Port distributed energy management system considering charging and discharging of unmanned ship under multi-modal network

Abstract

The invention provides a port distributed energy management system considering unmanned ship charging and discharging under a multi-mode network, belongs to the technical field of new energy and energy-saving technology / port micro-grid system energy scheduling, can effectively solve the problem of port distributed energy management considering unmanned ship charging and discharging under the multi-mode network, and aims at a port data center. A delay sensitive load of a port is considered, a data load delay constraint is established, and an energy consumption calculation model of a port data center is constructed. For a port micro-grid system, a port distributed energy management model considering unmanned ship charging and discharging is constructed by taking port micro-grid operation cost, main grid electricity purchasing or selling cost, carbon cost, data center operation cost and unmanned ship charging and discharging cost as objective functions. Based on dual decomposition mixed integer linear programming, a distributed energy optimization strategy is designed; and finally, an optimal energy management scheme is solved, so that port micro-grid energy management considering unmanned ship charging and discharging under the multi-modal network is realized.

Description

Technical Field

[0001] The present invention relates to the field of new energy and energy-saving technology / energy dispatching technology of port microgrid system, and in particular to a port distributed energy management system that considers the charging and discharging of unmanned ships under a multimodal network. Background Art

[0002] With the development of information technology, ports are increasingly inclined towards automation and digital transformation. Therefore, as an effective resource utilization optimization method, data centers integrate highly concentrated computing power and powerful data processing capabilities to solve the problem of insufficient computing resources in ports. Optimizing the energy management system of port microgrids is an important guarantee for the sustainable development of ports.

[0003] For complex large-scale port energy systems, there are few studies on energy management issues that consider port computing power requirements. Most existing studies only consider the port's power system load requirements. Therefore, designing port distributed energy optimization strategies is an issue that needs attention. For port data centers, existing studies have mostly ignored the situation of insufficient port computing resources. For port microgrid systems, existing studies have mostly used the operating cost of energy supply equipment as the optimization target, ignoring the additional cost requirements generated by unmanned ship charging and discharging and carbon emissions. In addition, distributed optimization strategies are limited to the continuity of decision variables between port equipment, and existing optimization strategies rely on real-time and continuous communication between devices.

[0004] However, as a complex and changeable operating environment, the port's data flow, equipment status, and energy demand are highly dynamic and uncertain. Therefore, a complete port distributed energy management system that considers the charging and discharging of unmanned ships in a multimodal network is urgently needed to ensure the reliable operation of the port. Summary of the invention

[0005] Aiming at the energy management problem of the port microgrid system considering the charging and discharging of unmanned ships, the present invention provides a port distributed energy management system considering the charging and discharging of unmanned ships under a multimodal network. The present invention takes the port microgrid as the research object, and proposes energy management models and distributed energy optimization strategies for the port data center, power supply equipment and unmanned ships charging at the port. The port distributed energy management system proposed by the present invention can reduce the operating costs of the port while defining different routes according to business needs, realize management between multimodal networks, and ensure the smooth operation of the port.

[0006] To this end, the present invention provides the following technical solutions:

[0007] A port distributed energy management system considering unmanned ship charging and discharging under a multimodal network comprises: a data layer, a control layer, and a service layer; the data layer is used to store relevant data of port energy management; the control layer is responsible for domain management and authorization management; the service layer is responsible for the implementation of port distributed energy management, defines different routes according to business needs, realizes management between polymorphic networks, and ensures distributed energy management in the port; the implementation of the distributed energy management comprises: taking the port microgrid operation cost, the main power grid power purchase or sales cost, the carbon cost, the data center operation cost, and the unmanned ship charging and discharging cost as the first objective function, combining the unmanned ship's charging demand, discharge capacity, and power demand in the port, constructing the unmanned ship charging and discharging physical constraints, and considering the port operation characteristics, constructing the port's physical constraints; based on the first objective function, the unmanned ship charging and discharging physical constraints, and the port's physical constraints, constructing a port distributed energy management model considering the unmanned ship charging and discharging; solving the port distributed energy management model considering the unmanned ship charging and discharging, and obtaining and implementing the optimal energy management plan.

[0008] Furthermore, the operating cost of the data center is obtained, including: taking the minimum energy consumption of the port data center as the second objective function, constructing delay constraints based on the port delay-sensitive load, and establishing an energy consumption calculation model of the port data center; and obtaining the operating cost of the data center based on the energy consumption calculation model of the port data center.

[0009] Furthermore, the physical constraints for charging and discharging of the unmanned ship include: the charging time window, charging rate, discharge power limit and interaction rules with the port microgrid.

[0010] Furthermore, the physical constraints of the port include: load changes, renewable energy power generation conditions and ship arrival time.

[0011] Furthermore, the energy consumption of the port data center is expressed as:

[0012]

[0013] α=PUE*P n,min

[0014] β=[PUE*L t (P n,max -P n,min )] / μ n

[0015] in, is the energy consumption of the port data center, is the number of active servers in the data center, α, β are the energy consumption coefficients of the data center, PUE is the ratio of the total energy consumption of the data center to the energy consumption for computing and storage, Pn,max ,P n,min is the peak power and quiet power of a single server in the data center, L t is the data load generated by the port, μ n is the service rate of the active server, and T = {1, 2, …, t} is the number of time slots.

[0016] Furthermore, the delay constraints include: server quantity constraints and port data load processing delay constraints;

[0017] The server quantity constraint is expressed as:

[0018]

[0019] Among them, M n is the total number of servers in the data center;

[0020] The port data load processing delay constraint is expressed as:

[0021]

[0022] Where Q is the maximum processing delay time of the port data load.

[0023] Furthermore, the first objective function is:

[0024] F=min{F1+F2+F3+F4+F5}

[0025] Among them, F is the total cost, F1 is the operating cost of functional equipment, F2 is the main power grid cost, F3 is the carbon tax cost, F4 is the unmanned ship charging and discharging cost, and F5 is the data center operating cost;

[0026] in:

[0027]

[0028] Among them, F w ,F pv ,F fu is the cost of wind power generation, photovoltaic power generation and fossil fuel power generation, P n,w ,P n,pv ,P n,fu The wind turbine output power is the photovoltaic output power and the diesel generator output power, α w ,α pv ,α fu is the cost coefficient of power generation equipment; K M is the cost coefficient of the main power grid purchasing electricity, P M is the power supply of the main power grid; K C is the carbon tax, ι is the coefficient of carbon dioxide produced by fossil fuel combustion; PS is the charge and discharge power, K SA ,K SB is the charge and discharge cost coefficient, a n ,b n is the charge and discharge state; K D is the data center power consumption cost factor.

[0029] Furthermore, the physical constraints for charging and discharging of the unmanned boat are constructed, including:

[0030] The charging and discharging cost of the unmanned ship in the port is:

[0031]

[0032] Among them, P S is the charge and discharge power, K SA ,K SB is the charge and discharge cost coefficient, a n ,b n It is the charging and discharging state;

[0033] Construct the unmanned ship's charging and discharging capacity constraints:

[0034]

[0035] Among them, P min ,P max They are the upper and lower limits of the unmanned ship’s charging and discharging capacity;

[0036] Construct the energy level constraint of the unmanned ship:

[0037]

[0038] Among them, e n is the energy level of the unmanned ship, They are initial energy storage, calculated reference energy storage, minimum energy storage and maximum energy storage. is the energy level coefficient;

[0039] Construct the unmanned ship operation status constraints:

[0040] a n +b n ≤1.

[0041] Furthermore, the distributed energy management model of the port considering the charging and discharging of unmanned ships is solved, including:

[0042] The port energy management problem is a mixed integer linear programming problem, which can be expressed as:

[0043]

[0044] Where V is the number of nodes; Z iis the decision variable;

[0045] Design the Lagrangian function, expressed as:

[0046]

[0047] in, Expressed as is a vector of Lagrange multipliers;

[0048] The design dual problem is expressed as:

[0049]

[0050] Distributed algorithms for mixed integer linear programming based on dual decomposition, including:

[0051] Repeat the following steps until convergence:

[0052] based on Update i (k);

[0053] based on Update Z i (k+1);

[0054] based on Update λ i (k+1);

[0055] based on renew

[0056] Where k is the number of iterations;

[0057] Using two different sequences For the result Z i (k) Correction, Z i (k) is the weighted average and optimal solution of the port energy management problem, expressed as follows:

[0058]

[0059] Advantages and positive effects of the present invention: The present invention can effectively handle the port distributed energy management problem considering the charging and discharging of unmanned ships under multimodal networks. For the port data center, the delay-sensitive load of the port is considered, the data load delay constraint is established, and the energy consumption calculation model of the port data center is constructed. For the port microgrid system, the operating cost of the port microgrid, the main grid electricity purchase or sales cost, the carbon cost, the data center operating cost and the unmanned ship charging and discharging cost are used as the objective function to construct a port distributed energy management model considering the charging and discharging of unmanned ships. And based on the mixed integer linear programming of dual decomposition, a distributed energy optimization strategy is designed; finally, the optimal energy management plan is solved, and then the port microgrid energy management considering the charging and discharging of unmanned ships under multimodal networks is realized. The present invention is aimed at promoting the protection of the marine ecological environment and improving the level of green and low-carbon development of the ocean, and injecting vitality into the development of the marine economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0061] Figure 1 This is a schematic diagram of a port energy management system according to an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the port energy management system in an embodiment of the present invention;

[0063] Figure 3 A node diagram in an embodiment of the present invention;

[0064] Figure 4 A schematic diagram of power supply of a power supply device and power consumption of a data center in an embodiment of the present invention;

[0065] Figure 5 Schematic diagram of the charging and discharging state of the unmanned boat in an embodiment of the present invention;

[0066] Figure 6 Schematic diagram of dual variable iteration in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0068] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0069] In order to reduce the environmental impact of port carbon emissions, promote the sustainable development of ports, and achieve safe and reliable operation of ports, this invention takes the port microgrid considering the charging and discharging of unmanned ships as the research object, reduces the port operation cost, defines different routes according to business needs, realizes the management between multimodal networks, and provides a port distributed energy management method considering the charging and discharging of unmanned ships under a multimodal network, focusing on the port electrification transformation and green energy application, aiming to lead the port to develop in a green and sustainable direction. Figure 1As shown in the figure, a port energy management system considering the charging and discharging needs of unmanned ships includes a data layer, a control layer, and a service layer. The data layer is used to store relevant data of port energy management. The control layer is responsible for domain management and authorization management. The service layer is responsible for the implementation of distributed energy management in ports. Different routes are defined according to business needs to achieve management between polymorphic networks and ensure distributed energy management in ports. Based on this, the energy consumption of the port data center is designed as the objective function. Based on the port delay-sensitive load, delay constraints are constructed to establish an energy consumption calculation model for the port data center; combined with the charging needs and discharge capabilities of the unmanned ship and the power demand in the port, the physical constraints for the charging and discharging of the unmanned ship are constructed. This includes the charging time window, charging rate, discharge power limit, and interaction rules with the port microgrid. At the same time, considering the port operation characteristics, such as load changes, renewable energy generation, ship arrival time, etc., physical constraints are constructed; based on mixed integer linear programming, a distributed energy optimization algorithm is designed to solve the port distributed energy management model considering the charging and discharging of unmanned ships, and the optimal energy management plan is formulated.

[0070] Port distributed energy management relies on information interaction between various devices. The structure framework of the port microgrid energy system in the present invention is as follows: Figure 2 As shown in the figure, the system takes the current, data flow and carbon flow transmission lines between port equipment as the research carrier, and can be divided into three parts according to different energy flows: energy supply equipment, unmanned ship and data center. Among them, the data center is composed of IT equipment, cooling system, and other equipment (lighting, security, fire protection). Considering the data processing delay constraint, a port data center energy consumption calculation model is constructed. With carbon tax cost, data center operating cost, energy supply equipment operating cost and unmanned ship charging and discharging cost as the objective function, an energy management model is established. The energy supply equipment includes traditional fuel equipment, new energy photovoltaic equipment and wind turbine equipment. For the port energy management model, a mixed integer linear programming distributed optimization algorithm based on dual decomposition is used to solve it, and then the optimal energy management solution is obtained.

[0071] In the embodiment of the present invention, node classification is as follows Figure 3 As shown in the figure, the distributed energy management of ports considering the charging and discharging of unmanned ships under multimodal networks is implemented. The technical solution is mainly divided into energy consumption calculation for port data centers and energy management for port microgrid systems, which specifically includes the following steps:

[0072] Step 1: Take the energy consumption generated by data processing in the port data center as the first objective function; build constraints based on the data delay and transmission of sensitive loads, and then establish an energy consumption calculation model for the port data center.

[0073] Among them, the power consumption of the data center can be expressed as:

[0074]

[0075] in, is the energy consumption of the port data center, is the number of active servers in the data center, α, β are the energy consumption coefficients of the data center, PUE is the ratio of the total energy consumption of the data center to the energy consumption for computing and storage, set to 1.2, P n,max ,P n,min is the peak power and quiet power of a single server in the data center, L t is the data load generated by the port, μ n is the service rate of the active server, T = {1, 2, …, t} is the number of time slots, and T = 1 represents the set of all time periods in the voyage.

[0076] Constraints include:

[0077] The server quantity constraint can be specifically expressed as:

[0078]

[0079] Among them, M n is the total number of servers in the data center.

[0080] The port data load processing delay constraint can be specifically expressed as:

[0081]

[0082] Where Q is the maximum processing delay time of the port data load.

[0083] Step 2: Taking the port microgrid operating cost, the main grid power purchase or sales cost, carbon cost, data center operating cost and unmanned ship charging and discharging cost as the objective function, considering the port operation characteristics, constructing physical constraints, and establishing a port distributed energy management model considering the charging and discharging of unmanned ships.

[0084] Among them, the objective function includes:

[0085] Step 2.1.1 : Based on the cost function of energy supply equipment operation, unmanned boat charging and discharging, data center operation and main power grid electricity purchase or sales, an economic benefit objective function is established, including:

[0086] F = min{F1+F2+F3+F4+F5};

[0087] Among them, F is the total cost, F1 is the operating cost of functional equipment, F2 is the main power grid cost, F3 is the carbon tax cost, F4 is the unmanned ship charging and discharging cost, and F5 is the data center operating cost.

[0088] Step 2.1.2 : Calculate the operating cost of port functional equipment, the cost of purchasing or selling electricity from the main power grid, the carbon cost, the operating cost of the data center, and the charging and discharging cost of the unmanned ship, which can be expressed as:

[0089]

[0090] Among them, F w ,F pv ,F fu is the cost of wind power generation, photovoltaic power generation and fossil fuel power generation, P n,w ,P n,pv ,P n,fu The wind turbine output power is the photovoltaic output power and the diesel generator output power, α w ,α pv ,α fu is the cost coefficient of the power generation equipment. K M is the cost coefficient of the main power grid purchasing electricity, P M is the power supply of the main power grid. K C is the carbon tax, the price is $50, and ι is the coefficient of carbon dioxide produced by fossil fuel combustion. S is the charge and discharge power, K SA ,K SB is the charge and discharge cost coefficient, a n ,b n It is the charging and discharging state. K D is the data center power consumption cost factor.

[0091] Among them, physical constraints include:

[0092] Step 2.2.1: Construct the power balance constraint as follows:

[0093]

[0094] Among them, P load is the port load demand, the port operating load is 2000kWh, and the data load generation rate is 10000 / h.

[0095] Step 2.2.2: Construct the capacity constraints of power generation equipment as follows:

[0096]

[0097] in, are the upper and lower limits of the power of each power generation equipment. The cost coefficients of each power generation equipment are [0.610, 0.615, 0.650, 0.600], and the upper and lower limits of the power generation equipment are [1100, 1100, 900], [0, 0, 200] respectively.

[0098] Step 2.2.3: Construct the unmanned ship charging and discharging constraints as follows:

[0099]

[0100] Among them, e n is the energy level of the unmanned ship, They are initial energy storage, calculated reference energy storage, minimum energy storage and maximum energy storage. is the energy level coefficient.

[0101] Step 2.2.4: Construct data center operation constraints as follows:

[0102]

[0103] Step 2.2.5: The energy management model is integrated into a form that includes all coupling constraints, as shown below:

[0104]

[0105] Among them, V d ,V s ,V D They are the number of electrical nodes, the number of unmanned ship nodes and the number of data center nodes. d ,Θ ship ,Θ IDC They are the electrical node set, the unmanned ship node set and the data center node set.

[0106] At this point, step 2, the port distributed energy management model considering the charging and discharging of unmanned ships is established.

[0107] Step 3: Based on mixed integer linear programming, design a distributed energy optimization algorithm and formulate an optimal energy management plan.

[0108] Among them, the distributed energy optimization algorithm and optimal energy management solution include:

[0109] Step 3.1: The port energy management problem is a mixed integer linear programming problem, which can be expressed as follows:

[0110]

[0111] Where V is the number of nodes; Z i is the decision variable.

[0112] Step 3.2: Design the Lagrangian function, which can be expressed as follows

[0113]

[0114] in, is represented as a vector of Lagrange multipliers.

[0115] Step 3.3: Design the dual problem, which can be expressed as follows:

[0116]

[0117] Step 3.4: Design a distributed algorithm for mixed integer linear programming based on dual decomposition, expressed as follows:

[0118]

[0119] Step 3.5: Using two different sequences For the result Z i (k) Correction, Z i (k) is the weighted average and optimal solution of the port energy management problem, expressed as follows:

[0120]

[0121] Step 3.6: Based on the port distributed energy management model considering the charging and discharging of unmanned ships in step 2 and the distributed energy optimization algorithm in step 3, calculate and execute the optimal energy management plan.

[0122] At this point, a port distributed energy management considering the charging and discharging of unmanned ships under a multimodal network has been completed. The power supply of the port power supply equipment and the power consumption of the data center, the charging and discharging status of the unmanned ship, and the dual variable iteration are shown in the attached figure. Figure 4-6 Specifically, the computing power requirements of the port are met, and the port load and unmanned ship charging and discharging requirements based on the optimal energy management strategy meet the expected settings, which can achieve reliable operation of the port. According to the above simulation diagram, the effectiveness of the energy management system proposed in the present invention can be seen.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A port distributed energy management system considering unmanned ship charging and discharging under a multimodal network, characterized in that: include: Includes data layer, control layer and service layer; The data layer is used to store relevant data of port energy management; The control layer is responsible for domain management and authorization management; the service layer is responsible for the implementation of port distributed energy management, defines different routes according to business needs, realizes management between polymorphic networks, and ensures distributed energy management in the port; the implementation of the distributed energy management includes: taking the port microgrid operating cost, the main power grid electricity purchase or sales cost, carbon cost, data center operating cost and unmanned ship charging and discharging cost as the first objective function, combining the unmanned ship's charging demand, discharge capacity and power demand in the port, constructing the physical constraints of the unmanned ship charging and discharging, considering the port operation characteristics, and constructing the physical constraints of the port; based on the first objective function, the physical constraints of the unmanned ship charging and discharging and the physical constraints of the port, construct a port distributed energy management model considering the charging and discharging of unmanned ships; solving the port distributed energy management model considering the charging and discharging of unmanned ships, and obtaining and implementing the optimal energy management plan.

2. According to claim 1, a port distributed energy management system considering unmanned ship charging and discharging in a multimodal network is characterized in that: Obtaining the operating cost of the data center includes: taking the minimum energy consumption of the port data center as the second objective function, constructing a delay constraint condition based on the port delay-sensitive load, and establishing an energy consumption calculation model of the port data center; and obtaining the operating cost of the data center based on the energy consumption calculation model of the port data center.

3. According to claim 1, a port distributed energy management system considering unmanned ship charging and discharging in a multimodal network is characterized in that: The physical constraints for charging and discharging of the unmanned ship include: the charging time window, charging rate, discharge power limit and interaction rules with the port microgrid.

4. According to claim 1, a port distributed energy management system considering unmanned ship charging and discharging in a multimodal network is characterized in that: The physical constraints of the port include: load changes, renewable energy generation and ship arrival time.

5. According to claim 1, a port distributed energy management system considering unmanned ship charging and discharging in a multimodal network is characterized in that: The energy consumption of the port data center is expressed as: α=PUE*P n,min β=[PUE*L t (P n,max -P n,min )] / μ n in, is the energy consumption of the port data center, is the number of active servers in the data center, α, β are the energy consumption coefficients of the data center, PUE is the ratio of the total energy consumption of the data center to the energy consumption for computing and storage, P n,max ,P n,min is the peak power and quiet power of a single server in the data center, L t is the data load generated by the port, μ n is the service rate of the active server, and T = {1, 2, …, t} is the number of time slots.

6. The port distributed energy management system considering the charging and discharging of unmanned ships in a multimodal network according to claim 5 is characterized in that: Delay constraints, including: server quantity constraints and port data load processing delay constraints; The server quantity constraint is expressed as: Among them, M n is the total number of servers in the data center; The port data load processing delay constraint is expressed as: Where Q is the maximum processing delay time of the port data load.

7. The port distributed energy management system considering the charging and discharging of unmanned ships in a multimodal network according to claim 1 is characterized in that: The first objective function is: F=min{F1+F2+F3+F4+F5} Among them, F is the total cost, F1 is the operating cost of functional equipment, F2 is the main power grid cost, F3 is the carbon tax cost, F4 is the unmanned ship charging and discharging cost, and F5 is the data center operating cost; in: Among them, F w ,F pv ,F fu is the cost of wind power generation, photovoltaic power generation and fossil fuel power generation, P n,w ,P n,pv ,P n,fu The wind turbine output power is the photovoltaic output power and the diesel generator output power, α w ,α pv ,α fu is the cost coefficient of power generation equipment; K M is the cost coefficient of the main power grid purchasing electricity, P M is the power supply of the main power grid; K C is the carbon tax, ι is the coefficient of carbon dioxide produced by fossil fuel combustion; P S is the charge and discharge power, K SA ,K SB is the charge and discharge cost coefficient, a n ,b n is the charge and discharge state; K D is the data center power consumption cost factor.

8. The port distributed energy management system considering the charging and discharging of unmanned ships in a multimodal network according to claim 3 is characterized in that: Construct the physical constraints for charging and discharging of unmanned boats, including: The charging and discharging cost of the unmanned ship in the port is: Among them, P S is the charge and discharge power, K SA ,K SB is the charge and discharge cost coefficient, a n ,b n It is the charging and discharging state; Construct the unmanned ship's charging and discharging capacity constraints: Among them, P min ,P max They are the upper and lower limits of the unmanned ship’s charging and discharging capacity; Construct the energy level constraint of the unmanned ship: Among them, e n is the energy level of the unmanned ship, They are initial energy storage, calculated reference energy storage, minimum energy storage and maximum energy storage. is the energy level coefficient; Construct the unmanned ship operation status constraints: a n +b n ≤1。 9. The port distributed energy management system considering the charging and discharging of unmanned ships in a multimodal network according to claim 1 is characterized in that: Solve the port distributed energy management model considering the charging and discharging of unmanned ships, including: The port energy management problem is a mixed integer linear programming problem, which can be expressed as: Where V is the number of nodes; Z i is the decision variable; Design the Lagrangian function, expressed as: in, It is represented as a vector of Lagrange multipliers; The design dual problem is expressed as: Distributed algorithms for mixed integer linear programming based on dual decomposition, including: Repeat the following steps until convergence: based on Update i (k ) ; based on Update Z i (k+ 1) ; based on Update λ i (k+1); based on renew Where k is the number of iterations; Using two different sequences For the result Z i (k) Correction, Z i (k) is the weighted average and optimal solution of the port energy management problem, expressed as follows:

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