Zero-carbon logistics robust energy supply method and system fusing multiple uncertainties

Abstract

The invention belongs to the technical field of zero-carbon logistics energy supply, and provides a zero-carbon logistics robust energy supply method and system fusing multiple uncertainties, and the technical scheme is that a multiple-uncertainty model is constructed according to the energy structure and operation characteristics of a zero-carbon logistics energy supply system; constructing a robust optimization objective function, a day-ahead constraint, an intra-day constraint and a power balance constraint based on interaction of multiple energy sources in the system; optimizing the robust optimization objective function on the basis of a robust optimization theory by combining the multiple uncertain models and actual operation requirements of the carbon logistics system, and constructing a robust optimization model; and decomposing the robust optimization model into a main problem and a sub-problem, and solving the main problem and the sub-problem to obtain a scheduling strategy for coordinating the multiple energy systems. Therefore, the robustness and the high efficiency of the system under various uncertain conditions are ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of zero-carbon logistics energy supply, and particularly relates to a zero-carbon logistics robust energy supply method and system fusing multiple uncertainties. BACKGROUND

[0002] The statements in this section merely provide background information related to the application and do not necessarily constitute prior art.

[0003] In a zero-carbon logistics energy supply system, the uncertainty of energy supply mainly comes from the volatility of renewable energy (such as solar energy and wind energy) and the uncertainty of energy demand. These uncertain factors make it difficult for traditional energy supply models to meet the real-time and efficient scheduling requirements in practical applications. Moreover, the existing zero-carbon logistics system often does not fully consider the uncertainty factors in the design and operation process, resulting in a large difference in the stability and economy of system operation, especially when facing complex logistics scenarios, the system performance is often difficult to reach the optimal state.

[0004] Currently, robust optimization methods are proposed to solve this problem. Robust optimization can optimize system design and operation strategies by considering the worst-case scenario of uncertainty to ensure that the system remains highly robust and stable under different operating environments. However, in the zero-carbon logistics energy supply system, existing robust optimization methods mostly focus on the optimization of a single energy form and rely on traditional optimization algorithms, failing to effectively combine multiple uncertainty factors, making it difficult to adapt to real-time changes in dynamic environments. Especially in the logistics transportation process, due to the uncertainty of demand, the supply and scheduling of energy face greater challenges, and existing optimization methods are difficult to provide robust and efficient solutions in complex environments. SUMMARY

[0005] To solve at least one of the technical problems in the background art, the application provides a zero-carbon logistics robust energy supply method and system fusing multiple uncertainties, which fuses the robust energy supply technology of zero-carbon logistics fusing multiple uncertainties, coordinates the synergistic effect of multiple energy forms, solves the problems of energy volatility and demand uncertainty in the prior art, and improves the stability, flexibility and economy of the system.

[0006] To achieve the above purpose, the application adopts the following technical solutions: The first aspect of the application provides a zero-carbon logistics robust energy supply method fusing multiple uncertainties, comprising the following steps: According to the energy structure and operating characteristics of the zero-carbon logistics energy supply system, a multiple uncertainty model is constructed; Based on the interaction of multiple energies in the system, a robust optimization objective function, day-ahead constraints, intra-day constraints and power balance constraints are constructed; The robust optimization model is decomposed into a main problem and a sub-problem, and a scheduling strategy of the coordinated multi-energy system is obtained by solving the main problem and the sub-problem. The robust optimization model is decomposed into a main problem and a sub-problem, and a scheduling strategy of the coordinated multi-energy system is obtained by solving the main problem and the sub-problem.

[0007] Further, the multi-uncertainty model comprises: The renewable energy uncertainty variable and the logistics demand uncertainty variable are respectively established; The uncertainty set is established for the uncertainty variable, and the uncertainty set is represented as: , Wherein, The uncertainty variable comprises the renewable energy uncertainty variable and the logistics demand uncertainty variable; and The lower bound and the upper bound of the uncertainty variable in each scheduling period are represented as and respectively; and The single-day lower bound and the single-day upper bound of the uncertainty variable are represented as and respectively, represents, represents a time period.

[0008] Further, the robust optimization objective function comprises a power purchase cost, a hybrid energy storage operation cost, a renewable energy utilization cost and a carbon emission cost; The day-ahead constraints comprise energy storage capacity constraints of each energy storage system, charge and discharge power constraints and non-simultaneous charge and discharge constraints of each energy storage system, minimum start-up and shutdown time constraints of the electrolytic hydrogen production equipment and the hydrogen fuel cell, working temperature constraints of the electrolytic hydrogen production equipment, constraints for ensuring that the logistics loading and unloading operations are performed after the traffic load, constraints for ensuring that the logistics equipment loads and unloads the goods corresponding to the traffic load i within the planned time, constraints for ensuring that all refrigerated goods are loaded and unloaded within the planned time and connected to the logistics refrigeration system, period loading and unloading quantity constraints, constraints for ensuring that all goods are transported to the yard in time, period loading and unloading quantity constraints, refrigerated goods internal temperature constraints, constraints for ensuring that the yard provides refrigeration power immediately after the refrigerated goods arrive, and maximum refrigeration power and internal temperature constraints of the refrigerated goods; The day-ahead constraints comprise wind turbine generator set power constraints, photovoltaic generator set power constraints and main grid tie-line power constraints; The power balance constraints comprise cold power balance constraints and electric power balance constraints.

[0009] Further, the robust optimization objective function is: , , , , The current constraints include: the energy storage range of each energy storage system; the charging and discharging power of each energy storage system and the inability to charge and discharge simultaneously; the minimum start-up and shutdown time constraints for electrolysis hydrogen production equipment and hydrogen fuel cells; the operating temperature constraints for electrolysis hydrogen production equipment; ensuring that logistics loading and unloading operations are carried out after traffic loads arrive; ensuring that logistics equipment completes the loading and unloading of goods corresponding to traffic load i within the planned time; and ensuring that all refrigerated goods are loaded and unloaded and connected to the logistics cooling system within the planned time. Time-limited loading and unloading quantity constraints, constraints to ensure all goods are transported to the yard in a timely manner, internal temperature constraints of refrigerated goods, constraints on the yard to provide refrigeration power immediately after the arrival of refrigerated goods, maximum refrigeration power and internal temperature constraints of refrigerated goods, and refrigeration power constraints of refrigerated goods. Intraday constraints include: power constraints for wind turbine generators, power constraints for photovoltaic generators, and power constraints for main grid interconnection lines. Power balance constraints: cold power balance condition and electric power balance condition constraints; in, This represents the total operating cost. Indicates the cost of hydrogen storage. Indicates the cost of battery energy storage. Indicates the cost of cold storage. Indicates the cost of purchasing electricity. Indicates the cost of renewable energy use. Indicates the cost of carbon emissions; This indicates the unit cost of electricity. Indicates the power purchased. Indicates the time span of the scheduling period. Indicates the unit cost of photovoltaic power. Indicates the power output of photovoltaic power generation. This represents the unit cost of wind power. This indicates the power output of wind power generation. Indicates the cost per unit of carbon emissions. and These represent the carbon emissions per unit of electricity purchased from the main grid and generated from renewable energy sources, respectively (kg / kWh). This represents the carbon emission share per unit of electricity generated (kg / kWh).

[0010] Furthermore, combining the multiple uncertainty model and the actual operational requirements of the carbon logistics system, the robust optimization objective function is optimized based on robust optimization theory to construct a robust optimization model. This model includes determining the worst-case stochastic scenario and the switching status and power of the hybrid energy storage system and logistics system under this scenario during the daytime operation phase. During the intraday operation phase, the power of wind power generation, photovoltaic generator sets, and tie line are adjusted in real time based on ultra-short-term forecast data and fixed dispatch instructions.

[0011] Furthermore, the expression for the robust optimization model is: , , , , , in, This indicates the decision variables during the day-ahead operation phase, including power dispatch instructions for hydrogen energy storage systems, natural gas energy storage systems, electrochemical energy storage systems, cold storage systems, electric refrigeration systems, absorption refrigeration systems, quay cranes, yard cranes, and refrigerated cargo. This represents the binary decision variables during the daytime operation phase, including the operating status of each energy storage system, and the operating status of the quay crane and yard crane; This represents the decision variables during the intraday operation phase, specifically including the actual power values ​​of wind power, photovoltaic power, and tie lines; This represents uncertain variables, including the maximum power values ​​of wind turbines and photovoltaic generators, local load, and traffic load demand values. Indicates and The relevant unit operating cost vector, and Respectively represent and Matching unit coefficient vector and constant term, Represents a set of uncertainties, with constraints The range of values ​​for , Indicates the relationship with the two-stage adjustment variable The corresponding unit adjustment cost vector.

[0012] Furthermore, the scheduling strategy for coordinating multiple energy systems, obtained by solving the main problem and sub-problems, specifically includes: The main problem calculates the optimal decision scheme based on the most extreme running scenario obtained by the column constraint generation algorithm. The subproblem searches for the most extreme running scenario given the first-stage decision variables. Then, the main problem adjusts the first-stage decision variables based on the set of potential most extreme running scenarios returned by the subproblem. This process is repeated until the difference between the objective functions of the main problem and the subproblem meets the stopping condition. At this point, a robust scheduling instruction is output.

[0013] A second aspect of the invention provides a zero-carbon logistics robust energy supply system that integrates multiple uncertainties, comprising: The uncertainty modeling module is used to construct multiple uncertainty models based on the energy structure and operational characteristics of zero-carbon logistics energy supply systems. The robust optimization module is used to construct a robust optimization objective function, day-ahead constraints, intraday constraints, and power balance constraints based on the interaction of multiple energy sources in the system. Combining the multi-uncertainty model and the actual operational requirements of the carbon logistics system, the robust optimization objective function is optimized based on robust optimization theory to construct a robust optimization model. The scheduling module is used to decompose the robust optimization model into a main problem and sub-problems, and obtain the scheduling strategy for coordinating multiple energy systems by solving the main problem and sub-problems.

[0014] A third aspect of the present invention provides a computer-readable storage medium.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method for robust zero-carbon logistics power supply incorporating multiple uncertainties.

[0016] A fourth aspect of the present invention provides a computer device.

[0017] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the robust zero-carbon logistics energy supply method for incorporating multiple uncertainties as described above.

[0018] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention proposes a multi-timescale adaptive robust optimization method for zero-carbon logistics energy supply systems. This method constructs multiple uncertainty sets to accurately characterize disturbances such as renewable energy output fluctuations, changes in logistics demand, and uncertainties in market electricity prices. A worst-case optimization mechanism is embedded in the scheduling model to ensure stable system operation even under extreme operating scenarios. By introducing uncertainty-driven optimization boundaries and resource coordination adjustment strategies, the model can dynamically adapt to changes in operating conditions while meeting feasibility requirements, accurately solving for the optimal scheduling scheme under different uncertainty scenarios, and achieving the dual minimization of operating costs and carbon emissions. Compared with existing day-ahead operation methods and robust optimization operation methods, the method proposed in this invention, while meeting feasibility requirements, makes fuller use of flexible resources to absorb renewable energy, achieving optimal operating results.

[0019] 2. In terms of uncertainty variable modeling, this invention establishes an energy-power fluctuation boundary model that considers both hourly and daily uncertainty variables. Compared to the overly conservative nature of traditional robust optimization methods, this method provides a more accurate characterization of uncertainty variables. In practical operation, this invention's method can further reduce operating costs and carbon emissions without compromising feasibility.

[0020] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0022] Figure 1 This is a flowchart of a zero-carbon logistics robust energy supply method that integrates multiple uncertainties, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a robust operation scheme for a zero-carbon logistics energy supply system provided in an embodiment of the present invention; Figure 3 This is a flowchart of the column constraint generation algorithm provided in an embodiment of the present invention; Figure 4 This refers to the day-ahead operating electrical power of the energy system provided in this embodiment of the invention; Figure 5 This refers to the day-ahead operating thermal power of the energy system provided in this embodiment of the invention; Figure 6 These are the flexible electrical and thermal load requirements provided by the embodiments of the present invention; Figure 7 This is the transportation situation of refrigerated goods provided in the embodiments of the present invention; Figure 8 These are the intraday operation results provided by the embodiments of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0026] As mentioned in the background section, in zero-carbon logistics energy supply systems, most existing robust optimization methods focus on optimizing a single energy form and rely on traditional optimization algorithms. They fail to effectively incorporate multiple uncertainties, making it difficult to adapt to real-time changes in dynamic environments. Especially during logistics transportation, the uncertainty of demand presents greater challenges to energy supply and scheduling, making it difficult for existing optimization methods to provide robust and efficient solutions in complex environments.

[0027] This invention addresses the shortcomings of existing zero-carbon logistics energy supply systems, which fail to fully utilize multiple uncertainties and effectively coordinate the load interactions between different energy forms. It proposes a robust zero-carbon logistics energy supply technology that integrates multiple uncertainties. This technology introduces robust optimization methods and combines the synergistic effects of multiple energy forms (such as renewable energy and energy storage systems) to ensure the system's robustness and efficiency under various uncertain conditions.

[0028] Example 1 like Figure 1 As shown, this embodiment provides a robust zero-carbon logistics energy supply method that integrates multiple uncertainties, including the following steps: Step 1: Construct a multiple uncertainty model based on the energy structure and operational characteristics of the zero-carbon logistics energy supply system; like Figure 2 The diagram shows a robust operating scheme for a zero-carbon logistics energy supply system, which includes multiple energy sources such as electricity, hydrogen, gas, heat, and cooling.

[0029] Uncertain variables include maximum photovoltaic output, maximum wind power output, shore power load, and local load demand.

[0030] This invention models the uncertainty variables using hourly power and daily energy boundaries, establishing multi-timescale uncertainty boundaries to provide more accurate uncertainty conditions for day-ahead robust scheduling.

[0031] Recently, robust dispatching has calculated power dispatch instructions for various equipment, including electrolysis equipment, hydrogen fuel cells, methanation equipment, natural gas turbines, electrochemical energy storage systems, cold storage systems, electric refrigeration equipment, absorption refrigeration equipment, refrigerated cargo yards, quay cranes, and yard cranes, based on multi-timescale uncertainty boundaries. This provides operational boundaries for intraday real-time operation. During the intraday real-time operation phase, the power of wind turbines, photovoltaic generators, and tie lines is adjusted within the operational boundaries based on uncertain ultra-short-term time series forecast data to ensure the operational reliability of the zero-carbon logistics energy supply system.

[0032] To effectively describe the uncertainties in a zero-carbon logistics system, this embodiment introduces multiple uncertainty factors and constructs a corresponding mathematical model. The main sources of uncertainty include: (1) Uncertainty of renewable energy generation: Renewable energy sources such as wind power and photovoltaic power are greatly affected by weather factors. Their fluctuations can be modeled using probability distributions, such as normal distribution or Beta distribution.

[0033] (2) Fluctuations in logistics demand: Logistics demand may be affected by market changes, emergencies, etc., and can be modeled using interval variables or scenario analysis.

[0034] In order to accurately describe the uncertainties in the zero-carbon logistics energy supply system and to ensure the stability of the energy supply system, this embodiment needs to consider the uncertainties of multiple influencing factors and establish corresponding mathematical models.

[0035] Specifically, the steps include the following: Step 101: Establish uncertainties for renewable energy and logistics demand respectively. The power generation capacity of renewable energy (such as wind and solar power) is affected by weather conditions and is highly volatile. To model this uncertainty, this embodiment uses a probability distribution method to construct wind power generation and photovoltaic power generation models to describe the uncertainties of wind power and photovoltaic power generation.

[0036] The wind power generation model is as follows: wind speed Follows a normal distribution: (1), in, and These represent the mean and variance of the wind speed, respectively.

[0037] Among them, wind power output It can be represented as: (2), in, This indicates the overall conversion efficiency of the wind turbine. This represents the functional relationship between wind speed and wind power output; The photovoltaic power generation model is as follows: Solar radiation Follows a Beta distribution: (3), Photovoltaic power generation It can be represented as: (4), in, For photovoltaic conversion efficiency, For the area of ​​the photovoltaic panel, , The shape parameter of the Beta distribution reflects the mean and variance characteristics of solar radiation and can be obtained by fitting historical meteorological data.

[0038] Logistics demand can fluctuate due to factors such as market conditions and unforeseen events. To model this uncertainty, this embodiment uses interval variables and scenario analysis.

[0039] Specifically, the steps include the following: Define logistics requirements Subject to interval uncertainty: (5), in, and These represent the minimum and maximum requirements, respectively.

[0040] Using historical data for Monte Carlo sampling, multiple possible logistics demand scenarios are generated: (6), in, Follows a uniform distribution. To simulate the number of scenes, This represents the historical average or forecast value of logistics demand.

[0041] Step 102: Establish an uncertainty set for the uncertain variables; In this embodiment, an uncertainty set is established for the uncertain variables, specifically including the maximum output power of photovoltaic and wind turbine generators, local load, and shore power load demand. The model is shown below: (7), In the formula, This represents uncertain variables, including uncertain variables related to renewable energy and uncertain variables related to logistics demand. and These represent the lower and upper bounds of the uncertain variable in each scheduling period, respectively. and These represent the lower and upper bounds of the uncertainty variable for a single day, respectively. Indicates the historical average. Indicates a time period; The construction of the uncertainty set can be extended using the Distributed Robust Optimization (DRO) method to improve the robustness of the model: (8), in, Let be the set of possible probability distributions.

[0042] In the optimization model, a robust optimization method is used to incorporate uncertain variables into the objective function and constraints, and robust constraints are applied to ensure that the system remains feasible even under the worst-case scenario.

[0043] Step 2: Based on the interaction of multiple energy forms in the system, construct a robust optimization objective function and constraints; In this embodiment, the robust optimization objective function is: The optimization objective of this invention is to minimize daily operating costs, including electricity purchase costs, hybrid energy storage operating costs, renewable energy consumption costs, and carbon emission costs, expressed as: (9), (10) (11), (12), in, This represents the total operating cost. Indicates the cost of hydrogen storage. Indicates the cost of battery energy storage. Indicates the cost of cold storage. Indicates the cost of purchasing electricity. Indicates the cost of renewable energy use. Indicates the cost of carbon emissions; This indicates the unit cost of electricity. Indicates the power purchased. Indicates the time span of the scheduling period. Indicates the unit cost of photovoltaic power. Indicates the power output of photovoltaic power generation. This represents the unit cost of wind power. This indicates the power output of wind power generation. Indicates the cost per unit of carbon emissions. and These represent the carbon emissions per unit of electricity purchased from the main grid and the carbon emissions per unit of renewable energy generation, respectively (kg / kWh). This represents the carbon emission share per unit of electricity generated (kg / kWh).

[0044] Constraints include day-ahead constraints and intraday constraints; The day-ahead constraints include: Energy storage range constraints for each energy storage system: (13) HES stands for Hybrid Energy Storage System, specifically hydrogen energy storage, natural gas energy storage, electrochemical energy storage, and cold storage system. This represents the energy level of each energy storage system at time t; and These represent the lower and upper limits of the storage state of each energy storage system, respectively; This indicates the upper limit of the energy stored by each energy storage system; The charging and discharging power of each energy storage system and the constraints on simultaneous charging and discharging: (14) (15) (16) in, and They represent Start-up and shutdown status of each energy storage system during the time period; These represent the minimum power required to start and stop the energy storage system. and They represent Start-up and shutdown power of each energy storage system during the time period and These represent the maximum start-up and shutdown power of the energy storage system; Minimum start-up and shutdown time constraints for electrolytic hydrogen production equipment and hydrogen fuel cells: (17) (18) in, and These represent the unit start-up and shutdown times of the hydrogen electrolysis production equipment and the hydrogen fuel cell, respectively. Indicates an electrolysis device. Indicates fuel cell; Operating temperature constraints for electrolytic hydrogen production equipment: (19) in, express Operating temperature of the time-phase electrolysis unit and Indicates the minimum and maximum operating temperatures of the electrolysis unit; Ensure that loading and unloading operations are carried out after traffic load has reached its peak: (20) in, Indicates in Time, work site Energy load rate, Indicates logistics task Arrival time, Indicates logistics task departure time; Logistics equipment must ensure that the corresponding traffic load is loaded and unloaded within the planned timeframe. The cargo loaded: (twenty one), in, Indicates the quantity of goods per unit. Indicates time Internal logistics nodes Loading and unloading Operational decision variables for this type of goods Indicates traffic load The total demand for the loaded goods; All refrigerated goods must be loaded and unloaded within the planned timeframe and connected to the logistics refrigeration system. (twenty two), in, Indicates time Is it a node for refrigerated goods? A Boolean variable (0 or 1) indicating the execution of loading and unloading operations.

[0045] Time-limited loading and unloading quantity constraints; (twenty three), in, express Loading and unloading volume during time period and These represent the minimum and maximum loading / unloading volumes (containers) of the quay crane within a unit scheduling period, respectively. Ensure all goods are transported to the yard in a timely manner to avoid cargo delays; (twenty four), in, Indicates time Transport goods from the node Transported to the The workload of each storage yard Indicates the first yIndividual piles; Constraints on the quantity of goods loaded and unloaded within a given time period: (25) in, express The number of goods loaded and unloaded during the time period and These represent the minimum and maximum loading / unloading volume (containers) of the yard crane within a unit scheduling period, respectively. Internal temperature control for refrigerated goods: (26) in, express Maintain the internal temperature of the goods at all times. and These represent the lowest and highest internal temperatures of refrigerated goods, respectively. The storage yard provides refrigeration capacity immediately upon arrival of refrigerated goods. (27) in, Indicates time At that time, the first A certain state or control variable of refrigerated goods Indicates time At that time, the first The same state or control variables of refrigerated goods; The relationship between the maximum refrigeration capacity and the internal temperature of refrigerated goods is as follows: (28) in, Indicates time At that time, the first The maximum refrigeration capacity of refrigerated goods and This represents the coefficient relating the maximum cooling capacity to the internal temperature. Indicates time At that time, the first The internal temperature of refrigerated goods; Refrigeration power constraints for refrigerated goods: (29) in, Indicates the first The minimum refrigeration capacity for refrigerated goods. Indicates the on / off status of the refrigeration equipment. Indicates the first Refrigeration capacity of refrigerated goods Indicates the maximum cooling capacity; Intraday constraints include: Power constraints of wind turbine generator sets: (30) in, Indicates time The actual output power of the wind turbine generator set and These represent the minimum and maximum permissible electrical power of the wind turbine generator set, respectively. Photovoltaic generator power constraints: (31), in, Indicates time The actual output power of the photovoltaic generator set at that time and These represent the minimum and maximum allowable electrical power of the photovoltaic generator set, respectively. Main grid tie line power constraints: (32), in, and These represent the lower and upper limits of the electrical power of the wind turbine generator set, respectively. and These represent the lower and upper limits of the electrical power output of the photovoltaic generator set, respectively. Power balance constraints include: Cold power balance condition: (33), Electric power balance condition: (34), in, and For the power generation of fuel cells and gas turbines, This refers to the battery discharge power. The power consumption of the electric chiller (ETC). To charge the electrochemical energy storage power, The cooling capacity of the cold storage equipment. To charge the cooling capacity of the cold storage equipment.

[0046] Step 3: Combining various uncertainties and the actual operational needs of the zero-carbon logistics system, construct a comprehensive robust optimization model based on robust optimization theory.

[0047] The uncertainties considered in this embodiment include the maximum output power of photovoltaic and wind turbine generator sets, local load, and shore power load demand.

[0048] The original objective function in step 2 is rewritten into a robust optimization model. During the daytime operation phase, the worst-case stochastic scenario and the switching status and power of the hybrid energy storage system and logistics system under this scenario are determined. During the intraday operation phase, the power of wind power generation, photovoltaic generator sets and tie line power are adjusted in real time based on ultra-short-term forecast data and fixed dispatch instructions.

[0049] The expression for the robust optimization model is shown below: (35), (36) (37) (38), (39) in, This indicates the decision variables during the day-ahead operation phase, including power dispatch instructions for hydrogen energy storage systems, natural gas energy storage systems, electrochemical energy storage systems, cold storage systems, electric refrigeration systems, absorption refrigeration systems, quay cranes, yard cranes, and refrigerated cargo. This represents the binary decision variables during the daytime operation phase, including the operating status of each energy storage system, and the operating status of the quay crane and yard crane; This represents the decision variables during the intraday operation phase, specifically including the actual power values ​​of wind power, photovoltaic power, and tie lines; This represents uncertain variables, including the maximum power values ​​of wind turbines and photovoltaic generators, local load, and traffic load demand values. Indicates and The relevant unit operating cost vector, and Respectively represent and Matching unit coefficient vector and constant term, Represents a set of uncertainties, with constraints The range of values ​​for , Indicates the relationship with the two-stage adjustment variable The corresponding unit adjustment cost vector.

[0050] Equation (35) is the objective function of the robust optimization model, where the terms are as follows: Total operating costs during the day-ahead operation phase, including the day-ahead operating costs of the hydrogen energy storage system, electrochemical energy storage system, and cold storage system; (40) The power fluctuation costs of electrolysis equipment and hydrogen fuel cells, and the cycle degradation costs of electrochemical energy storage: (41), Start-up and shutdown costs of electrolysis equipment and hydrogen fuel cells: (42), Maintenance costs and lifespan degradation costs of electrolysis equipment and hydrogen fuel cells; lifespan degradation costs and maintenance costs of electrochemical energy storage; maintenance costs of cold storage systems. (43), Total operating costs during the daytime operation phase include electricity purchase costs, renewable energy consumption costs, and carbon emission costs.

[0051] (44), in, This represents the power fluctuation cost of electrolysis equipment and hydrogen fuel cells. This indicates the shutdown costs of electrolysis equipment and hydrogen fuel cells. This indicates the maintenance costs of electrolysis equipment and hydrogen fuel cells. This represents the lifespan degradation cost of electrolysis equipment and hydrogen fuel cells. This represents the cost of electrochemical energy storage lifespan degradation. Indicates the maintenance cost of electrochemical energy storage; It should be noted that all relevant uncertain parameters originate from the uncertainty set Ω constructed in step 1 and are embedded into the scheduling model through constraints. The parameter perturbation range and total quantity limit conditions defined by the uncertainty set Ω are explicitly embedded into the objective function and constraints of the robust optimization model. For example, in the problem of minimizing operating costs in the robust scheduling model, parameters such as wind power output, logistics demand, and electricity price are not used as deterministic values, but are replaced with uncertain parameter vectors that satisfy the internal conditions of set Ω.

[0052] In the scheduling optimization model constructed in step 3, a single predicted value is no longer used directly as input. Instead, uncertain parameter variables that satisfy the Ω constraint are used for modeling to ensure that the scheduling scheme is effective in all disturbance ranges.

[0053] Step 4: Decompose the robust optimization model into a main problem and sub-problems, and obtain the scheduling strategy for coordinating the multi-energy system by solving the main problem and sub-problems.

[0054] The adaptive robust optimization model established in this invention is solved by a column constraint generation algorithm that considers uncertainties at multiple time scales. The original robust model is decomposed into a main problem and subproblems. The main problem calculates the optimal decision scheme based on the most extreme running scenarios obtained by the column constraint generation algorithm; the subproblems, given the decision variables in the first stage, search for the most extreme running scenarios. Then, the main problem adjusts the decision variables in the first stage based on the set of potential most extreme running scenarios returned by the subproblems, and this process is repeated until the difference between the objective functions of the main problem and the subproblems meets the stopping condition. The robust scheduling instruction column constraint generation algorithm is then output.Figure 3 As shown.

[0055] The main problem is as follows: (45) (46) (47) (48) (49) (50), In the formula, and Uncertainty variables returned by subproblems. Uncertainty variables returned by subproblems in the main problem. The parameters are fixed in parametric form, thus transforming the main problem into a mixed-integer linear programming model to determine the optimal solution under the current operating environment; once the optimal parameters are determined, the decision variables of the main problem... It will be passed to the subproblem as a fixed parameter.

[0056] The subproblems are as follows: (51), (52), (53), In the formula, This represents the fixed decision variables in the main problem. The subproblem uses the Karush-Kuhn-Tucker (KKT) conditions to transform the max-min optimization problem into a mixed-integer linear programming problem. This is used to compute the parameters for the most extreme operating scenario.

[0057] Based on historical operational data of a logistics system, a simulation model was constructed to verify the proposed method. The simulation cycle was 24 hours, with each unit of operation lasting 30 minutes. Key parameters of the hybrid energy storage system are shown in Table 1. Temperature range parameters for refrigerated goods are shown in Table 2.

[0058] Table 1 Key parameters of the robust optimization model

[0059] Table 2 Parameters of Refrigerated Goods

[0060] Day-ahead operating results: Day-ahead operating results include power commands for the energy system and the logistics system, with the energy system operating results as follows: Figure 4 and Figure 5As shown, the operational results of the logistics system are as follows: Figure 6 and Figure 7 As shown. The goal of optimizing the operation of the energy system is to minimize the overall cost of electricity and carbon emissions. Figure 4 The day-ahead operating power of the energy system. Figure 5 For the day-ahead operating thermal power of the energy system, from Figure 4 and Figure 5 It can be seen that the electrolysis equipment and hydrogen fuel cells in the hydrogen energy storage system have relatively large power outputs, but the overall fluctuations are relatively small. The electrolysis equipment mainly produces stable power to generate hydrogen and natural gas when wind and solar power output reaches its peak, while the hydrogen fuel cells mainly release electrical energy during periods of high load demand and high electricity prices. In order to quickly respond to instantaneous power changes in the energy network, the power fluctuation frequency of the battery energy storage system is relatively high, which plays a role in smoothing power fluctuations and peak shaving.

[0061] Because the energy conversion efficiency of methanation equipment and gas turbines in natural gas energy storage systems is relatively low, natural gas energy storage systems mainly serve as a supplement to hydrogen energy storage systems and electrochemical energy storage systems. Electric refrigeration systems and absorption refrigeration systems are key to multi-energy coupling, converting the heat energy generated by electrical energy and the heat energy generated by hydrogen and natural gas energy storage systems into cold energy, respectively, to provide refrigeration power for refrigerated goods. Redundant cold energy is stored in the refrigeration system and released during periods of high cooling load demand. In the electrical system, the storage state of hydrogen and electrochemical energy storage systems exhibits similar trends: energy is stored during periods of redundant renewable energy generation and released during periods of excessive total load demand or high electricity prices. Although the energy storage state changes of natural gas energy storage systems are relatively smaller than those of hydrogen and electrochemical energy storage systems, the charging and discharging logic is similar: charging during periods of redundant renewable energy generation and discharging during periods of high electricity prices, playing a supporting role in energy system power balance and reducing the overall cost of electricity.

[0062] Electrolysis equipment, hydrogen fuel cells, and natural gas turbines generate heat energy, which is then converted into cold energy via an absorption refrigeration system; electrical energy is also converted into cold energy via an electro-refrigeration system, thus achieving multi-energy coupling. Figure 4 and Figure 5 It can be seen that when the supply of electricity exceeds the demand, the power of the electric refrigeration system and the absorption refrigeration system increases, the excess electricity is converted into cold energy, the energy stored in the cold storage system increases, and the refrigeration power is provided for the load of refrigerated goods, thereby reducing the internal temperature of refrigerated goods; when the supply of electricity is insufficient, the refrigeration power is mainly provided by the heat energy of the cold storage system, hydrogen fuel cells, and natural gas turbines after conversion, in order to minimize operating costs.

[0063] Figure 6This indicates the results of flexible electrical and cold load operation in the logistics system. The results show that the flexible load was demand-side responsively scheduled according to the optimization objectives. Quay cranes and yard cranes flexibly adjusted their unit-time loading and unloading volume based on the calculated energy-power operating range, dynamically adjusting electrical load demand while ensuring the loading and unloading volume to achieve power balance and the lowest overall electricity and carbon cost. Figure 7 It can be seen that the refrigeration of refrigerated goods is concentrated during the period when the wind and photovoltaic generators are at their highest output, indicating that the optimized scheduling method proposed in this invention is also applicable to the flexible operation of refrigerated goods and reduces operating costs.

[0064] Intraday Operation Results: During the intraday operation phase, intraday decision variables are adjusted sequentially based on the intraday ultra-short-term forecast results of the previously fixed day-ahead variables and uncertain variables. Intraday decision variables include the power output commands of photovoltaic generators, wind turbine generators, and tie lines. The specific intraday scheduling results are as follows: Figure 8 As shown. From Figure 8 It can be seen that the optimized operation method proposed in this invention prioritizes the use of energy generated by wind power and photovoltaic generators, and the tie-line power is mainly concentrated during periods when renewable energy output is insufficient or electricity prices are low. In summary, the optimized scheduling strategy proposed in this invention also makes full use of flexible resources during the intraday phase, thereby minimizing operating costs and carbon emission costs.

[0065] Comparative analysis with deterministic optimization methods To demonstrate the feasibility of the proposed adaptive robust optimization method in practical operation, the method is compared with the day-ahead deterministic operation method. The specific results are shown in Table 3. Table 3 shows that the day-ahead optimization operation method has a low feasibility rate in practical operation, only 26.5%, while the proposed adaptive robust scheduling method achieves a 100% feasibility rate. That is, after testing with 1000 random scenarios generated using the Monte Carlo sampling method, feasible solutions can be calculated for all of them. The operational results demonstrate that, compared with the day-ahead optimization operation method, the proposed robust optimization operation method can provide a reliable operating boundary for the port energy-logistics coupled system under multiple uncertainties.

[0066] Table 3 Comparison results with existing day-ahead operating methods

[0067] Comparative analysis with single-time-scale robust optimization methods Unlike existing robust optimization methods that only model hourly uncertainties, the multi-timescale robust optimization method proposed in this invention models both hourly power and daily energy fluctuation boundaries of uncertainties. A detailed comparative analysis of the results is shown in Table 4.

[0068] Table 4 Comparison results with existing single-time-scale robust optimization methods

[0069] The multi-timescale optimization method proposed in this invention reduces overall operating costs by 6.23% and carbon emissions by 12.07%. In summary, simulation results demonstrate that the multi-timescale adaptive robust optimization operation scheme proposed in this invention can effectively reduce energy costs and carbon emissions without compromising operational feasibility.

[0070] Example 2 This embodiment provides a zero-carbon logistics robust energy supply system that integrates multiple uncertainties, including: The uncertainty modeling module is used to construct multiple uncertainty models based on the energy structure and operational characteristics of zero-carbon logistics energy supply systems. The robust optimization module is used to construct a robust optimization objective function, day-ahead constraints, intraday constraints, and power balance constraints based on the interaction of multiple energy sources in the system. Combining the multi-uncertainty model and the actual operational requirements of the carbon logistics system, the robust optimization objective function is optimized based on robust optimization theory to construct a robust optimization model. The scheduling module is used to decompose the robust optimization model into a main problem and sub-problems, and obtain the scheduling strategy for coordinating multiple energy systems by solving the main problem and sub-problems.

[0071] It should be noted that the specific implementation of the zero-carbon logistics robust energy supply system integrating multiple uncertainties in the embodiments of the present invention is similar to the specific implementation of the zero-carbon logistics robust energy supply method integrating multiple uncertainties in the embodiments of the present invention. For details, please refer to the description in the method section. In order to reduce redundancy, it will not be repeated here.

[0072] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the robust zero-carbon logistics energy supply method for incorporating multiple uncertainties as described above.

[0073] Example 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the zero-carbon logistics robust energy supply method that integrates multiple uncertainties as described above.

[0074] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0075] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0076] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0077] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0078] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0079] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A robust zero-carbon logistics energy supply method integrating multiple uncertainties, characterized in that, Includes the following steps: Based on the energy structure and operational characteristics of zero-carbon logistics energy supply systems, a multi-uncertainty model is constructed. Based on the interaction of multiple energy sources in the system, a robust optimization objective function, day-ahead constraints, intraday constraints, and power balance constraints are constructed. Combining the multiple uncertainty model and the actual operational requirements of the carbon logistics system, a robust optimization model is constructed by optimizing the robust optimization objective function based on robust optimization theory. The robust optimization model is decomposed into a main problem and sub-problems. By solving the main problem and sub-problems, the scheduling strategy for coordinating multiple energy systems is obtained.

2. The zero-carbon logistics robust energy supply method integrating multiple uncertainties as described in claim 1, characterized in that, Multiple uncertainty models include: Establish separate uncertainty variables for renewable energy and uncertainty variables for logistics demand; An uncertainty set is established for the uncertain variables, and the uncertainty set is represented as: ， in, This represents uncertain variables, including uncertain variables related to renewable energy and uncertain variables related to logistics demand. and These represent the lower and upper bounds of the uncertain variable in each scheduling period, respectively. and These represent the lower and upper bounds of the uncertainty variable for a single day, respectively. express, It represents a time period.

3. The zero-carbon logistics robust energy supply method integrating multiple uncertainties as described in claim 1, characterized in that, The robust optimization objective function includes electricity purchase cost, hybrid energy storage operation cost, renewable energy consumption cost, and carbon emission cost; Current constraints include: energy storage capacity limits for each energy storage system; charging and discharging power limits for each energy storage system and the inability to charge and discharge simultaneously; minimum start-up and shutdown time limits for hydrogen electrolysis equipment and hydrogen fuel cells; operating temperature limits for hydrogen electrolysis equipment; ensuring that logistics loading and unloading operations are carried out after traffic loads arrive; ensuring that logistics equipment completes loading and unloading of goods corresponding to traffic load i within the planned time; and ensuring that all refrigerated goods are loaded and unloaded and connected to the logistics cooling system within the planned time. Time-limited loading and unloading quantity constraints, and constraints to ensure all goods are transported to the yard in a timely manner. Constraints on the quantity of goods loaded and unloaded within a time period, constraints on the internal temperature of refrigerated goods, constraints on the refrigeration capacity provided by the yard immediately after the arrival of refrigerated goods, and constraints on the maximum refrigeration capacity and internal temperature of refrigerated goods. Intraday constraints include power constraints for wind turbine generators, power constraints for photovoltaic generators, and power constraints for main grid interconnection lines. Power balance constraints include cold power balance constraints and electric power balance constraints.

4. The zero-carbon logistics robust energy supply method integrating multiple uncertainties as described in claim 1, characterized in that, The robust optimization objective function is: ， ， ， ， The current constraints include: the energy storage range of each energy storage system; the charging and discharging power of each energy storage system and the inability to charge and discharge simultaneously; the minimum start-up and shutdown time constraints for electrolysis hydrogen production equipment and hydrogen fuel cells; the operating temperature constraints for electrolysis hydrogen production equipment; ensuring that logistics loading and unloading operations are carried out after traffic loads arrive; ensuring that logistics equipment completes the loading and unloading of goods corresponding to traffic load i within the planned time; and ensuring that all refrigerated goods are loaded and unloaded and connected to the logistics cooling system within the planned time. Time-limited loading and unloading quantity constraints, constraints to ensure all goods are transported to the yard in a timely manner, internal temperature constraints of refrigerated goods, constraints on the yard to provide refrigeration power immediately after the arrival of refrigerated goods, maximum refrigeration power and internal temperature constraints of refrigerated goods, and refrigeration power constraints of refrigerated goods. Intraday constraints include: power constraints for wind turbine generators, power constraints for photovoltaic generators, and power constraints for main grid interconnection lines. Power balance constraints: cold power balance condition and electric power balance condition constraints; in, This represents the total operating cost. Indicates the cost of hydrogen storage. Indicates the cost of battery energy storage. Indicates the cost of cold storage. Indicates the cost of purchasing electricity. Indicates the cost of renewable energy use. Indicates the cost of carbon emissions; This indicates the unit cost of electricity. Indicates the power purchased. Indicates the time span of the scheduling period. Indicates the unit cost of photovoltaic power. Indicates the power output of photovoltaic power generation. This represents the unit cost of wind power. This indicates the power output of wind power generation. Indicates the cost per unit of carbon emissions. and These represent the carbon emissions per unit of electricity purchased from the main grid and generated from renewable energy sources, respectively (kg / kWh). This represents the carbon emission share per unit of electricity generated (kg / kWh).

5. The zero-carbon logistics robust energy supply method integrating multiple uncertainties as described in claim 1, characterized in that, Combining multiple uncertainty models and the actual operational requirements of carbon logistics systems, and based on robust optimization theory, the robust optimization objective function is optimized to construct a robust optimization model. This model includes determining the worst-case stochastic scenario and the switching status and power of the hybrid energy storage system and logistics system under this scenario during the daytime operation phase. During the intraday operation phase, the power of wind power generation, photovoltaic generator sets, and tie line are adjusted in real time based on ultra-short-term forecast data and fixed dispatch instructions.

6. The zero-carbon logistics robust energy supply method integrating multiple uncertainties as described in claim 5, characterized in that, The expression for the robust optimization model is: ， ， ， ， ， in, This indicates the decision variables during the day-ahead operation phase, including power dispatch instructions for hydrogen energy storage systems, natural gas energy storage systems, electrochemical energy storage systems, cold storage systems, electric refrigeration systems, absorption refrigeration systems, quay cranes, yard cranes, and refrigerated cargo. This represents the binary decision variables during the daytime operation phase, including the operating status of each energy storage system, and the operating status of the quay crane and yard crane; This represents the decision variables during the intraday operation phase, specifically including the actual power values ​​of wind power, photovoltaic power, and tie lines; This represents uncertain variables, including the maximum power values ​​of wind turbines and photovoltaic generators, local load, and traffic load demand values. Indicates and The relevant unit operating cost vector, and Respectively represent and Matching unit coefficient vector and constant term, Represents a set of uncertainties, with constraints The range of values ​​for , Indicates the relationship with the two-stage adjustment variable The corresponding unit adjustment cost vector.

7. The zero-carbon logistics robust energy supply method integrating multiple uncertainties as described in claim 1, characterized in that, The scheduling strategy for coordinating a multi-energy system is obtained by solving the main problem and its sub-problems, specifically including: The main problem calculates the optimal decision scheme based on the most extreme running scenario obtained by the column constraint generation algorithm. The subproblem searches for the most extreme running scenario given the first-stage decision variables. Then, the main problem adjusts the first-stage decision variables based on the set of potential most extreme running scenarios returned by the subproblem. This process is repeated until the difference between the objective functions of the main problem and the subproblem meets the stopping condition. At this point, a robust scheduling instruction is output.

8. A robust zero-carbon logistics energy supply method integrating multiple uncertainties, characterized in that, include: The uncertainty modeling module is used to construct multiple uncertainty models based on the energy structure and operational characteristics of zero-carbon logistics energy supply systems. The robust optimization module is used to construct robust optimization objective functions, day-ahead constraints, intraday constraints, and power balance constraints based on the interaction of multiple energy sources in the system. Combining the multiple uncertainty model and the actual operational requirements of the carbon logistics system, a robust optimization model is constructed by optimizing the robust optimization objective function based on robust optimization theory. The scheduling module is used to decompose the robust optimization model into a main problem and sub-problems, and obtain the scheduling strategy for coordinating multiple energy systems by solving the main problem and sub-problems.

9. A 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 in the zero-carbon logistics robust energy supply method that incorporates multiple uncertainties as described in any one of claims 1-7.

10. A computer 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 in the zero-carbon logistics robust energy supply method that incorporates multiple uncertainties as described in any one of claims 1-7.

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