A port system electric-hydrogen energy resource configuration optimization scheduling method

Abstract

The present application relates to a kind of optimization scheduling methods for port system electric-hydrogen energy resource configuration, belong to resource optimization scheduling field.The method includes: the mapping relationship between logistics operation organization mode and energy resource demand is established, and unified collaborative information model is formed;Based on collaborative information model, configuration decision evaluation model considering electrochemical energy storage and hydrogen energy resource is constructed, for generating the evaluation index corresponding to resource configuration scheme under the given resource configuration scheme and logistics operation organization scheme;Three-dimensional decision space with operating cost, carbon emissions and ship average time in port as dimension is constructed, and by constructing observation grid on the preset discrete value of corresponding index, boundary state point set is solved point by point, and the envelope surface of three-dimensional decision space is constructed using three-dimensional convex hull algorithm;Based on the decision boundary features of three-dimensional decision space, configuration scheme is screened.The present application can improve the applicability of electric-hydrogen energy resource configuration result under complex operation scenario.

Description

An Optimal Scheduling Method for Electricity-Hydrogen Energy Resource Allocation in Port Systems Technical Field

[0001] This invention belongs to the field of resource optimization and scheduling, specifically relating to an optimization scheduling method for the allocation of electric and hydrogen energy resources in port systems. Background Technology

[0002] In the context of near-zero carbon port construction, the large-scale integration of renewable energy into the port's integrated energy system to replace traditional high-carbon energy supply has become a crucial support for the port's low-carbon operation. However, renewable energy output is characterized by strong fluctuations and poor predictability, easily leading to situations where surplus electricity cannot be absorbed in certain periods and energy supply capacity is limited during critical periods, creating a time mismatch with the continuous energy consumption of port operations. Port loading, unloading, transshipment, and storage yard operations are characterized by high concurrency and frequent demand changes. Their resource demand characteristics dynamically change with the operation organization methods and ship schedules, making port energy resource allocation decisions face strong uncertainty and complexity. To alleviate the above contradictions, the system urgently needs energy storage systems with power regulation and energy time-shifting capabilities to improve the stability of port energy supply. Based on this, conducting resource allocation research for port scenarios has become a necessary link to support the reliable operation of ports.

[0003] Existing research on port energy resource allocation typically addresses multi-objective allocation problems through weighted compromises, making it difficult to explicitly characterize the constraints between multiple evaluation indicators and the decision margin of allocation schemes. This results in allocation conclusions being highly sensitive to weight settings and limited applicability and reusability across different operational scenarios. Furthermore, some methods only consider capacity selection at the equipment level, lacking a holistic allocation decision-making framework that considers the characteristics of port operations and the synergistic relationships of energy resources. This easily leads to mismatches between the allocation schemes applied and the actual operational status of the port. Therefore, it is necessary to provide a resource allocation optimization and scheduling method centered on information processing and decision rules. Based on a comprehensive consideration of operating costs, carbon emission levels, and operational timeliness evaluation indicators, this method constructs a three-dimensional decision space for evaluating and selecting allocation schemes, thereby improving the scientific rigor and interpretability of the optimized scheduling of port integrated energy system resources.

[0004] Although a series of studies have been carried out on the resource allocation optimization and scheduling problem in port integrated energy systems, existing technologies still have shortcomings in information modeling methods for energy flow-logistics coordination and research on multi-resource allocation optimization and scheduling methods.

[0005] On the one hand, existing configuration studies mostly follow an energy system-dominated analytical paradigm, and the characterization of port logistics operations is usually limited to empirical or static descriptions, making it difficult to fully reflect the changing resource demands of operating equipment under conditions such as switching operating modes, parallel operations, and overlapping tasks. This simplification at the information modeling level leads to incomplete expression of constraints during configuration optimization and scheduling, resulting in inconsistencies between the feasibility and scenario adaptability of some configuration schemes in the application stage. This manifests as insufficient adaptability of resource allocation results to high-load conditions and complex operational organization methods.

[0006] On the other hand, existing research at the allocation decision-making level mostly focuses on the analysis of single types of energy resources, with insufficient consideration of the complementary relationships between different types of resources in terms of time scale and functional attributes. A collaborative allocation decision-making framework oriented towards multi-time scale demand characteristics has not yet been formed. This makes it difficult to balance resource utilization efficiency and allocation rationality when simultaneously facing short-term demand fluctuations and long-term energy allocation demands, and the robustness and universality of allocation schemes need to be improved.

[0007] Furthermore, in decision-making regarding multi-evaluation index configuration, traditional methods generally employ weighted summation and other methods to synthesize multi-dimensional indices into a single objective for solution. These methods are highly sensitive to the setting of weight parameters, and different weight combinations can easily yield significantly different configuration results, even biased towards a single index. Simultaneously, these methods struggle to intuitively express the constraints between multiple indices and the decision margin of the configuration scheme, resulting in a lack of interpretability in the optimization scheduling results and limited reusability across different application scenarios. Summary of the Invention

[0008] The purpose of this invention is to address the problems existing in the background technology and provide an optimized scheduling method for the allocation of electric and hydrogen energy resources in port systems.

[0009] To achieve the above objectives, the technical solution of the present invention is: an optimized scheduling method for the allocation of electric-hydrogen energy resources in port systems, comprising:

[0010] Construct an energy flow-logistics coupling configuration decision information model for port integrated energy systems. By extracting key information variables including operation status, operation concurrency, operation rhythm and equipment operation mode, establish a mapping relationship between logistics operation organization mode and energy resource demand, and form a unified collaborative information model.

[0011] Based on the collaborative information model, a configuration decision evaluation model that takes into account electrochemical energy storage and hydrogen energy resources is constructed. This model is used to perform information-based simulation of the resource supply and demand relationship of the port system under a given resource allocation scheme and logistics operation organization scheme, and generate evaluation indicators corresponding to the resource allocation scheme.

[0012] A three-dimensional decision space is constructed with operating cost, carbon emissions, and average ship time in port as dimensions. An observation grid is constructed on preset discrete values ​​of operating cost, carbon emissions, and average ship time in port. The boundary state point set is solved point by point. The envelope surface of the three-dimensional decision space is constructed using the three-dimensional convex hull algorithm.

[0013] Based on the decision boundary characteristics of the three-dimensional decision space, configuration schemes are selected.

[0014] Furthermore, the port integrated energy system includes a port energy flow system and a port logistics system; the configuration decision information model treats the port energy flow system and the port logistics system as a unified information processing object; wherein, the port logistics system forms a description of energy resource demand through ship planning, operation task arrangement and equipment operation mode information, and the port energy flow system forms a description of resource supply capacity through the capacity attributes and constraints of various energy resources.

[0015] Furthermore, the configuration decision evaluation model includes an energy-side resource evaluation model and a logistics-side demand information model; the energy-side resource evaluation model includes at least an electrochemical energy storage resource model, a hydrogen storage tank model, a hydrogen fuel cell model, and a thermal storage tank model; the logistics-side demand information model includes at least a logistics loading and unloading operation model, a logistics transportation operation model, and a logistics yard operation model.

[0016] Furthermore, the electrochemical energy storage resource model is based on a discrete-time information derivation model constructed from the state of charge, and considers the mutual exclusion of charging and discharging and the state recovery constraint at the end of the cycle; the hydrogen storage tank model describes the dynamic evolution of the hydrogen mass in the hydrogen storage tank based on hydrogen mass balance constraints and upper and lower limits of storage capacity constraints; the hydrogen fuel cell model is constructed based on energy balance relationships, considers the characteristics of electrical and thermal energy conversion, and is constrained by upper and lower limits of input hydrogen energy power; the thermal storage tank model is constructed based on thermal mass balance, describes the dynamic evolution of heat storage, and is constrained by upper and lower limits of storage capacity and charging / discharging heat power.

[0017] Furthermore, the logistics loading and unloading operation model characterizes the resource demand features of the loading and unloading process through variables such as ship status in port, shore power allocation, and quay crane operation, including berth conflict constraints, shore power constraints, and loading and unloading schedule constraints; the logistics transportation operation model describes the resource consumption of the transportation link based on the allocation and power demand of automated guided container trucks, including allocation constraints, calculation of cumulative transportation volume, and constraints matching with loading and unloading schedule; the logistics yard operation model describes the resource demand of the yard link through the allocation of gantry cranes and yard schedule, including allocation constraints, calculation of cumulative yard volume, and constraints synchronizing with transportation schedule.

[0018] Furthermore, the construction of the three-dimensional decision space is specifically as follows: the multi-type energy resource allocation problem of the port integrated energy system is modeled as a two-layer configuration evaluation structure coupled with planning and operation; wherein, the planning layer aims to make medium- and long-term energy storage capacity configuration decisions with the goal of minimizing the full life cycle cost of electric and hydrogen energy resources; the operation layer is used to evaluate the operational performance of the port integrated energy system on multi-dimensional performance indicators under a given configuration scheme.

[0019] Furthermore, the objective function of the planning layer is used to measure the comprehensive economic performance of different electric-hydrogen energy resource allocation schemes within the planning period, providing a quantitative evaluation basis for allocation decisions. The objective function is expressed as follows:

[0020]

[0021] In the formula, This represents the annualized total lifecycle cost; Represents the net present value of total lifecycle costs; denoted as the equal payment coefficient, and x represents the electric-hydrogen energy resource allocation scheme.

[0022] Furthermore, the solution to the boundary state point set must satisfy the constraints of electric power balance, thermal power balance, demand response, and total power demand for logistics.

[0023] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0024] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] (1) Improve the completeness of information characterization and the consistency of allocation results in port resource allocation decision-making.

[0027] This invention incorporates information related to the entire logistics operation process into a configuration optimization scheduling information model. It reflects the concurrent relationship of operations and the superposition characteristics of demand at the constraint information level, enabling configuration optimization scheduling to be evaluated and screened based on the complete operation process and resource requirements, thereby improving the applicability of configuration results in complex operation scenarios.

[0028] (2) Improve the ability of configuration decisions to adapt to energy regulation needs across multiple time scales.

[0029] This invention differentiates and synergistically describes the functional attributes of electrochemical energy storage resources and hydrogen energy resources at the configuration decision level, enabling the configuration value of different types of resources in energy regulation at different time scales to be reflected in the optimal scheduling process. Compared with configuration methods that only consider a single type of energy resource, this decision mechanism helps to improve the coverage of configuration schemes for energy regulation needs at multiple time scales, thereby enhancing the stability of the optimal scheduling of configuration results in diverse application scenarios.

[0030] (3) Improve the stability and interpretability of the multi-index configuration optimization scheduling results.

[0031] This invention constructs a multi-dimensional decision space, explicitly expressing the constraints between multiple evaluation indicators such as operating costs, carbon emission levels, and operational efficiency in the form of decision space boundaries and decision margins. This avoids the high dependence on weight parameter settings in traditional weight-based configuration optimization scheduling methods. The configuration optimization scheduling results can be traced and interpreted through the characteristics of the decision space boundaries, forming a reusable basis for configuration optimization scheduling, thereby improving the transparency and stability of the multi-indicator configuration optimization scheduling process. Attached Figure Description

[0032] Figure 1 shows the structure of the port's integrated energy system.

[0033] Figure 2 is a flowchart of the method of the present invention. Detailed Implementation

[0034] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0035] As shown in Figure 1, this invention provides an optimized scheduling method for the allocation of electric-hydrogen energy resources in port systems, including:

[0036] Construct an energy flow-logistics coupling configuration decision information model for port integrated energy systems. By extracting key information variables including operation status, operation concurrency, operation rhythm and equipment operation mode, establish a mapping relationship between logistics operation organization mode and energy resource demand, and form a unified collaborative information model.

[0037] Based on the collaborative information model, a configuration decision evaluation model that takes into account electrochemical energy storage and hydrogen energy resources is constructed. This model is used to perform information-based simulation of the resource supply and demand relationship of the port system under a given resource allocation scheme and logistics operation organization scheme, and generate evaluation indicators corresponding to the resource allocation scheme.

[0038] A three-dimensional decision space is constructed with operating cost, carbon emissions, and average ship time in port as dimensions. An observation grid is constructed on preset discrete values ​​of operating cost, carbon emissions, and average ship time in port. The boundary state point set is solved point by point. The envelope surface of the three-dimensional decision space is constructed using the three-dimensional convex hull algorithm.

[0039] Based on the decision boundary characteristics of the three-dimensional decision space, configuration schemes are selected.

[0040] The following is a detailed implementation process of the present invention.

[0041] This invention provides an optimized scheduling method for the allocation of electricity and hydrogen energy resources in port systems, mainly comprising:

[0042] (1) A configuration decision information modeling method is proposed for the energy flow-material flow coupling characteristics of a port integrated energy system. For the complete process of port logistics operations, key logistics information variables such as operation status, operation concurrency relationship, operation rhythm and equipment operation mode are extracted. The mapping relationship between logistics operation organization mode and energy resource demand is constructed, and logistics side information and energy side constraints are described with a unified data structure, thereby forming a collaborative information model for configuration decision-making, providing a consistent constraint information basis for the evaluation and selection of configuration schemes.

[0043] (2) Construct a collaborative allocation decision-making mechanism for electricity-hydrogen energy resources oriented towards energy regulation needs at multiple time scales. In the case of port scenarios where there are both short-term demand fluctuations and long-term energy allocation characteristics, the functional attributes of electrochemical energy storage resources and hydrogen energy resources are distinguished and collaboratively described at the allocation decision level. This allows the allocation value of different types of resources under different time scale demand conditions to be uniformly reflected in the decision-making process, thereby achieving collaborative matching of multiple types of energy resources at the allocation decision level.

[0044] (3) A decision-making method for electric-hydrogen energy resource allocation based on a multi-dimensional decision space is proposed. Operating cost, carbon emission, and operational timeliness are selected as evaluation dimensions for allocation decisions. Based on these dimensions, a three-dimensional decision space for the system under multi-indicator constraints is constructed, and its boundary features are extracted. The multi-dimensional decision space is used to explicitly quantify the constraints between multiple indicators and the decision margin of allocation schemes. This transforms the traditional allocation decision-making method, which relies on weight trade-offs, into a scheme selection process based on decision space constraints, thereby achieving scientific decision-making for electric-hydrogen energy resource allocation schemes without relying on subjective weight settings.

[0045] Specifically, the technical solution of this invention is implemented as follows:

[0046] 1. Construct an energy flow-material flow coupled information model for configuration decision-making.

[0047] This invention is applicable to integrated energy systems for ports, and its structure is shown in Figure 1.

[0048] This invention describes the Port Energy System (PES) and Port Logistics System (PLS) as unified information processing objects for resource allocation decision-making. The PLS describes energy resource demand based on information such as ship schedules, operational task arrangements, and equipment operating modes; the PLS describes resource supply capacity based on the capacity attributes and constraints of various energy resources. Through their information-level correlation, they form a collaborative information framework for resource allocation decision analysis. Within this framework, power generation units, energy conversion units, electrochemical energy storage resources, and hydrogen energy resources involved in the port's integrated energy system participate in the allocation decision-making evaluation process as a set of resource capacity parameters and constraints, without directly solving for the operating status or control strategies of specific equipment.

[0049] 2. Construct a decision-making and evaluation model for the allocation of electricity and hydrogen energy resources.

[0050] To support multi-indicator evaluation of resource allocation schemes, this invention constructs an allocation decision evaluation model that considers electrochemical energy storage resources and hydrogen energy resources. This model is used to perform an information-based simulation of the potential resource supply and demand relationships within a port system, given a resource allocation scheme and a logistics operation organization scheme, thereby generating evaluation index results corresponding to the allocation scheme.

[0051] (1) Energy resource assessment model

[0052] Energy-side models are used to describe the capability attributes of different types of energy resources at the allocation decision level, including the capacity range, power or energy conversion capability and corresponding constraints of electrochemical energy storage resources and hydrogen energy resources.

[0053] 1) Electrochemical resource model

[0054] To evaluate the energy feasibility and consistency of candidate electrochemical energy storage resource configuration schemes under different demand scenarios, a discrete-time extrapolation model based on State of Charge (SoC) is constructed to calculate the possible energy state changes of the configuration schemes during the evaluation period.

[0055] (1)

[0056] (2)

[0057] Binary variables are used to constrain mutual exclusion between charging and discharging, and the charging and discharging power is limited to not exceeding the rated power.

[0058] (3)

[0059] (4)

[0060] To ensure energy cycle consistency, a state recovery is set at the end of the cycle:

[0061] (5)

[0062] In the formula, t represents the time period, T is the cycle length, and Δt represents the time period length; This represents the SoC value at time t=1. This represents the SoC value at time t. Indicates the initial SoC value of the scheduling cycle; Indicates the battery's charge and discharge efficiency; , E represents the battery charging power and discharging power at time t, respectively. BAT Indicates the rated energy capacity of the battery; Let be the mutually exclusive state variables for charging and discharging at time t. = 1 indicates charging. = 0 indicates discharge; This indicates the upper limit of the rated charging / discharging power of the battery.

[0063] 2) Hydrogen storage tank model

[0064] Based on hydrogen mass balance constraints, the hydrogen produced by the electrolyzer is used for net filling of the hydrogen storage tank and consumption by hydrogen-using equipment:

[0065] (6)

[0066] Upper and lower limits are set for the hydrogen storage capacity to limit the acceptable resource range of the configuration scheme in the evaluation scenario:

[0067] (7)

[0068] The dynamic evolution of hydrogen quality in the hydrogen storage tank is described using a state recursion method. The initial time period is obtained by adding the initial storage quantity to the net charge quantity, and subsequent time periods are updated recursively by the storage quantity of the previous time period.

[0069] (8)

[0070] (9)

[0071] In the formula, g elz Indicates the hydrogen production rate of the electrolyzer; Δm tank This indicates the net amount of hydrogen added to the hydrogen storage tank during a certain period of time. This indicates the amount of hydrogen consumed by the gas turbine. α represents the mass of hydrogen in the hydrogen storage tank at time t; min α represents the lower limit operating coefficient.max Indicates the upper limit operating coefficient; Indicates the capacity of the hydrogen storage tank; This indicates the initial hydrogen mass in the hydrogen storage tank.

[0072] 3) Hydrogen fuel cell model

[0073] The hydrogen fuel cell model is used to describe the electrical and thermal energy conversion results corresponding to the allocation scheme evaluation of hydrogen energy resources. Its modeling needs to consider energy balance relationship, battery input and output power constraints, etc.

[0074] (10)

[0075] (11)

[0076] (12)

[0077] In the formula, This indicates the hydrogen energy consumed by the fuel cell; Indicates the output electrical power of the fuel cell; This indicates the waste heat power generated during fuel cell operation; Indicates the electrical efficiency of a fuel cell; This indicates the upper limit of the hydrogen energy input to the fuel cell.

[0078] 4) Thermal storage tank model

[0079] The operating constraints of the thermal storage tank in the system can be expressed by the following formula:

[0080] (13)

[0081] (14)

[0082] (15)

[0083] (16)

[0084] (17)

[0085] In the formula, This indicates the amount of heat stored in the hot water tank at the end of time period t; This indicates the rated maximum heat storage capacity of the hot water tank; to ensure the rationality of resource allocation in the evaluation scenario, upper and lower limits [0.2, 0.9] are set for the storage level of the heat storage resources to limit the acceptable range of the configuration scheme; , These represent the heating power and heat dissipation power of the hot water tank, respectively. This represents a mutually exclusive state variable for charging and discharging heat. = 1 when heated, Heat is released when the value is 0. This represents the heat loss coefficient of the hot water tank per unit time period; This indicates the initial heat storage.

[0086] (2) Logistics-side demand information model

[0087] The logistics-side model extracts information such as ship berthing and departure plans, loading and unloading task sequences, equipment concurrent operation relationships, and operation rhythms to construct a mapping relationship between operational tasks and energy resource demands, and generates energy resource demand descriptions for different assessment periods. This section describes the impact of different operational organization conditions on energy resource demands during the configuration decision assessment process.

[0088] 1) Logistics loading and unloading operation model

[0089] This invention introduces state variables to describe the state of a ship in port, with the following logical constraints:

[0090] (18)

[0091] (19)

[0092] (20)

[0093] (twenty one)

[0094] (twenty two)

[0095] Further consideration should be given to berthing and unberthing operations conflicts and berth length and capacity constraints:

[0096] (twenty three)

[0097] (twenty four)

[0098] In the formula, This represents the port status variable of vessel i during time period t; t arr (i), t dep (i) indicates the arrival and departure times of the vessel; , These represent the start and end events of the status in Hong Kong, respectively; L ship (i) indicates the length of the ship; L max This indicates the maximum length of the berth.

[0099] Shore-ship power supply (SPS) interface allocation is determined by variables. This indicates that the constraints of each device serving only one vessel per time period and the availability of allocation only for vessels in port are met:

[0100] (25)

[0101] (26)

[0102] SPS power supply upper and lower limit constraints:

[0103] (27)

[0104] During berthing, consistency check constraints are used to determine whether the SPS resource allocation scheme can meet the ship's energy needs:

[0105] (28)

[0106] In the formula, Let s represent the shore power interface allocation variable, where s represents the s-th shore power interface and S represents the set of shore power interfaces. Indicates the number of SPS interfaces; Indicates the corresponding SPS power supply; , These represent the upper and lower bounds of the SPS interface power, respectively. This indicates the electricity demand of ship i when berthing.

[0107] The allocation of electrically driven quay cranes (QC) meets the constraints of serving only one vessel per unit per time period and the constraint of being available only for vessels in port:

[0108] (29)

[0109] (30)

[0110] The equivalent operating power of QC is converted to a constant based on the equipment kinematics and energy consumption coefficient, and the cumulative QC loading and unloading volume is calculated accordingly:

[0111] (31)

[0112] (32)

[0113] (33)

[0114] (34)

[0115] The Big-M linearization method is used to determine whether the cumulative loading and unloading volume has reached the demand volume and to define the variables. Perform constraint binding to ensure = 1 indicates that loading and unloading are complete. = 0 indicates that loading and unloading are not complete:

[0116] (35)

[0117] (36)

[0118] (37)

[0119] (38)

[0120] In the formula, Let q represent the quay crane assignment variable, where q represents the q-th quay crane and Q represents the set of quay cranes. This indicates the maximum number of QC personnel that can be deployed simultaneously on a vessel; This represents the equivalent power per unit obtained through conversion; This indicates the processing capacity of the quay crane per unit time. , , , , , , These represent the effective lifting height of QC operations, the horizontal movement distance of QC from ship to shore or from shore to ship, the working stroke of QC on the ship, the lifting / lowering speed of QC, the horizontal movement speed of QC, the unit energy consumption coefficient of QC vertical movement, and the unit energy consumption coefficient of QC horizontal movement, respectively. Indicates the operating power of the quay crane; Indicates the maximum power of a single QC unit; Indicates the cumulative loading and unloading volume; This represents the demand for ship loading and unloading; M is the Big-M constant. This indicates the completion of loading and unloading; ε is a small positive number.

[0121] 2) Logistics and transportation operation model

[0122] Automated Guided Vehicle (AGV) transportation meets the constraints of serving only one vessel per unit per time period and being available only for vessels in port:

[0123] (39)

[0124] (40)

[0125] The AGV operating power is converted using an equivalent constant, and the cumulative transportation volume is calculated accordingly:

[0126] (41)

[0127] (42)

[0128] (43)

[0129] To ensure that loading and unloading requirements are met during transportation:

[0130] (44)

[0131] By setting work process consistency constraints, the matching relationship between the transportation operation organization plan and the loading and unloading operation schedule can be evaluated:

[0132] (45)

[0133] In the formula, Indicates the AGV allocation variable. Indicates the first A represents a set of all AGV automated guided container trucks. This indicates the maximum number of AGVs that can be deployed on a ship at the same time. Indicates the AGV's operating power; This represents the equivalent power constant of the AGV; Indicates the cumulative transport volume; This indicates the AGV's transportation and processing capacity per unit time.

[0134] 3) Logistics yard operation model

[0135] The gantry crane (GC) operates in accordance with the constraints of serving only one vessel per unit per time period and the constraints of being available only for vessels in port:

[0136] (46)

[0137] (47)

[0138] GC operation power is converted using an equivalent constant, and the cumulative completion amount of the stockpile is calculated accordingly:

[0139] (48)

[0140] (49)

[0141] (50)

[0142] (51)

[0143] To assess the extent to which the yard operation organization plan meets loading and unloading requirements during the configuration decision-making cycle:

[0144] (52)

[0145] GC storage progress should not exceed transportation progress:

[0146] (53)

[0147] In the formula, Let g represent the GC allocation variable, where g represents the g-th gantry crane GC, and G represents the gantry crane GC set. This indicates the maximum number of GCs that a ship can deploy simultaneously; Indicates the equivalent power of a single unit; Indicates the stacking processing capacity per unit time; , , , , , These are the lifting height, horizontal movement distance, vertical lifting speed, horizontal movement speed, vertical lifting energy consumption coefficient, and horizontal movement energy consumption coefficient, respectively. Indicates GC job power; Indicates the maximum power of a single GC unit; This indicates that the cumulative stockpile operation has been completed.

[0148] The aforementioned logistics loading and unloading, transportation, and yard operation models are all used to describe the state characteristics and resource demand characteristics of different operation organization schemes in the configuration decision evaluation, and their calculation results serve as input information for the evaluation indicators of resource allocation schemes.

[0149] 3. Construct an electric-hydrogen energy resource allocation framework for port scenarios.

[0150] The problem of allocating multiple types of energy resources in a port integrated energy system is modeled as a two-layer configuration evaluation structure coupled with planning and operation. The planning layer is used for medium- and long-term energy storage capacity configuration decisions, while the operation layer is used to evaluate the system's operational performance on multi-dimensional performance indicators under a given configuration scheme.

[0151] (1) Planning layer model

[0152] 1) Objective function

[0153] To reflect the long-term impact of energy storage capacity configuration on the coupled system, the planning layer aims to minimize the life-cycle cost (LCC) of the electric-hydrogen energy resources. Taking into account factors such as initial investment costs, annual fixed operation and maintenance costs, equipment replacement costs, and residual value within the planning period (m years), the life-cycle cost is discounted and summarized using net present cost (NPC), and annualized using equivalent annual cost (EAC). The planning layer's objective function measures the comprehensive economic performance of different electric-hydrogen energy resource configuration schemes within the planning period, providing a quantitative evaluation basis for configuration decisions. The objective function is constructed as follows:

[0154] (54)

[0155] In the formula, This represents the annualized total lifecycle cost; Represents the net present value of total lifecycle costs; This represents the equal payment coefficient.

[0156] in, and The calculation formula is as follows:

[0157] (55)

[0158] (56)

[0159] In the formula, m represents the planning period; r represents the discount rate; and y represents the number of years. Indicates the initial investment cost; This represents the annual fixed operating and maintenance cost; This represents the replacement cost in year y. This represents the residual value at the end of the period.

[0160] Each cost item is linearly represented by its capacity as follows:

[0161] (57)

[0162] (58)

[0163] (59)

[0164] (60)

[0165] In the formula, , , , These represent the unit investment costs for the hydrogen storage tank, hot water tank, and battery power and energy sides, respectively. This indicates the maximum hydrogen storage capacity of the hydrogen storage tank. This indicates the maximum heat storage capacity of the hot water tank. and These represent the battery's maximum charging and discharging power and energy capacity, respectively. , , This indicates the annual maintenance coefficient of the corresponding equipment; This indicates the battery replacement price coefficient. This represents the set of years in which batteries were replaced. Indicates battery life; , , This indicates the residual value rate.

[0166] 2) Constraints

[0167] To ensure that the configuration scheme meets the performance constraints of electric-hydrogen energy resources and the feasible domain constraints of the operation layer, the specific details are as follows:

[0168] (61)

[0169] (62)

[0170] (63)

[0171] (64)

[0172] In the formula, , This indicates the lower and upper limits of the maximum hydrogen storage capacity of the hydrogen storage tank; , This indicates the lower and upper limits of the maximum heat storage capacity of the hot water tank; , This indicates the lower and upper limits of the battery's maximum charge and discharge power; , This indicates the lower and upper limits of the battery's energy capacity.

[0173] For any given configuration scheme x, under the premise of satisfying the energy flow-material flow coupling relationship, the corresponding three-dimensional decision space boundary point set is obtained through operational status evaluation calculation:

[0174] (65)

[0175] In the formula, B(x) represents the set of boundary points in the three-dimensional decision space; J k (x) represents the system operating cost, E k (x) represents the system's carbon emissions, T k(x) represents the average time a ship spends in port; K represents the number of boundary points.

[0176] Construct an approximate decision space for this configuration scheme based on the boundary point set:

[0177] (66)

[0178] Given three acceptable performance thresholds , , These serve as the upper limits for operating costs, carbon emissions, and average ship time in port, respectively. To quantify the degree to which configuration scheme x meets the threshold requirements, a coverage margin index is defined based on the boundary point set:

[0179] (67)

[0180] Further requirements include:

[0181] (68)

[0182] In the formula, This indicates the coverage margin indicator; , , These represent the upper limits for operating costs, carbon emissions, and average port time, respectively. This represents the minimum acceptable margin. When... When ≥0, it means that at least one running state simultaneously meets the threshold requirement; when ≥ When the value is greater than 0, it indicates that the configuration scheme not only meets the threshold requirement, but also has a value not less than 0. The overall margin level.

[0183] (2) Runtime layer model

[0184] 1) Constraints

[0185] When evaluating the operational status of a given configuration scheme, in addition to satisfying the system structural constraints mentioned above, the following balance and demand constraints must also be met.

[0186] Electric power balance constraints:

[0187] (69)

[0188] In the formula, Indicates the power purchased by the power grid; , These represent wind power and solar power output, respectively. Indicates the electrical power of the gas turbine; , These represent the battery charging power and discharging power, respectively. Indicates the port's basic electrical load; This represents the equivalent power consumption after demand response; Indicates the power consumption on the logistics side; This indicates the total electrical power consumption of the electrolytic cell.

[0189] Thermal power balance constraint:

[0190] (70)

[0191] In the formula, Indicates the heat output of the electrolytic cell; This indicates the output thermal power of the gas turbine. , These represent the heating power and heat dissipation power of the hot water tank, respectively. This indicates the port's heat load demand.

[0192] Demand response constraints:

[0193] (71)

[0194] (72)

[0195] (73)

[0196] (74)

[0197] (75)

[0198] (76)

[0199] In the formula, , These represent the upper and lower bounds of the demand response power, respectively. Indicates the baseline power for demand response; , These represent the upward and downward adjustments relative to the reference power, respectively. This indicates the total demand response within the period.

[0200] Total power consumption requirements for logistics:

[0201] (77)

[0202] In the formula, This indicates the total power demand on the logistics side; , , , These represent the electrical power consumption of SPS, QC, AGV, and GC respectively when providing services to ship i during time period t.

[0203] 2) Three-dimensional decision space model

[0204] To characterize the rational space of a port integrated energy system across the three dimensions of "economy, low carbon emissions, and operational efficiency" under a given configuration, this invention proposes the concept of a three-dimensional decision space for the system. This space is defined by system operating cost J and carbon emissions E. car and the average time a ship spends in port, T ship As observed variables, the corresponding three-dimensional decision space is defined as follows:

[0205] (78)

[0206] In the formula, x represents the set of variables describing the system's operating state and resource allocation state; g(x) = 0 represents constraints such as power balance, energy coupling, and logistics operation continuity; h(x) ≤ 0 represents inequality constraints such as equipment output limits, energy storage state of charge constraints, logistics operation capacity constraints, and carbon emission limits.

[0207] The decision space constructed in this invention is a bounded convex set in the three-dimensional observation space. Therefore, the effective value intervals of the three-dimensional observation variables are discretized separately, assuming:

[0208] (79)

[0209] The specific process for solving the decision space boundary is as follows: An observation grid is constructed based on preset discrete values ​​for operating costs, carbon emissions, and average ship port time. For each given combination of observations, under complete energy-material flow constraints, the achievable extreme values ​​of the system in the remaining observation dimensions are evaluated through numerical calculation, corresponding to the obtained (J, E) values. car T ship Points can be considered as boundary locations of feasible states of the system under that observation combination. By repeating the above process for all observation combinations and summarizing the obtained boundary state points, an approximate set of boundary points in the three-dimensional decision space can be obtained.

[0210] The specific steps are as follows:

[0211] Step 1: Assessment of Port Time Boundaries Given Operating Costs and Carbon Emissions

[0212] For each group (J) i E j Add the following to the basic constraints g(x) = 0, h(x) ≤ 0:

[0213] (80)

[0214] Construct a status assessment model for time spent in port:

[0215] (81)

[0216] If a feasible solution exists for this problem, then let the minimum reachable time in Hong Kong be T. ij and (J) i E j T ij This serves as one of the boundary state points of the decision space in the time dimension of being in Hong Kong.

[0217] Step 2: Assessment of the operating cost boundary under given carbon emissions and port stay conditions

[0218] For each group (E) j T k ), adding the following to the basic constraints:

[0219] (82)

[0220] Constructing an evaluation model:

[0221] (83)

[0222] If a feasible solution J is obtained jk Then (J) jk E j T k The boundary state point set is included to characterize the boundary features of the decision space in the cost dimension.

[0223] Step 3: Assessment of the carbon emission boundary given operating costs and time spent in port

[0224] For each group (J) i T k ), adding the following to the basic constraints:

[0225] (84)

[0226] Constructing an evaluation model:

[0227] (85)

[0228] If a feasible solution E is obtained ik Then (J) i E ik T k () serves as the boundary state point in the carbon emission dimension of the decision space.

[0229] By traversing the three types of problems mentioned above, the discrete grid within the range of three-dimensional observation values ​​is solved point by point. The obtained boundary state points are deduplicated and filtered to form a set of feasible boundary state points. Based on the final set of boundary state points, the envelope surface is constructed using the three-dimensional convex hull algorithm, resulting in a visualized representation of the three-dimensional decision space of the port integrated energy system.

[0230] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0231] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0232] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. An optimized scheduling method for the allocation of electricity-hydrogen energy resources in port systems, characterized in that, include: Construct an energy flow-logistics coupling configuration decision information model for port integrated energy systems. By extracting key information variables including operation status, operation concurrency, operation rhythm and equipment operation mode, establish a mapping relationship between logistics operation organization mode and energy resource demand, and form a unified collaborative information model. Based on a collaborative information model, a resource allocation decision-making and evaluation model incorporating electrochemical energy storage and hydrogen energy resources is constructed. This model is used to perform an information-based simulation of the resource supply and demand relationship in a port system under given resource allocation and logistics operation organization schemes, generating evaluation indicators corresponding to the resource allocation schemes. A three-dimensional decision space is constructed, with operating costs, carbon emissions, and average ship time in port as dimensions. An observation grid is constructed on preset discrete values ​​of operating costs, carbon emissions, and average ship time in port. The boundary state point set is solved point by point, and the envelope surface of the three-dimensional decision space is constructed using a three-dimensional convex hull algorithm. Based on the decision boundary characteristics of the three-dimensional decision space, resource allocation is performed... The selection of configuration options; the configuration decision evaluation model includes an energy-side resource evaluation model and a logistics-side demand information model; the energy-side resource evaluation model includes at least an electrochemical energy storage resource model, a hydrogen storage tank model, a hydrogen fuel cell model, and a thermal storage tank model; the logistics-side demand information model includes at least a logistics loading and unloading operation model, a logistics transportation operation model, and a logistics yard operation model; the electrochemical energy storage resource model is based on a discrete-time information extrapolation model constructed based on the state of charge, and considers charging and discharging mutual exclusion and end-of-cycle state recovery constraints; the hydrogen storage tank model describes the dynamics of hydrogen mass in the hydrogen storage tank based on hydrogen mass balance constraints and upper and lower limits of storage capacity constraints. The evolution of the hydrogen fuel cell model is described. The hydrogen fuel cell model is based on energy balance, considering the conversion characteristics of electrical and thermal energy, and is constrained by upper and lower limits of input hydrogen power. The thermal storage tank model is based on thermal mass balance, describing the dynamic evolution of stored heat, and is constrained by upper and lower limits of storage capacity and charging / discharging heat power. The logistics loading and unloading operation model characterizes the resource demand characteristics of the loading and unloading process through variables such as ship port status, shore power allocation, and quay crane operation, including berth conflict constraints, shore power constraints, and loading / unloading schedule constraints. The logistics transportation operation model describes the resource consumption of the transportation link based on the allocation and power demand of automated guided vehicle (AGV) container trucks, including allocation constraints and transportation... The cumulative quantity calculation and matching constraints with loading and unloading schedule; the logistics yard operation model describes the resource requirements of the yard process through gantry crane allocation and yard schedule, including allocation constraints, cumulative quantity calculation of the yard, and synchronization constraints with transportation schedule; the construction of the three-dimensional decision space is specifically as follows: the multi-type energy resource allocation problem of the port integrated energy system is modeled as a two-layer configuration evaluation structure coupled with planning and operation; wherein, the planning layer aims to minimize the full life cycle cost of electric-hydrogen energy resources to make medium- and long-term energy storage capacity configuration decisions; the operation layer is used to evaluate the operational performance of the port integrated energy system on multi-dimensional performance indicators under a given configuration scheme.

2. The optimized scheduling method for the allocation of electricity-hydrogen energy resources in a port system according to claim 1, characterized in that, The port integrated energy system includes a port energy flow system and a port logistics system; the configuration decision information model treats the port energy flow system and the port logistics system as a unified information processing object; the port logistics system forms a description of energy resource demand through ship planning, operation task arrangement and equipment operation mode information, while the port energy flow system forms a description of resource supply capacity through the capacity attributes and constraints of various energy resources.

3. The optimized scheduling method for the allocation of electric-hydrogen energy resources in a port system according to claim 1, characterized in that, The objective function at the planning level is used to measure the comprehensive economic performance of different electric-hydrogen energy resource allocation schemes within the planning period, providing a quantitative evaluation basis for allocation decisions. The objective function is expressed as follows: In the formula, This represents the annualized total lifecycle cost; Represents the net present value of total lifecycle costs; denoted as the equal payment coefficient, and x represents the electric-hydrogen energy resource allocation scheme.

4. The optimized scheduling method for the allocation of electricity-hydrogen energy resources in a port system according to claim 1, characterized in that, The solution to the boundary state point set must satisfy the constraints of electric power balance, thermal power balance, demand response, and total power demand for logistics.

5. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 4.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 4.

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