ship under extreme weather. The higher its value, the stronger the system resilience. The expression of the resilience coordination index is as follows:

[0028] In the formula, represents the resilience coordination index, represents the energy transmitted in an emergency, represents the total demand of the port's critical loads in the kth period, represents the synchronization delay of the control instruction, represents the duration of extreme weather, the absolute value of the frequency deviation, η , ξ , ζ all represent calibration coefficients.

[0029] In step S13, according to the power purchase price and the maximum revenue objective function of the ship, determine the agreed power supply of the ship's reverse power supply.

[0030] In the present disclosure, according to the power purchase price released by the port, combined with the limitation of the ship's remaining power, the ship equipment status, the voltage of the port-ship coupling point, etc., on the basis of this power purchase price, calculate the maximum revenue objective function of the ship, so as to determine the agreed power supply of the ship's reverse power supply. That is, according to the current power purchase price released by the port, the power supply provided by the ship to the port.

[0031] In step S14, according to the real-time requested power supply and the agreed power supply, determine the real-time power supply of the port's power generation equipment.

[0032] In the embodiment of the present disclosure, according to the agreed power supply of the ship and the real-time requested power supply of the port, the real-time power supply provided by the port's power generation equipment is updated in real time.

[0033] Among them, if the real-time power supply provided by the port power generation equipment is insufficient, non-critical loads can also be deactivated, thereby reducing the power consumption.

[0034] Furthermore, the first instantaneous frequency of the real-time power supply and the second instantaneous frequency of the protocol power supply can be obtained, and the frequency deviation between the first instantaneous frequency and the second instantaneous frequency can be calculated. When the frequency deviation is greater than the set threshold, the protocol power supply is re-determined based on the frequency deviation.

[0035] Among them, the expression of the frequency deviation is as follows:

[0036] In the formula, represents the frequency deviation at time t, represents the frequency synchronization error at time t, represents the real-time frequency of the port power grid at time t, represents the real-time frequency of the ship's reverse power supply at time t, represents the proportional gain, represents the integral gain, represents the derivative gain, represents the frequency synchronization error at the current moment.

[0037] The collaborative enhancement method of the port microgrid and the ship energy system provided by the present disclosure can predict the real-time requested power supply of the port and the actual power supply of the ship's reverse power supply under extreme weather through the data of the automatic identification system of ships, the measured extreme weather data, and the state of the port power system, and can improve the prediction accuracy of the real-time requested power supply and the actual power supply of the ship's reverse power supply under extreme weather scenarios. Secondly, by calculating the minimum operating cost and the resilience coordination index of the port according to the real-time requested power supply and the actual power supply, and determining the power purchase price, the resilience coordination index combined with the minimum operating cost can quantify that the recovery speed of the port ship energy system under extreme weather is increased by 22%. Furthermore, according to the power purchase price and the maximum revenue objective function of the ship, the protocol power supply of the ship's reverse power supply is determined, and according to the real-time requested power supply and the protocol power supply, the real-time power supply of the port power generation equipment is controlled, realizing a closed-loop strategy of prediction, game, protocol, and control, thereby achieving the goal of coordinated enhancement between the port and the ship energy system, shortening the power outage time of the port under the influence of extreme weather, and improving the robustness of the coordination strategy.

[0038] In the present disclosure, the port and ship power coupling system is represented by the following formula:

[0039] In the formula, represents the state of the port and ship power coupling system at time t, represents the system state vector at time t, represents the port control input power at time t, represents the port control input power, represents the ship control input power at time t, represents the ship control input power, represents the disturbance caused by weather at time t.

[0040] Among them, the function f is used to describe the differential changes of the system state, port control input power, ship control input power, and weather-induced disturbance in the port and ship power coupling system at time t.

[0041] In the embodiments of the present disclosure, a physical information digital twin fusion model can be determined in advance, so that the ship automatic identification system data, measured extreme weather data, and port power system state are input into the physical information digital twin fusion model to obtain the real-time requested power supply of the port and the actual power supply of the ship's reverse power supply under extreme weather.

[0042] Furthermore, according to the extreme weather data, the adaptive fusion parameters of fluid dynamics and long short-term memory artificial neural network can be determined respectively, and the sum of the adaptive fusion parameters of fluid dynamics and the adaptive fusion parameters of the long short-term memory artificial neural network is 1. An observation operator is determined according to fluid dynamics and the long short-term memory artificial neural network and their respective corresponding adaptive fusion parameters. A data-physical fusion model is constructed according to the observation operator, fluid dynamics, and the long short-term memory artificial neural network. Historical data is obtained, and the data-physical fusion model is trained to obtain a physical information digital twin fusion model.

[0043] Exemplarily, the adaptive fusion parameter of fluid dynamics under extreme weather can be 0.7, and the adaptive fusion parameter of the long short-term memory artificial neural network is 0.3. In general, the adaptive fusion parameter of fluid dynamics under extreme weather can be 0.3, and the adaptive fusion parameter of the long short-term memory artificial neural network is 0.7.

[0044] Among them, the expression of the observation operator is as follows:

[0045] In the formula, represents the observation operator, represents the adaptive fusion parameter of fluid dynamics, represents the physical simulation of fluid dynamics, represents the environmental parameters, represents the ship state, represents the data-driven of the long short-term memory artificial neural network, Represents Automatic Identification System (AIS) data, Represents the status of the port power system, Represents the predicted target variable.

[0046] In the present disclosure, a hardware-in-the-loop test method can be adopted to test the obtained digital twin fusion model of physical information. For example, the digital twin fusion model of physical information can be tested by simulating a 300% load step change during the passage of the typhoon eye, synchronous fault recovery under a 2.5 Hz frequency deviation, etc.

[0047] Furthermore, historical data of different time scales can be fused, a long short-term memory artificial neural network can be trained based on the fused historical data to obtain a long short-term memory ship trajectory prediction model, and the hydrodynamic model can be trained based on the fused historical data to obtain a ship wave force offset prediction model. According to the observation operator, the long short-term memory ship trajectory prediction model and the ship wave force offset prediction model are fused to obtain the digital twin fusion model of physical information.

[0048] Among them, the heterogeneous data stream obtained by fusing historical data of different time scales is expressed as follows:

[0049] In the formula, contains the ship position / speed from the Automatic Identification System, is the ECMWF weather ensemble, is the phasor measurement unit data, is the port equipment status.

[0050] The expression of the further digital twin fusion model of physical information is as follows:

[0051] represents the hydrodynamic model, represents the power system simulation, represents the trajectory prediction neural network according to the observation operator, represents the meteorological data, represents the ship dynamic information, represents the tensor fusion of multimodal data, V represents the ship speed, and W represents the wave height, represents the physical-data fusion digital twin model, x represents the input data, t represents the time, θ represents the phase angle.

[0052] In the embodiments of the present disclosure, it is also necessary to align the obtained historical data of the Automatic Identification System (AIS) of ships, historical data of extreme weather, historical data of the state of the port power system, and historical data of the state of the power grid based on timestamps after removing abnormal data, so as to obtain the historical data.

[0053] In the present disclosure, the data of a certain region in one year can be selected as the historical data.

[0054] In the embodiments of the present disclosure, according to the real-time requested power supply and the actual power supply, the implementation method of calculating the minimum operating cost and the resilience coordination index of the port and determining the power purchase price is as follows.

[0055] Calculate the expenditure cost of purchasing electricity from ships according to the real-time requested power supply. Determine the minimum operating cost of the port based on the fuel cost of the diesel generator of the energy storage system, the equipment usage cost of the energy storage system, and the expenditure cost. Based on the actual power supply, determine the support ratio of the ship's reverse power supply to the critical load of the port. Calculate the resilience coordination index according to the support ratio, the power supply speed of the ship in response to the port, and the power grid frequency deviation. Determine the power purchase price of the port's electricity purchase from ships according to the minimum operating cost and the resilience coordination index.

[0056] In the embodiments of the present disclosure, it is possible to solve for the minimum sum of the minimum operating cost and the resilience coordination index, that is, to optimize the port control input (such as energy storage scheduling, diesel engine start / stop, etc.), so as to determine the power purchase price of the port's electricity purchase from ships. The expression for minimizing the supply-demand imbalance and voltage deviation is as follows:

[0057] In the formula, represents minimizing the port control input, represents a time step of 24 hours, represents the port operating cost, is the resilience coordination index, α , β is the weight coefficient, represents the resilience target value, In the embodiments of the present disclosure, the following implementation will detail the determination of the agreed power supply amount of the ship's reverse power supply according to the power purchase price and the maximum revenue objective function of the ship.

[0058] Obtain the power supply plan of the ship's reverse power supply per hour, and minimize the deviation between the real-time power supply amount of the ship and the real-time requested power supply amount of the port according to the power supply plan; determine the voltage at the point of common coupling between the ship and the port; obtain the ship's own state, and calculate the maximum revenue objective function of the ship according to the power purchase price with the minimization of the deviation, the voltage at the point of common coupling, and the ship's own state as constraints, so as to determine the agreed power supply amount of the ship's reverse power supply.

[0059] Among them, the voltage at the point of common coupling is stabilized at 1.0 pu to prevent cascading failures caused by voltage collapse.

[0060] The expressions with the constraints of minimizing the deviation, the voltage at the point of common coupling, and the ship's own state are as follows:

[0061] In the formula, represents minimizing the imbalance between supply and demand and the voltage deviation, represents a time step of 60 seconds, represents the actual power supply of the ship at time period m, represents the requested power supply of the port at time period m, is the voltage at the point of common coupling, γ and δ represent the penalty weights.

[0062] Exemplarily, by issuing a power purchase price to the ship through the port and purchasing electricity from the ship, the ship can then feedback the available power supply to the port based on multi-dimensional factors, thus conducting multiple rounds of games. Figure 2 is the evolutionary trajectory diagram of the collaborative enhancement of the port microgrid and the ship energy system provided by the embodiment of the present invention. Refer to Figure 2 , which shows convergence to a stable Nash equilibrium within 45 iterations in a storm scenario, while the traditional method requires 82 iterations. The collaborative resilience index reaches a peak of 0.78 during the coordination of ship-port power transmission. These optimizations fundamentally change the fault propagation dynamics: PMU data confirms that under coordinated control, the voltage deviation propagation delay increases from the baseline of 8 - 12 minutes to 18 - 22 minutes, which is a key buffer for implementing corrective measures.

[0063] Based on the same principle as the method shown in Figure 1 , Figure 3 shows a schematic structural diagram of a collaborative enhancement system for a port microgrid and a ship energy system provided by an embodiment of the present disclosure. As shown in Figure 3 , the collaborative enhancement system 300 for the port microgrid and the ship energy system may include: A prediction module 301, configured to predict the real-time requested power supply of the port and the actual power supply of the ship for reverse power supply under extreme weather according to the ship automatic identification system data, the measured extreme weather data, and the state of the port power system; A determination module 302, configured to calculate the minimum operating cost and the resilience coordination index of the port according to the real-time requested power supply and the actual power supply, determine the power purchase price; determine the agreed power supply of the ship for reverse power supply according to the power purchase price and the maximum revenue objective function of the ship; determine the real-time power supply of the port power generation equipment according to the real-time requested power supply and the agreed power supply.

[0064] In the present disclosure, the prediction module 301 is configured to determine a cyber-physical digital twin fusion model; input Automatic Identification System (AIS) data of a ship, measured extreme weather data, and the state of a port power system into the cyber-physical digital twin fusion model to obtain the real-time required power supply of the port under extreme weather and the actual power supply of reverse power supply by the ship.

[0065] In the present disclosure, the determination module 302 is configured to respectively determine adaptive fusion parameters of hydrodynamics and a long short-term memory (LSTM) artificial neural network according to the extreme weather data, where the sum of the adaptive fusion parameter of hydrodynamics and the adaptive fusion parameter of the LSTM artificial neural network is 1; determine an observation operator according to the hydrodynamics, the LSTM artificial neural network, and their respective corresponding adaptive fusion parameters; construct a data-physical fusion model according to the observation operator, the hydrodynamics, and the LSTM artificial neural network; obtain historical data and train the data-physical fusion model to obtain a cyber-physical digital twin fusion model.

[0066] In the present disclosure, the determination module 302 is configured to fuse the historical data of different time scales; train the LSTM artificial neural network according to the fused historical data to obtain an LSTM ship trajectory prediction model, and train the hydrodynamics according to the fused historical data to obtain a ship wave force offset prediction model; fuse the LSTM ship trajectory prediction model and the ship wave force offset prediction model according to the observation operator to obtain a cyber-physical digital twin fusion model.

[0067] In the present disclosure, the determination module 302 is configured to align the acquired historical AIS data of a ship, historical extreme weather data, historical state data of a port power system, and historical grid state data based on timestamps after removing abnormal data to obtain historical data.

[0068] In the present disclosure, the determination module 302 is configured to calculate the expenditure cost of purchasing electricity from the ship according to the real-time required power supply; determine the minimum operating cost of the port according to the fuel cost of a diesel generator of an energy storage system, the equipment usage cost of the energy storage system, and the expenditure cost; determine the support ratio of the ship's reverse power supply to the critical load of the port based on the actual power supply; calculate the resilience coordination index according to the support ratio, the power supply speed of the ship in response to the port, and the grid frequency deviation; determine the electricity purchase price for the port to purchase electricity from the ship according to the minimum operating cost and the resilience coordination index.

[0069] In the present disclosure, the determining module 302 is configured to obtain a power supply plan for the ship's reverse power supply per hour, and minimize the deviation between the real-time power supply of the ship and the real-time requested power supply of the port according to the power supply plan; determine the voltage at the point of common coupling between the ship and the port; obtain the ship's own state, and calculate the maximum revenue objective function of the ship based on minimizing the deviation, the voltage at the point of common coupling, and the ship's own state as constraints, and determine the agreed power supply for the ship's reverse power supply.

[0070] In the present disclosure, the determining module 302 is configured to obtain a first instantaneous frequency of the real-time power supply and a second instantaneous frequency of the agreed power supply; calculate a frequency deviation between the first instantaneous frequency and the second instantaneous frequency; and when the frequency deviation is greater than a set threshold, re-determine the agreed power supply based on the frequency deviation.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features. However, such modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A collaborative enhancement method for a port microgrid and a ship energy system, characterized in that, The method includes: Predicting the real-time requested power supply of the port and the actual power supply of the ship's reverse power supply under extreme weather based on the Automatic Identification System (AIS) data of ships, the measured extreme weather data, and the state of the port power system; Calculating the minimum operating cost and the resilience coordination index of the port based on the real-time requested power supply and the actual power supply, and determining the electricity purchase price; the resilience coordination index is used to quantify the collaborative ability between the port and the ship under extreme weather; Determining the agreed power supply of the ship's reverse power supply according to the electricity purchase price and the maximum revenue objective function of the ship; Determining the real-time power supply of the port's power generation equipment according to the real-time requested power supply and the agreed power supply.

2. The collaborative enhancement method of the port microgrid and ship energy system according to claim 1, characterized in that, The predicting the real-time requested power supply of the port and the actual power supply of the ship's reverse power supply under extreme weather based on the AIS data of ships, the measured extreme weather data, and the state of the port power system includes: Determining a cyber-physical digital twin fusion model; Inputting the AIS data of ships, the measured extreme weather data, and the state of the port power system into the cyber-physical digital twin fusion model to obtain the real-time requested power supply of the port and the actual power supply of the ship's reverse power supply under extreme weather.

3. The method for collaboratively enhancing the port microgrid and ship energy system according to claim 2, characterized in that: The determining the cyber-physical digital twin fusion model includes: Respectively determining the adaptive fusion parameters of fluid dynamics and the long short-term memory (LSTM) artificial neural network according to the extreme weather data, and the sum of the adaptive fusion parameters of fluid dynamics and the LSTM artificial neural network is 1; Determining an observation operator according to the fluid dynamics, the LSTM artificial neural network, and their respective adaptive fusion parameters; Constructing a data-physical fusion model according to the observation operator, the fluid dynamics, and the LSTM artificial neural network; Obtaining historical data and training the data-physical fusion model to obtain a cyber-physical digital twin fusion model.

4. The method for collaboratively enhancing the port microgrid and ship energy system according to claim 3 is characterized in that: The obtaining historical data and training the data-physical fusion model to obtain a cyber-physical digital twin fusion model includes: Fusing the historical data at different time scales; Training the LSTM artificial neural network according to the fused historical data to obtain an LSTM ship trajectory prediction model, and training the fluid dynamics according to the fused historical data to obtain a ship wave force offset prediction model; Fusing the LSTM ship trajectory prediction model and the ship wave force offset prediction model according to the observation operator to obtain a cyber-physical digital twin fusion model.

5. The collaborative enhancement method of the port microgrid and ship energy system according to claim 3, characterized in that The obtaining historical data includes: After removing the abnormal data from the obtained AIS historical data, extreme weather historical data, port power system state historical data, and power grid state historical data, aligning them based on the time stamp to obtain historical data.

6. The method for collaboratively enhancing port microgrid and ship energy system according to claim 1, characterized in that: The calculating the minimum operating cost and the resilience coordination index of the port based on the real-time requested power supply and the actual power supply, and determining the electricity purchase price includes: Calculating the expenditure for purchasing electricity from the ship according to the real-time requested power supply; Determine the minimum operating cost of the port according to the fuel cost of the diesel generator of the energy storage system, the usage cost of the energy storage system equipment, and the said expenditure cost; Based on the actual power supply amount, determine the support ratio of the ship to reverse power supply for the key loads of the port; Calculate the resilience coordination index according to the support ratio, the power supply speed of the ship in response to the port, and the grid frequency deviation; Determine the power purchase price for the port to purchase electricity from the ship according to the minimum operating cost and the resilience coordination index; 7. The collaborative enhancement method of the port microgrid and the ship energy system according to claim 1, characterized in that, The determining the agreed power supply amount of the ship's reverse power supply according to the power purchase price and the maximum revenue objective function of the ship includes: Obtain the power supply plan for the ship's reverse power supply per hour, and minimize the deviation between the real-time power supply amount of the ship and the real-time requested power supply amount of the port according to the power supply plan; Determine the voltage at the point of common coupling between the ship and the port; Obtain the ship's own state, and with minimizing the deviation, the voltage at the point of common coupling, and the ship's own state as constraints, calculate the maximum revenue objective function of the ship according to the power purchase price, and determine the agreed power supply amount of the ship's reverse power supply.

8. The collaborative enhancement method of the port microgrid and ship energy system according to claim 1, characterized in that After the determining the real-time power supply amount of the port's power generation equipment according to the real-time requested power supply amount and the agreed power supply amount, the method further includes: Obtain the first instantaneous frequency of the real-time power supply amount and the second instantaneous frequency of the agreed power supply amount; Calculate the frequency deviation between the first instantaneous frequency and the second instantaneous frequency; When the frequency deviation is greater than the set threshold, re-determine the agreed power supply amount based on the frequency deviation.

9. A port microgrid and ship energy system collaborative enhancement system, characterized in that: The system includes: A prediction module, configured to predict the real-time requested power supply amount of the port and the actual power supply amount of the ship's reverse power supply under extreme weather according to the data of the Automatic Identification System (AIS) of the ship, the measured extreme weather data, and the state of the port power system; A determination module, configured to calculate the minimum operating cost and the resilience coordination index of the port according to the real-time requested power supply amount and the actual power supply amount, and determine the power purchase price; determine the agreed power supply amount of the ship's reverse power supply according to the power purchase price and the maximum revenue objective function of the ship; determine the real-time power supply amount of the port's power generation equipment according to the real-time requested power supply amount and the agreed power supply amount.

10. The collaborative enhancement system of the port microgrid and the ship energy system according to claim 9, wherein, The prediction module is configured to: Determine the physical information digital twin fusion model; Input the data of the Automatic Identification System (AIS) of the ship, the measured extreme weather data, and the state of the port power system into the physical information digital twin fusion model to obtain the real-time requested power supply amount of the port and the actual power supply amount of the ship's reverse power supply under extreme weather.

Images