Marine mobile micro-grid group logistics and energy collaborative scheduling method and system

Abstract

The application provides a marine mobile micro-grid group logistics and energy collaborative scheduling method and system, comprising the following steps: constructing a fleet logistics deployment model based on a multi-layer discrete space-time network; constructing a multi-objective ship energy management model; combining the fleet logistics deployment model and the multi-objective ship energy management model to construct a scheduling model based on the multi-layer discrete space-time network, optimizing the scheduling model based on the multi-layer discrete space-time network, and realizing the distribution of the ship sailing link and the optimized deployment of the energy equipment under the premise of meeting the logistics demand of each port; the fleet logistics deployment model is used for realizing the logistics cluster scheduling of the all-electric ship and the distribution of the sailing space-time link based on the marine discrete space-time network; and the multi-objective ship energy management model is used for minimizing the ship energy system operation cost and carbon emission under the premise of guaranteeing the logistics demand of each port and the on-time arrival of the ship, thereby effectively realizing the double optimization of energy utilization and carbon emission.

Description

Technical Field

[0001] This invention relates to the field of maritime logistics and energy dispatching technology, specifically to a method and system for coordinated dispatching of logistics and energy in a maritime mobile microgrid cluster, and more specifically to a method and system for coordinated dispatching of logistics and energy in a mobile microgrid cluster based on a multi-layer discrete spatiotemporal network. Background Technology

[0002] In the global logistics and transportation system, shipping plays a crucial role, handling nearly 80% of the logistics volume. However, while steadily improving transportation efficiency, the shipping industry's management of carbon emissions has not kept pace, resulting in a relatively extensive operational approach and increasing carbon emissions and environmental costs. Simultaneously, with the implementation of policies and regulations by various governments and the establishment of the EU's carbon emissions trading market, the shipping industry is facing an urgent need to address carbon reduction. Therefore, how to precisely manage the energy of ship micro-energy systems and regulate shipping operations through flexible and efficient logistics allocation strategies to achieve economical and low-carbon operation of the maritime shipping network has become a critical issue that urgently needs to be addressed. Especially with the increasing maturity of all-electric ship technology, electrification and optimized logistics allocation strategies will open up new possibilities for energy conservation, efficiency improvement, and green shipping development in the shipping industry.

[0003] Unlike fixed microgrids on land, all-electric vessel ensembles at sea, while fulfilling logistics and transportation tasks, constitute a unique type of mobile marine microgrid. Multiple all-electric vessels shuttle between ports, creating a highly dynamic network with significant spatiotemporal coupling, requiring adjustments based on vessel navigation conditions and mission demands. Furthermore, the energy allocation of all-electric vessels is closely linked to their speed; different speeds correspond to different propulsion power requirements. Therefore, the vessel's energy allocation strategy directly impacts the efficiency of logistics operations. For example, excessively high speeds may lead to insufficient power supply, while excessively low speeds may cause delays. Simultaneously, different energy allocation strategies also result in varying operating costs. Choosing an appropriate energy allocation strategy not only ensures efficient completion of logistics tasks but also minimizes operating costs.

[0004] However, there is currently no research on the coordinated optimization and scheduling of logistics and energy in all-electric ship microgrid clusters. It is worth noting that while there are some existing studies on energy management and speed optimization for all-electric ships, these studies only focus on single all-electric ships with fixed navigation routes. However, in real-world scenarios, maritime transport typically involves logistics transportation in the form of fleets of multiple ships. Ship owners have the ability to flexibly allocate the navigation routes of the entire fleet and the energy output of each ship's equipment. Therefore, it is necessary to coordinate and optimize the logistics and energy allocation of all-electric ship clusters.

[0005] Zhao Yue, Gao Xiaoyong, Kui Guofeng, et al. Green scheduling optimization of shuttle tanker routes considering carbon emissions [J]. Chemical Industry and Engineering, 2022, 39(02):23-31. DOI:10.13353 / j.issn.1004.9533.20210396. This paper discloses that offshore shuttle tankers play an important role in the oil export system of deep-sea and offshore marginal oil fields, and their route planning determines the transportation efficiency of the entire oil export system. Maritime crude oil transportation accounts for 80% of the total crude oil volume. To improve the market competitiveness of crude oil companies and reduce their shipping costs, the rational design and route scheduling optimization of shuttle tanker fleets are crucial. In recent years, scholars have focused on researching and establishing a green logistics system that is environmentally friendly and can promote the healthy development of the economy and consumer life. Based on this, a green scheduling optimization model for shuttle tanker routes is proposed. This model aims to minimize the total shipping cost of shuttle tankers, which consists of two parts: traditional fixed transportation costs and variable costs related to carbon emissions. The proposed model also optimizes shuttle tanker fleet design and route planning. Through case studies and cost comparisons, the crucial role of speed selection in shuttle tanker shipping costs is verified. While the previous literature presented shuttle tanker fleet design and route planning, it did not optimize energy allocation for all-electric ship microgrids containing multiple energy devices, although it reduced costs by adjusting speed. Furthermore, the fleet allocation and navigation link design presented in that literature are based on a continuous-time model and are not applicable to energy allocation in discrete time periods. In contrast, the method based on discrete spatiotemporal networks proposed in this invention can simultaneously adapt to fleet logistics and energy scheduling problems involving spatial decision-making and discrete time intervals.

[0006] K. Hein, Y. Xu, G. Wilson and AKGupta, "Coordinated Optimal Voyage Planning and Energy Management of All-Electric Ship With Hybrid Energy Storage System," in IEEE Transactions on Power Systems, vol. 36, no. 3, pp. 2355-2365, May 2021, doi:10.1109 / TPWRS.2020.3029331. This paper proposes a joint optimization method for power generation and speed of all-electric propulsion ships, taking into account carbon emissions and operational economics. Although it covers energy management and speed optimization of all-electric ships, its main research object is still a single all-electric ship with a fixed sailing route and predetermined arrival and departure times. In contrast, the research object of this invention is expanded from a single ship to the entire fleet, and from single energy management to the coordinated optimization of logistics and energy of the fleet, with flexible adjustment of the fleet's routes.

[0007] In view of this, this invention aims to effectively improve the energy utilization efficiency of all-electric ship clusters. It proposes a pioneering method for the coordinated optimization and scheduling of logistics and energy within all-electric ship clusters. Based on a multi-layered discrete spatiotemporal network, this method achieves logistics cluster scheduling and navigation link allocation for all-electric ships while ensuring that the actual logistics needs of each port are met. Simultaneously, by combining the physical constraints of each all-electric ship's energy equipment with the traffic constraints of the navigation links, the optimal output of each ship's energy equipment is achieved, thereby improving the ship's energy efficiency and reducing carbon emissions. Summary of the Invention

[0008] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for coordinated scheduling of logistics and energy in a maritime mobile microgrid cluster.

[0009] A method for coordinated scheduling of logistics and energy in a maritime mobile microgrid cluster, provided by the present invention, includes:

[0010] Step S1: Construct a fleet logistics allocation model based on a multi-layer discrete spatiotemporal network;

[0011] Step S2: Construct a multi-objective ship energy management model;

[0012] Step S3: Combine the fleet logistics allocation model and the multi-objective ship energy management model to construct a scheduling model based on a multi-layer discrete spatiotemporal network. Optimize the scheduling model based on the multi-layer discrete spatiotemporal network to achieve the allocation of ship navigation links and the optimized allocation of energy equipment while meeting the logistics needs of each port.

[0013] The fleet logistics dispatch model is based on a maritime discrete spatiotemporal network to realize the logistics cluster scheduling and navigation spatiotemporal link allocation of all-electric ships.

[0014] The multi-objective ship energy management model effectively optimizes both energy utilization and carbon emissions by minimizing the operating costs and carbon emissions of the ship energy system, while ensuring the logistics needs of each port and the timely arrival of ships.

[0015] Preferably, the fleet logistics allocation model based on a multi-layer discrete spatiotemporal network adopts:

[0016] The horizontal axis of the discrete spatiotemporal network represents the time scale and is divided into several time periods; the vertical axis represents the spatial dimension, including a supply / warehouse port and multiple customer / demand ports.

[0017] In the current discrete-time network, each node integrates both location and time dimensions; the current discrete-time network is divided into multiple layers, each layer corresponding to a fully electric ship in the fleet, and the number of layers in the spatiotemporal network is... This reflects the size of the fleet; in the spatiotemporal network layer corresponding to the k-th all-electric ship. In the middle, spatiotemporal nodes This represents the t-th scheduling time interval, where the k-th ship is located at the j-th port. Indicates a collection of ports. Indicates the scheduling time period; spacetime arc It represents the movement of a ship between points in time and space;

[0018] Objective function: Minimize the total running time of all ships;

[0019]

[0020] Where, x ε This is a binary variable; if it is 1, it indicates that ship k used arc ε, otherwise it is 0. A collection of all-electric vessels in the fleet; For the entire set of scheduling time periods; The sailing time between ports i and j is related to the sailing distance between the ports; d ij Indicates the sailing distance between ports; Indicates average speed; δ + (n) represents the arc originating from node n in the spatiotemporal network;

[0021] The constraints include: network flow balancing constraints and operational constraints;

[0022] The network flow balance constraint adopts:

[0023]

[0024] Where, δ + (n), δ - (n) represent the arcs starting and ending at node n in the spatiotemporal network, respectively; x ε This is a binary variable; if it is 1, it indicates that ship k used arc ε, otherwise it is 0. This represents the set of nodes (intermediate nodes) in the maritime spatiotemporal network; T represents the entire scheduling time period.

[0025] The operational constraints

[0026]

[0027] Among them, I (j，t-1) I (j，t) The inventory levels at port j at times t-1 and t are respectively; I k，t-1 I k，t Z represents the cargo load of the k-th ship at times t-1 and t, respectively; k，n This is a binary indicator variable; if it is 1, it represents the k-th ship at the spatiotemporal node n; otherwise, it is 0. k，n p represents the amount of cargo loaded by the k-th ship at time t from the warehouse port or unloaded at the customer port. (j，t) d (j，t) These are warehouse port productivity and customer port consumption rate, respectively. This indicates the maximum cargo capacity of the vessel, in meters (m). j This indicates the maximum number of times a ship has visited a port.

[0028] Preferably, the multi-objective ship energy management model adopts:

[0029] The all-electric ship model includes: a diesel engine motor module, an energy storage device module, and a propulsion load module;

[0030] The diesel engine motor module adopts:

[0031] The relationship between the fuel cost of a diesel generator and its power generation can be expressed as a second-order polynomial function:

[0032]

[0033] Among them, Q k Fuel consumption for shipboard mobile micro-energy grids; P represents the fuel consumption coefficient of the i-th diesel generator; t DG，i This represents the power output of the i-th diesel generator set at time t; Represents a collection of shipborne diesel generator sets; T kThis indicates the duration of the logistics task for the k-th ship.

[0034] The energy storage module uses lithium batteries as energy storage devices in the ship's energy system. The mathematical model of the battery energy storage power station is as follows:

[0035]

[0036] Among them, SOC k，t-1 SOC k，t Let represent the state of charge of the onboard energy storage battery of the k-th all-electric ship at time t-1 and time t, respectively; Let represent the self-discharge loss coefficient of the onboard energy storage battery of the k-th all-electric ship, and take 0.05; This represents the charging and discharging power of the onboard energy storage battery of the k-th all-electric ship at time t; These represent the charging and discharging efficiencies of the energy storage battery, respectively; Δt represents the time interval.

[0037] The propulsion load module includes: adjusting the ship's speed to change the electric propulsion power, with the two satisfying the following relationship:

[0038]

[0039] Among them, v k，t This represents the ship's speed in still water at time t; σk represents the electrical power consumed by the propeller of the k-th all-electric ship at time t; σ1 and σ2 represent the coefficients of the functional relationship between the propulsion load and the ship's speed.

[0040] Objective function: The economic cost and carbon emissions of each all-electric ship are taken as the optimization objective function, and its expression is as follows:

[0041] min(C k GHG k (7)

[0042] Among them, C k and GHG k For each of the following, the carbon emission cost of cargo delivery is calculated for the all-electric vessel k based on the spatiotemporal navigation link obtained from the first phase of logistics distribution optimization.

[0043] The constraints include: power balance constraints, equipment operation safety constraints, and traffic and navigation constraints;

[0044] The power balance constraints include:

[0045]

[0046] in, This represents the power output of the onboard diesel generator of the k-th all-electric ship during time period t; This represents the charging and discharging power of the onboard energy storage battery of the k-th all-electric ship at time t; These represent the charging and discharging efficiencies of the energy storage battery, respectively. These represent the electrical loads for daily life services and electric propulsion during time period t, respectively.

[0047] The equipment operation safety constraints are as follows:

[0048]

[0049] In the formula: These represent the maximum charging and discharging power of the energy storage unit, respectively; Boolean variables. These represent the charging and discharging status indicators for energy storage during time period t. A value of 1 indicates charging, while a value of 0 indicates otherwise. (SOC) k，max SOC k，min These represent the upper and lower limits of the state of charge of the energy storage unit, respectively. Let be the upper and lower limits of the output of the i-th diesel generator set; The maximum climbing power of the i-th diesel generator set;

[0050] The traffic and navigation constraints are as follows:

[0051]

[0052] Among them, D k，t , These represent the distances the ship has traveled at time t and at the end of the navigation cycle, respectively. This refers to the total voyage from the warehouse port to the delivery location and back to the warehouse port; k，t Let t be the ship's speed at time t; This indicates the upper and lower limits of acceptable navigation deviation for a vessel at the end of the navigation cycle; These are the upper and lower limits for ship speed; This indicates the distance the ship has traveled at the end of the sailing cycle.

[0053] Preferably, step S3 employs the following methods:

[0054] Step S3.1: The first-stage fleet logistics allocation model is a mixed-integer linear programming model. Solve the current mixed-integer linear programming model to obtain the logistics allocation results;

[0055] Step S3.2: Based on the logistics allocation results obtained in the first stage, solve the multi-objective energy management problem for each ship in the fleet of the multi-objective ship energy management model in the second stage to obtain the optimal energy allocation decision for the ships.

[0056] Preferably, step S3.2 employs the following:

[0057] Step S3.2.1: Linearize the nonlinear function using the piecewise linearization method;

[0058] Step S3.2.2: The bi-objective problem is transformed into two single-objective mathematical programming problems by using the linear boundary crossing method, and then the Pareto front decision solution set is obtained.

[0059] Step S3.2.3: Based on the concept of maximizing the trade-offs among multiple objectives, select the knee point from the Pareto front as the optimal energy allocation decision for the ship.

[0060] Preferably, step S3.1 employs the following:

[0061] MinimizeF1(x) = f1(x)

[0062] subjectto:x∈Ω1

[0063] g 1,i (x)≤0,i=1,...,p1

[0064] h 1,j (x)=0,j=1,...,q1

[0065] Where F1(x) represents the first-stage optimization objective, f1(x) represents the total operating time of all ships, x represents the first-stage optimization variable, Ω1 represents the decision space of the first-stage optimization variable, and g 1,i (x) represents the first-stage inequality constraint, h 1,j (x) represents the first-stage equality constraint, p1 represents the number of first-stage inequality constraints, and q1 represents the number of first-stage equality constraints.

[0066] Step S3.2:

[0067] Minimize F2(y|x)=(h 2,1 (y|x),h 2,2 (y|x))

[0068] subjectto:y∈Ω2

[0069] g 2,i (y|x)≤0,i=1,...,p2

[0070] h 2,j (y|x)=0,j=1,...,q2

[0071] Where F2(y|x) represents the optimization objective of the second stage, h 2,1(y|x) represents the economic operating cost, h 2,2 (y|x) represents carbon emissions, x represents the first-stage optimization decision variable, y represents the second-stage optimization decision variable, Ω2 represents the second-stage optimization decision space, and g represents the second-stage optimization decision space. 2,i (y|x) represents the second-stage inequality constraint, h 2,j (y|x) represents the second-stage equality constraint, p2 represents the number of second-stage inequality constraints, and q2 represents the number of second-stage equality constraints.

[0072] A maritime mobile microgrid cluster logistics and energy collaborative scheduling system according to the present invention includes:

[0073] Module M1: Constructing a fleet logistics dispatch model based on a multi-layer discrete spatiotemporal network;

[0074] Module M2: Constructing a multi-objective ship energy management model;

[0075] Module M3: Combines the fleet logistics allocation model and the multi-objective ship energy management model to construct a scheduling model based on a multi-layer discrete spatiotemporal network, optimizes the scheduling model based on the multi-layer discrete spatiotemporal network, and realizes the allocation of ship navigation links and the optimized allocation of energy equipment under the premise of meeting the logistics needs of each port.

[0076] The fleet logistics dispatch model is based on a maritime discrete spatiotemporal network to realize the logistics cluster scheduling and navigation spatiotemporal link allocation of all-electric ships.

[0077] The multi-objective ship energy management model effectively optimizes both energy utilization and carbon emissions by minimizing the operating costs and carbon emissions of the ship energy system, while ensuring the logistics needs of each port and the timely arrival of ships.

[0078] Preferably, the fleet logistics allocation model based on a multi-layer discrete spatiotemporal network adopts:

[0079] The horizontal axis of the discrete spatiotemporal network represents the time scale and is divided into several time periods; the vertical axis represents the spatial dimension, including a supply / warehouse port and multiple customer / demand ports.

[0080] In the current discrete-time network, each node integrates both location and time dimensions. The current discrete-time network is divided into multiple layers, each corresponding to a fully electric ship in a fleet. The number of layers in the spatiotemporal network is... This reflects the size of the fleet; in the spatiotemporal network layer corresponding to the k-th all-electric ship. In the middle, spatiotemporal nodes This represents the t-th scheduling time interval, where the k-th ship is located at the j-th port. Indicates a collection of ports. Indicates the scheduling time period; spacetime arc It represents the movement of a ship between points in time and space;

[0081] Objective function: Minimize the total running time of all ships;

[0082]

[0083] Where, x ε This is a binary variable; if it is 1, it indicates that ship k used arc ε, otherwise it is 0. A collection of all-electric vessels in the fleet; For the entire set of scheduling time periods; The sailing time between ports i and j is related to the sailing distance between the ports; d ij Indicates the sailing distance between ports; Indicates average speed; δ + (n) represents the arc originating from node n in the spatiotemporal network;

[0084] The constraints include: network flow balancing constraints and operational constraints;

[0085] The network flow balance constraint adopts:

[0086]

[0087] Where, δ + (n), δ - (n) represent the arcs starting and ending at node n in the spatiotemporal network, respectively; x ε This is a binary variable; if it is 1, it indicates that ship k used arc ε, otherwise it is 0. This represents a node in the maritime spatiotemporal network; T represents the entire scheduling time period.

[0088] The operational constraints

[0089]

[0090] Among them, I (j，t-1) I (j，t) The inventory levels at port j at times t-1 and t are respectively; I k，t-1 I k，t Z represents the cargo load of the k-th ship at times t-1 and t, respectively; k，n This is a binary indicator variable; if it is 1, it represents the k-th ship at the spatiotemporal node n; otherwise, it is 0. k，n p represents the amount of cargo loaded by the k-th ship at time t from the warehouse port or unloaded at the customer port. (j，t) d (j，t) These are respectively warehouse port productivity and customer port consumption rate; fj max This indicates the maximum cargo capacity of the vessel, in meters (m). j This indicates the maximum number of times a ship has visited a port.

[0091] Preferably, the multi-objective ship energy management model adopts:

[0092] The all-electric ship model includes: a diesel engine motor module, an energy storage device module, and a propulsion load module;

[0093] The diesel engine motor module adopts:

[0094] The relationship between the fuel cost of a diesel generator and its power generation can be expressed as a second-order polynomial function:

[0095]

[0096] Among them, Q k Fuel consumption for shipboard mobile micro-energy grids; P represents the fuel consumption coefficient of the i-th diesel generator; t DG，i This represents the power output of the i-th diesel generator set at time t; Represents a collection of shipborne diesel generator sets; T k This indicates the duration of the logistics task for the k-th ship.

[0097] The energy storage module uses lithium batteries as energy storage devices in the ship's energy system. The mathematical model of the battery energy storage power station is as follows:

[0098]

[0099] Among them, SOC k，t-1 SOC k，t Let represent the state of charge of the onboard energy storage battery of the k-th all-electric ship at time t-1 and time t, respectively; Let represent the self-discharge loss coefficient of the onboard energy storage battery of the k-th all-electric ship, and take 0.05; This represents the charging and discharging power of the onboard energy storage battery of the k-th all-electric ship at time t; These represent the charging and discharging efficiencies of the energy storage battery, respectively; Δt represents the time interval.

[0100] The propulsion load module includes: adjusting the ship's speed to change the electric propulsion power, with the two satisfying the following relationship:

[0101]

[0102] Among them, v k，t This represents the ship's speed in still water at time t; σk represents the electrical power consumed by the propeller of the k-th all-electric ship at time t; σ1 and σ2 represent the coefficients of the functional relationship between the propulsion load and the ship's speed.

[0103] Objective function: The economic cost and carbon emissions of each all-electric ship are taken as the optimization objective function, and its expression is as follows:

[0104] min(C k GHG k (7)

[0105] Among them, C k and GHG k For each of the following, the carbon emission cost of cargo delivery is calculated for the all-electric vessel k based on the spatiotemporal navigation link obtained from the first phase of logistics distribution optimization.

[0106] The constraints include: power balance constraints, equipment operation safety constraints, and traffic and navigation constraints;

[0107] The power balance constraints include:

[0108]

[0109] in, This represents the power output of the onboard diesel generator of the k-th all-electric ship during time period t; This represents the charging and discharging power of the onboard energy storage battery of the k-th all-electric ship at time t; These represent the charging and discharging efficiencies of the energy storage battery, respectively. These represent the electrical loads for daily life services and electric propulsion during time period t, respectively.

[0110] The equipment operation safety constraints are as follows:

[0111]

[0112] In the formula: These represent the maximum charging and discharging power of the energy storage unit, respectively; Boolean variables. These represent the charging and discharging status indicators for energy storage during time period t. A value of 1 indicates charging, while a value of 0 indicates otherwise. (SOC) k，max SOC k，min These represent the upper and lower limits of the state of charge of the energy storage unit, respectively. Let be the upper and lower limits of the output of the i-th diesel generator set; The maximum climbing power of the i-th diesel generator set;

[0113] The traffic and navigation constraints are as follows:

[0114]

[0115] Among them, D k，t , These represent the distances the ship has traveled at time t and at the end of the navigation cycle, respectively. This refers to the total voyage from the warehouse port to the delivery location and back to the warehouse port; k，t Let t be the ship's speed at time t; This indicates the upper and lower limits of acceptable navigation deviation for a vessel at the end of the navigation cycle; These are the upper and lower limits for ship speed; This indicates the distance the ship has traveled at the end of the sailing cycle.

[0116] Preferably, the module M3 adopts:

[0117] Module M3.1: The first-stage fleet logistics allocation model is a mixed-integer linear programming model. Solving the current mixed-integer linear programming model yields the logistics allocation results.

[0118] Module M3.2: Based on the logistics allocation results obtained in the first stage, the multi-objective energy management problem of each ship in the fleet of the second stage multi-objective ship energy management model is solved to obtain the optimal energy allocation decision of the ships;

[0119] The module M3.2 adopts:

[0120] Module M3.2.1: Linearizes nonlinear functions using piecewise linearization;

[0121] Module M3.2.2: The linear boundary crossing method is used to transform the bi-objective problem into two single-objective mathematical programming problems for optimization and solution, thereby obtaining the Pareto front decision solution set;

[0122] Module M3.2.3: Based on the concept of maximizing the trade-offs among multiple objectives, selects the knee point from the Pareto front as the optimal energy allocation decision for the ship;

[0123] The module M3.1 adopts:

[0124] Minimize F1(x)=f1(x)

[0125] subject to: x∈Ω1

[0126] g 1,i (x)≤0,i=1,...,p1

[0127] h 1,j (x)=0,j=1,...,q1

[0128] Where F1(x) represents the first-stage optimization objective, f1(x) represents the total operating time of all ships, x represents the first-stage optimization variable, Ω1 represents the decision space of the first-stage optimization variable, and g 1,i (x) represents the first-stage inequality constraint, h 1,j (x) represents the first-stage equality constraint, p1 represents the number of first-stage inequality constraints, and q1 represents the number of first-stage equality constraints.

[0129] Module M3.2:

[0130] Minimize F2(y|x)=(h 2,1 (y|x),h 2,2 (y|x))

[0131] subject to: y∈Ω2

[0132] g 2,i (y|x)≤0,i=1,...,p2

[0133] h 2,j (y|x)=0,j=1,...,q2

[0134] Where F2(y|x) represents the optimization objective of the second stage, h 2,1 (y|x) represents the economic operating cost, h 2,2 (y|x) represents carbon emissions, x represents the first-stage optimization decision variable, y represents the second-stage optimization decision variable, Ω2 represents the second-stage optimization decision space, and g represents the second-stage optimization decision space. 2,i (y|x) represents the second-stage inequality constraint, h 2,j (y|x) represents the second-stage equality constraint, p2 represents the number of second-stage inequality constraints, and q2 represents the number of second-stage equality constraints.

[0135] Compared with the prior art, the present invention has the following beneficial effects:

[0136] 1. This invention combines logistics and energy scheduling of all-electric ship fleets, which to a certain extent optimizes the energy efficiency of ship operation, improves the overall energy utilization efficiency, and reduces carbon emissions, providing a new solution for the development of green shipping.

[0137] 2. This invention enhances the operational flexibility of ship clusters by using logistics cluster scheduling and navigation link allocation based on multi-layer discrete spatiotemporal networks, providing highly adaptable scheduling strategies for different marine environments and transportation needs.

[0138] 3. This invention takes into account the physical constraints of the energy equipment of each all-electric ship and the traffic constraints of the navigation link, and realizes the optimal output of the ship's energy equipment, thereby effectively reducing operating costs and improving the energy efficiency of the ship.

[0139] 4. The collaborative optimization method proposed in this invention integrates logistics and energy scheduling of an all-electric ship cluster for the first time, deepens the coupling between logistics transportation and energy management, greatly enhances the overall operating efficiency and flexibility of the ship cluster, and strengthens the dynamic collaborative scheduling of the maritime mobile microgrid cluster.

[0140] 5. In actual maritime transportation scenarios, this invention considers the transportation tasks of maritime vessel clusters and the complex interactions between vessels. Based on the maritime discrete spatiotemporal network, it realizes the logistics cluster scheduling and navigation link allocation of all-electric vessels.

[0141] 6. Under the premise of ensuring the logistics needs of various ports and the timely arrival of ships, and with the goal of minimizing the operating costs and carbon emissions of ship energy systems, it effectively achieves the dual optimization of energy utilization and carbon emissions, and has significant environmental and economic benefits. Attached Figure Description

[0142] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0143] Figure 1 This is a diagram of the architecture of a mobile microgrid cluster at sea.

[0144] Figure 2 This is a multi-layer discrete spatiotemporal network diagram.

[0145] Figure 3 This is a spatiotemporal navigation link diagram. Detailed Implementation

[0146] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0147] Example 1

[0148] This invention aims to improve the energy efficiency and reduce carbon emissions of all-electric ship clusters by using a collaborative optimization scheduling method for logistics and energy. Based on a multi-layer discrete spatiotemporal network, it optimizes logistics scheduling and navigation links to meet the logistics needs of various ports, while considering the energy equipment and navigation constraints of each ship to achieve optimal energy output, improve energy efficiency, and reduce operating costs.

[0149] According to the present invention, a method and system for coordinated scheduling of logistics and energy in a marine mobile microgrid cluster is proposed, wherein the marine mobile microgrid cluster is as follows: Figure 1 As shown, a scheduling model based on a multi-layer discrete spatiotemporal network is constructed by innovatively combining logistics and energy management based on a maritime mobile microgrid cluster. This model can effectively allocate ship navigation links and optimize the allocation of energy equipment while meeting the logistics needs of various ports.

[0150] The method for coordinated scheduling of logistics and energy in the marine mobile microgrid cluster includes:

[0151] Step S1: Construct a fleet logistics allocation model based on a multi-layer discrete spatiotemporal network;

[0152] Step S2: Construct a multi-objective ship energy management model;

[0153] Step S3: Combine the fleet logistics allocation model and the multi-objective ship energy management model to construct a scheduling model based on a multi-layer discrete spatiotemporal network. Optimize the scheduling model based on the multi-layer discrete spatiotemporal network to achieve the allocation of ship navigation links and the optimized allocation of energy equipment while meeting the logistics needs of each port.

[0154] The fleet logistics dispatch model is based on a maritime discrete spatiotemporal network to realize the logistics cluster scheduling and navigation spatiotemporal link allocation of all-electric ships.

[0155] The multi-objective ship energy management model aims to minimize the operating costs and carbon emissions of ship energy systems while ensuring the logistics needs of various ports and the timely arrival of ships. It effectively optimizes both energy utilization and carbon emissions, resulting in significant environmental and economic benefits.

[0156] The fleet logistics allocation model based on multi-layer discrete spatiotemporal networks includes:

[0157] Multilayer Discrete Spatiotemporal Networks

[0158] like Figure 2 As shown, the horizontal axis of this discrete spatiotemporal network represents the time scale, divided into several time periods; the vertical axis represents the spatial dimension, including a supply / warehouse port and multiple customer / demand ports. In this spatiotemporal network diagram, each node integrates both location and time dimensions. The spatiotemporal network is divided into multiple layers, each corresponding to a fully electric vessel in the fleet, i.e., the number of layers in the spatiotemporal network. This reflects the size of the fleet. In the spatiotemporal network layer corresponding to the k-th all-electric ship... In the middle, spatiotemporal nodes This represents the t-th scheduling time interval, where the k-th ship is located at the j-th port (including warehouse ports and customer ports), and the spatiotemporal arc. It represents the movement of ships between points in time and space.

[0159] In this maritime logistics spatiotemporal network, there are three types of spatiotemporal nodes: 1) source nodes 1) The ship is located in the warehouse port at the start of the scheduling cycle representing all-electric ships; 2) The set of maritime spatiotemporal network nodes (intermediate nodes) This represents that ship k is located at customer port j during the t-th scheduling time interval; 3) Dock node This represents the location at the warehouse port at the end of the all-electric vessel scheduling cycle. Similarly, four types of spatiotemporal arcs exist within this spatiotemporal network. ) navigation arc Connecting two customer port spatiotemporal nodes, representing a fully electric vessel sailing from one customer port to another; 2) Dwelling arc Connecting two spatiotemporal nodes of the same customer port represents all-electric vessels loading and unloading cargo at the customer port; 3) Source arc A spatiotemporal node connecting the source node and a customer port indicates that an all-electric vessel departs from the warehouse port and sails to the customer port; 4) Merging arc Connecting the spatiotemporal nodes and rendezvous nodes of a customer port indicates that an all-electric vessel returns to the warehouse port after completing its logistics transportation task; 5) Arc not used This indicates that the vessel remained in the warehouse port throughout the entire scheduling cycle and did not participate in logistics transportation tasks.

[0160] The objective function includes:

[0161] In shipping logistics management, the effective utilization and management of vessel resources is crucial for achieving excellent operational performance. Minimizing the total operating time of all vessels—the entire process from departure from the warehouse port to delivery to various customer ports and back—including sailing time and cargo unloading time at customer ports, is a key optimization goal. This not only promotes efficient vessel utilization and reduces idle time but also ensures vessels can quickly return to the warehouse for the next mission, thereby reducing ineffective operating time and improving operational efficiency. More importantly, this goal responds to a market environment with high demand for logistics services, enabling vessels to handle cargo quickly and efficiently under high-load conditions.

[0162]

[0163] In the formula: x ε This is a binary variable; if it is 1, it indicates that ship k used arc ε, otherwise it is 0. A collection of all-electric vessels in the fleet; For the entire set of scheduling time periods; The voyage time between ports i and j is related to the voyage distance between the ports; δ + (n) represents the arc originating from node n in the spatiotemporal network.

[0164] The constraints include: network flow balancing constraints and operational constraints;

[0165] The network flow balance constraint adopts:

[0166]

[0167] Where: δ + (n), δ - (n) represent the arcs starting and ending at node n in the spatiotemporal network, respectively; x ε It is a binary variable; if it is 1, it indicates that the ship k used arc ε, otherwise it is 0.

[0168] The operational constraints are as follows:

[0169]

[0170] In the formula: I (j，t-1) I (j，t) The inventory levels at port j at times t-1 and t are respectively; I k，t-1 I k，t Z represents the cargo load of the k-th ship at times t-1 and t, respectively; k，n This is a binary indicator variable; if it is 1, it represents the k-th ship at the spatiotemporal node n; otherwise, it is 0. k，n p represents the amount of cargo loaded by the k-th ship at time t from the warehouse port or unloaded at the customer port. (j，t) d (j，t) These are warehouse port productivity and customer port consumption rate, respectively.

[0171] The multi-objective ship energy management model includes:

[0172] All-electric ship model

[0173] diesel generator

[0174] As the primary power source for shipboard mobile microgrids, diesel generators provide energy to the microgrids by burning diesel fuel. Different shipboard load levels correspond to different operating points of the diesel generators, leading to variations in fuel consumption. The relationship between the fuel cost and power output of a diesel generator can be approximated as a second-order polynomial function:

[0175]

[0176] In the formula: Q k Fuel consumption for shipboard mobile micro-energy grids; P represents the fuel consumption coefficient of the i-th diesel generator; t DG，i This represents the power output of the i-th diesel generator set at time t; This represents a collection of shipborne diesel generator sets.

[0177] Energy storage devices

[0178] Lithium batteries are used as energy storage devices in ship energy systems. They have the advantages of high energy density and stable performance. The mathematical model of a battery energy storage power station can be expressed as:

[0179]

[0180] In the formula, SOC k,t-1 SOC k,t Let represent the state of charge of the onboard energy storage battery of the k-th all-electric ship at time t-1 and time t, respectively; Let represent the self-discharge loss coefficient of the onboard energy storage battery of the k-th all-electric ship, and take 0.05; This represents the charging and discharging power of the onboard energy storage battery of the k-th all-electric ship at time t; These represent the charging and discharging efficiencies of the energy storage battery, respectively, and are set to 0.95 and 0.95.

[0181] propulsion load

[0182] It is worth noting that, unlike traditional mechanically propelled ships, the propulsion power of all-electric ships is closely related to their speed. The electric propulsion power can be changed by adjusting the speed, and the two satisfy the following relationship:

[0183]

[0184] In the formula: v k，t This represents the ship's speed in still water at time t; σk represents the electrical power consumed by the propeller of the k-th all-electric ship at time t; σ1 and σ2 represent the coefficients of the functional relationship between the propulsion load and the ship's speed.

[0185] The objective function includes:

[0186] After optimizing the spatiotemporal navigation links of each ship, energy management optimization is needed for each ship to further reduce operating costs and carbon emissions. This invention uses the economic cost and carbon emissions of each all-electric ship as the optimization objective function, expressed as follows:

[0187] min(C k GHG k (7)

[0188] In the formula: Ck and GHG k Let k be an all-electric vessel, and k be the carbon emission cost of cargo delivery based on the spatiotemporal navigation link obtained from the first phase of logistics optimization. The specific expressions for each part are as follows:

[0189] The economic costs include:

[0190] The main costs of diesel generator sets and hydrogen fuel cells are the costs of diesel fuel and hydrogen fuel, which are expressed as follows:

[0191]

[0192] In the formula: Q k Indicates the consumption of diesel and hydrogen fuel, m 3 ; Price per unit for diesel and hydrogen fuel, $ / m 3 .

[0193] The carbon emissions include:

[0194]

[0195] In the formula: This represents the carbon emission cost coefficient of the i-th diesel generator.

[0196] The constraints include: power balance constraints, equipment operation safety constraints, and traffic and navigation constraints.

[0197] The power balance constraint adopts:

[0198]

[0199] In the formula: This represents the power output of the onboard diesel generator of the k-th all-electric ship during time period t; This represents the charging and discharging power of the onboard energy storage battery of the k-th all-electric ship at time t; These represent the charging and discharging efficiencies of the energy storage battery, respectively, and are set to 0.95 and 0.95. These represent the electrical loads for daily life services and electric propulsion during time period t, respectively.

[0200] The equipment operation constraints are as follows:

[0201]

[0202] In the formula: These represent the maximum charging and discharging power of the energy storage unit, respectively; Boolean variables. These represent the charging and discharging status indicators for energy storage during time period t. A value of 1 indicates charging, while a value of 0 indicates otherwise. (SOC)k，max SOC k，min These represent the upper and lower limits of the state of charge of the energy storage unit, respectively. Let be the upper and lower limits of the output of the i-th diesel generator set; Let be the maximum climbing power of the i-th diesel generator set.

[0203] The traffic and navigation constraints are as follows:

[0204]

[0205] In the formula: D k，t , These represent the distances the ship has traveled at time t and at the end of the navigation cycle, respectively. This refers to the total voyage from the warehouse port to the delivery location and back to the warehouse port; k，t Let t be the ship's speed at time t; This indicates the upper and lower limits of acceptable navigation deviation for a vessel at the end of the navigation cycle; These are the upper and lower limits for ship speed.

[0206] Step S3 employs the following:

[0207] In this invention, the problem of coordinated scheduling of logistics and energy in mobile microgrid clusters based on multi-layer discrete spatiotemporal networks is a two-stage multi-objective optimization problem, and the optimization scheduling model can be summarized as follows:

[0208]

[0209] The first stage of this optimization problem is a mixed-integer linear programming model, in which some decision variables are continuous, such as ship cargo capacity and designated port delivery volume; while some decision variables are integer, such as binary decision variables of ship spatiotemporal links. The optimization solution of this stage can be obtained by directly calling an efficient commercial solver, such as Cplex.

[0210] The second stage is a bi-objective optimization problem involving nonlinear functions. This invention first uses piecewise linearization to linearize the nonlinear functions, and then employs the normal boundary crossing method to transform the bi-objective problem into two single-objective mathematical programming problems for optimization, thereby obtaining the Pareto front decision solution set. Based on the concept of maximizing the trade-offs between multiple objectives, the "knee point" is selected from the Pareto front as the optimal energy allocation decision for the ship.

[0211] The marine mobile microgrid cluster logistics and energy coordinated dispatch system includes:

[0212] Module M1: Constructing a fleet logistics dispatch model based on a multi-layer discrete spatiotemporal network;

[0213] Module M2: Constructing a multi-objective ship energy management model;

[0214] Module M3: Combines the fleet logistics allocation model and the multi-objective ship energy management model to construct a scheduling model based on a multi-layer discrete spatiotemporal network, optimizes the scheduling model based on the multi-layer discrete spatiotemporal network, and realizes the allocation of ship navigation links and the optimized allocation of energy equipment under the premise of meeting the logistics needs of each port.

[0215] The fleet logistics dispatch model is based on a maritime discrete spatiotemporal network to realize the logistics cluster scheduling and navigation spatiotemporal link allocation of all-electric ships.

[0216] The multi-objective ship energy management model aims to minimize the operating costs and carbon emissions of ship energy systems while ensuring the logistics needs of various ports and the timely arrival of ships. It effectively optimizes both energy utilization and carbon emissions, resulting in significant environmental and economic benefits.

[0217] The fleet logistics allocation model based on multi-layer discrete spatiotemporal networks includes:

[0218] Multilayer Discrete Spatiotemporal Networks

[0219] like Figure 2 As shown, the horizontal axis of this discrete spatiotemporal network represents the time scale, divided into several time periods; the vertical axis represents the spatial dimension, including a supply / warehouse port and multiple customer / demand ports. In this spatiotemporal network diagram, each node integrates both location and time dimensions. The spatiotemporal network is divided into multiple layers, each corresponding to a fully electric vessel in the fleet, i.e., the number of layers in the spatiotemporal network. This reflects the size of the fleet. In the spatiotemporal network layer corresponding to the k-th all-electric ship... In the middle, spatiotemporal nodes This represents the t-th scheduling time interval, where the k-th ship is located at the j-th port (including warehouse ports and customer ports), and the spatiotemporal arc. It represents the movement of ships between points in time and space.

[0220] In this maritime logistics spatiotemporal network, there are three types of spatiotemporal nodes: 1) source nodes 1) The ship is located in the warehouse port at the start of the scheduling cycle representing all-electric ships; 2) The set of maritime spatiotemporal network nodes (intermediate nodes) This represents that ship k is located at customer port j during the t-th scheduling time interval; 3) Dock node This represents the location at the warehouse port at the end of the all-electric vessel scheduling cycle. Similarly, four types of spatiotemporal arcs exist within this spatiotemporal network. ) navigation arc Connecting two customer port spatiotemporal nodes, representing a fully electric vessel sailing from one customer port to another; 2) Dwelling arc Connecting two spatiotemporal nodes of the same customer port represents all-electric vessels loading and unloading cargo at the customer port; 3) Source arc A spatiotemporal node connecting the source node and a customer port indicates that an all-electric vessel departs from the warehouse port and sails to the customer port; 4) Merging arc Connecting the spatiotemporal nodes and rendezvous nodes of a customer port indicates that an all-electric vessel returns to the warehouse port after completing its logistics transportation task; 5) Arc not used This indicates that the vessel remained in the warehouse port throughout the entire scheduling cycle and did not participate in logistics transportation tasks.

[0221] The objective function includes:

[0222] In shipping logistics management, the effective utilization and management of vessel resources is crucial for achieving excellent operational performance. Minimizing the total operating time of all vessels—the entire process from departure from the warehouse port to delivery to various customer ports and back—including sailing time and cargo unloading time at customer ports, is a key optimization goal. This not only promotes efficient vessel utilization and reduces idle time but also ensures vessels can quickly return to the warehouse for the next mission, thereby reducing ineffective operating time and improving operational efficiency. More importantly, this goal responds to a market environment with high demand for logistics services, enabling vessels to handle cargo quickly and efficiently under high-load conditions.

[0223]

[0224] In the formula: x ε This is a binary variable; if it is 1, it indicates that ship k used arc ε, otherwise it is 0. A collection of all-electric vessels in the fleet; For the entire set of scheduling time periods; The voyage time between ports i and j is related to the voyage distance between the ports; δ + (n) represents the arc originating from node n in the spatiotemporal network.

[0225] The constraints include: network flow balancing constraints and operational constraints;

[0226] The network flow balance constraint adopts:

[0227]

[0228] Where: δ + (n), δ - (n) represent the arcs starting and ending at node n in the spatiotemporal network, respectively; x ε It is a binary variable; if it is 1, it indicates that the ship k used arc ε, otherwise it is 0.

[0229] The operational constraints are as follows:

[0230]

[0231] In the formula: I (j，t-1) I (j，t) The inventory levels at port j at times t-1 and t are respectively; I k，t-1 I k，t Z represents the cargo load of the k-th ship at times t-1 and t, respectively; k，n This is a binary indicator variable; if it is 1, it represents the k-th ship at the spatiotemporal node n; otherwise, it is 0. k，n p represents the amount of cargo loaded by the k-th ship at time t from the warehouse port or unloaded at the customer port. (j，t) d (j，t) These are warehouse port productivity and customer port consumption rate, respectively.

[0232] The multi-objective ship energy management model includes:

[0233] All-electric ship model

[0234] diesel generator

[0235] As the primary power source for shipboard mobile microgrids, diesel generators provide energy to the microgrids by burning diesel fuel. Different shipboard load levels correspond to different operating points of the diesel generators, leading to variations in fuel consumption. The relationship between the fuel cost and power output of a diesel generator can be approximated as a second-order polynomial function:

[0236]

[0237] In the formula: Q k Fuel consumption for shipboard mobile micro-energy grids; P represents the fuel consumption coefficient of the i-th diesel generator; t DG，i This represents the power output of the i-th diesel generator set at time t; This represents a collection of shipborne diesel generator sets.

[0238] Energy storage devices

[0239] Lithium batteries are used as energy storage devices in ship energy systems. They have the advantages of high energy density and stable performance. The mathematical model of a battery energy storage power station can be expressed as:

[0240]

[0241] In the formula, SOC k，t-1 SOC k，tLet represent the state of charge of the onboard energy storage battery of the k-th all-electric ship at time t-1 and time t, respectively; Let represent the self-discharge loss coefficient of the onboard energy storage battery of the k-th all-electric ship, and take 0.05; This represents the charging and discharging power of the onboard energy storage battery of the k-th all-electric ship at time t; These represent the charging and discharging efficiencies of the energy storage battery, respectively, and are set to 0.95 and 0.95.

[0242] propulsion load

[0243] It is worth noting that, unlike traditional mechanically propelled ships, the propulsion power of all-electric ships is closely related to their speed. The electric propulsion power can be changed by adjusting the speed, and the two satisfy the following relationship:

[0244]

[0245] In the formula: v k，t This represents the ship's speed in still water at time t; σk represents the electrical power consumed by the propeller of the k-th all-electric ship at time t; σ1 and σ2 represent the functional relationship coefficients between the propulsion load and the ship's speed.

[0246] The objective function includes:

[0247] After optimizing the spatiotemporal navigation links of each ship, energy management optimization is needed for each ship to further reduce operating costs and carbon emissions. This invention uses the economic cost and carbon emissions of each all-electric ship as the optimization objective function, expressed as follows:

[0248] min(C k GHG k (7)

[0249] In the formula: C k and GHG k Let k be an all-electric vessel, and k be the carbon emission cost of cargo delivery based on the spatiotemporal navigation link obtained from the first phase of logistics optimization. The specific expressions for each part are as follows:

[0250] The economic costs include:

[0251] The main costs of diesel generator sets and hydrogen fuel cells are the costs of diesel fuel and hydrogen fuel, which are expressed as follows:

[0252]

[0253] In the formula: Q k Indicates the consumption of diesel and hydrogen fuel, m 3 ; Price per unit for diesel and hydrogen fuel, $ / m 3 .

[0254] The carbon emissions include:

[0255]

[0256] In the formula: This represents the carbon emission cost coefficient of the i-th diesel generator.

[0257] The constraints include: power balance constraints, equipment operation safety constraints, and traffic and navigation constraints.

[0258] The power balance constraint adopts:

[0259]

[0260] In the formula: This represents the power output of the onboard diesel generator of the k-th all-electric ship during time period t; This represents the charging and discharging power of the onboard energy storage battery of the k-th all-electric ship at time t; These represent the charging and discharging efficiencies of the energy storage battery, respectively, and are set to 0.95 and 0.95. These represent the electrical loads for daily life services and electric propulsion during time period t, respectively.

[0261] The equipment operation constraints are as follows:

[0262]

[0263] In the formula: These represent the maximum charging and discharging power of the energy storage unit, respectively; Boolean variables. These represent the charging and discharging status indicators for energy storage during time period t. A value of 1 indicates charging, while a value of 0 indicates otherwise. (SOC) k，max SOC k，min These represent the upper and lower limits of the state of charge of the energy storage unit, respectively. Let be the upper and lower limits of the output of the i-th diesel generator set; Let be the maximum climbing power of the i-th diesel generator set.

[0264] The traffic and navigation constraints are as follows:

[0265]

[0266] In the formula: D k，t , These represent the distances the ship has traveled at time t and at the end of the navigation cycle, respectively. This refers to the total voyage from the warehouse port to the delivery location and back to the warehouse port;k，t Let t be the ship's speed at time t; This indicates the upper and lower limits of acceptable navigation deviation for a vessel at the end of the navigation cycle; These are the upper and lower limits for ship speed.

[0267] The module M3 adopts:

[0268] In this invention, the problem of coordinated scheduling of logistics and energy in mobile microgrid clusters based on multi-layer discrete spatiotemporal networks is a two-stage multi-objective optimization problem, and the optimization scheduling model can be summarized as follows:

[0269]

[0270] The first stage of this optimization problem is a mixed-integer linear programming model, in which some decision variables are continuous, such as ship cargo capacity and designated port delivery volume; while some decision variables are integer, such as binary decision variables of ship spatiotemporal links. The optimization solution of this stage can be obtained by directly calling an efficient commercial solver, such as Cplex.

[0271] The second stage is a bi-objective optimization problem involving nonlinear functions. This invention first uses piecewise linearization to linearize the nonlinear functions, and then employs the normal boundary crossing method to transform the bi-objective problem into two single-objective mathematical programming problems for optimization, thereby obtaining the Pareto front decision solution set. Based on the concept of maximizing the trade-offs between multiple objectives, the "knee point" is selected from the Pareto front as the optimal energy allocation decision for the ship.

[0272] Example 2

[0273] Example 2 is a preferred example of Example 1.

[0274] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0275] This research was supported by the Hainan Provincial Science and Technology Program Sanya Yazhou Bay Science and Technology City Joint Project "Key Technologies for Low-Carbon Operation of Green Ships for Hainan Free Trade Port" (Project No.: 2021JJLH0026).

[0276] This case study includes one warehouse port and four customer ports. After loading cargo at the warehouse port, the vessel transports it to the customer ports and finally returns empty to the warehouse port, completing the logistics delivery mission and forming a closed-loop navigation system. The distances between ports are calculated using Euclidean distances, with a scheduling interval of 3 hours and a scheduling cycle of 24 intervals, resulting in a total scheduling time of 72 hours. To verify the effectiveness of the proposed logistics and energy collaborative optimization method, three different scenarios were set up for comparative analysis, with scenario 3 representing the optimized scheduling method proposed in this invention. The scenarios are as follows:

[0277] Scenario 1: No optimized scheduling strategy, the fleet adopts a fixed route, and the output of each all-electric vessel is fixed;

[0278] Scenario 2: The fleet travels on a fixed route, but optimizes the power output of the ship's onboard energy equipment during the voyage;

[0279] Scenario 3: The fleet adopts a method based on discrete spatiotemporal networks for flexible scheduling, optimizes the spatiotemporal links of navigation, and optimizes the output of the shipboard energy equipment of each all-electric vessel during navigation.

[0280] Table 1 Comparison of operating costs and carbon emissions under three scenarios

[0281] Calculation example navigation links energy System operating cost / mu Carbon emissions / ton 1 Fixed route round trip Fixed output 127401 42401 2 Fixed route round trip Flexible allocation 101327 39642 3 Flexible allocation Flexible allocation 87345 37825

[0282] Example 1: In this scenario, an all-electric ship convoy travels along a fixed route, and each ship's onboard energy equipment operates at a fixed output, meaning no energy optimization or allocation is performed. Under these conditions, the convoy does not adjust its speed based on logistics tasks or energy supply. Due to this lack of flexibility, the system operating cost in this mode is high, at 127,401 m.u., and the carbon emissions are also significant, at 42,401 ton.

[0283] Example 2: In this operating mode, the all-electric ship fleet still travels along fixed routes, but the energy equipment of each ship in the fleet is flexibly allocated. This means that, based on the navigation link, ships can flexibly adjust their sailing speed and energy output. This optimized scheduling strategy reduces system operating costs to 101,327 m.u. and carbon emissions to 39,642 tons.

[0284] Example 3: The all-electric ship cluster not only allows for flexible allocation of energy equipment, but also allows for flexible allocation of navigation links. Figure 3 The spatiotemporal navigation links of each vessel in the fleet under this scenario are presented. This means that, based on logistics mission requirements, vessels can flexibly select the optimal navigation link and adjust their speed and energy output according to the navigation link allocated for logistics. This collaborative optimization operation mode further reduces system operating costs to 87,345 m.u. and carbon emissions are also significantly reduced to 37,825 tons.

[0285] In summary, the case study results demonstrate that the collaborative optimization method of this invention fully utilizes the coupling relationship between logistics and energy dispatch in all-electric ship convoys, significantly reducing operating costs and carbon emissions. Particularly in Example 3, the collaborative optimization of navigation planning and energy supply further highlights the superiority of this method. By fully considering the interaction between the power network and the logistics network, and by performing collaborative optimization of both during the operation phase, it achieves better economic efficiency and environmental friendliness while meeting the energy demands of logistics and ship microgrids. This invention can be applied to the energy management of all-electric ship convoys, effectively realizing the economical and environmentally friendly operation of the ship convoy's mobile energy network.

[0286] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0287] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for coordinated scheduling of logistics and energy in a maritime mobile microgrid cluster, characterized in that, include: Step S1: Construct a fleet logistics allocation model based on a multi-layer discrete spatiotemporal network; Step S2: Construct a multi-objective ship energy management model; Step S3: Combine the fleet logistics allocation model and the multi-objective ship energy management model to construct a scheduling model based on a multi-layer discrete spatiotemporal network. Optimize the scheduling model based on the multi-layer discrete spatiotemporal network to achieve the allocation of ship navigation links and the optimized allocation of energy equipment while meeting the logistics needs of each port. The fleet logistics dispatch model is based on a maritime discrete spatiotemporal network to realize the logistics cluster scheduling and navigation spatiotemporal link allocation of all-electric ships. The multi-objective ship energy management model effectively optimizes both energy utilization and carbon emissions by minimizing the operating costs and carbon emissions of the ship energy system, while ensuring the logistics needs of each port and the timely arrival of ships. The fleet logistics allocation model based on multi-layer discrete spatiotemporal network adopts: The horizontal axis of the discrete spatiotemporal network represents the time scale and is divided into several time periods; the vertical axis represents the spatial dimension, including a supply / warehouse port and multiple customer / demand ports. In the current discrete-time network, each node integrates both location and time dimensions. The current discrete-time network is divided into multiple layers, each corresponding to a fully electric ship in a fleet. The number of layers in the spatiotemporal network is... This reflects the size of the fleet; in the corresponding number k Spatiotemporal network layer of an all-electric ship In the middle, spatiotemporal nodes This represents the t-th scheduling time interval, the... k The ship is located in port j. Indicates a collection of ports. Indicates the scheduling time period; spatiotemporal arc It represents the movement of a ship between points in time and space; Objective function: Minimize the total running time of all ships; (1) in, This is a binary variable; if it is 1, it indicates that ship k used an arc. Otherwise, it is 0; A collection of all-electric vessels in the fleet; For the entire set of scheduling time periods; The sailing time between ports i and j is related to the sailing distance between the ports. Indicates the sailing distance between ports; Indicates average speed; Let n be the arc originating from node n in the spatiotemporal network. The constraints include: network flow balancing constraints and operational constraints; The network flow balance constraint adopts: (2) in, , Let n represent the arcs that start and end at node n in the spatiotemporal network, respectively. This is a binary variable; if it is 1, it indicates that ship k used an arc. Otherwise, it is 0; Represents the set of nodes in a maritime spatiotemporal network; Indicates the entire scheduling time period; The operational constraints (3) in, , They are respectively t- 1 and t time j Port inventory levels; , They are respectively t- 1 and t Time of the first k The cargo capacity of a ship; This is a binary indicator variable; if it is 1, it means that the kth ship is at the spatiotemporal node n, otherwise it is 0. This represents the amount of cargo loaded by the k-th ship at time t from the warehouse port or unloaded at the customer port. , These are warehouse port productivity and customer port consumption rate, respectively. Indicates the maximum cargo capacity of the vessel. This indicates the maximum number of ports visited by a vessel. The multi-objective ship energy management model adopts: The all-electric ship model includes: a diesel engine motor module, an energy storage device module, and a propulsion load module; The diesel engine motor module adopts: The relationship between the fuel cost of a diesel generator and its power generation can be expressed as a second-order polynomial function: in, Let be the fuel consumption of the mobile micro-energy grid of the kth ship; The fuel consumption coefficient represents the i-th diesel generator; This represents the power output of the i-th diesel generator set on the k-th ship at time t; This represents a collection of shipborne diesel generator sets; This indicates the duration of the logistics task for the k-th ship. The energy storage module uses lithium batteries as energy storage devices in the ship's energy system. The mathematical model of the battery energy storage power station is as follows: in, , Let represent the state of charge of the onboard energy storage battery of the k-th all-electric ship at time t-1 and time t, respectively; Let represent the self-discharge loss coefficient of the onboard energy storage battery of the k-th all-electric ship, and take 0.05; , This represents the charging and discharging power of the onboard energy storage battery of the k-th all-electric ship at time t; , These represent the charging and discharging efficiencies of the energy storage battery, respectively. Indicates a time interval; The propulsion load module includes: adjusting the ship's speed to change the electric propulsion power, with the two satisfying the following relationship: (6) in, Indicates a ship t The speed of the ship in still water at any given moment; Indicates the first k All-electric ships t The electrical power consumed by the ship's propulsion system at any given moment; The coefficients representing the functional relationship between propulsion electrical load and ship speed; Objective function: The economic cost and carbon emissions of each all-electric ship are taken as the optimization objective function, and its expression is as follows: (7) in, and All-electric ships k Based on the logistics time-space navigation link obtained from the first phase of logistics distribution optimization, calculate the carbon emission cost of cargo distribution. The constraints include: power balance constraints, equipment operation safety constraints, and traffic and navigation constraints; The power balance constraints include: (10) in, Indicates the first k All-electric ships t The power output of the shipborne diesel generator during the specified time period; , Indicates the first k All-electric ships t The charging and discharging power of the ship's onboard energy storage battery at all times; , These represent the charging and discharging efficiencies of the energy storage battery, respectively. , They represent t Electric loads for time-of-use services and electric propulsion; The equipment operation safety constraints are as follows: (11) In the formula: , These represent the maximum charging and discharging power of the energy storage unit, respectively; Boolean variables. , They represent t The indicator bit for the charging and discharging status of the energy storage during a given period is 1 if it indicates charging and 0 otherwise. , These represent the upper and lower limits of the state of charge of the energy storage unit, respectively. , For the first i The upper and lower limits of the output of the diesel generator set; For the first i The maximum climbing power of the diesel generator set; The traffic and navigation constraints are as follows: (12) in, , They represent t The time and the distance the ship has traveled at the end of the navigation cycle; The total voyage from the warehouse port to the delivery destination and back to the warehouse port; for t Ship speed at any time; This indicates the upper and lower limits of acceptable navigation deviation for a vessel at the end of the navigation cycle; , These are the upper and lower limits for ship speed; This indicates the distance the ship has traveled at the end of the sailing cycle.

2. The method for coordinated scheduling of logistics and energy in a maritime mobile microgrid cluster according to claim 1, characterized in that, Step S3 employs the following: Step S3.1: The first-stage fleet logistics allocation model is a mixed-integer linear programming model. Solve the current mixed-integer linear programming model to obtain the logistics allocation results; Step S3.2: Based on the logistics allocation results obtained in the first stage, solve the multi-objective energy management problem for each ship in the fleet of the multi-objective ship energy management model in the second stage to obtain the optimal energy allocation decision for the ships.

3. The method for coordinated scheduling of logistics and energy in a maritime mobile microgrid cluster according to claim 2, characterized in that, Step S3.2 adopts the following: Step S3.2.1: Linearize the nonlinear function using the piecewise linearization method; Step S3.2.2: The bi-objective problem is transformed into two single-objective mathematical programming problems by using the linear boundary crossing method, and then the Pareto front decision solution set is obtained. Step S3.2.3: Based on the concept of maximizing the trade-offs among multiple objectives, select the knee point from the Pareto front as the optimal energy allocation decision for the ship.

4. The method for coordinated scheduling of logistics and energy in a maritime mobile microgrid cluster according to claim 2, characterized in that, Step S3.1 adopts the following: in, This indicates the optimization goal for the first phase. This represents the total travel time of all ships. This represents the first-stage optimization variable. This represents the decision space of the first-stage optimization variables. This represents the first-stage inequality constraint. This indicates the first-stage equality constraint. This indicates the number of inequality constraints in the first stage. Indicates the number of equality constraints in the first stage; Step S3.2: in, This indicates the optimization goal for the second phase. Indicates the cost of economic operation. Indicates carbon emissions, This represents the decision variables for the first stage of optimization. This represents the decision variables for the second stage of optimization. Let u represent the decision space of the optimization variables in Section 2. This indicates the second-stage inequality constraint. This indicates the second-stage equality constraint. This indicates the number of second-stage inequality constraints. This indicates the number of equality constraints in the second stage.

5. A marine mobile microgrid cluster logistics and energy collaborative dispatch system, characterized in that, include: Module M1: Constructing a fleet logistics dispatch model based on a multi-layer discrete spatiotemporal network; Module M2: Constructing a multi-objective ship energy management model; Module M3: Combines the fleet logistics allocation model and the multi-objective ship energy management model to construct a scheduling model based on a multi-layer discrete spatiotemporal network, optimizes the scheduling model based on the multi-layer discrete spatiotemporal network, and realizes the allocation of ship navigation links and the optimized allocation of energy equipment under the premise of meeting the logistics needs of each port. The fleet logistics dispatch model is based on a maritime discrete spatiotemporal network to realize the logistics cluster scheduling and navigation spatiotemporal link allocation of all-electric ships. The multi-objective ship energy management model effectively optimizes both energy utilization and carbon emissions by minimizing the operating costs and carbon emissions of the ship energy system, while ensuring the logistics needs of each port and the timely arrival of ships. The fleet logistics allocation model based on multi-layer discrete spatiotemporal network adopts: The horizontal axis of the discrete spatiotemporal network represents the time scale and is divided into several time periods; the vertical axis represents the spatial dimension, including a supply / warehouse port and multiple customer / demand ports. In the current discrete-time network, each node integrates both location and time dimensions. The current discrete-time network is divided into multiple layers, each corresponding to a fully electric ship in a fleet. The number of layers in the spatiotemporal network is... This reflects the size of the fleet; in the corresponding number k Spatiotemporal network layer of an all-electric ship In the middle, spatiotemporal nodes This represents the t-th scheduling time interval, the... k The ship is located in port j. Indicates a collection of ports. Indicates the scheduling time period; spatiotemporal arc It represents the movement of a ship between points in time and space; Objective function: Minimize the total running time of all ships; (1) in, This is a binary variable; if it is 1, it indicates that ship k used an arc. Otherwise, it is 0; A collection of all-electric vessels in the fleet; For the entire set of scheduling time periods; The sailing time between ports i and j is related to the sailing distance between the ports. Indicates the sailing distance between ports; Indicates average speed; Let n be the arc originating from node n in the spatiotemporal network. The constraints include: network flow balancing constraints and operational constraints; The network flow balance constraint adopts: (2) in, , Let n represent the arcs that start and end at node n in the spatiotemporal network, respectively. This is a binary variable; if it is 1, it indicates that ship k used an arc. Otherwise, it is 0; Represents the set of nodes in a maritime spatiotemporal network; Indicates the entire scheduling time period; The operational constraints (3) in, , They are respectively t- 1 and t time j Port inventory levels; , They are respectively t- 1 and t Time of the first k The cargo capacity of a ship; This is a binary indicator variable; if it is 1, it means that the kth ship is at the spatiotemporal node n, otherwise it is 0. This represents the amount of cargo loaded by the k-th ship at time t from the warehouse port or unloaded at the customer port. , These are warehouse port productivity and customer port consumption rate, respectively. Indicates the maximum cargo capacity of the vessel. This indicates the maximum number of ports visited by a vessel. The multi-objective ship energy management model adopts: The all-electric ship model includes: a diesel engine motor module, an energy storage device module, and a propulsion load module; The diesel engine motor module adopts: The relationship between the fuel cost of a diesel generator and its power generation can be expressed as a second-order polynomial function: in, Let be the fuel consumption of the mobile micro-energy grid of the kth ship; The fuel consumption coefficient represents the i-th diesel generator; This represents the power output of the i-th diesel generator set on the k-th ship at time t; This represents a collection of shipborne diesel generator sets; This indicates the duration of the logistics task for the k-th ship. The energy storage module uses lithium batteries as energy storage devices in the ship's energy system. The mathematical model of the battery energy storage power station is as follows: in, , Let represent the state of charge of the onboard energy storage battery of the k-th all-electric ship at time t-1 and time t, respectively; Let represent the self-discharge loss coefficient of the onboard energy storage battery of the k-th all-electric ship, and take 0.05; , This represents the charging and discharging power of the onboard energy storage battery of the k-th all-electric ship at time t; , These represent the charging and discharging efficiencies of the energy storage battery, respectively. Indicates a time interval; The propulsion load module includes: adjusting the ship's speed to change the electric propulsion power, with the two satisfying the following relationship: (6) in, Indicates a ship t The speed of the ship in still water at any given moment; Indicates the first k All-electric ships t The electrical power consumed by the ship's propulsion system at any given moment; The coefficients representing the functional relationship between propulsion electrical load and ship speed; Objective function: The economic cost and carbon emissions of each all-electric ship are taken as the optimization objective function, and its expression is as follows: (7) in, and All-electric ships k Based on the logistics time-space navigation link obtained from the first phase of logistics distribution optimization, calculate the carbon emission cost of cargo distribution. The constraints include: power balance constraints, equipment operation safety constraints, and traffic and navigation constraints; The power balance constraints include: (10) in, Indicates the first k All-electric ships t The power output of the shipborne diesel generator during the specified time period; , Indicates the first k All-electric ships t The charging and discharging power of the ship's onboard energy storage battery at all times; , These represent the charging and discharging efficiencies of the energy storage battery, respectively. , They represent t Electric loads for time-of-use services and electric propulsion; The equipment operation safety constraints are as follows: (11) In the formula: , These represent the maximum charging and discharging power of the energy storage unit, respectively; Boolean variables. , They represent t The indicator bit for the charging and discharging status of the energy storage during a given period is 1 if it indicates charging and 0 otherwise. , These represent the upper and lower limits of the state of charge of the energy storage unit, respectively. , For the first i The upper and lower limits of the output of the diesel generator set; For the first i The maximum climbing power of the diesel generator set; The traffic and navigation constraints are as follows: (12) in, , They represent t The time and the distance the ship has traveled at the end of the navigation cycle; The total voyage from the warehouse port to the delivery destination and back to the warehouse port; for t Ship speed at any time; This indicates the upper and lower limits of acceptable navigation deviation for a vessel at the end of the navigation cycle; , These are the upper and lower limits for ship speed; This indicates the distance the ship has traveled at the end of the sailing cycle.

6. The maritime mobile microgrid cluster logistics and energy collaborative dispatch system according to claim 5, characterized in that, The module M3 adopts: Module M3.1: The first-stage fleet logistics allocation model is a mixed-integer linear programming model. Solving the current mixed-integer linear programming model yields the logistics allocation results. Module M3.2: Based on the logistics allocation results obtained in the first stage, the multi-objective energy management problem of each ship in the fleet of the second stage multi-objective ship energy management model is solved to obtain the optimal energy allocation decision of the ships; The module M3.2 adopts: Module M3.2.1: Linearizes nonlinear functions using piecewise linearization; Module M3.2.2: The linear boundary crossing method is used to transform the bi-objective problem into two single-objective mathematical programming problems for optimization and solution, thereby obtaining the Pareto front decision solution set; Module M3.2.3: Based on the concept of maximizing the trade-offs among multiple objectives, selects the knee point from the Pareto front as the optimal energy allocation decision for the ship; The module M3.1 adopts: in, This indicates the optimization goal for the first phase. This represents the total travel time of all ships. This represents the first-stage optimization variable. This represents the decision space of the first-stage optimization variables. This represents the first-stage inequality constraint. This indicates the first-stage equality constraint. This indicates the number of inequality constraints in the first stage. Indicates the number of equality constraints in the first stage; Module M3.2: in, This indicates the optimization goal for the second phase. Indicates the cost of economic operation. Indicates carbon emissions, This represents the decision variables for the first stage of optimization. This represents the decision variables for the second stage of optimization. Let u represent the decision space of the optimization variables in Section 2. This indicates the second-stage inequality constraint. This indicates the second-stage equality constraint. This indicates the number of second-stage inequality constraints. This indicates the number of equality constraints in the second stage.

Images