A method and device for coordinating and scheduling a multi-energy fusion system in a port dynamic scene

Abstract

The application discloses a port dynamic scene multi-energy fusion system cooperative scheduling method and device, relates to the field of port energy system optimal scheduling, and comprises the following steps: constructing a dynamic coupling relationship model according to port data; constructing a multi-objective optimization model according to the energy system power of each region at a current time point; the objective function of the multi-objective optimization model comprises minimization of operation cost, minimization of total carbon emission and maximization of service life of energy storage equipment; the constraint conditions of the multi-objective optimization model are power balance, energy storage state of charge, electric-hydrogen equipment capacity, berth allocation continuity and load demand priority; determining a target cooperative scheduling operation strategy of each region at the current time point according to the multi-objective optimization model and the port data of each region at the current time point, wherein the target cooperative scheduling operation strategy comprises a multi-energy output distribution scheme, a load response instruction and equipment operation parameters; and executing the target cooperative scheduling operation strategy of each region at the current time point.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of port energy system optimal scheduling, in particular to a port multi-energy fusion system collaborative scheduling method and device under dynamic scenarios. BACKGROUND

[0002] At present, the port energy system still generally adopts a traditional single energy supply mode.

[0003] The traditional single energy supply mode is dominated by fossil energy, and the port energy system mainly relies on traditional thermal power transmitted by the power grid or fossil fuel generators (such as diesel generators and heavy oil generators) provided by the port as the only or absolute dominant energy source.

[0004] This mode does not integrate multiple complementary energies such as wind energy, photovoltaic energy, energy storage, and hydrogen energy, and cannot realize the collaborative scheduling of multiple energies to balance the operating cost, carbon emission, and energy storage device life in complex dynamic scenarios, that is, it is difficult to realize efficient collaboration of multiple energies and accurate matching of supply and demand under the premise of dynamic load and balanced multiple targets. SUMMARY

[0005] The purpose of the present application is to provide a port multi-energy fusion system collaborative scheduling method and device under dynamic scenarios, which can realize efficient collaboration of multiple energies and accurate matching of supply and demand.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a port multi-energy fusion system collaborative scheduling method under dynamic scenarios, which comprises:

[0008] According to the port data, a dynamic coupling relationship model is constructed; the port data includes real-time output data of each available energy of each region of the port at the current time point and load data of each region of the port; the each available energy includes at least one of the following energy types: wind energy, photovoltaic energy, energy storage, and hydrogen energy; the load data includes at least one of the following data types: dynamic load demand data of ships, logistics equipment, and infrastructure; the dynamic coupling relationship model integrates the port data and is used to output the energy system power of each region at the current time point according to the port data, wherein the energy system power includes: photovoltaic output power, wind energy output power, real-time power purchased from the power grid, energy storage charging and discharging power, hydrogen energy output power, and total power;

[0009] a multi-objective optimization model is constructed according to the energy system power of each region at the current time point output by the dynamic coupling relationship model, objective functions of the multi-objective optimization model include: minimum operation cost, minimum total carbon emission, maximum service life of energy storage equipment, constraint conditions of the multi-objective optimization model are: power balance, energy storage state of charge, electric and hydrogen equipment capacity, berth allocation continuity, and load demand priority;

[0010] a target collaborative scheduling operation strategy of each region at the current time point is determined according to the multi-objective optimization model and the port data of each region at the current time point, the target collaborative scheduling operation strategy includes: a multi-energy output allocation scheme, a load response instruction, and equipment operation parameters;

[0011] the target collaborative scheduling operation strategy of each region at the current time point is executed.

[0012] In a second aspect, the present application provides a multi-energy fusion system collaborative scheduling device in a port dynamic scenario, the device includes:

[0013] a dynamic coupling relationship model construction module, configured to construct a dynamic coupling relationship model according to port data, the port data includes: real-time output data of each available energy of each region of the port at a current time point and load data of each region of the port, the available energy at least includes at least one of the following energy types: wind energy, photovoltaic energy, energy storage, and hydrogen energy, the load data includes at least one of the following data types: dynamic load demand data of a ship, logistics equipment, and infrastructure, the dynamic coupling relationship model integrates the port data, and is configured to output energy system power of each region at the current time point according to the port data, the energy system power includes: photovoltaic output power, wind energy output power, real-time power purchased by a power grid, energy storage charging and discharging power, hydrogen energy output power, and total power;

[0014] a multi-objective optimization model construction module, configured to construct a multi-objective optimization model according to the energy system power of each region at the current time point output by the dynamic coupling relationship model, objective functions of the multi-objective optimization model include: minimum operation cost, minimum total carbon emission, maximum service life of energy storage equipment, constraint conditions of the multi-objective optimization model are: power balance, energy storage state of charge, electric and hydrogen equipment capacity, berth allocation continuity, and load demand priority;

[0015] a determination module, configured to determine a target collaborative scheduling operation strategy of each region at the current time point according to the multi-objective optimization model and the port data of each region at the current time point, the target collaborative scheduling operation strategy includes: a multi-energy output allocation scheme, a load response instruction, and equipment operation parameters;

[0016] an execution module, configured to execute the target collaborative scheduling operation strategy of each region at the current time point.

[0017] According to the specific embodiments provided in the present application, the present application discloses the following technical effects:

[0018] The present application provides a port dynamic scene multi-energy fusion system collaborative scheduling method and device, a dynamic coupling relationship model is constructed based on the obtained multi-energy real-time output data and the load data of each area of the port, the dynamic coupling relationship model is not simply integrated data, but through quantifying the real-time correlation of each energy output and load demand, the dispersed multi-energy data is converted into an energy system power index that can be used for scheduling decision, further, taking the minimization of operation cost, the minimization of total carbon emissions and the maximization of energy storage device life as objective functions, taking power balance, energy storage state of charge, electric hydrogen device capacity, berth allocation continuity, load demand priority as constraint conditions, a multi-objective optimization model is constructed, avoiding the trade-off caused by single objective optimization, such as increasing carbon emissions or wasting energy storage devices by only pursuing the lowest cost, finally, based on the multi-objective optimization model and real-time port data, a multi-energy output distribution scheme, load response instruction and device operation parameter are generated, and the strategy is executed, wherein the multi-energy output distribution scheme clearly defines the real-time output proportion of wind energy, photovoltaic energy, energy storage and hydrogen energy, maximizing the use of clean energy; the load response instruction dynamically adjusts the operating state of various loads according to the priority, avoiding energy waste; the device operation parameter provides a quantitative basis for actual operation. At the same time, the strategy is generated based on the real-time data of the current time point, ensuring synchronization and adaptation with the dynamic scene of the port. That is, in the present disclosure, the full data coverage breaks the information island, the three-element optimization target is combined with the port-specific constraints to construct a global optimization system, relying on the deep synergy of each technical link to resolve the multi-element target conflict in the dynamic scene, promoting the transition of multi-energy from dispersed operation to collaborative operation, and finally realizing efficient collaboration and accurate matching of supply and demand of multi-energy, while balancing multiple targets naturally, fully adapting to the complex dynamic operation requirements of the port. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0020] Figure 1 is a flow chart of a port dynamic scene multi-energy fusion system collaborative scheduling method according to an exemplary embodiment;

[0021] Figure 2 is a structural diagram of a port dynamic scene multi-energy fusion system collaborative scheduling device according to an exemplary embodiment;

[0022] Figure 3 A structural schematic diagram of a computer device provided for an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0024] The above purposes, features and advantages of the present application can be more obvious and easy to understand. The present application will be described in further detail below with reference to the drawings and specific embodiments.

[0025] Figure 1 is a flowchart of a multi-energy fusion system cooperative scheduling method in a port dynamic scenario according to an exemplary embodiment, as shown in FIG. 1, the method comprises the following steps S101-S104: Figure 1

[0026] In step S101, a dynamic coupling relationship model is constructed according to port data; the port data includes real-time output data of each available energy of each region of the port at the current time point and load data of each region of the port; the each available energy at least includes at least one of the following energy types: wind energy, photovoltaic energy, energy storage and hydrogen energy; the load data includes at least one of the following data types: dynamic load demand data of ships, logistics equipment and infrastructure; the dynamic coupling relationship model integrates the port data, and is used to output energy system power of each region at the current time point according to the port data, the energy system power includes: photovoltaic output power, wind energy output power, real-time power purchased by the power grid, energy storage charging and discharging power, hydrogen energy output power and total power.

[0027] The object is collected to cover each region of the port (such as the wharf area, the storage area, the office area, etc.), ensuring the spatial integrity of the data.

[0028] The load data at least includes dynamic demand data of one or more of ships, logistics equipment and infrastructure, such as dynamic demand data of ships, for example: ship berthing state, shore power load corresponding to operation time sequence, dynamic demand data of logistics equipment, for example: running load of logistics equipment such as shore cranes and electric trucks, dynamic demand data of infrastructure, for example: heat and cold demand load of infrastructure, etc.

[0029] The collection can be performed at a time granularity of 15 minutes or 30 minutes. The current time point in the above embodiments can be referred to as the current collection time point.

[0030] ​In constructing the dynamic coupling relationship model, the following data of each port is obtained based on the port area real-time sensing system: 、 、 、 , represents the wind energy output power in the wind energy real-time output data of region n at the current time point t, represents the photovoltaic output power of region n at the current time point t, represents the energy storage charging and discharging power of region n at the current time point t, represents the hydrogen energy output power of region n at the current time point t, the corresponding efficiencies are , and the corresponding capacities are .

[0031] The total load is represented as: ;

[0032] wherein, represents the ship load of region n at the current time point t, ; represents the ship berthing time sequence of region n at the current time point t, represents the ship operation time sequence of region n at the current time point t, is a quantitative mapping relationship model of the ship load and the berthing time sequence and the operation time sequence, which is used to accurately depict the dynamic change law of the ship load with time, and can be realized by a piecewise linear function in combination with the port operation scene; represents the load of the logistics equipment of region n at the current time point t, represents the infrastructure heat load of region n at the current time point t, represents the infrastructure cold load of region n at the current time point t.

[0033] The wind energy output power fluctuation of each region at the current time point is obtained according to the wind energy output power in the wind energy real-time output data at the current time point and the wind energy output power at the adjacent previous time point, and the wind energy output power at the adjacent previous time point is obtained by a wind power prediction model.

[0034] The wind energy output power fluctuation at the current time point is , which reflects the change of the wind energy output power with time, and the difference is positive, indicating that the power increases, and is negative, indicating that the power decreases; wherein, represents the wind energy output power fluctuation of region n at the current time point t, represents the wind energy output power in the wind energy real-time output data of region n at the current time point t, represents the wind energy output power of region n at the adjacent previous time point t-1.

[0035] The wind power output power of the region n at the adjacent previous time point is obtained through the wind power prediction model.

[0036] The wind power output power of the adjacent previous time point t-1 , wherein, is a wind power prediction model trained based on historical wind power and historical wind speed data related to the wind power. The wind power prediction model trained, the trained is used to perform prediction of the wind power output power (the wind power output power and the electric power output power).

[0037] The historical wind speed data can be obtained through historical statistical data of a meteorological station, mesoscale meteorological data or local wind measurement tower wind measurement data. The historical wind power is calculated by wind speed, airflow passing area, air density, formula or wind power prediction system historical data constructed with wind power.

[0038] The photovoltaic power fluctuation of each region at the current time point is obtained according to the photovoltaic output power in the photovoltaic real-time output data at the current time point and the photovoltaic output power at the adjacent previous time point, and the photovoltaic output power at the adjacent previous time point is obtained through a photovoltaic prediction model.

[0039] The photovoltaic power fluctuation of the region n at the current time point is , which is the difference of the photovoltaic output power and reflects the change trend of the photovoltaic power, wherein, represents the photovoltaic power fluctuation of the region n at the current time point , represents the photovoltaic output power of the region n at the current time point , represents the photovoltaic output power of the region n at the adjacent previous time point .

[0040] The photovoltaic output power of the region n at the adjacent previous time point is obtained through the photovoltaic prediction model.

[0041] The photovoltaic output power of the region n at the adjacent previous time point is , wherein, is a photovoltaic prediction model trained based on historical photovoltaic output power, historical data related to solar radiation .

[0042] The calculation of photovoltaic power can be obtained by the following formula: P = E / t, wherein E is the light energy, the unit is joule (J); t is the current time point, the unit is second (s). The light energy is calculated from the total solar radiation. The historical data related to solar radiation can come from the historical data of data centers such as meteorological stations, NASA, solargis or photovoltaic supporting construction light power prediction system.

[0043] The energy storage device release power of each region at the current time point, when the wind power output power fluctuation and / or photovoltaic power fluctuation is less than 0, the energy storage device release power is obtained according to the wind power output power fluctuation and / or photovoltaic power fluctuation.

[0044] When or , the adjustment is made by the energy storage device.

[0045] , The energy storage device release power of region n at the current time point t is used to make up the amount of wind and light power reduction, maintain system power stability, and avoid the impact of wind and light power reduction on power supply reliability. The constraint is , which specifies the upper and lower limits of the charge and discharge power of the energy storage device at the current time point t, is the maximum charge and discharge power of the energy storage device, which ensures that the energy storage operates in a safe and reasonable power range to prevent overcharging and overdischarging damage to the device.

[0046] The energy storage state of charge satisfies , which reflects the proportion of the remaining capacity of the energy storage device to the total capacity, The energy storage state of charge of region n at the current time point t is is the capacity of the energy storage device. The energy storage state of charge changes with time, When it is positive (energy storage charging), the SOC rises; when it is negative (energy storage discharging), the SOC falls, which is used to monitor and manage the available capacity of the energy storage to ensure that it is in a reasonable range (such as the SOC needs to be maintained within a certain range to avoid excessive or low impact on life and performance) to ensure stable and effective work of the energy storage device.

[0047] The hydrogen energy compensation power of each region at the current time point, when the wind power output power fluctuation and / or photovoltaic power fluctuation is less than 0, and the real-time state of charge of the energy storage device at the current time point is less than the minimum safety threshold, the hydrogen energy compensation power at the current time point is the difference between the total load and the wind power output power at the current time point, the photovoltaic output power at the current time point and the energy storage charge and discharge power.

[0048] If The minimum safety threshold of the state of charge of the energy storage, the minimum value set in advance, at which point the hydrogen energy system is started as a supplement to ensure stable power supply. The hydrogen energy compensation power is:

[0049]

[0050] The constraint is , is the maximum output power of the hydrogen energy system (determined by the capacity of the hydrogen energy system, hydrogen production and storage scale), represents the hydrogen energy compensation power of region n at the current time point t.

[0051] Predicted total load of each region; total load of each region predicted by the total load prediction model.

[0052] Predicted total load of each region , wherein represents the predicted total load of region n at the current time point t, is a total load prediction model trained based on historical load curves, represents the logistics equipment operating state of region n at the adjacent previous time point in the historical load curve, represents the ship operation schedule of region n at the adjacent previous time point in the historical load curve.

[0053] Coupling relationship table; the coupling relationship table includes the wind power output power, photovoltaic output power, real-time state of charge, hydrogen energy compensation power and total load of each region at the current time point.

[0054] Constructing multi-dimensional data space-time coupling relationship, combining time and space nodes , the coupling relationship is represented as: , which fully describes the space-time distribution characteristics of the multi-energy complementary system (such as strong photovoltaic in the daytime and strong wind power at night in a certain area, and different node loads), wherein represents the coupling relationship of region n at the current time point t. Wherein refers to the upper limit of the time period of the port multi-energy fusion system cooperative scheduling, which has no fixed length and needs to be determined by matching the data acquisition granularity and scheduling target according to the actual scheduling demand of the port, and the optimal is 24 hours, represents the total number of port regions, and each port region represents a space node.

[0055] Supply and demand balance relationship; the supply and demand balance relationship is determined according to the total available energy power of each region at the current time point and the total load of each region at the current time point.

[0056] The operation efficiency of each energy conversion equipment is:​ , η (t, n) is the energy conversion efficiency of region n at the current time point t (such as the electricity-to-hydrogen efficiency), P (t, n) represents the output power of each device of region n at the current time point t (such as the equivalent electric power of the hydrogen production of the electricity-to-hydrogen system), P (t, n) represents the input power of each device of region n at the current time point t (such as the electric power consumed by the electricity-to-hydrogen system), and finally the supply and demand balance relationship can be represented as:

[0057] , P (t, n) represents the difference between the total available energy power and the total load of region n at the current time point t, P (t, n) represents the total available energy power of region n at the current time point t (the sum of all available power / electricity supplementing power such as wind, light, and hydrogen storage); P avail (t, n) is calculated by the formula P avail (t, n) = P wind (t, n) + P pv (t, n) + P storage (t, n) + P H2 (t, n), and the hydrogen energy output power P H2 (t, n) is taken as an example. The hydrogen energy output power P H2 (t, n) needs to be calculated through the energy conversion efficiency formula η conv (t, n) = P out (t, n) / P in (t, n), where P H2 (t, n) is the P out (t, n) of the hydrogen energy system, and the multiplication of P in (t, n) and η conv (t, n) gives the specific relationship between the two; P (t, n) represents the total load of region n at the current time point t.

[0058] The input of the dynamic coupling relationship model is P wind (t, n), P pv (t, n), P storage (t, n), P H2 (t, n), and the corresponding energy conversion efficiency η, the capacity C under the corresponding energy conversion efficiency of the available energy, the ship berthing time sequence B(t, n), the ship operation time sequence Q ship (t, n), the logistics device running state Q device (t, n) of region n at the current time point t, the infrastructure heat load and cold load L heat (t, n), and L cold(t, n), and historical load curve and weather forecast data; the output is a "source-storage-load-grid" dynamic coupling relationship model covering the spatiotemporal matching characteristics of energy output and load demand, the operation efficiency and response relationship of energy conversion equipment, containing G(t, n) and F(t, n) = P avail (t, n)-L total (t, n).

[0059] In one embodiment, port data can be obtained by deploying sensors and data interfaces (such as Modbus protocol).

[0060] After collecting the port data, the collected port data can also be cleaned (remove outliers, complete missing values), standardized (unify power / energy units), and spatiotemporally aligned (match the timestamps and spatial positions of different devices to a unified coordinate system), and then a dynamic coupling relationship model is established based on the processed port data.

[0061] The present disclosure explicitly defines the specific composition of the dynamic coupling relationship model, covering core elements such as multi-energy power fluctuation, energy storage release power, hydrogen energy compensation power, load prediction, and supply-demand balance relationship. It not only accurately captures the intermittent fluctuation characteristics of wind energy and photovoltaic and the complementary regulation capacity of energy storage and hydrogen energy, but also realizes the correlation and integration of data through prediction models and coupling relationship tables. It makes the dynamic coupling relationship model more comprehensive and accurate in reflecting the dynamic correlation of port multi-energy and load, provides more reliable model support for subsequent energy system power output, effectively avoids the imbalance of supply and demand matching caused by model simplification, and improves the data foundation reliability of dispatching decision-making.

[0062] In step S102, a multi-objective optimization model is constructed according to the energy system power of each region at the current time point output by the dynamic coupling relationship model. The objective function of the multi-objective optimization model includes: minimization of operation cost, minimization of total carbon emissions, maximization of energy storage device life, and the constraint conditions of the multi-objective optimization model are: power balance, energy storage state of charge, electric-hydrogen device capacity, berth allocation continuity, and load demand priority.

[0063] The objective function corresponding to the minimization of operation cost is represented as:

[0064] ;

[0065] Wherein, represents the minimized operation cost, represents the electricity purchase price of the power grid in region n at the current time point t, which changes with time t, represents the real-time power purchased from the power grid in region n at the current time point t (if , , indicating that there is no need to purchase electricity from the power grid, if , then ); represents the unit loss cost in the energy storage charging and discharging process, represents the energy storage charging and discharging power of the region n at the current time point t, represents the unit cost of hydrogen production and storage, represents the hydrogen energy output power of the region n at the current time point t, represents the unit operation and maintenance cost of the equipment, represents the total power of the region n at the current time point t; , , are the outputs of the dynamic coupling relationship model in step S101, which together constitute the calculation dimension of the operation cost, and the optimization goal of the port multi-energy fusion system operation economy is realized by quantifying each cost component.

[0066] In the present disclosure, the outputs of the dynamic coupling relationship model are: photovoltaic output power, wind energy output power, real-time power purchased from the power grid, energy storage charging and discharging power, hydrogen energy output power, and total power.

[0067] The present disclosure quantifies and integrates key expenditure items such as power grid purchase cost, energy storage charging and discharging loss cost, hydrogen energy output cost, and equipment operation and maintenance cost by explicitly expressing the objective function expression of operation cost minimization, realizes the accurate accounting of port energy operation cost and the calculability of optimization goal. It can guide the dispatching strategy to focus on low cost, reasonably allocate the proportion of each energy output, effectively reduce the expenditure of port from power grid purchase, equipment loss, etc., improve the economy of energy system operation, and solve the problem of lack of quantitative basis for cost control in traditional dispatching mode.

[0068] The target function corresponding to the total carbon emission minimization is represented as:

[0069]

[0070] wherein, represents the minimized total carbon emission, represents the real-time power purchased from the power grid by the region n at the current time point t, represents the carbon emission coefficient of the power grid, represents the photovoltaic output power of the region n at the current time point t, represents the carbon emission coefficient of photovoltaic power generation represents the wind energy output power of the region n at the current time point t represents the carbon emission coefficient of wind power generation represents the hydrogen energy output power of the region n at the current time point t, Carbon emission coefficient of hydrogen energy.

[0071] The total carbon emission minimization is calculated based on the proportion of fossil energy replacement (such as hydrogen energy replacing oil, wind and light replacing traditional electricity) and the carbon emission coefficient of each energy.

[0072] The present disclosure realizes the quantitative calculation and optimization of the total carbon emission by constructing a target function containing the carbon emission coefficients of various energies such as grid power, photovoltaic, wind energy, hydrogen energy, etc. It can guide the dispatching strategy to preferentially consume photovoltaic, wind energy and other low-carbon energy, reduce the dependence on high-carbon grid power, and reasonably control the carbon emission contribution of the hydrogen energy system, providing a quantitative basis for the accurate control of the total carbon emission of the port energy system, and effectively promoting the low-carbon transformation process of the port energy system.

[0073] The target function corresponding to the maximum life of the energy storage device is represented as:

[0074] ;

[0075] Wherein, represents the maximum life of the energy storage device, represents the energy storage charge and discharge power of the region n at the current time point t, represents the capacity of the energy storage device, i.e. the maximum energy that the energy storage device can store, represents the cycle life of the energy storage device. The formula calculates the consumption proportion of the actual charge and discharge cycle to the life of the energy storage device, the denominator is the total theoretical cycle energy, and the numerator is the total actual charge and discharge energy. The remaining life of the energy storage device is quantified by "1 minus the consumption proportion", and the goal is to maximize this value, thereby achieving the purpose of prolonging the service life of the energy storage device.

[0076] The maximum life of the energy storage device is realized by constraining the energy storage charge and discharge depth, cycle number and remaining life decay rate.

[0077] The present disclosure quantifies the influence of charge and discharge behavior on the life of the energy storage device by associating the energy storage charge and discharge power, device capacity and cycle life through the explicit expression of the target function of the maximum life of the energy storage device. It can guide the dispatching strategy to optimize the charge and discharge rhythm of the energy storage, avoid behaviors such as overcharging, overdischarging and frequent cycling that damage the device, prolong the service life of the energy storage device, reduce the replacement and maintenance cost of the device, solve the problem of rapid device wear caused by ignoring the life protection of the energy storage in the traditional dispatching mode, and improve the long-term stable operation ability and comprehensive benefit of the port energy system.

[0078] The constraint condition of the power balance is represented as:

[0079] ;

[0080] Wherein, Pn(t) represents the wind power output of region n at the current time point t, Pn(t) represents the photovoltaic power output of region n at the current time point t, Pn(t) represents the energy storage charging and discharging power of region n at the current time point t, Pn(t) represents the hydrogen energy output of region n at the current time point t, Pn(t) represents the total load of region n at the current time point t, including the sum of ship, logistics equipment, heat and cold energy loads.

[0081] The above equation represents the total energy output of a certain time point The total output of all energy needs to be balanced with the total load of the port;

[0082] The constraint condition of the state of charge of the energy storage is represented as:

[0083] ;

[0084] wherein, represents the minimum safety threshold, Pn(t) represents the state of charge of the energy storage of region n at the current time point t, represents the maximum safety threshold;

[0085] By maintaining the state of charge of the energy storage device within the safety range, overcharging or overdischarging is avoided;

[0086] The constraint condition of the capacity of the hydrogen equipment is represented as: , and ; wherein, Pn(t) represents the hydrogen energy output of region n at the current time point t, represents the maximum output power of the hydrogen energy output, Pn(t) represents the energy storage charging and discharging power of region n at the current time point t, represents the maximum charging and discharging power of the energy storage;

[0087] The constraint condition of the berth allocation continuity is represented as: No work interruption; wherein, represents the ship operation timing of region n at the current time point t in the historical load curve, represents the ship operation timing of region n at the previous time point t-1 in the historical load curve.

[0088] The constraint condition of the load demand priority is represented as: - wherein, is a critical load, such as a cruise ship heat load, is a non-critical load, The total power of the available energy of the area n at the current time point t is represented. The critical load is determined by the load level, which refers to the load that directly guarantees the safety of port operations, ship rigidity services and the continuity of the main process, and the interruption of which will cause safety risks, ship delays or full process stagnation. The core scenarios include high-voltage shore power safety monitoring systems, cruise life heat loads, shore crane main lifting mechanism power supply, cold chain container refrigeration systems, etc. The non-critical load refers to the load that has no direct impact on safety, whose operation timing can be flexibly adjusted or has alternative solutions, and the interruption of which only affects local efficiency and has no major losses. The core scenarios include non-emergency lighting in the terminal, air conditioning in the office area, etc.

[0089] The present disclosure defines clear operating boundaries for the multi-objective optimization model through mathematical expressions of the constraints of power balance, energy storage state of charge, and electric-hydrogen equipment capacity. Among them, the power balance constraint ensures supply-demand matching, the energy storage state of charge constraint avoids equipment damage, and the electric-hydrogen equipment capacity constraint adapts to the actual capacity of hardware. The continuity of berth allocation and the priority of load demand constraints take into account the characteristics of port operations. This enables the solution process of the optimization model to strictly follow the actual limitations of port operations, avoiding scheduling schemes that are theoretically optimal but cannot be implemented, and improving the operability and safety of the target collaborative scheduling operation strategy.

[0090] After the multi-objective optimization model of the objective function and the constraint condition is output, in the multi-objective optimization model that has been constructed, the core targets of minimizing the operation cost, minimizing the total carbon emissions, and maximizing the energy storage life have been clearly defined, and the optimization boundaries have been defined through the constraints of power balance, energy storage state of charge, etc. The operation cost target has clearly quantified the key cost components such as grid power purchase, energy storage loss, hydrogen storage and injection process, and equipment operation and maintenance. The multi-energy and load data input into the multi-objective optimization model are all derived from the output of the dynamic coupling relationship model in step S101.

[0091] In the present disclosure, when the operation cost minimization target is executed, the grid power purchase cost is reduced by adjusting the grid power purchase power, the energy storage charging and discharging loss is reduced by adjusting the energy storage charging and discharging power, the hydrogen storage and injection process is optimized by adjusting the hydrogen energy processing power, and the equipment operation and maintenance efficiency is improved by adjusting the total power.

[0092] In step S103, according to the multi-objective optimization model and the port data of each area at the current time point, a target collaborative scheduling operation strategy of each area at the current time point is determined, which includes a multi-energy output allocation scheme, a load response instruction and a device operation parameter.

[0093] In one implementation, determining the target collaborative scheduling operation strategy of each area at the current time point according to the multi-objective optimization model and the port data of each area at the current time point includes the following sub-steps A1-A2:

[0094] A1, determining a preliminary collaborative scheduling operation strategy of each region at the current time point according to the multi-objective optimization model and the port data of each region at the current time point.

[0095] The multi-objective optimization model and the port data can also be used to formulate a collaborative operation strategy, time-sharing scheduling and equipment linkage. The collaborative operation strategy, time-sharing scheduling and equipment linkage are as follows:

[0096] Time-sharing scheduling: according to the wind and light power generation power curve, the priority period (wind power dominated), the smoothing period (energy storage regulation) and the regulation period (hydrogen energy supplement) are divided.

[0097] Equipment linkage: through the mechanisms such as "wind-light-energy storage collaboration", "wind-light-hydrogen energy collaboration" and "load-energy collaboration", the linkage operation of multiple devices is realized, including:

[0098] Wind-light-energy storage collaboration: when wind and light fluctuate, energy storage is used to preferentially smooth short-term gaps;

[0099] Wind-light-hydrogen energy collaboration: long-term energy storage (hydrogen energy) is used to cover the wind and light valley period;

[0100] Load-energy collaboration: adjusting the ship berthing time or equipment operation time sequence to match the energy output curve.

[0101] The ship berthing operation management system can receive ship arrival forecast to formulate a berthing schedule, according to the ship type (container ship, bulk carrier), tonnage, loading and unloading demand, assign the appropriate berth, assign the shore crane in advance for the berthing ship, and plan the operation sequence of the shore crane to ensure that the ship can start loading and unloading immediately after berthing, and track the loading and unloading progress in real time. Obtain the future energy output curve, based on the collected information, complete "operation load prediction" and "energy matching degree evaluation", according to the ship loading capacity and equipment configuration, calculate the hourly operation load under different berthing times (such as the total power consumption of shore crane + truck in a certain period), output the adjusted ship berthing schedule (including berth allocation result) and equipment operation time sequence plan (such as the loading and unloading box capacity of shore crane per hour, truck transportation route), to ensure the matching of operation load curve and energy output curve.

[0102] If , it is identified as "priority period"; if and , energy storage smoothing is performed; if , switch to hydrogen energy supplement.

[0103] For example, the time periods can be divided according to the port load characteristics (peak time: 08:00-12:00, 14:00-18:00; flat time: 12:00-14:00, 18:00-22:00; valley time: 22:00-next day 08:00), and the scheduling priorities of each time period are defined, for example: peak time: preferentially calling energy storage discharge, hydrogen energy compensation, and reducing power grid purchase; valley time: preferentially using low-cost power grid electricity to charge energy storage and prepare hydrogen energy (hydrogen storage), and maximizing cost savings.

[0104] The time-sharing scheduling is performed by dividing the priority period, the smoothing period and the adjustment period, and the device linkage strategies such as wind-solar-energy storage coordination, wind-solar-hydrogen coordination, and load-energy coordination are implemented, to form the preliminary coordinated scheduling operation strategy scheme. The preliminary coordinated scheduling strategy scheme includes multi-energy output distribution, load response scheme and device linkage rule, and provides an operable operation strategy basis for subsequent multi-objective optimization algorithm to solve the optimal scheme, so as to promote the efficient matching and coordinated operation of multi-energy and load in the port scene.

[0105] A2, according to the multi-objective optimization model and the preliminary coordinated scheduling operation strategy of each region, a non-dominated sorting genetic algorithm is used to obtain the target coordinated scheduling operation strategy of each region at the current time point.

[0106] The two-step strategy determination mode of the preliminary strategy formulation and the non-dominated sorting genetic algorithm (NSGA-II algorithm) optimization fully gives play to the advantages of the non-dominated sorting genetic algorithm in multi-objective optimization problems, and can efficiently screen out non-dominated optimal solutions in the port scene with multi-objective conflicts and complex constraint conditions. Compared with directly generating a scheduling strategy, this mode not only ensures the scene adaptability of the preliminary strategy based on real-time port data, but also improves the global optimality of the strategy through algorithm optimization, so as to ensure that the target coordinated scheduling operation strategy of each region at the current time point can better balance the multi-element objectives and constraint conditions, and improve the scientificity and accuracy of the scheduling decision.

[0107] In one embodiment, according to the multi-objective optimization model and the preliminary coordinated scheduling operation strategy of each region, a non-dominated sorting genetic algorithm is used to obtain the target coordinated scheduling operation strategy of each region at the current time point, including the following sub-steps A21-A25:

[0108] A21, a non-dominated sorting genetic algorithm is used to optimize and solve the multi-objective optimization model and the preliminary coordinated scheduling operation strategy, to generate a Pareto frontier solution set; each solution in the Pareto frontier solution set is a non-dominated optimal solution.

[0109] A22, quantifying the comprehensive membership of each solution in the Pareto front solution set by a fuzzy membership function; the fuzzy membership function first converts the actual performance value of each solution under the three objectives of minimizing operation cost, minimizing total carbon emissions, and maximizing energy storage device life into a single-objective membership in the interval [0, 1], and then obtains the comprehensive membership of each solution by weighted sum of the single-objective membership according to the weight distribution of each objective by the three operators.

[0110] A23, selecting the solution with the highest comprehensive membership from each solution in the Pareto front solution set as the candidate optimal compromise solution.

[0111] A24, determining the candidate optimal compromise solution that satisfies all constraint conditions as the final optimal compromise solution.

[0112] A25, converting the final optimal compromise solution into a target collaborative scheduling operation strategy, which includes a multi-energy output allocation scheme, a load response instruction, and a device operation parameter; the multi-energy output allocation scheme specifies the power allocation of wind energy, photovoltaic energy, energy storage, hydrogen energy, and grid purchase in different time periods, the load response instruction includes ship berthing time adjustment and optimized logistics device operation schedule, and the device operation parameter includes upper and lower limits of energy storage charging and discharging power and hydrogen energy system start-stop threshold.

[0113] The present disclosure refines the solution process of the non-dominated sorting genetic algorithm, realizes the closed loop of algorithm optimization, demand adaptation, and landing verification by generating a Pareto front solution set, quantifying the comprehensive membership of multiple parties, screening the optimal compromise solution, and verifying the constraint conditions. It not only covers the optimal trade-off space of multiple objectives with the Pareto solution set, but also integrates the demand preferences of port operators, energy system operators, and logistics system operators through the fuzzy membership function, and ensures the feasibility of the scheme through constraint verification. The final output scheduling strategy can balance the interests of multiple parties and actual operation restrictions, improving the acceptance and landing execution effect of the scheduling scheme.

[0114] In step S104, the target collaborative scheduling operation strategy is executed.

[0115] The corresponding device operation parameters are sent to each device, the power adjustment instructions are sent to each energy device, and the corresponding berthing time and logistics device operation schedule are sent to each ship.

[0116] A "center-edge" architecture can be used, in which the center node (port energy management center) is responsible for global scheduling, and the edge node (deployed in each energy device control cabinet) is responsible for local execution, covering the dispersed energy devices in the port (such as photovoltaic arrays in the wharf area, energy storage cabinets in the warehouse area, and hydrogen energy stations).

[0117] The target coordinated scheduling operation strategy is converted into device recognizable control instructions (such as energy storage charging and discharging power instructions, hydrogen energy electrolyzer start and stop instructions, shore power switch instructions), and is transmitted in real time to the edge node through 5G / industrial Ethernet.

[0118] The edge node collects the device execution state (such as the actual charging and discharging power of the energy storage, the hydrogen energy output power), and feeds back to the center node in real time, forming a "instruction issuing - state feedback" closed loop control.

[0119] The present disclosure integrates the real-time output data of multiple energies at the current time point of each area of the port and the dynamic load data of the ship, logistics equipment, infrastructure, etc., constructs a dynamic coupling relationship model, and provides integrated data support for the coordinated scheduling of multiple energies; further, according to the output of the dynamic coupling relationship model, a multi-objective optimization model covering the minimization of operation cost, the minimization of total carbon emissions, and the maximization of energy storage device life is constructed, and key constraints such as power balance and berth allocation continuity are included to fit the actual port operation, and finally the efficient coordinated scheduling of multiple energies in complex dynamic scenarios is realized, which not only accurately adapts to the dynamic load fluctuations of the port, but also effectively balances the multi-dimensional demands of economic cost, carbon emission control and energy storage device life protection, helping the port energy system to get rid of single dependence on fossil energy, and providing core technical support for its clean and low-carbon, intelligent and efficient transformation.

[0120] The present disclosure realizes the efficient coordinated operation of wind, light, storage, hydrogen and other multiple energies in the complex dynamic scenario of the port through dynamic modeling and multi-objective optimization, and solves the non-obvious technical problem of matching the complementary characteristics of multiple energies and dynamic demand. The core problem solved is that in the port scenario, it is difficult to efficiently match the complementary characteristics of wind, light, storage, hydrogen and other multiple energies with the dynamic load such as ship berthing state, logistics equipment operation, infrastructure heat and cold demand, which is specifically manifested in that the existing technology cannot realize the coordinated scheduling of multiple energies in complex dynamic scenarios to balance the operation cost, carbon emissions and energy storage life, and it is difficult to cope with the cases of wind and light power fluctuations, real-time changes of load demand with operation progress and climate conditions, etc., resulting in low energy utilization efficiency, poor operation economy, insufficient environmental protection performance and shortened equipment life, etc.

[0121] The port energy demand has the characteristics of severe load fluctuation, high reliability requirement, and complex energy form, and the traditional single energy supply mode is difficult to meet the green and intelligent development demand of the port. The energy demand of the port power grid has diversity, complexity and dynamics. How to accurately model the multi-energy fusion system and consider the complementary characteristics and dynamic characteristics of various energy forms is the basis for realizing the optimal scheduling of the system. The port energy system has large load fluctuation (such as shore power and loading and unloading equipment start-stop), and the intermittency of wind and light renewable energy is strong. The traditional scheduling model cannot realize multi-energy complementation. In addition, the optimal scheduling of the multi-energy fusion system needs to consider energy cost, equipment operation efficiency, environmental impact and other multiple objectives, and the complexity of the optimization model is high.

[0122] At present, a port AC-DC hybrid power distribution network and its comprehensive scheduling control method are disclosed, which belongs to the field of port power distribution and distribution network automation. The method includes a total step-down station, a sub-station or an open and close station, and various distribution subsystems. The multi-way feeder of the total step-down station provides AC power for each distribution subsystem through the sub-station or the open and close station. The distribution subsystems include a shore power subsystem, a shore crane subsystem, a yard crane subsystem, a charging and swapping station subsystem for port electric tractors, a new energy subsystem, and a production and life subsystem. The AC distribution transformer and the AC / DC converter are used to build the DC bus of each distribution subsystem, and the DC buses of each distribution subsystem are interconnected through DC interconnection protection switches. For the distribution subsystems with multiple DC buses, the DC buses are interconnected through DC interconnection protection switches. This scheme focuses on the power grid structure rather than multi-energy collaborative scheduling. Another disclosure discloses a modular power flexible networking method suitable for port intelligent energy stations, which focuses on the modular networking mode in a static environment.

[0123] The above disclosed schemes do not integrate long-term regulation resources such as hydrogen energy, and only rely on on-site consumption of photovoltaic and potential feedback. In the face of large fluctuations in port wind and light power (such as no light and no wind period at night), the scheme only focuses on on-site power consumption and tidal flow balance, and does not consider carbon emissions and equipment life. At the same time, in terms of scheduling, the scheme relies on fixed power difference calculation and lacks active adjustment on the load side, such as ship berthing time and equipment operation timing. When the load curve and energy output curve do not match, only AC distribution network or resistance consumption can be used to solve the problem, resulting in energy waste. The present disclosure can arrange high-load operation in the wind and light peak period through load-side strategy adjustment, which greatly improves the energy utilization efficiency.

[0124] The application provides a multi-energy fusion system cooperative scheduling method and device in a port dynamic scene. A dynamic coupling relationship model is constructed based on obtained multi-energy real-time output data and load data of each area of the port. The dynamic coupling relationship model is not simply integrated data, but quantifies the real-time correlation between energy output and load demand, converts scattered multi-energy data into energy system power indicators that can be used for scheduling decisions. Further, a multi-objective optimization model is constructed with the minimum operation cost, the minimum total carbon emissions, and the maximum service life of energy storage devices as objective functions, and with power balance, energy storage state of charge, electric and hydrogen device capacity, berth allocation continuity, and load demand priority as constraint conditions, to avoid the trade-off caused by single objective optimization, such as increasing carbon emissions or wasting energy storage devices when only pursuing the lowest cost. Finally, based on the multi-objective optimization model and real-time port data, a multi-energy output allocation scheme, load response instructions, and device operation parameters are generated and the strategy is executed. The multi-energy output allocation scheme specifies the real-time output proportion of wind energy, photovoltaic energy, energy storage, and hydrogen energy, maximizing the use of clean energy. The load response instructions dynamically adjust the operating state of various loads according to priority to avoid energy waste. The device operation parameters provide quantitative basis for actual operation. At the same time, the strategy is generated based on real-time data at the current time point, ensuring synchronization and adaptation to the dynamic scene of the port. That is, in the present disclosure, the full data coverage breaks the information silos, the three-optimized objectives are combined with the port-specific constraints to construct a global optimization system, and the multi-objective conflicts in the dynamic scene are resolved through deep coordination of each technical link, promoting the transition of multi-energy from dispersed operation to coordinated operation. Ultimately, efficient coordination of multi-energy and precise matching of supply and demand can be achieved, while naturally balancing multiple objectives, fully adapting to the complex and dynamic operation requirements of the port.

[0125] In one embodiment, the present disclosure also introduces an abnormal response mechanism to ensure system safety and stability, specifically including: when a preset abnormal state is monitored, re-executing the steps of obtaining the target cooperative scheduling operation strategy and executing the target cooperative scheduling operation strategy; the preset abnormal state includes at least one of the following states: over-discharge of energy storage, over-pressure of hydrogen energy storage tank, and overload of logistics equipment.

[0126] When the scheduling scheme and real-time monitoring data are executed, an abnormal response mechanism is introduced. When abnormal states such as over-discharge of energy storage, over-pressure of hydrogen energy storage tank, and overload of shore bridge are monitored, an emergency adjustment mechanism is triggered to re-allocate multi-energy output and load demand, prioritize critical loads (such as cruise ship heat loads), and adjust energy storage device and hydrogen energy device operation parameters to restore safety constraints. At the same time, the cooperative scheduling scheme is corrected and the corrected cooperative scheduling scheme is output, ensuring the safe and stable operation of the system in abnormal scenes.

[0127] The present disclosure can monitor three types of abnormalities in real time, such as device abnormalities, overcharging / overdischarging of energy storage through current / voltage sensors, hydrogen energy system leakage, photovoltaic inverter failure; power abnormalities, monitoring sudden changes in wind and light output (such as a fluctuation of >30% within 1 minute), sudden increase in load (such as the simultaneous shore power of more than 3 ships), and communication abnormalities, monitoring the interruption of center-edge node communication (such as loss of 5G signal). Pre-stored abnormal response solutions are also provided, such as: device failure, starting backup equipment (such as switching to backup hydrogen energy compensation when energy storage fails); power surge, triggering temporary capacity increase application for grid power purchase + emergency discharge of energy storage. When an abnormality occurs, the emergency strategy is automatically triggered, and when the abnormality is resolved (such as equipment repair or communication recovery), the normal scheduling mode is automatically switched back, and an abnormality processing log is recorded.

[0128] The present disclosure adds an abnormal response mechanism, which triggers the process of reacquiring and executing the target coordinated scheduling operation strategy for preset abnormal states such as overdischarge of energy storage, overpressure of hydrogen energy storage tank, and overload of logistics equipment. This effectively improves the fault tolerance and stability of the port multi-energy fusion system, quickly responds to sudden abnormalities during operation, avoids the expansion of abnormal states leading to equipment damage, operation interruption, or safety accidents, ensures the continuity of port energy supply and the stability of port operation, and solves the problem of lack of dynamic emergency adjustment mechanism in traditional scheduling mode.

[0129] By verifying the effect of the present disclosure scheme, the efficiency improvement indicators are quantified. Through the scheduling scheme operation data after execution, the multi-energy coordination efficiency (wind and light utilization rate, energy storage peak shaving contribution rate, hydrogen energy adjustment proportion) and economic indicators (purchase power cost reduction rate, carbon emission reduction amount) are calculated. Compared with the traditional static scheduling scheme, the improvement effect of the dynamic coupling modeling and multi-objective optimization method on energy coordination efficiency is verified. The coordination effect evaluation report (including efficiency improvement rate, economic improvement, and carbon emission reduction amount) is output.

[0130] The calculation of multi-energy coordination efficiency (wind and light utilization rate, energy storage peak shaving contribution rate, hydrogen energy adjustment proportion) and economic indicators (purchase power cost reduction rate, carbon emission reduction amount) includes: based on the real-time collected multi-energy output data, load consumption data, and system operation parameters, the corresponding calculation logic (such as wind and light utilization rate = actual consumption of wind and light = total wind and light power generation, purchase power cost reduction rate = (original purchase power cost - adjusted purchase power cost) / original purchase power cost, etc., carbon emission is calculated by standard coal according to the carbon dioxide emission per unit area or per unit energy generated in a unit time, “Port operation enterprise carbon emission accounting guide” and the like) is used to calculate these indicators.

[0131] The step realizes verification effect, quantifies efficiency improvement index, and evaluates the actual operation effect of the scheduling system. Specifically, it further includes: 1) defining and calculating core indexes, such as energy utilization efficiency: wind power / photovoltaic consumption rate (consumed power / total power generation), multi-energy complementary rate (non-grid power supply / total load power); economic index: unit load energy consumption cost (total energy cost / total load power), annual cost saving (compared with traditional scheduling scheme); reliability index: power supply reliability rate (1-lack of power time / total running time), power fluctuation suppression rate (suppressed fluctuation amplitude / original fluctuation amplitude); 2) comparing the current scheduling effect with "non-collaborative scheduling scheme" (such as wind and light direct grid connection, no energy storage / hydrogen compensation), "traditional single-objective scheduling scheme" (such as only optimizing economy), and quantifying the efficiency improvement range; 3) generating verification reports by day / week / month, including index trend chart, abnormal processing effect analysis, and optimization space suggestion.

[0132] The present disclosure can also perform system iteration and model optimization to adapt to dynamic changes in port demand.

[0133] The step can achieve the purpose of system iteration and model optimization, adapt to dynamic changes in port demand, and continuously optimize system performance. Through port operation data (such as annual throughput change, ship type proportion (container ship / bulk carrier), new equipment commissioning plan), identify demand changes (such as throughput growth leading to 10% annual growth of load, new energy equipment (such as newly added photovoltaic power station) access); based on demand changes and verification results, update core model parameters (such as the first dynamic coupling model: update time and space data granularity (such as adjust the time granularity to 10 minutes after the load increases), add data sources of newly connected equipment; the second multi-objective optimization model: adjust the target weight (such as increase the environmental protection target weight after the proportion of new energy increases), add constraint conditions (such as the output constraint of the newly added photovoltaic power station); optimize collaborative operation strategy (such as adding ship shore power reservation scheduling strategy), upgrade optimization algorithm (such as introducing reinforcement learning algorithm to improve dynamic adaptation ability); record the iteration version of the model, strategy, and algorithm, support version backtracking (such as restore to the historical optimal version when the effect decreases after iteration).

[0134] The generated synergistic effect evaluation report result is used to correct the dynamic coupling model parameters (such as wind and light prediction error correction, load priority adjustment (firstly, real-time wind and light output power and actual load data are collected, and then wind and light prediction values are compared with real-time values to correct wind and light prediction errors, and the importance of actual load is evaluated to adjust the priority of load, and the correction parameters are obtained from the comparison results and the importance evaluation results of load. For example, the dynamic correction method of introducing a correction factor to correct the prediction model in real time is used to implement the comparison of real-time wind and light output power and prediction values, and the priority of load is confirmed by the port owner or the specific load level) ) ; the multi-objective optimization model is optimized based on the new operation data (such as introducing a berth allocation flexibility constraint and updating a carbon emission coefficient) (the corresponding objective function is a multi-objective function constructed based on the premise of meeting the berth allocation flexibility constraint, and the goals of reducing port power purchase cost, improving wind and light energy utilization rate, and reducing system carbon emission based on the updated carbon emission coefficient. The optimization based on the new operation data is the multi-objective function of minimizing the operation cost, minimizing the total carbon emission, and maximizing the service life of the energy storage device in S102, and the berth allocation flexibility constraint and the updated carbon emission coefficient are introduced based on the new data during optimization, and then the NSGA-II algorithm is used for solution), the model is periodically retrained to ensure that the scheduling scheme adapts to changes in port ship types, fluctuations in weather conditions, and policy adjustments. The dynamic coupling model and the multi-objective optimization strategy are continuously optimized.

[0135] The present disclosure also includes providing data storage and cross-module data interaction support, providing a visual monitoring interface and a manual intervention interface for port operation and maintenance personnel, and other hardware devices required for the implementation method.

[0136] The present disclosure can use parallel computing technology (such as GPU acceleration and distributed computing framework) to control the solution time within 5 minutes (to meet the real-time requirements of port dynamic scheduling).

[0137] Advantages of the present scheme:

[0138] 1) The core parameters of the operation cost objective in the present scheme 、 、 are not static values, but real-time outputs from the dynamic coupling relationship model constructed in the first step. The dynamic coupling relationship model integrates real-time output data (power, efficiency, capacity) of port wind energy, photovoltaic energy, energy storage, hydrogen energy, as well as dynamic demand data such as ship berthing state, logistics equipment operation time sequence, and infrastructure heat and cold load, to construct a time and space coupling relationship of the whole "source-storage-load-network" link.

[0139] 2) Operation cost objective formula The whole-chain cost composition of the port multi-energy fusion system is covered, and each cost item corresponds to a specific physical process and technical logic. Among them, the grid electricity purchase cost , changes dynamically over time, starts only when local energy cannot meet the load, embodying the economic logic of "local energy priority consumption"; the energy storage charging and discharging loss cost is directly related to the charging and discharging efficiency loss of the energy storage device, and its value is related to the number of cycles and depth of the energy storage; the hydrogen production and storage injection cost covers the whole process cost of hydrogen production, storage, and refueling, and starts only when the state of charge of the energy storage is below the safety threshold, avoiding the inefficient consumption of hydrogen energy; the equipment operation and maintenance cost is related to the total power of the system, quantifying the daily maintenance and repair expenses of wind, light, storage, hydrogen, and other equipment, and realizing the direct correlation between cost and equipment operating state.

[0140] 3) The minimization of operating cost is not an isolated goal, but a core of the multi-objective optimization model together with the minimization of total carbon emissions and the maximization of energy storage life. The three are optimized through parameter linkage. For example, reducing the grid electricity purchase power can simultaneously reduce the electricity purchase cost ( ) and carbon emissions, but may increase the dependence on energy storage or hydrogen energy; controlling the energy storage charging and discharging power not only affects the loss cost ( ), but also directly relates to the energy storage life goal through the number of charging and discharging cycles , avoiding the problem of decreased environmental performance or shortened equipment life caused by single cost optimization.

[0141] 4) The scheme realizes dynamic optimization of operating cost through implicit energy calling priority. In energy output allocation, renewable energy such as wind and photovoltaic with zero marginal cost is preferentially consumed, and only when wind and light output is insufficient, the order of "energy storage smoothing short-term gap → hydrogen energy supplementing long-term gap → grid bottoming out" is called, reducing the use of high-cost energy from the source. For example, when , the excess power can be used for energy storage charging (reducing subsequent discharge loss cost) or hydrogen production (storing as low-cost energy); when wind and light output is insufficient and energy storage SOC is sufficient, energy storage is preferentially called to avoid the high production cost of hydrogen energy; only when energy storage and hydrogen energy cannot meet the demand, grid electricity purchase is started to minimize the overall cost.

[0142] 5) The parameters in the operating cost formula are dynamically adjusted according to the equipment operating state, embodying the deep binding of cost and technical characteristics. For example, the energy storage charging and discharging loss cost coefficient , with the change of the energy storage SOC state (the charging and discharging efficiency decreases and the loss cost increases when the SOC is too high or too low); hydrogen cost associated with the hydrogen production method (if hydrogen is produced by using abandoned wind and light power, the cost can be greatly reduced, and at this time the cost contribution of the hydrogen energy decreases); and the equipment operation and maintenance cost coefficient is related to the equipment operation time and the load rate.

[0143] 6) The scheme reduces the operation cost through the load-energy collaborative strategy from the demand side to assist in reducing the operation cost, and forms a closed loop with the cost control on the energy side. In the collaborative operation strategy of S103, the load demand curve is matched with the energy output curve by adjusting the ship berthing time, the electric truck charging period, and the shore crane operation time sequence (for example, high load operation is arranged in the peak period of wind and light output), so as to reduce the peak regulation pressure of the energy storage, hydrogen energy, and power grid. For example, the electric truck charging period is concentrated in the peak (maximum) of the photovoltaic output, which can directly reduce the dependence on the power grid purchase or energy storage discharge, and reduce the cost contribution of the and and The operation cost minimization reduces the power purchase cost term in the objective function.

[0144] Based on the same inventive concept, the embodiments of the present application also provide a port dynamic scene multi-energy fusion system collaborative scheduling device for implementing the above-mentioned port dynamic scene multi-energy fusion system collaborative scheduling method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more port dynamic scene multi-energy fusion system collaborative scheduling device embodiments provided below can be referred to the limitations of the port dynamic scene multi-energy fusion system collaborative scheduling method in the foregoing, which will not be repeated here.

[0145] In an exemplary embodiment, as shown in Figure 2 a port dynamic scene multi-energy fusion system collaborative scheduling device is provided, which includes:

[0146] The dynamic coupling relationship model construction module is configured to construct a dynamic coupling relationship model according to port data, wherein the port data includes real-time output data of each available energy of each region of the port at a current time point and load data of each region of the port, the each available energy at least includes at least one of the following energy types: wind energy, photovoltaic energy, energy storage and hydrogen energy, and the load data includes at least one of the following data types: dynamic load demand data of a ship, logistics equipment and infrastructure, and the dynamic coupling relationship model integrates the port data and is configured to output energy system power of each region at the current time point according to the port data, wherein the energy system power includes photovoltaic output power, wind energy output power, real-time power purchased by a power grid, energy storage charging and discharging power, hydrogen energy output power and total power;

[0147] The multi-objective optimization model construction module is configured to construct a multi-objective optimization model according to the energy system power of each region at the current time point output by the dynamic coupling relationship model, wherein an objective function of the multi-objective optimization model includes minimization of operating cost, minimization of total carbon emission and maximization of service life of energy storage equipment, and a constraint condition of the multi-objective optimization model includes power balance, energy storage state of charge, electric-hydrogen equipment capacity, berth allocation continuity and load demand priority.

[0148] The determination module is configured to determine a target collaborative scheduling operation strategy of each region at the current time point according to the multi-objective optimization model and the port data of each region at the current time point, wherein the target collaborative scheduling operation strategy includes a multi-energy output allocation scheme, a load response instruction and equipment operation parameters.

[0149] The execution module is configured to execute the target collaborative scheduling operation strategy of each region at the current time point.

[0150] In one embodiment, in the aspect of determining the target collaborative scheduling operation strategy of each region at the current time point according to the multi-objective optimization model and the port data of each region at the current time point, the determination module is specifically configured to:

[0151] determine a preliminary collaborative scheduling operation strategy of each region at the current time point according to the multi-objective optimization model and the port data of each region at the current time point;

[0152] obtain the target collaborative scheduling operation strategy of each region at the current time point by using a non-dominated sorting genetic algorithm according to the multi-objective optimization model and the preliminary collaborative scheduling operation strategy of each region.

[0153] In one embodiment, the dynamic coupling relationship model at least includes at least one of the following:

[0154] a wind power fluctuation of each region at a current time point, the wind power fluctuation being obtained according to a wind power in wind real-time output data at the current time point and a wind power at an adjacent previous time point, the wind power at the adjacent previous time point being obtained by a wind power prediction model;

[0155] a photovoltaic power fluctuation of each region at the current time point, the photovoltaic power fluctuation being obtained according to a photovoltaic power in photovoltaic real-time output data at the current time point and a photovoltaic power at the adjacent previous time point, the photovoltaic power at the adjacent previous time point being obtained by a photovoltaic prediction model;

[0156] a storage device release power of each region at the current time point, the storage device release power being obtained according to the wind power fluctuation and / or the photovoltaic power fluctuation when the wind power fluctuation and / or the photovoltaic power fluctuation is less than 0;

[0157] a hydrogen energy compensation power of each region at the current time point, the hydrogen energy compensation power at the current time point being a difference between a total load and a difference between a wind power at the current time point, a photovoltaic power at the current time point and a storage charge-discharge power when the wind power fluctuation and / or the photovoltaic power fluctuation is less than 0 and a real-time state of charge of the storage device at the current time point is less than a minimum safety threshold;

[0158] a total load of each region at the adjacent previous time point, the total load at the adjacent previous time point being obtained by a total load prediction model;

[0159] a coupling relationship table, the coupling relationship table including the wind power at the current time point, the photovoltaic power at the current time point, the real-time state of charge, the hydrogen energy compensation power, the total load of each region at the current time point;

[0160] a supply-demand balance relationship, the supply-demand balance relationship being determined according to a total available energy power of each region at the current time point and the total load of each region at the current time point.

[0161] In one embodiment, the objective function corresponding to the operation cost minimization is expressed as:

[0162]

[0163] wherein, represents the minimized operation cost, represents a unit price of grid electricity purchase of region n at a current time point t, represents a real-time power purchased from the grid by region n at the current time point t, represents a unit loss cost in a storage charge-discharge process, represents a storage charge-discharge power of region n at the current time point t, represents a unit cost of hydrogen production and storage injection,​ represents the hydrogen energy output power of region n at the current time point t, represents the unit operation and maintenance cost of the equipment, represents the total power of region n at the current time point t.

[0164] In one embodiment, the total carbon emission minimization corresponding objective function is represented as:

[0165] ;

[0166] wherein, represents the minimized total carbon emission, represents the real-time power purchased by region n from the power grid at the current time point t, represents the carbon emission coefficient of the power grid power, represents the photovoltaic output power of region n at the current time point t, represents the carbon emission coefficient of photovoltaic power generation, represents the wind energy output power of region n at the current time point t, represents the carbon emission coefficient of wind power generation, represents the hydrogen energy output power of region n at the current time point t, represents the carbon emission coefficient of hydrogen energy.

[0167] In one embodiment, the energy storage device life maximization corresponding objective function is represented as:

[0168] ;

[0169] wherein, represents the maximized energy storage device life, represents the energy storage charge and discharge power of region n at the current time point t, represents the capacity of the energy storage device, represents the cycle life of the energy storage device.

[0170] In one embodiment, the power balance constraint condition is represented as:

[0171] ;

[0172] wherein, represents the wind energy output power of region n at the current time point t, represents the photovoltaic output power of region n at the current time point t, represents the energy storage charge and discharge power of region n at the current time point t, represents the hydrogen energy output power of region n at the current time point t, represents the total load of region n at the current time point t;

[0173] The constraint condition of the energy storage state of charge is expressed as:

[0174]

[0175] wherein, represents the minimum safety threshold, represents the energy storage state of charge of the region n at the current time point t, represents the maximum safety threshold;

[0176] The constraint condition of the capacity of the electric hydrogen equipment is expressed as: , and

[0177] wherein, represents the hydrogen energy output power of the region n at the current time point t, represents the maximum output power of the hydrogen energy output, represents the energy storage charging and discharging power of the region n at the current time point t, represents the maximum charging and discharging power of the energy storage;

[0178] The constraint condition of the berth allocation continuity is expressed as: ; wherein, represents the ship operation time sequence of the region n at the current time point t in the historical load curve.

[0179] In an embodiment, in the aspect of obtaining the target collaborative scheduling operation strategy according to the multi-objective optimization model and the preliminary collaborative scheduling operation strategy, the determining module is specifically configured to:

[0180] The multi-objective optimization model and the preliminary collaborative scheduling operation strategy are optimized and solved by using the non-dominated sorting genetic algorithm to generate a Pareto frontier solution set; each solution in the Pareto frontier solution set is a non-dominated optimal solution;

[0181] The comprehensive membership degrees of the solutions in the Pareto frontier solution set by the port operator, the energy system operator and the logistics system operator are quantified by using a fuzzy membership function; the fuzzy membership function first converts the actual performance values of the solutions under the three objectives of minimizing the operation cost, minimizing the total carbon emission and maximizing the service life of the energy storage equipment into single-objective membership degrees in the interval of 0-1, and then performs weighted summation on the single-objective membership degrees according to the weight distribution of the three operators on the objectives to obtain the comprehensive membership degrees of the solutions;

[0182] The solution with the highest comprehensive membership degree is selected from the solutions in the Pareto frontier solution set as a candidate optimal compromise solution;

[0183] ​​verify whether the candidate optimal compromise solution meets all constraint conditions of the multi-objective optimization model, including power balance constraint, energy storage state of charge constraint, electro-hydrogen equipment capacity constraint, berth allocation continuity constraint and load demand priority constraint, and determine the candidate optimal compromise solution meeting all constraint conditions as the final optimal compromise solution;

[0184] convert the final optimal compromise solution into a target coordinated scheduling operation strategy, which includes a multi-energy output allocation scheme, a load response instruction and an equipment operation parameter; the multi-energy output allocation scheme explicitly allocates power of wind energy, photovoltaic energy, energy storage, hydrogen energy and grid purchase in different time periods, the load response instruction includes ship berthing time adjustment and optimized logistics equipment operation time sequence, and the equipment operation parameter includes upper and lower limits of energy storage charging and discharging power and hydrogen energy system start-stop threshold.

[0185] In one embodiment, the device further comprises:

[0186] The re-execution module is configured to re-execute the steps of obtaining the target coordinated scheduling operation strategy and executing the target coordinated scheduling operation strategy when a preset abnormal state is monitored; the preset abnormal state includes at least one of the following states: over-discharge of energy storage, over-pressure of hydrogen energy storage tank and overload of logistics equipment.

[0187] In an exemplary embodiment, a computer device, which can be a server or a terminal, is provided, and an internal structure diagram of the computer device can be as shown in Figure 3 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement the multi-energy fusion system coordinated scheduling method in a port dynamic scenario.

[0188] Those skilled in the art can understand that, Figure 3The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0189] In an exemplary embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0190] In an exemplary embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implements the steps in the above method embodiments when executed by a processor.

[0191] In an exemplary embodiment, a computer program product is provided, including a computer program, and the computer program implements the steps in the above method embodiments when executed by a processor.

[0192] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0193] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0194] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0195] The principles and implementation modes of the present application are described by using specific examples in this paper, and the above-mentioned embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for coordinated scheduling of multi-energy fusion systems in a port dynamic scenario, characterized in that, The method comprises: According to the port data, a dynamic coupling relationship model is constructed; the port data comprises real-time output data of each available energy of each region of the port at a current time point and load data of each region of the port; the each available energy at least comprises at least one of the following energy types: wind energy, photovoltaic energy, energy storage and hydrogen energy; the load data comprises at least one of the following data types: dynamic load demand data of ships, logistics equipment and infrastructure; the dynamic coupling relationship model integrates the port data and is used for outputting energy system power of each region at the current time point according to the port data, wherein the energy system power comprises photovoltaic output power, wind energy output power, real-time power purchased by the power grid, energy storage charging and discharging power, hydrogen energy output power and total power; A multi-objective optimization model is constructed according to the energy system power of each region at the current time point output by the dynamic coupling relationship model, wherein an objective function of the multi-objective optimization model comprises minimization of operation cost, minimization of total carbon emission and maximization of energy storage device life, and a constraint condition of the multi-objective optimization model is power balance, energy storage state of charge, electric-hydrogen device capacity, berth allocation continuity and load demand priority; A target collaborative scheduling operation strategy of each region at the current time point is determined according to the multi-objective optimization model and the port data of each region at the current time point, wherein the target collaborative scheduling operation strategy comprises a multi-energy output allocation scheme, a load response instruction and a device operation parameter; The target collaborative scheduling operation strategy of each region at the current time point is executed; The determination of the target collaborative scheduling operation strategy of each region at the current time point according to the multi-objective optimization model and the port data of each region at the current time point comprises: A preliminary collaborative scheduling operation strategy of each region at the current time point is determined according to the multi-objective optimization model and the port data of each region at the current time point; A target collaborative scheduling operation strategy of each region at the current time point is obtained by using a non-dominated sorting genetic algorithm according to the multi-objective optimization model and the preliminary collaborative scheduling operation strategy of each region.

2. The method of claim 1, wherein, The dynamic coupling relationship model at least comprises at least one of the following: Wind energy output power fluctuation of each region at the current time point; the wind energy output power fluctuation is obtained according to wind energy output power in real-time output data of wind energy at the current time point and wind energy output power at an adjacent previous time point, and the wind energy output power at the adjacent previous time point is obtained by using a wind power prediction model; Photovoltaic power fluctuation of each region at the current time point; the photovoltaic power fluctuation is obtained according to photovoltaic output power in real-time output data of photovoltaic energy at the current time point and photovoltaic output power at an adjacent previous time point, and the photovoltaic output power at the adjacent previous time point is obtained by using a photovoltaic prediction model; Energy storage device release power of each region at the current time point; when the wind energy output power fluctuation and / or the photovoltaic power fluctuation is less than 0, the energy storage device release power is obtained according to the wind energy output power fluctuation and / or the photovoltaic power fluctuation; hydrogen energy compensation power of each region at the current time point; when the wind energy output power fluctuation and / or the photovoltaic power fluctuation is less than 0, and the real-time state of charge of the energy storage device at the current time point is less than the minimum safety threshold, the hydrogen energy compensation power at the current time point is the total load minus the wind energy output power at the current time point, the photovoltaic output power at the current time point, and the energy storage charging and discharging power; predicted total load of each region; the total load of each region predicted by a total load prediction model; a coupling relationship table; the coupling relationship table includes the wind energy output power, the photovoltaic output power, the real-time state of charge, the hydrogen energy compensation power, and the total load of each region at the current time point; a supply-demand balance relationship; the supply-demand balance relationship is determined according to the total available energy power of each region at the current time point and the total load of each region at the current time point.

3. The method of claim 1, wherein the objective function corresponding to the operation cost minimization is represented as:

4. The method of claim 1, wherein the objective function corresponding to the total carbon emission minimization is represented as: ； wherein, represents the minimized operation cost, represents the grid purchase price of electricity of region n at the current time point t, represents the real-time power purchased from the grid of region n at the current time point t, represents the unit loss cost in the energy storage charging and discharging process, represents the energy storage charging and discharging power of region n at the current time point t, represents the unit cost of hydrogen production and storage injection, represents the hydrogen energy output power of region n at the current time point t, represents the unit operation and maintenance cost of the equipment, represents the total power of region n at the current time point t.

5. The method of claim 1, wherein the objective function corresponding to the energy storage device life maximization is represented as:

6. The method of any one of claims 3-5, wherein the constraint condition of the power balance is represented as: ； wherein represents the total amount of carbon emissions minimized, represents the real-time power purchased from the grid by region n at the current point in time t, represents the carbon emission factor of grid power, represents the photovoltaic output power of region n at the current point in time t, represents the carbon emission factor of photovoltaic power generation, represents the wind energy output power of region n at the current point in time t, represents the carbon emission factor of wind power generation, represents the hydrogen energy output power of region n at the current point in time t, represents the carbon emission factor of hydrogen energy. the constraint condition of the energy storage state of charge is represented as: the target collaborative scheduling operation strategy is obtained by using a non-dominated sorting genetic algorithm based on the multi-objective optimization model and the preliminary collaborative scheduling operation strategy, and the obtaining includes: ； wherein, represents the maximized device lifetime, represents the energy storage charge and discharge power of region n at the current time point t, represents the capacity of the energy storage device, represents the cycle life of the energy storage device. the multi-objective optimization model and the preliminary collaborative scheduling operation strategy are optimized and solved by using the non-dominated sorting genetic algorithm to generate a Pareto frontier solution set; each solution in the Pareto frontier solution set is a non-dominated optimal solution; a fuzzy membership function is used to quantify the comprehensive membership degrees of each solution in the Pareto frontier solution set by the port operator, the energy system operator, and the logistics system operator; the fuzzy membership function first converts actual performance values of each solution under the three targets of operation cost minimization, total carbon emission minimization, and energy storage device life maximization into single-target membership degrees in the interval of 0-1, and then obtains the comprehensive membership degree of each solution by weighted sum of the single-target membership degrees according to the weight distribution of the three operators on each target; ； wherein represents the wind energy output power of region n at the current point in time t, represents the photovoltaic output power of region n at the current point in time t, represents the storage charging and discharging power of region n at the current point in time t, represents the hydrogen energy output power of region n at the current point in time t, represents the total load of region n at the current point in time t, a solution with the highest comprehensive membership degree is selected from each solution in the Pareto frontier solution set as a candidate optimal compromise solution; ； wherein denotes the minimum safety threshold, denotes the state of charge of the energy storage of the region n at the current point in time t, denotes the maximum safety threshold; The constraint on the capacity of the electrohydrogenation device is expressed as: , and ; wherein, represents the hydrogen energy output power of region n at the current time point t, represents the maximum output power of the hydrogen energy system output, represents the energy storage charge-discharge power of region n at the current time point t, represents the maximum energy storage charge-discharge power; The constraint of berth allocation continuity is expressed as: No work interruption; wherein, denotes the ship operation timing of region n in the historical load curve at the current time point t, denotes the ship operation timing of region n in the historical load curve at the previous time point t-1; The constraint condition of load demand priority is expressed as: - wherein, Pn(t) represents the total available energy power of the area n at the current time point t, Pn,cr is a critical load, Pn,nc is a non-critical load; the critical load refers to a load directly guaranteeing the safety of port operation, rigid service of a ship and continuity of a main process; the non-critical load refers to a load having no direct influence on the safety of port operation, flexible adjustment of operation timing or an alternative solution.

7. The method of claim 6, wherein, the candidate optimal compromise solution that meets all the constraint conditions is determined as the final optimal compromise solution; the final optimal compromise solution is converted into the target collaborative scheduling operation strategy, and the target collaborative scheduling operation strategy includes a multi-energy output allocation scheme, a load response instruction, and a device operation parameter; the multi-energy output allocation scheme clearly allocates power of wind energy, photovoltaic energy, energy storage, hydrogen energy, and grid purchase at different time periods; the load response instruction includes ship berthing time adjustment and optimized logistics device operation timing; and the device operation parameter includes upper and lower limits of energy storage charging and discharging power and hydrogen energy system start-stop threshold. The method further includes: ​ ​ ​ 8. The method of claim 1, wherein, ​ When a preset abnormal state is monitored, a target collaborative scheduling operation strategy is re-executed, and the steps of executing the target collaborative scheduling operation strategy are performed. The preset abnormal state at least includes at least one of the following states: over-discharge of energy storage, overpressure of hydrogen energy storage tank, and overload of logistics equipment.

9. A multi-energy fusion system cooperative scheduling device in a port dynamic scene, characterized in that, The device comprises: A dynamic coupling relationship model construction module is configured to construct a dynamic coupling relationship model according to port data, wherein the port data comprises real-time output data of each available energy of each region of the port at a current time point and load data of each region of the port, the each available energy at least includes at least one of the following energy types: wind energy, photovoltaic energy, energy storage and hydrogen energy, and the load data comprises at least one of the following data types: dynamic load demand data of a ship, logistics equipment and infrastructure; the dynamic coupling relationship model integrates the port data and is configured to output energy system power of each region at the current time point according to the port data, wherein the energy system power comprises photovoltaic output power, wind energy output power, real-time power purchased from a power grid, energy storage charging and discharging power, hydrogen energy output power and total power; A multi-objective optimization model construction module is configured to construct a multi-objective optimization model according to the energy system power of each region at the current time point output by the dynamic coupling relationship model, wherein the objective function of the multi-objective optimization model comprises minimization of operation cost, minimization of total carbon emission and maximization of service life of energy storage equipment, and the constraint condition of the multi-objective optimization model comprises power balance, energy storage state of charge, electric-hydrogen equipment capacity, berth allocation continuity and load demand priority; A determination module is configured to determine a target collaborative scheduling operation strategy of each region at the current time point according to the multi-objective optimization model and the port data of each region at the current time point, wherein the target collaborative scheduling operation strategy comprises a multi-energy output distribution scheme, a load response instruction and equipment operation parameters; An execution module is configured to execute the target collaborative scheduling operation strategy of each region at the current time point. In the aspect of determining the target collaborative scheduling operation strategy of each region at the current time point according to the multi-objective optimization model and the port data of each region at the current time point, the determination module is specifically configured to: determine a preliminary collaborative scheduling operation strategy of each region at the current time point according to the multi-objective optimization model and the port data of each region at the current time point; and obtain the target collaborative scheduling operation strategy of each region at the current time point by using a non-dominated sorting genetic algorithm according to the multi-objective optimization model and the preliminary collaborative scheduling operation strategy of each region.

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