Port energy management optimization methods and devices, control devices and storage media

Abstract

This invention relates to the field of port energy management technology, specifically providing a port energy management optimization method and apparatus, control device, and storage medium. The aim is to solve the problem of power and energy mismatch between new energy sources and loads in port energy management, which leads to significant disturbances to the port's main power grid. To this end, the port energy management optimization method of this invention includes: in the day-ahead scheduling phase, establishing a day-ahead economic scheduling model and constraints for the port area with the aim of minimizing the overall daily operating cost; obtaining day-ahead scheduling results based on the day-ahead economic scheduling model and constraints; in the intraday adjustment phase, establishing a predictive model and constraints for intraday future operating conditions with the aim of reducing power changes in the main power grid; obtaining intraday adjustment results based on the intraday future operating condition predictive model and constraints; and correcting the day-ahead scheduling results based on the intraday adjustment results to improve port energy autonomy.

Description

Technical Field

[0001] This invention relates to the field of port energy management technology, specifically providing a port energy management optimization method and apparatus, control device and storage medium. Background Technology

[0002] Currently, port energy includes load, new energy sources, and hybrid energy storage systems. Load mainly includes quay crane load and shore power load, while hybrid energy storage systems mainly include fuel cells, electrolyzers, and lithium-ion batteries. The main raw materials for the production of fuel cells and electrolyzers are hydrogen energy.

[0003] With the electrification of port equipment, traditional traffic dispatching also possesses electrical characteristics, forming a coupled "transportation-electricity" operation mode in the port area. With the development of new energy sources and energy storage technologies, the clean energy of ports will greatly promote low-carbon development in the port area. As the penetration rate of new energy sources in the port area increases, handling power uncertainty becomes a key issue in energy management.

[0004] Accordingly, there is a need in this field for a new port energy management optimization scheme to address the above problems. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, namely the mismatch in power and energy between new energy sources and loads in existing port energy management, which leads to significant disturbances to the main power grid in the port area and negatively impacts the quality and stable operation of port power, this invention provides a port energy management optimization method and apparatus, control device, and storage medium.

[0006] In a first aspect, the present invention provides a port energy management optimization method, the method comprising:

[0007] During the day-ahead scheduling phase, a day-ahead economic scheduling model and constraints for the port area are established with the aim of minimizing the overall daily operating cost of the port area.

[0008] The day-ahead scheduling results are obtained based on the day-ahead economic scheduling model and constraints.

[0009] During the intraday adjustment phase, a predictive model and constraints for future intraday operating conditions are established with the aim of reducing power changes in the main power grid.

[0010] Intraday adjustment results are obtained based on the predictive model of future working conditions and constraints.

[0011] The day-ahead scheduling results will be revised based on intraday adjustments to enhance port energy autonomy.

[0012] In one technical solution of the aforementioned port energy management optimization method, the objective function of the day-ahead economic scheduling model is:

[0013]

[0014] In the formula, k is the kth hour of the day, C ope The total daily operating cost of the port area, The daily operating cost of a fuel cell-electrolyzer in the port area. The daily operating cost of lithium-ion batteries in the port area. The daily operating cost of ships in the port area, The daily operating cost of the main power grid in the port area. The penalty cost for the main power grid in the port area for one day.

[0015] In one technical solution of the aforementioned port energy management optimization method, the constraints of the objective function of the day-ahead economic scheduling model include:

[0016] The daily power generation and power consumption balance in the port area is as follows: P pv (k)+P wt (k)+P bat (k)+P fc-elz (k)+P grid (k)=P port (k)

[0017] Among them, P pv P represents the power generated by photovoltaics in one day. wt P is the power generated by the wind turbine in one day. bat P is the power generated by a lithium-ion battery in one day. fc-elz P represents the power generated by the fuel cell-electrolyte in one day. grid The power generated by the main grid in one day, P port The power generated by the load in one day,

[0018] The load includes quay crane load and shore power load;

[0019] The power constraints of fuel cells, electrolyzers, lithium-ion batteries, and the main power grid itself are as follows:

[0020]

[0021] Among them, P bat min P is the minimum power of a lithium-ion battery. ba tmax P is the maximum power of a lithium-ion battery. bat (k) represents the operating power of the lithium-ion battery at hour k, P fc min P is the minimum power of the fuel cell. fcmax P is the maximum power of the fuel cell. fc (k) represents the operating power of the fuel cell in the kth hour, P elz min P is the minimum power of the electrolytic cell. elz max P is the maximum power of the electrolytic cell. elz (k) represents the operating power of the electrolytic cell in the kth hour, P grid min The minimum power of the main power grid, P grid max The minimum power of the main power grid, P grid (k) represents the operating power of the main power grid in the kth hour;

[0022] The energy constraints of hybrid energy storage systems are as follows:

[0023]

[0024] in, This represents the minimum remaining lifespan of a lithium-ion battery. For the maximum remaining lifespan of a lithium-ion battery, SOC bat (k) represents the remaining lifespan of the lithium-ion battery at hour k. The minimum hydrogen capacity for hydrogen fuel. For the maximum hydrogen capacity of hydrogen fuel, LOH tank (k) represents the hydrogen capacity of the hydrogen fuel in the kth hour;

[0025] The charging and discharging constraints of the power circulation current within the hybrid energy storage system are specifically: P bat (k)·P fc-elz (k)≥0

[0026] Among them, P bat (k) represents the power of the lithium-ion battery at hour k, P fc-elz (k) represents the power of the fuel cell-electrolyte in the kth hour;

[0027] The energy constraints of the hybrid energy storage system at the first and last moments are as follows:

[0028] in, The first limiting factor for the remaining lifespan of a lithium-ion battery at the initial moment. This is the second limiting factor for the remaining lifespan of the lithium-ion battery at the end of the time frame. The third limiting factor for the hydrogen capacity of hydrogen fuel at the initial moment. This is the fourth limiting factor for the hydrogen capacity of hydrogen fuel at the final time step.

[0029] In one technical solution of the aforementioned port energy management optimization method, the daily operating cost C of the fuel cells and electrolyzers in the port area is... fc-elz for:

[0030]

[0031] in, The operating depreciation cost of the fuel cell-electrolyte The start-up and shutdown costs of the fuel cell-electrolyzer;

[0032] The daily operating cost C of lithium-ion batteries in the port area bat for:

[0033]

[0034] Among them, C WC (k) represents the degradation cost of the lithium-ion battery, and S() is a 0-1 function;

[0035] The daily operating cost of a ship in the port area (C) ves for:

[0036]

[0037] in, The cost of a ship waiting for a berth per unit of time. The carbon emission cost of a ship due to waiting for a berth per unit of time;

[0038] The daily operating cost of the main power grid in the port area, C grid for:

[0039]

[0040] Among them, c grid The unit operating cost of electricity in the main power grid, P grid (k) represents the operating power of the main power grid, T d For a unit of time, This represents the carbon emission cost incurred by the port's use of the main power grid per unit of time.

[0041] In one technical solution of the aforementioned port energy management optimization method, the objective function of the intraday future operating condition prediction model is:

[0042]

[0043] in, x i x is the state variable of a node in the scene tree. ref SOC at time k bat and P fc-elz Reference quantity, ui Let Q be the input vector of the tree node, Q be the first weight coefficient of the objective function, and R be the second weight coefficient of the objective function. State variable SOC bat The weighting coefficients, State variable P fc-elz The weighting coefficients, The control quantity ΔP fc-elz The weighting coefficients, For control quantity P grid The weighting coefficients.

[0044] In one technical solution of the aforementioned port energy management optimization method, the constraints of the objective function of the intraday future operating condition prediction model include:

[0045] Any non-rooted tree node N i The state variable x must be satisfied. i The constraints are as follows:

[0046]

[0047] Any non-leaf tree node N i The control quantity u must be satisfied. i The constraints are as follows:

[0048]

[0049] Any non-rooted tree node N i The output quantity y must be satisfied i The constraints are as follows:

[0050]

[0051] The charging and discharging constraints of the power circulation current within the hybrid energy storage system are as follows:

[0052]

[0053] In one technical solution of the aforementioned port energy management optimization method, obtaining the intraday adjustment result based on the intraday future operating condition prediction model and constraints includes:

[0054] Obtain control quantity u i The optimal solution;

[0055] According to the control quantity u i The optimal solution yields the control quantity u0;

[0056] The operating power of the lithium-ion battery and the operating power of the fuel cell-electrolyte in the hybrid energy storage system are obtained from the control quantity u0 in the intraday adjustment results.

[0057] The operating power of lithium-ion batteries and fuel cell-electrolyzers in the hybrid energy storage system from the intraday adjustment results will be used to correct the operating power of lithium-ion batteries and fuel cell-electrolyzers in the day-ahead scheduling results, in order to improve port energy autonomy.

[0058] In a second aspect, the present invention provides a port energy management optimization device, the device comprising:

[0059] The first module is used to establish a day-ahead economic scheduling model and constraints for the port area with the aim of minimizing the overall daily operating cost of the port area during the day-ahead scheduling phase.

[0060] The first calculation module is used to calculate the day-ahead scheduling results based on the day-ahead economic scheduling model and constraints.

[0061] The second module is used to establish a predictive model and constraints for future operating conditions during the day, with the aim of reducing power changes in the main power grid.

[0062] The second calculation module is used to calculate the intraday adjustment results based on the prediction model and constraints of the intraday future operating conditions.

[0063] The correction module is used to correct the day-ahead scheduling results based on the intraday adjustment results, in order to improve port energy autonomy.

[0064] In a third aspect, the present invention provides a control device comprising a processor and a storage device, the storage device being adapted to store a plurality of program codes, the program codes being adapted to be loaded and run by the processor to perform the method described in any of the above-described port energy management optimization methods.

[0065] In a fourth aspect, the present invention provides a computer-readable storage medium storing a plurality of program codes adapted to be loaded and run by a processor to perform the method described in any of the technical solutions of the above-described port energy management optimization method.

[0066] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects:

[0067] In implementing the technical solution of this invention, a port energy management optimization method is proposed. On the one hand, in the day-ahead scheduling stage, a day-ahead economic scheduling model and constraints for the port area are established with the aim of minimizing the overall daily operating cost of the port area, and the day-ahead scheduling results are obtained. On the other hand, in the intraday adjustment stage, a prediction model and constraints for the intraday future operating conditions are established with the aim of reducing the power variation of the main power grid, and the intraday adjustment results are obtained. The day-ahead scheduling results are corrected based on the intraday adjustment results, thereby reducing the power disturbance to the main power grid and improving the port's energy autonomy. Attached Figure Description

[0068] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. Furthermore, similar numbers in the drawings are used to denote similar components, wherein:

[0069] Figure 1 This is a schematic diagram of the main structure of a port area in an application scenario according to an embodiment of the present invention;

[0070] Figure 2 This is a schematic diagram of the main steps of a port energy management optimization method according to an embodiment of the present invention;

[0071] Figure 3 This is a schematic diagram of the scene tree structure of the multi-scene prediction model of random power according to the present invention;

[0072] Figure 4 This is a schematic diagram of the main structure of a port energy management optimization device according to an embodiment of the present invention. Detailed Implementation

[0073] Some embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0074] In the description of this invention, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and may also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0075] In one application scenario of the present invention, the port area structure of the present invention is as follows: Figure 1 As shown, the system includes load 10, new energy source 20, hybrid energy storage system 30, and main grid 40. Load 10, new energy source 20, and hybrid energy storage system 30 are all connected to the main grid 40. Load 10 includes quay crane load 101 and shore power load 102. New energy source 20 includes photovoltaic 201 and wind turbine 202. Hybrid energy storage system 30 includes fuel cell 301, hydrogen storage tank 302, electrolyzer 303, and lithium-ion battery 304. The port energy management optimization method provided by this invention is applied to the port area structure. In the day-ahead scheduling stage, a day-ahead economic scheduling model and constraints for the port area are established with the aim of minimizing the overall daily operating cost of the port area. The day-ahead scheduling results are obtained based on the day-ahead economic scheduling model and constraints. The day-ahead scheduling results include the power of lithium-ion batteries in the hybrid energy storage system, the power of fuel cell-electrolyzer, the power of loads, and the power of the main grid. During the intraday adjustment phase, a predictive model and constraints for future intraday operating conditions are established with the aim of reducing power changes in the main grid. Intraday adjustment results are obtained based on the predictive model and constraints for future intraday operating conditions. The intraday adjustment results include the power changes of new energy sources and lithium-ion batteries and fuel cell-electrolyzers in the hybrid energy storage system, while keeping the load power and main grid power as constant as possible. The day-ahead dispatch results are then corrected based on the power changes to improve port energy autonomy.

[0076] See appendix Figure 2 , Figure 2 This is a schematic flowchart illustrating the main steps of a port energy management optimization method according to an embodiment of the present invention. Figure 2As shown, the port energy management optimization method in this embodiment of the invention mainly includes the following steps S101-S105.

[0077] Step S101: In the day-ahead scheduling phase, establish a day-ahead economic scheduling model and constraints for the port area with the aim of minimizing the overall daily operating cost of the port area;

[0078] Step S102: Obtain the day-ahead scheduling results based on the day-ahead economic scheduling model and constraints;

[0079] Step S103: During the intraday adjustment phase, establish a predictive model and constraints for future intraday operating conditions with the aim of reducing power changes in the main power grid.

[0080] Step S104: Obtain the intraday adjustment result based on the intraday adjustment phase and constraints;

[0081] Step S105: Revise the day-ahead scheduling results based on the intraday adjustment results to improve port energy autonomy.

[0082] In a specific example, the goal of port area energy management is to coordinate the power and energy mismatch between the port area's daily new energy sources and loads through the power and energy of fuel cells-electrolyzers, lithium-ion batteries, etc. in a hybrid energy storage system, so as to reduce the disturbance to the port area's main power grid and improve energy autonomy.

[0083] There are two key issues in port energy management: the dispatchability of port load demand and the randomness of renewable energy.

[0084] Regarding the first key issue, port load demand is directly related to port logistics. That is, the first issue is essentially a "transportation-electricity" integration problem. Therefore, the best way to solve the first problem is to combine port energy management with port traffic scheduling. To this end, in the day-ahead scheduling phase, with the goal of minimizing the overall daily operating cost of the port area, a day-ahead economic scheduling model and constraints for the port area were established. This model can both increase the penetration rate of new energy sources in the port area and reduce carbon emissions, ultimately achieving day-ahead optimal scheduling to coordinate the mismatch between new energy sources and load in the port area. To establish the day-ahead economic scheduling model and constraints, firstly, a berth-quay crane (B-QC) scheduling model based on load power was established. Then, an operating cost model for fuel cells, hydrogen storage tanks, electrolyzers, and lithium-ion batteries within a hybrid energy storage system considering lifespan degradation was established. Finally, a day-ahead economic scheduling model and constraints for the port area were established with the goal of minimizing the overall daily operating cost of the port area.

[0085] The B-QC scheduling model based on load power and the operating cost model for fuel cells, hydrogen storage tanks, electrolyzers, and lithium-ion batteries in a hybrid energy storage system that considers lifespan degradation are both preparatory stages for establishing a day-ahead economic dispatch model and constraints for the port area.

[0086] In a specific example, we will first explain the B-QC scheduling model based on load power.

[0087] B-QC problems include vessel berth location, berth time, and the number and sequence of quay cranes allocated. Since different B-QC scheduling schemes affect port load power changes, a B-QC scheduling model based on load power can be established.

[0088] The port area's load power mainly consists of two parts: the power of the quay cranes and the shore power of the ships. This study focuses on standard container ships, therefore the power of each quay crane during operation can be considered constant. Meanwhile, the power of the ships using shore power remains unchanged. The total power of the quay cranes responsible for loading and unloading ship i is:

[0089]

[0090] The time a vessel spends in port depends primarily on the volume of cargo loaded and unloaded and the number of quay cranes allocated. Vessel time in port t ber for:

[0091]

[0092] Departure time of the vessel t lev for:

[0093]

[0094] Therefore, in each optimization cycle k, the port load power model is:

[0095] P port (k)=∑ i P iron,i (k)+∑ i P cra,i (k) (4)

[0096] The constraints of the B-QC scheduling model are as follows:

[0097] (1) Berth location constraints: This invention studies continuous berths, therefore the ship's berthing location must be within the coastline, i.e.:

[0098]

[0099] The stern is located at:

[0100]

[0101] (2) Berthing time constraint: The actual berthing time should be within a certain range of the expected berthing time, i.e.:

[0102]

[0103] (3) No two ships are allowed to conflict in time and space, that is, if

[0104] Then it must satisfy:

[0105]

[0106] (4) Constraints on the number of quay cranes allocated to ships, namely:

[0107]

[0108] The constraint on the total number of k quaybridges allocated in any optimization cycle is as follows:

[0109]

[0110] At any given moment k, no quay cranes are allowed to operate simultaneously, that is:

[0111]

[0112] (7) The scheduling cycle is one day, i.e., 24 hours. Vessels should complete the loading and unloading tasks at the berth within one day, i.e.:

[0113] At this point, the B-QC scheduling model based on load power is ready.

[0114] In a specific example, the operating cost model for fuel cells, hydrogen storage tanks, electrolyzers, and lithium-ion batteries in a hybrid energy storage system with declining lifespan will be explained next.

[0115] In a specific example, the economics of hybrid energy storage systems are affected by their high initial investment costs and limited cycle life. To address this, a hybrid energy storage operating cost model considering lifespan degradation is established, specifically including an operating depreciation model for lithium-ion batteries and an operating depreciation model for fuel cell-electrolyzer systems.

[0116] (1) The depreciation model of lithium-ion batteries: Many factors affect the lifespan of lithium-ion batteries, among which the depth of discharge (DOD) is the main factor influencing lifespan degradation. The usable cycle count (ACC) of a lithium-ion battery (LiB) exhibits a non-linear relationship with DOD. The ACC-DOD characteristic can be fitted by the following formula:

[0117] N ACC (D oD ) = A h / (D oD ) R (13)

[0118] Where A h R is the first cycle life parameter of the lithium-ion battery, and R is the second cycle life parameter of the lithium-ion battery.

[0119] The nonlinear characteristics of ACC-DOD cause the degradation cost of LiB to exhibit a nonlinear relationship with DOD. Average wear cost (AWC) represents the energy conversion cost per unit battery capacity and is related to DOD. Therefore, the degradation cost can be obtained through an AWC model of lithium-ion batteries. AWC can be calculated using the following formula:

[0120]

[0121] Multiplying the AWC by the battery capacity yields the degradation cost CWC of LiB. Furthermore, since the DOD (Degradation Cost) of LiB can only be determined after the lithium battery power direction changes, a recursive function for DOD can be established. The DOD in the k-th cycle... oD (k) is:

[0122] D oD (k)=(1-S(k))·D oD (k-1)+P bat (k)·T d / E bat (15)

[0123] Where S() is a 0-1 function, specifically:

[0124]

[0125] Through D oD (k) gives the degradation cost of LiB. The operational degradation cost of LiB is:

[0126] C bat (k)=C WC (k)-(1-S(k))·C WC (k-1) (17)

[0127] Daily operating degradation cost of LiB for:

[0128]

[0129] (2) The operating depreciation model of FE: Hydrogen energy, as a clean energy source, has great application potential. However, due to the limited working life of fuel cell-electrolyzers and expensive fuels, hydrogen energy remains an expensive energy storage technology. The operating cost of a fuel cell-electrolyzer consists of two parts: operating depreciation cost and start-up and shutdown cost. The working life of FE is 10,000 to 20,000 hours, therefore, in each cycle T... d Its operating cost can be obtained from the following formula:

[0130]

[0131] To better reflect the hydrogen storage status of the hydrogen storage tank and facilitate energy management, the equivalent state of charge (LOH) of the hydrogen storage tank is:

[0132]

[0133] After the preparation phase of the above-mentioned B-QC dispatch model based on load power and the hybrid energy storage system operating cost model considering life degradation are completed, in order to establish the day-ahead economic dispatch model of the port area, the total daily operating cost of the port area should first be determined.

[0134] In one embodiment of the present invention, the objective function of the day-ahead economic scheduling model is:

[0135]

[0136] In the formula, k is the kth hour of the day, C ope The total daily operating cost of the port area, The daily operating cost of a fuel cell-electrolyzer in the port area. The daily operating cost of lithium-ion batteries in the port area. The daily operating cost of ships in the port area, The daily operating cost of the main power grid in the port area. The penalty cost for the main power grid in the port area for one day.

[0137] In one embodiment of the present invention, the daily operating cost C of the fuel cells and electrolyzers in the port area is... fc-elz for:

[0138] , the same as equation (19)

[0139] in, The operating depreciation cost of the fuel cell-electrolyte The start-up and shutdown costs of the fuel cell-electrolyzer;

[0140] The daily operating cost C of lithium-ion batteries in the port area bat for:

[0141] Same as formula (17)

[0142] Among them, C WC (k) represents the degradation cost of the lithium-ion battery, and S() is a 0-1 function;

[0143] The daily operating cost of a ship in the port area (C) ves for:

[0144] in, The cost of a ship waiting for a berth per unit of time. The carbon emission cost of a ship due to waiting for a berth per unit of time;

[0145] The CO2 emissions from a ship per unit time can be obtained by fitting the ship's electrical power consumption to the ship's auxiliary engine consumption, i.e.:

[0146]

[0147] Wherein, α0 is the first correlation coefficient of fuel consumption of ship auxiliary machinery, α1 is the second correlation coefficient of fuel consumption of ship auxiliary machinery, and α2 is the third correlation coefficient of fuel consumption of ship auxiliary machinery.

[0148] While waiting, if ship i exceeds its carbon emission limit within a unit of time, it will need to pay a CO2 emission fee, i.e.:

[0149]

[0150] in, Cost per unit of carbon emissions.

[0151] The daily operating cost of the main power grid in the port area, C grid for:

[0152] Among them, c grid The unit operating cost of electricity in the main power grid, P grid (k) represents the operating power of the main power grid, T d For a unit of time, This represents the carbon emission cost incurred by the port's use of the main power grid per unit of time.

[0153] If the carbon emissions from the port's electricity consumption per unit time exceed its maximum allowable value, a CO2 emission fee must be paid, i.e.:

[0154]

[0155] Among them, E grid This refers to the carbon emissions of the main power grid.

[0156] E grid (k)=α co2 ·P grid (k)·T d (27)

[0157] Where, α co2 This is the CO2 emission factor of the main power grid.

[0158] In one embodiment of the present invention, the constraints of the objective function of the day-ahead economic scheduling model include:

[0159] The daily power generation and power consumption balance in the port area is as follows:

[0160] P pv (k)+P wt (k)+P bat (k)+P fc-elz (k)+P grid (k)=P port (k) (28)

[0161] Among them, P pv P represents the power generated by photovoltaics in one day. wt P is the power generated by the wind turbine in one day. bat P is the power generated by a lithium-ion battery in one day. fc-elz P represents the power generated by the fuel cell-electrolyte in one day. grid The power generated by the main grid in one day, P port The power generated by the load in a day, where the load includes shore crane load and shore power load;

[0162] The power constraints of fuel cells, electrolyzers, lithium-ion batteries, and the main power grid itself are as follows:

[0163]

[0164] Among them, P bat min P is the minimum power of a lithium-ion battery. ba tmax P is the maximum power of a lithium-ion battery. bat (k) represents the operating power of the lithium-ion battery at hour k, P fc min P is the minimum power of the fuel cell. fc max P is the maximum power of the fuel cell. fc (k) represents the operating power of the fuel cell in the kth hour, Pelz min P is the minimum power of the electrolytic cell. elz max P is the maximum power of the electrolytic cell. elz (k) represents the operating power of the electrolytic cell in the kth hour, P grid min The minimum power of the main power grid, P grid max The minimum power of the main power grid, P grid (k) represents the operating power of the main power grid in the kth hour;

[0165] The energy constraints of hybrid energy storage systems are as follows:

[0166]

[0167] in, This represents the minimum remaining lifespan of a lithium-ion battery. For the maximum remaining lifespan of a lithium-ion battery, SOC bat (k) represents the remaining lifespan of the lithium-ion battery at hour k. The minimum hydrogen capacity for hydrogen fuel. For the maximum hydrogen capacity of hydrogen fuel, LOH tank (k) represents the hydrogen capacity of the hydrogen fuel in the kth hour;

[0168] The charging and discharging constraints of the power circulation current within the hybrid energy storage system are as follows:

[0169] P bat (k)·P fc-elz (k)≥0 (31)

[0170] Among them, P bat (k) represents the power of the lithium-ion battery at hour k, P fc-elz (k) represents the power of the fuel cell-electrolyte in the kth hour;

[0171] The energy constraints of the hybrid energy storage system at the first and last moments are as follows:

[0172]

[0173] in, The first limiting factor for the remaining lifespan of a lithium-ion battery at the initial moment. This is the second limiting factor for the remaining lifespan of the lithium-ion battery at the end of the time frame. The third limiting factor for the hydrogen capacity of hydrogen fuel at the initial moment. This is the fourth limiting factor for the hydrogen capacity of hydrogen fuel at the final time step.

[0174] Thus far, the objective function (21) of the current scheduling model includes continuous variables (t) ber x sta P bat P fc-elz P grid ) and integer variable S m,i (k). The day-ahead scheduling model composed of equations (1) to (32) above is a mixed-integer nonlinear programming (MINLP) problem. In actual optimization, in order to improve computational efficiency, it is necessary to relax the constraints, which will not be explained here.

[0175] To address the second key issue, during the intraday adjustment phase, a predictive model and constraints for future intraday operating conditions are established with the aim of reducing power fluctuations in the main power grid. This can reduce the disturbances to the port area's power grid caused by the randomness of new energy sources under high penetration rates, thereby improving energy autonomy; it can also reduce the impact on the port area's power quality and stable operation, ultimately achieving power disturbances to the main power grid in the port area and improving energy autonomy in the port area.

[0176] To establish a predictive model and constraints for intraday future operating conditions, a control system model is first proposed. Then, a multi-scenario prediction model for stochastic power is proposed. Both the establishment of the control system model and the proposal of the multi-scenario prediction model for stochastic power serve as preparatory stages for establishing the predictive model and constraints for intraday future operating conditions.

[0177] In a specific example, the establishment of the control system model will be explained first.

[0178] Each control period T adjusted intraday s The load should be balanced with the power of other inputs:

[0179] P bat (k)+P fc-elz (k)-P s (k)+P grid (k)=0 (33)

[0180] Since the ship's load is relatively fixed after docking, this invention only considers the randomness of new energy sources, where P s (k) is:

[0181] P s (k)=P port (k)-P pv (k)-P wt (k) (34)

[0182] The SOC of LiB and the power of FE at time k+1 can be obtained from the following equations:

[0183]

[0184] According to equations (33)-(35), the SOC of LiB and the power of FE are selected as the state vector x(k) = [SOC]. bat (k)P fc-elz (k)] T The power of LiB is represented by the output vector y(k) = [P bat (k)] T The power change of FE and the grid power are used as the control vector u(k) = [ΔP] fc-elz (k)P grid (k)] T The random power is the disturbance w(k) = P s (k), the state-space model of the system is established as follows:

[0185]

[0186] in, C = D1 = [0 -1], D2 = 1.

[0187] In a specific example, the multi-scenario prediction model for random power will be explained next.

[0188] Accurate prediction of stochastic power is crucial for improving control performance. Therefore, this invention proposes a multi-scenario prediction model for stochastic power during the intraday adjustment phase to address the stochastic disturbance P in the system model. s (k) Modeling. First, based on the power prediction model, a candidate scene set is obtained using the Latin hypercube sampling (LHS) method. Then, a multi-scene model is obtained through the proposed scene tree generation method.

[0189] (1) Stochastic power prediction model and candidate scenario set generation. The ARIMA model is commonly used for new energy power prediction due to its ease of implementation and parameter selection. ARIMA(p,d,q) can be expressed as:

[0190]

[0191] Among them, y k-p Let e ​​be the power at time kp. k-q Let be the error at time kq. Let εk and εk be the predicted power value and prediction error at time k, respectively. φ represents the parameters of the autoregressive model (AR), and θ represents the parameters of the moving average model (MA). p and q are the orders of AR and MA, respectively. d is the difference order. Generally, p, d, and q are all taken as 1. In general, the prediction errors for photovoltaic and wind power follow a normal distribution, i.e., ek = εk. k ~N(μ,σ 2 ), where μ and σ are the expected value and standard deviation of the prediction error, respectively.

[0192] LHS (Layered Hierarchical Sampling) uses stratified sampling to ensure that the sampling points cover the distribution of the random variable, thus effectively reflecting the overall distribution of the random variable. LHS has the advantages of low computational cost and high accuracy. It is widely used in stochastic analysis. This paper uses the LHS method to sample the power of photovoltaic and wind turbines and generate N. s There are several scenarios. The probability of each scenario is 1 / N. s .

[0193] Multi-scene prediction models based on scene trees (LHS) suffer from the following drawbacks: If the candidate scene set obtained by LHS is directly bundled and reduced to similar scenarios, the resulting scenes are either independent or exhibit a fan-shaped structure. This makes it difficult to reflect the continuous variation of stochastic power across multiple prediction time domains, particularly the gradual refinement of predicted scenes as the prediction domain increases. This can lead to a shortage of effective scenes as the prediction domain expands.

[0194] Therefore, during the intraday adjustment phase of this invention, a scene tree generation method is proposed to obtain a multi-scene model. This method is based on the Maximum Scene Reduction (MRS) method. MRS is used for the optimal selection of a single scene. The key to the proposed method is to add branches on higher probability paths in the prediction time domain, thereby increasing the number of higher probability nodes, as these have a greater impact on control performance. A schematic diagram of the scene tree structure is shown below. Figure 3 As shown.

[0195] The steps for generating the scene tree are as follows:

[0196] (1) The random power P of each prediction step s (k), based on LHS, obtain the scene set P that needs to be reduced. cand :

[0197]

[0198] Among them, P pv P wt These are candidate scenario sets for photovoltaic and wind power in the prediction time domain, respectively.

[0199] (2) For node N in step n j The selection begins with the scene set H of its parent node. pre Clustering is performed on j to determine the number of child nodes. If this is the first prediction step, the scene set of its parent node is P. cand .

[0200] This paper proposes a method to dynamically adjust the number of child nodes to increase the number of paths for higher-probability scenarios. Its parent node N preThe higher the probability of node j, the higher the probability of that path occurring, and therefore the number of child nodes of that path should be increased. Conversely, the number of branches should be reduced. K-means clustering is used, where the number of clusters K is set as follows:

[0201] K = round(exp(τ·p(N)) pre,j (39)

[0202] Where τ is the adjustment coefficient, and the round() function is the floor function.

[0203] (3) For each clustered subset H sub,j The optimal scene is preserved using MRS, resulting in child node N. u It should meet the following requirements:

[0204]

[0205] Where, p i The probability of each scenario, D r Distance between scenes:

[0206]

[0207] (4) Since the probability of each sample is 1 / N s Then node N u The probability of the scenario is:

[0208] p(N u )=s / N s (42)

[0209] Where s is H sub,j The number of scenes in the middle.

[0210] Its node probabilities (starting from the root node) are:

[0211]

[0212] (5) Based on the clustering results, classify P... cand Reorder and update.

[0213] (6) Select the parent node N according to the above steps (3) to (5). pre,j Other child nodes and their node probabilities.

[0214] (7) Repeat steps (2) to (5) to cluster each prediction step and select each node. Finally, the number of nodes N in each prediction step can be obtained. i and the corresponding node probability ζ i This refers to the scene tree that has been built.

[0215] After the above-mentioned control system model and multi-scenario prediction model of stochastic power are prepared, intraday stochastic model predictive control of port power is carried out in order to establish a prediction model of future operating conditions within the day.

[0216] The intraday adjustment target is for each T s Based on the recent dispatch results, the output power of the Hybrid Energy Storage System (HESS) will be adjusted to reduce errors caused by stochastic power. Specifically, this includes adjusting the State of Charge (SOC). bat (k), P fc-elz (k) The circuit should operate according to the planned trajectory, while power fluctuations in the FE and the grid should be reduced, i.e., the fluctuations in the control quantity should be reduced. In summary, at time k, according to the MPC framework, the established stochastic power multi-scenario model replaces the traditional MPC P... s (k), the objective function of the intraday future working conditions prediction model is as follows:

[0217]

[0218] in, x i x is the state variable of a node in the scene tree. ref SOC at time k bat and P fc-elz Reference quantity, u i Let Q be the input vector of the tree node, Q be the first weight coefficient of the objective function, and R be the second weight coefficient of the objective function. State variable SOC bat The weighting coefficients, State variable P fc-elz The weighting coefficients, The control quantity ΔP fc-elz The weighting coefficients, For control quantity P grid The weighting coefficients. and These are the weighting coefficients for the control variables ΔPfc-elz and Pgrid, respectively. They are both greater than zero.

[0219] Based on the proposed multi-scenario prediction model for random power, the objective function (44) transforms the stochastic problem into a deterministic problem by replacing the possible random power in the future prediction time domain with the nodes of the scenario tree and their corresponding node probabilities. The weight vector Q of the state variables aims to reduce the difference between the state variables and the reference values. The larger the value, the closer the SOC trajectory is to the day-ahead scheduling trajectory, thus minimizing the difference between LiB power and the reference power; similarly, The Pfc-elz variation affects FE; the control weight vector R is used to define the priority of different energy uses. The larger the value, the more priority is given to using the main grid power, and the smaller the power fluctuation of the FE; similarly, A larger weighting coefficient can reduce the power interaction between the port area and the main power grid. In practice, different weighting coefficients may yield contradictory optimization results, requiring reasonable adjustment based on performance.

[0220] In one embodiment of the present invention, the objective function (44) of the intraday future working conditions prediction model is subject to the following constraints:

[0221] (1) Any non-rooted tree node N i The state variable x must be satisfied. i The constraints are as follows:

[0222]

[0223] (2) Any non-leaf tree node N i The control quantity u must be satisfied. i The constraints are as follows:

[0224]

[0225] (3) Any non-rooted tree node N i The output quantity y must be satisfied i The constraints are as follows:

[0226]

[0227] (4) The charging and discharging constraints of the power circulation current within the hybrid energy storage system are as follows:

[0228]

[0229] The optimization problem proposed in equations (44) to (48) can be transformed into a standard quadratic programming (QP) problem to obtain the control variable u. i The optimal solution u is obtained. i Then, the power P of the HESS can be obtained from the control quantity u0. bat P fc-elz This is used to correct the planned values ​​for the day before. Then, at the next control time k+1, the system state is sampled and the entire optimization process is repeated.

[0230] In one embodiment of the present invention, obtaining the intraday adjustment result based on the intraday future operating condition prediction model and constraints includes:

[0231] Obtain the optimal solution for the control variable ui;

[0232] The control quantity u0 is obtained from the optimal solution of the control quantity ui;

[0233] The operating power of the lithium-ion battery and the operating power of the fuel cell-electrolyte in the hybrid energy storage system are obtained from the control quantity u0 in the intraday adjustment results.

[0234] The operating power of lithium-ion batteries and fuel cell-electrolyzers in the hybrid energy storage system from the intraday adjustment results will be used to correct the operating power of lithium-ion batteries and fuel cell-electrolyzers in the day-ahead scheduling results, in order to improve port energy autonomy.

[0235] Based on steps S101-S105 above, a port energy management optimization method is proposed. On the one hand, in the day-ahead scheduling stage, a day-ahead economic scheduling model and constraints for the port area are established with the aim of minimizing the overall daily operating cost of the port area, and the day-ahead scheduling results are obtained. On the other hand, in the intraday adjustment stage, a prediction model and constraints for the intraday future operating conditions are established with the aim of reducing the power variation of the main power grid, and the intraday adjustment results are obtained. The day-ahead scheduling results are corrected based on the intraday adjustment results, thereby reducing the power disturbance to the main power grid and improving the port's energy autonomy.

[0236] Furthermore, the present invention also provides a port energy management optimization device.

[0237] See appendix Figure 4 , Figure 4 This is a main structural block diagram of a port energy management optimization device according to an embodiment of this application. Figure 4 As shown, the port energy management optimization device in this embodiment mainly includes a first establishment module 11, a first calculation module 12, a second establishment module 13, a second calculation module 14, and a correction module 15. In some embodiments, the first establishment module 11 can be configured to establish a day-ahead economic dispatch model and constraints for the port area with the aim of minimizing the overall daily operating cost of the port area during the day-ahead dispatch phase; the first calculation module 12 can be configured to calculate the day-ahead dispatch result based on the day-ahead economic dispatch model and constraints; the second establishment module 13 can be configured to establish a prediction model and constraints for the intraday future operating conditions with the aim of reducing the power variation of the main power grid during the intraday adjustment phase; the second calculation module 14 can be configured to calculate the intraday adjustment result based on the prediction model and constraints for the intraday future operating conditions; and the correction module 15 can be configured to correct the day-ahead dispatch result based on the intraday adjustment result to improve port energy autonomy.

[0238] In one implementation, the specific function can be described in steps S101-S105.

[0239] The aforementioned port energy management optimization device is used for execution Figure 2The port energy management optimization method embodiments shown are similar in technical principle, the technical problems solved and the technical effects produced. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the xxx device can be found in the embodiments of the port energy management optimization method, which will not be repeated here.

[0240] Those skilled in the art will understand that all or part of the processes in the method of the above embodiment of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0241] Furthermore, the present invention also provides a control device. In one embodiment of the control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the port energy management optimization method of the above-described method embodiments, and the processor can be configured to execute the program in the storage device. This program includes, but is not limited to, a program for executing the port energy management optimization method of the above-described method embodiments. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. This control device can be a control device device comprising various electronic devices.

[0242] Furthermore, the present invention also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to the present invention, the computer-readable storage medium can be configured to store a program for executing the port energy management optimization method of the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described port energy management optimization method. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a storage device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0243] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of software and hardware. Therefore, the number of modules shown in the figures is merely illustrative.

[0244] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principles of the present invention; therefore, the technical solutions after splitting or combining will fall within the protection scope of the present invention.

[0245] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for optimizing port energy management, characterized in that, include: During the day-ahead scheduling phase, a day-ahead economic scheduling model and constraints for the port area are established with the aim of minimizing the overall daily operating cost of the port area. The day-ahead scheduling results are calculated based on the day-ahead economic scheduling model and constraints. During the intraday adjustment phase, a predictive model and constraints for future intraday operating conditions are established with the aim of reducing power changes in the main power grid. The intraday adjustment results are calculated based on the intraday forecasting model and constraints. The day-ahead scheduling results will be revised based on intraday adjustments to enhance port capacity autonomy; The objective function of the day-ahead economic scheduling model is: In the formula, k is the kth hour of the day, C ope The total daily operating cost of the port area, The daily operating cost of a fuel cell-electrolyzer in the port area. The daily operating cost of lithium-ion batteries in the port area. The daily operating cost of ships in the port area, The daily operating cost of the main power grid in the port area. The penalty cost for the main power grid in the port area for one day; The daily operating cost of fuel cells and electrolyzers in the port area. for: in, The operating depreciation cost of the fuel cell-electrolyte The start-up and shutdown costs of the fuel cell-electrolyzer; The daily operating cost of lithium-ion batteries in the port area for: Among them, C WC S(k) represents the degradation cost of a lithium-ion battery, and S(k) is a 0-1 function. The daily operating cost of the main power grid in the port area for: Among them, c grid The unit operating cost of electricity in the main power grid, P grid (k) represents the operating power of the main power grid, T d For a unit of time, The carbon emission cost caused by the port's use of the main power grid per unit of time; The objective function of the intraday future working conditions prediction model is: in, x i These are the state variables of the nodes in the scene tree. SOC at time k bat and P fc-elz Reference value, SOC bat For the remaining lifespan of lithium-ion batteries, P fc-elz u is the power generated by the fuel cell-electrolyte per day. i Let ζ be the input vector for the tree node. i Here, represents the model coefficients, Q represents the first weighting coefficient of the objective function, and R represents the second weighting coefficient of the objective function. State variable SOC bat The weighting coefficients, State variable P fc-elz The weighting coefficients, The control quantity ΔP fc-elz The weighting coefficients, For control quantity P grid The weighting coefficients.

2. The method according to claim 1, characterized in that, The constraints of the objective function of the day-ahead economic dispatch model include: The daily power generation and power consumption balance in the port area is as follows: P pv (k)+P wt (k)+P bat (k)+P fc-elz (k)+P grid (k)=P port (k) Among them, P pv P represents the power generated by photovoltaics in one day. wt P is the power generated by the wind turbine in one day. bat P is the power generated by a lithium-ion battery in one day. fc-elz P represents the power generated by the fuel cell-electrolyte in one day. grid The power generated by the main grid in one day, P port The power generated by the load in a day, where the load includes shore crane load and shore power load; The power constraints of fuel cells, electrolyzers, lithium-ion batteries, and the main power grid itself are as follows: Among them, P bat min P is the minimum power of a lithium-ion battery. bat max P is the maximum power of a lithium-ion battery. bat (k) represents the operating power of the lithium-ion battery at hour k, P fc min P is the minimum power of the fuel cell. fc max P is the maximum power of the fuel cell. fc (k) represents the operating power of the fuel cell in the kth hour, P elz min P is the minimum power of the electrolytic cell. elz max P is the maximum power of the electrolytic cell. elz (k) represents the operating power of the electrolytic cell in the kth hour, P grid min The minimum power of the main power grid, P grid max The maximum power of the main power grid, P grid (k) represents the operating power of the main power grid in the kth hour; The energy constraints of hybrid energy storage systems are as follows: in, This represents the minimum remaining lifespan of a lithium-ion battery. For the maximum remaining lifespan of a lithium-ion battery, SOC bat (k) represents the remaining lifespan of the lithium-ion battery at hour k. The minimum hydrogen capacity for hydrogen fuel. For the maximum hydrogen capacity of hydrogen fuel, LOH tank (k) represents the hydrogen capacity of the hydrogen fuel in the kth hour; The charging and discharging constraints of the power circulation current within the hybrid energy storage system are as follows: P bat (k)·P fc-elz (k)≥0 Among them, P bat (k) represents the power of the lithium-ion battery at hour k, P fc-elz (k ) The power of the fuel cell-electrolyte in hour k; The energy constraints of the hybrid energy storage system at the first and last moments are as follows: in, The first limiting factor for the remaining lifespan of a lithium-ion battery at the initial moment. This is the second limiting factor for the remaining lifespan of the lithium-ion battery at the end of the time frame. The third limiting factor for the hydrogen capacity of hydrogen fuel at the initial moment. This is the fourth limiting factor for the hydrogen capacity of hydrogen fuel at the final time step.

3. The method according to claim 1, characterized in that, The daily operating cost of ships in the port area for: Among them, t wait,i The time a ship waits for a berth. The cost of a ship waiting for a berth per unit of time. This refers to the carbon emission cost of a ship due to waiting for a berth per unit of time.

4. The method according to claim 2, characterized in that, The constraints of the objective function of the intraday future working conditions prediction model include: Any non-rooted tree node N i The state variable x must be satisfied. i The constraints are as follows: This represents the minimum remaining lifespan of a lithium-ion battery. This refers to the maximum remaining lifespan of a lithium-ion battery. This represents the minimum power output of the fuel cell-electrolyte per day. This represents the maximum power output of the fuel cell-electrolyte in one day. Any non-leaf tree node N i The control quantity u must be satisfied. i The constraints are as follows: The minimum power of the main power grid, The maximum power of the main power grid Any non-rooted tree node N i The output quantity y must be satisfied i The constraints are as follows: The charging and discharging constraints of the power circulation current within the hybrid energy storage system are as follows: P s (i)=P port (i)-P pv (i)-P wt (i), P bat (i) represents the power of the lithium-ion battery, P fc-elz (i) represents the power of the fuel cell-electrolyzer.

5. The method according to claim 4, characterized in that, The intraday adjustment results obtained based on the prediction model and constraints of future intraday operating conditions include: Obtain control quantity u i The optimal solution; According to the control quantity u i The optimal solution yields the control quantity u0, where u0 is the output vector of the tree node; The operating power of the lithium-ion battery and the operating power of the fuel cell-electrolyte in the hybrid energy storage system are obtained from the control quantity u0 in the intraday adjustment results. The operating power of lithium-ion batteries and fuel cell-electrolyzers in the hybrid energy storage system from the intraday adjustment results will be used to correct the operating power of lithium-ion batteries and fuel cell-electrolyzers in the day-ahead scheduling results, in order to improve port energy autonomy.

6. A port energy management optimization device, characterized in that, include: The first module is used to establish a day-ahead economic scheduling model and constraints for the port area with the aim of minimizing the overall daily operating cost of the port area during the day-ahead scheduling phase. The first calculation module is used to calculate the day-ahead scheduling results based on the day-ahead economic scheduling model and constraints. The second module is used to establish a predictive model and constraints for future operating conditions during the day, with the aim of reducing power changes in the main power grid. The second calculation module is used to calculate the intraday adjustment results based on the prediction model and constraints of the intraday future operating conditions. The correction module is used to revise the day-ahead scheduling results based on intraday adjustments, thereby enhancing port energy autonomy. The objective function of the day-ahead economic scheduling model is: In the formula, k is the kth hour of the day, C ope The total daily operating cost of the port area, The daily operating cost of a fuel cell-electrolyzer in the port area. The daily operating cost of lithium-ion batteries in the port area. The daily operating cost of ships in the port area, The daily operating cost of the main power grid in the port area. The penalty cost for the main power grid in the port area for one day; The daily operating cost of fuel cells and electrolyzers in the port area. for: in, The operating depreciation cost of the fuel cell-electrolyte The start-up and shutdown costs of the fuel cell-electrolyzer; The daily operating cost of lithium-ion batteries in the port area for: Among them, C WC S(k) represents the degradation cost of a lithium-ion battery, and S(k) is a 0-1 function. The daily operating cost of the main power grid in the port area for: Among them, c grid The unit operating cost of electricity in the main power grid, P grid (k) represents the operating power of the main power grid, T d For a unit of time, The carbon emission cost caused by the port's use of the main power grid per unit of time; The objective function of the intraday future working conditions prediction model is: in, x i These are the state variables of the nodes in the scene tree. SOC at time k bat and P fc-elz Reference value, SOC bat For the remaining lifespan of lithium-ion batteries, P fc-elz u is the power generated by the fuel cell-electrolyte per day. i Let ζ be the input vector for the tree node. i Here, represents the model coefficients, Q represents the first weighting coefficient of the objective function, and R represents the second weighting coefficient of the objective function. State variable SOC bat The weighting coefficients, State variable P fc-elz The weighting coefficients, The control quantity ΔP fc-elz The weighting coefficients, For control quantity P grid The weighting coefficients.

7. A control device, comprising a processor and a storage device, said storage device being adapted to store a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to perform the port energy management optimization method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the port energy management optimization method according to any one of claims 1 to 5.

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