SMES joint planning method considering carbon emission

Abstract

An SMES joint planning method considering carbon emission comprises the following steps: step 1, building a ship integrated energy system model based on a multi-energy coupling principle, the model comprising an AC / DC hybrid ship power system architecture model, a ship energy equipment model and a ship navigation model; 2, establishing a double-layer optimization planning model considering carbon emission by adopting the ship integrated energy system model obtained in the step 1, optimizing equipment configuration at the upper layer of the model by taking the minimum total investment cost as a target, and optimizing an equipment scheduling strategy at the lower layer by taking the operating cost and the carbon emission as double targets; and step 3, solving the double-layer optimization planning model considering the carbon emission obtained in the step 2 based on a mixed integer linear programming method, and realizing collaborative optimization of optimal capacity configuration and an operation strategy of the system through a CPLEX solver.

Description

Technical Field

[0001] The present invention relates to the field of power system technology, and in particular to ship power system optimization and low-carbon energy planning technology. Specifically, it relates to a technology suitable for the design and operation optimization of ship energy systems, which provides an effective solution for the low-carbonization and economic planning of ship multi-energy systems. Background Art

[0002] With the rapid growth of global ocean tourism in recent years, the energy needs of large cruise ships and other vessels have become increasingly complex, requiring the simultaneous provision of multiple energy sources, including cooling, heating, and electricity. To address this, modern ships commonly utilize ship multi-energy systems (SMES), which integrate distributed generators, energy storage systems, and multiple energy loads. Throughout the SMES lifecycle, factors such as system composition, power structure, and capacity configuration directly impact its operational efficiency and emission reduction potential. Therefore, the joint planning of SMES has attracted considerable attention.

[0003] Currently, research on SMES focuses primarily on system control, scheduling and operation, and navigation path planning. For example, to stabilize ship microgrids, the papers "Optimal planning and operation management of a ship electrical power system with energy storage system" (10.1109 / IECON.2016.7793272), "Optimization and control of electric ship microgrids with short-term energy storage systems" (10.1109 / TTE.2023.3326355), and "Two-layer model predictive control method for frequency regulation of ship hybrid power plants with diesel and energy storage systems" (10.16183 / j.cnki.jsjtu.2024.390) each focus on power capacity allocation, energy storage system optimization, and grid-connected presynchronization control. These studies design corresponding control strategies to reduce voltage disturbances caused by dynamic load variations while balancing frequency regulation quality and economic efficiency. To optimize ship energy management and scheduling, the papers "Risk-Aware Coordination of Logistics Scheduling and Energy Management for Maritime Mobile Microgrid Clusters" (10.1109 / TIV.2023.3336523) and "Coordinated optimal energy management and voyage scheduling for all-electric ships based on predicted shore-side electricity price" (10.1109 / TIA.2020.3034290) propose multi-objective optimization schemes for joint voyage scheduling and fleet collaborative scheduling, respectively, to maximize the provision of critical energy services and reduce overall operating costs. The paper "Ship Local Path Planning Method Based on a Three-Dimensional Potential Field Model" (0.19693 / j.issn.1673-3185.04076) designs a method for automatic ship berthing path planning to improve the adaptability of dynamic real-time local path planning for ships in different scenarios.

[0004] However, the aforementioned research on SMES has primarily focused on technical aspects such as system control, scheduling and operation, and navigation path planning. Although some progress has been made in specific methods and applications, research on its planning strategies is still in its infancy, primarily focusing on engineering operations or subsystem construction. This has the following technical drawbacks:

[0005] First, existing planning methods treat the cooling, heating, and electrical systems within SMES as fixed boundary conditions, failing to fully consider the dynamic coupling between multiple energy systems. In reality, the energy-saving and carbon-reduction benefits of SMES are highly dependent on the coordinated operation of these different energy systems. The fragmented optimization approach of existing methods results in reduced energy efficiency and increased costs.

[0006] Second, existing planning methods often prioritize investment and operating costs as single optimization objectives, failing to effectively incorporate carbon emission constraints. In the context of global green shipping development, carbon emissions have become a key factor influencing the design of ship energy systems. Planning methods that ignore this dimension struggle to meet environmental requirements.

[0007] Therefore, there is an urgent need for a SMES joint planning method that can comprehensively optimize cooling, heating, and electronic systems and consider carbon emission targets to solve the problems of lack of multi-energy coupling relationships and insufficient environmental protection goals in existing technologies. Summary of the Invention

[0008] The purpose of this invention is to solve the technical problems of insufficient consideration of multi-energy coupling relationships and lack of carbon emission targets in the existing ship cooling, heating and power multi-energy system planning methods, which leads to reduced energy efficiency, and to propose a joint planning technology for ship cooling, heating and power multi-energy systems considering carbon emissions.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0010] A SMES joint planning method considering carbon emissions includes the following steps:

[0011] Step 1: Based on the multi-energy coupling principle, a ship integrated energy system model is constructed. The model includes an AC / DC hybrid ship power system architecture model, a ship energy equipment model, and a ship navigation model.

[0012] Step 2: Use the ship integrated energy system model obtained in Step 1 to establish a two-level optimization planning model that considers carbon emissions. The upper level of the model optimizes equipment configuration with the goal of minimizing total investment cost, while the lower level optimizes equipment scheduling strategy with the dual objectives of operating cost and carbon emissions.

[0013] Step 3: Solve the two-level optimization planning model considering carbon emissions obtained in Step 2 based on the mixed integer linear programming method, and use the CPLEX solver to achieve collaborative optimization of the system's optimal capacity configuration and operation strategy.

[0014] The energy equipment in the ship energy equipment model is divided into power supply equipment, heating equipment, cooling equipment and energy storage equipment; the power supply equipment includes diesel generator sets, gas turbines in CCHP units and solar photovoltaic power generation; the heating equipment is the waste heat recovery device in the CCHP unit; the cooling equipment includes electric refrigerators and absorption refrigerators in CCHP units; the energy storage equipment includes marine batteries, marine heat storage tanks and marine cold storage tank models.

[0015] The output model and constraints of the diesel generator set are:

[0016] Establishing a diesel generator output model is the output power of DG at time t; k b is the diesel combustion efficiency, generally 40%; η is the diesel engine power generation efficiency; h f is the lower calorific value of diesel, generally taken as 49kJ / g; F0 is the no-load fuel consumption of DG unit power; F is the fuel consumption of DG.

[0017] Establishing the diesel generator set DG model is the output power of DG type n at time t; represents the fuel cost coefficient of DG; The switch variable representing the DG type n, 1 means DG is on, 0 means DG is off;

[0018] Construct a DG cost model that covers annual investment cost, maintenance cost, fuel cost, and unit start-up and shutdown cost, as follows:

[0019] The annual investment cost is

[0020] Maintenance cost is

[0021] Fuel cost is

[0022] The unit start-up and shutdown cost is

[0023] Among them, C in,dg is the investment cost of DG; n represents the model of diesel generator; P dg,n is the rated power of DG; μ dg,n is the unit investment cost of DG; T n Indicates the lifespan of DG of type n; is the maintenance cost of DG at time t; is the output power of DG type n at time t; dg,n is the unit maintenance cost of DG; represents the fuel cost required by DG type n at time t; represents the fuel cost coefficient of DG; The switch variable representing the DG type n, 1 means DG is on, 0 means DG is off; is the start-stop cost of DG at time t; and are the start and stop status of the DG of type n at time t and time t-1, respectively, 1 means on and 0 means off; is the unit start-stop cost of DG;

[0024] Set operational constraints, including:

[0025] ① Set the output power constraint to It is necessary to ensure that the output power of the diesel generator set is within the allowable range to avoid overload or underload operation;

[0026] ②Set the climbing rate constraint to Limit the power variation of diesel generator sets per unit time to ensure smooth regulation; Indicates the upper and lower limits of the output power of DG type n; Indicates the upper and lower limits of the climbing of DG type n;

[0027] ③Set the minimum continuous running time constraint to Prevent diesel generator sets from frequent starts and stops and meet the minimum continuous operation time requirement;

[0028] ④ Set the minimum downtime constraint to Ensure that the diesel generator set needs enough time to cool down before it can be restarted after shutdown; where, They represent the startup time and shutdown time of DG type n respectively; They represent the minimum contact startup and downtime of DG type n, respectively.

[0029] The output model and constraints of the gas turbine (GT) are:

[0030] The electric power output model is established as Calculate the electrical power output of the gas turbine at time t, which is proportional to the natural gas consumption and power generation efficiency;

[0031] The thermal power output model is established as Calculate the thermal power output of the gas turbine based on the electrical power output, reflecting the cogeneration characteristics; is the output power of GT at time t; is the amount of natural gas consumed by GT at time t; H r Indicates the calorific value of natural gas; represents the gas-to-electricity efficiency of GT; is the thermal output power of GT; is the ratio of the thermal output power to the electrical power of the GT;

[0032] The cost model for building GT is as follows:

[0033] Construct the annual investment cost model as Calculate the average annual investment cost of GT equipment, using the equal annuity method to consider the time value of money;

[0034] The operation and maintenance cost model is constructed as Calculate the operation and maintenance cost of GT at time t, which is proportional to the current output power;

[0035] Construct the fuel cost model as Calculate the natural gas fuel consumption cost of GT at time t based on the actual natural gas consumption; C in,gt represents the investment cost of GT; μ gt represents the unit investment cost of GT; P gt Indicates the rated electric power of GT; R gt Indicates the installed capacity of GT; represents the maintenance cost of GT at time t; represents the output power of GT at time t; λ gt represents the unit maintenance cost of GT, is the fuel cost of natural gas consumed by GT at time t; is the unit natural gas consumption cost.

[0036] The upper and lower limits of GT output are constructed as follows: Indicates the upper and lower limits of the GT output power.

[0037] The output model and constraints of the photovoltaic power generation PV model are as follows:

[0038] The PV output model is constructed as is the output power of PV at time t; P pv is the installed capacity of PV; G t , G stc is the light intensity of PV in actual environment and standard working conditions; β c Indicates the PV power temperature coefficient; Indicates the actual ambient temperature of the solar photovoltaic panel; T c-ref is the surface temperature of the solar photovoltaic panel under standard conditions.

[0039] The cost model for building a photovoltaic power generation system (PV) is as follows:

[0040] Construct the annual investment cost model as Calculate the average annual investment cost of the photovoltaic system and convert it into an equivalent annuity using the capital recovery factor method;

[0041] The operation and maintenance cost model is constructed as Calculate the operation and maintenance cost of the photovoltaic system in period i, which is linearly related to the actual output power of the system; C in,pv is the investment cost of PV; μ pv is the unit investment cost of PV; R pv is the installed capacity of PV; T pv is the life of PV; is the maintenance cost of PV at time t; is the output power of PV at time t; pv is the unit maintenance cost of PV.

[0042] When the PV is working in the maximum power tracking mode, the power consumption during the period t cannot exceed the maximum output power during the period, that is, Indicates the maximum output power of the PV.

[0043] The output model of the waste heat recovery device is: is the heating power of HB at time t; is the waste heat recovery efficiency of HB;

[0044] A cost model for waste heat recovery (HB) in a ship's integrated energy system is constructed. This model reflects the fact that ship heating mainly relies on HB:

[0045] Construct the annual investment cost model as Based on the capacity of the heat recovery system, the capital recovery factor method is used to calculate the average annual investment cost over the entire life cycle;

[0046] The annual investment cost model is constructed as Based on the capacity of the heat recovery system, the capital recovery factor method is used to calculate the average annual investment cost over the entire life cycle; C in,hb represents the investment cost of HB; η hb represents the unit investment cost of HB; R hb Indicates the installed capacity of HB; T hb is the life span of HB; represents the maintenance cost of HB at time t; represents the output power of HB at time t; λ hb Represents the unit maintenance cost of HB.

[0047] The upper and lower output constraints of HB are constructed as follows: Indicates the upper limit of HB output power.

[0048] The output model of the absorption chiller (AC) is COP AC It represents the ratio of output cooling power to input heating power and is defined as the thermal coefficient; They are AC output cooling power and input heating power respectively.

[0049] Construct the annual investment cost model as Use the capital recovery factor method to calculate the average annual investment cost of AC equipment, taking into account the time value of money and the equipment life cycle.

[0050] The operation and maintenance cost model is constructed as Calculate the maintenance cost of AC equipment based on real-time output power, reflecting operating losses; C in,ac represents the investment cost of AC; μ ac represents the unit investment cost of AC; Q ac Indicates the rated power of AC; T ac Indicates the life of AC; represents the maintenance cost of AC at time t; represents the output power of AC at time t; λ ac represents the unit maintenance cost of AC;

[0051] Set the upper and lower limits of AC output to Indicates the upper limit of the AC output power.

[0052] The output model of the electric cooling (EC) is is the EC cooling power during period t; is the electric power consumed by EC during period t; is the cooling efficiency of EC.

[0053] Constructing an EC cost model mainly includes:

[0054] Construct the annual investment cost model as The capital recovery factor method is used to calculate the average annual investment cost of EC equipment, based on the installed capacity and the full life cycle;

[0055] The operation and maintenance cost model is constructed as Calculate the maintenance cost of EC equipment based on real-time cooling power, reflecting the correlation of operating load; C in,ec represents the investment cost of EC; μ ec Represents the unit investment cost of EC; R ec represents the installed capacity of EC; Tec represents the lifespan of EC; represents the maintenance cost of EC at time t; represents the output power of EC at time t; λ ec Represents the unit maintenance cost of EC.

[0056] Set the upper and lower limits of EC output as Indicates the upper limit of the EC output power.

[0057] For the energy storage model, a unified model suitable for three types of energy storage devices is constructed as SOC t is the state of charge of ES during period t; SOC t-1 is the state of charge of ES at time t-1; are the charge and discharge power of ES in period t respectively; η cha ES charging efficiency; η dis is the energy release efficiency of ES; k is the self-consumption rate of ES;

[0058] The cost model for building ES is as follows:

[0059] Construct the annual investment cost model as Adopt the dynamic capacity-life conversion method and make differentiated calculations based on energy storage types;

[0060] The operation and maintenance cost model is constructed as z∈{EES, TES, CES}, a linear calculation model based on real-time output power; C in,es represents the investment cost of type z in ES; μ es,z represents the unit investment cost of type z in ES; R es Indicates the installed capacity of type z in ES; T es,z Represents the lifetime of type z in ES; represents the maintenance cost of type z in ES at time t; Output power of type z in ES at time t; λ es,z The maintenance cost of a unit of type z in ES at time t.

[0061] Set the upper and lower limits of ES output to Indicates the upper limit of ES output power.

[0062] Considering the direct impact of ship navigation conditions on energy demand, an accurate ship navigation model needs to be constructed.

[0063] In ship voyage analysis, the relationship between sailing distance and sailing speed is established as D t =Dt-1 +v t Δt, D t D is the distance the ship travels at time t; t is the distance the ship travels at time t-1; v t Indicates the speed of the ship at time t. s represents the distance from the initial port to the next port; τ m Indicates the allowable error in the distance between ports.

[0064] When a ship is sailing at sea, speed constraints are set to ensure that the sailing speed is between the specified upper and lower limits. The specific constraints are: Indicates the maximum and minimum speeds of a ship.

[0065] The propulsion load required by the ship at different times can be calculated using the following formula based on the ship's sailing speed. Different ship speeds correspond to different propulsion powers. c1, c2 are corresponding coefficients, generally taken as 0.003, 3; is the propulsion power of the ship at time t; different propulsion power formulas corresponding to different ship speeds It can be seen that the speed of the ship can represent different navigation states, such as v t =0, the propulsion load of the ship is 0, and the ship is in a moored state.

[0066] In step 2, establishing a two-level optimization planning model considering carbon emissions includes the following steps:

[0067] Step ① Construct an upper-level investment decision optimization model;

[0068] Step ②: Build the lower-level operation optimization model;

[0069] Step ③: Establish coupling constraints;

[0070] When constructing the upper-level investment decision optimization model, the planning period (20 years) is used as the time scale to solve the equipment capacity configuration problem. The upper-level investment decision optimization model is constructed as follows:

[0071] The upper investment decision optimization model serves as the long-term planning layer, focusing on solving the equipment capacity configuration problem. Its objective function is expressed as C P is the total cost of the system; C IN is the annual investment cost of the system; is the maintenance cost of the system during period t; is the fuel cost of diesel and natural gas required for the system during period t; is the system's unit start-up and shutdown cost at time t; is the shore power cost of the system purchased electricity during period t; x D represents the number of sailing days of a ship in a year; Δt represents the scheduling period of the ship;

[0072] Among them, the annual investment cost is C IN =C in,pv +C in,dg +C in,cchp +C in,ec +C in,es , C in,cchp =C in,gt +C in,hb +C in,ac The maintenance cost is C om,cchp =C om,gt +C om,hb +C om,ac The cost of shore power is Carbon emissions are is the carbon emission of the diesel generator of type n at time t; are the carbon emissions of the gas turbine at time t; Fuel cost coefficient of diesel generator; κ gt are the unit carbon emission coefficients of gas turbines respectively.

[0073] In step ②, when constructing the lower-level operation optimization model, the lower-level model uses the scheduling cycle (24 hours) as the time scale to optimize the daily operation strategy and construct the lower-level operation optimization model:

[0074] The dual objective function for constructing the lower layer operation is

[0075] Carbon emissions are C OP is the total operation and maintenance cost of the system; G EN is the total carbon emissions of the system; is the sum of the gas emissions generated by the equipment at time t; It represents the gas emission of diesel engine type n at time t; represents the carbon dioxide emissions of GT at time t.

[0076] In step ③, when establishing coupling constraints, the two-layer model collaborative optimization is achieved:

[0077] The system energy balance constraints are set as follows:

[0078] The power balance constraint is Ensure real-time balance between power generation, energy storage and electricity load;

[0079] The thermodynamic balance constraint is Maintain a dynamic balance between waste heat recovery, heat storage and heat load;

[0080] The cooling balance constraint is Coordinate the supply and demand of refrigeration equipment, cold storage and cooling load;

[0081] in, represents the discharge power of EES at time t; represents the charging power of ESS at time t; represents the power consumption of EC at time t; represents the propulsion load of the ship at time t; It represents the ship's life electricity load at time t; represents the heat release power of TES at time t; represents the thermal storage power of TES at time t; represents the thermal power consumed by AC at time t; represents the heat load of the ship at time t; represents the cooling power of AC at time t; represents the cooling power of CES at time t, represents the cold storage power of CES at time t; represents the cooling load of the ship at time t;

[0082] Construct the capacity-output correlation model as P i (t)≤x i ·u i (t); P i (t) represents the actual output of equipment i in period t; x i represents the configuration capacity of device i (upper-level decision variable); u i (t) represents the start / stop status of device i in period t (0 / 1 variable).

[0083] In step 3, when using the mixed integer linear programming method to solve the two-level optimization planning model considering carbon emissions, there are nonlinear equations. The specific solution includes the following steps:

[0084] Step 3.1: Linearize the nonlinear equation;

[0085] Step 3.2: Construct a mixed integer programming model;

[0086] Step 3.3: Use the solution algorithm to optimize the model.

[0087] In step 3.1, the speed-propulsion power relationship is converted and the fuel cost formula is corrected using piecewise linearization of nonlinear terms. Unified model formula applicable to three types of energy storage devices Formulas for different propulsion powers corresponding to the same ship speed And the dual objective function formula of the lower layer operation The nonlinear terms in are piecewise linearized.

[0088] When performing piecewise linearization, the following steps are taken:

[0089] Step ① Approximate the nonlinear function by piecewise linear combination:

[0090] Step ② is to calculate the slope.

[0091] Step ③ is to calculate the intercept:

[0092] Step ④ Set the segment interval activation constraint to

[0093] Step ⑤ Set the unique constraint to

[0094] in, and is the origin and the approximate linearization function; m is the index of the block; N R is the total number of blocks; k m and l m are the slope and intercept of the linear function; x m is the starting point of segment m; is the binary state (0-1 variable) of the mth segment.

[0095] Cost of starting and stopping the unit The formula line is personalized and the maximum function is separated into two separate parts, and we get

[0096] Formula for minimum continuous running time constraint Perform linearization:

[0097] Initial state processing, the remaining minimum running time at the beginning of the scheduling cycle is compensated and calculated to obtain

[0098] Set the continuous operation constraint to

[0099] Perform terminal period processing to obtain

[0100] Where w is the cycle index marking the on / off time of the generator, is the DG opening time before scheduling; G n It is the start time of the generator reset at the beginning of ship operation.

[0101] Formula for minimum downtime constraint Perform linearization:

[0102] Initial state compensation, calculate the number of downtime periods that need to be supplemented in the initial scheduling period, and get

[0103] Set the main runtime constraints to

[0104] Set the terminal constraint to

[0105] in, They represent the startup time and shutdown time of DG type n before operation; L n This is the shutdown time when the generator is reset to zero at the start of ship operation.

[0106] In step 3, after linearization, the ship coordinated operation model becomes a MILP problem, which can be effectively solved by industrially proven efficient solvers such as CPLEX. The specific process is shown in the attached figure. Figure 3 As shown, a brief description is as follows:

[0107] Step 1: Input equipment parameters, constraints, cooling, heating and electricity loads and other information;

[0108] Step ② generates a set of candidate equipment capacity configuration solutions;

[0109] Step 3: Calculate the annual equipment investment cost corresponding to each configuration plan;

[0110] Step ④ brings the upper-layer configuration plan into the lower-layer operation model;

[0111] Step 5: Establish operation and maintenance models for various types of equipment and consider operation constraints;

[0112] Step 6: Use the CPLEX solver to solve the minimum operating cost and the minimum carbon emissions respectively;

[0113] Step 7: Obtain the Pareto non-inferior solution set of multi-objective optimization and select the comprehensive optimal solution using the normalization method;

[0114] Step ⑧ Add the operating cost and investment cost to obtain the total annual cost;

[0115] Step 9: Determine whether the current solution is the minimum annual total cost. If not, iterate;

[0116] Step ⑩ outputs the final optimal equipment configuration plan and operation results.

[0117] A method for establishing a two-level optimization planning model considering carbon emissions includes the following steps:

[0118] Step ① Construct an upper-level investment decision optimization model;

[0119] Step ②: Build the lower-level operation optimization model;

[0120] Step ③: Establish coupling constraints;

[0121] This planning model, in which the upper layer takes minimizing total cost as the objective function, and the lower layer takes minimizing total operation and maintenance cost and carbon emissions as the goal, achieves the coordination of long-term planning and short-term scheduling compared to traditional single-layer optimization, reduces total cost, significantly reduces carbon emissions, and takes into account both economic and environmental performance.

[0122] When constructing the upper-level investment decision optimization model, the planning period (20 years) is used as the time scale to solve the equipment capacity configuration problem. The upper-level investment decision optimization model is constructed as follows:

[0123] The upper investment decision optimization model serves as the long-term planning layer, focusing on solving the equipment capacity configuration problem. Its objective function is expressed as C P is the total cost of the system; C IN is the annual investment cost of the system; is the maintenance cost of the system during period t; is the fuel cost of diesel and natural gas required for the system during period t; is the system's unit start-up and shutdown cost at time t; is the shore power cost of the system purchased electricity during period t; x D represents the number of sailing days of a ship in a year; Δt represents the scheduling period of the ship;

[0124] Among them, the annual investment cost is C IN =C in,pv +C in,dg +C in,cchp +C in,ec +C in,es , C in,cchp =C in,gt +C in,hb +C in,ac The maintenance cost is C om,cchp =C om,gt +C om,hb +C om,ac The cost of shore power is Carbon emissions are is the carbon emission of the diesel generator of type n at time t; are the carbon emissions of the gas turbine at time t; Fuel cost coefficient of diesel generator; κ gtare the unit carbon emission coefficients of gas turbines respectively.

[0125] In step ②, when constructing the lower-level operation optimization model, the lower-level model uses the scheduling cycle (24 hours) as the time scale to optimize the daily operation strategy and construct the lower-level operation optimization model:

[0126] The dual objective function for constructing the lower layer operation is

[0127] Carbon emissions are C OP is the total operation and maintenance cost of the system; G EN is the total carbon emissions of the system; is the sum of the gas emissions generated by the equipment at time t; It represents the gas emission of diesel engine type n at time t; represents the carbon dioxide emissions of GT at time t.

[0128] In step ③, when establishing coupling constraints, the two-layer model collaborative optimization is achieved:

[0129] The system energy balance constraints are set as follows:

[0130] The power balance constraint is Ensure real-time balance between power generation, energy storage and electricity load;

[0131] The thermodynamic balance constraint is Maintain a dynamic balance between waste heat recovery, heat storage and heat load;

[0132] The cooling balance constraint is Coordinate the supply and demand of refrigeration equipment, cold storage and cooling load;

[0133] in, represents the discharge power of EES at time t; represents the charging power of ESS at time t; represents the power consumption of EC at time t; represents the propulsion load of the ship at time t; It represents the ship's life electricity load at time t; represents the heat release power of TES at time t; represents the thermal storage power of TES at time t; represents the thermal power consumed by AC at time t; represents the heat load of the ship at time t; represents the cooling power of AC at time t; represents the cooling power of CES at time t, represents the cold storage power of CES at time t; represents the cooling load of the ship at time t;

[0134] Construct the capacity-output correlation model as P i (t)≤x i ·u i (t); P i (t) represents the actual output of equipment i in period t; x i represents the configuration capacity of device i (upper-level decision variable); u i (t) represents the start / stop status of device i in period t (0 / 1 variable).

[0135] The two-level optimization planning model considering carbon emissions is obtained based on the ship integrated energy system model, wherein the ship integrated energy system model is specifically:

[0136] The energy equipment in the ship energy equipment model is divided into power supply equipment, heating equipment, cooling equipment and energy storage equipment; the power supply equipment includes diesel generator sets, gas turbines in CCHP units and solar photovoltaic power generation; the heating equipment is the waste heat recovery device in the CCHP unit; the cooling equipment includes electric refrigerators and absorption refrigerators in CCHP units; the energy storage equipment includes marine batteries, marine heat storage tanks and marine cold storage tank models.

[0137] The output model and constraints of the diesel generator set are:

[0138] Establishing a diesel generator output model is the output power of DG at time t; k b is the diesel combustion efficiency, generally 40%; η is the diesel engine power generation efficiency; h f is the lower calorific value of diesel, generally taken as 49kJ / g; F0 is the no-load fuel consumption of DG unit power; F is the fuel consumption of DG.

[0139] Establishing the diesel generator set DG model is the output power of DG type n at time t; represents the fuel cost coefficient of DG; The switch variable representing the DG type n, 1 means DG is on, 0 means DG is off;

[0140] Construct a DG cost model that covers annual investment cost, maintenance cost, fuel cost, and unit start-up and shutdown cost, as follows:

[0141] The annual investment cost is

[0142] Maintenance cost is

[0143] Fuel cost is

[0144] The unit start-up and shutdown cost is

[0145] Among them, C in,dg is the investment cost of DG; n represents the model of diesel generator; P dg,n is the rated power of DG; μ dg,n is the unit investment cost of DG; T n Indicates the lifespan of DG of type n; is the maintenance cost of DG at time t; is the output power of DG type n at time t; dg,n is the unit maintenance cost of DG; represents the fuel cost required by DG type n at time t; represents the fuel cost coefficient of DG; The switch variable representing the DG type n, 1 means DG is on, 0 means DG is off; is the start-stop cost of DG at time t; and are the start and stop status of the DG of type n at time t and time t-1, respectively, 1 means on and 0 means off; is the unit start-stop cost of DG;

[0146] Set operational constraints, including:

[0147] ① Set the output power constraint to It is necessary to ensure that the output power of the diesel generator set is within the allowable range to avoid overload or underload operation;

[0148] ②Set the climbing rate constraint to Limit the power variation of diesel generator sets per unit time to ensure smooth regulation; Indicates the upper and lower limits of the output power of DG type n; Indicates the upper and lower limits of the climbing of DG type n;

[0149] ③Set the minimum continuous running time constraint to Prevent diesel generator sets from frequent starts and stops and meet the minimum continuous operation time requirement;

[0150] ④ Set the minimum downtime constraint to Ensure that the diesel generator set needs enough time to cool down before it can be restarted after shutdown; where, They represent the startup time and shutdown time of DG type n respectively; They represent the minimum contact startup and downtime of DG type n, respectively.

[0151] The output model and constraints of the gas turbine (GT) are:

[0152] The electric power output model is established as Calculate the electrical power output of the gas turbine at time t, which is proportional to the natural gas consumption and power generation efficiency;

[0153] The thermal power output model is established as Calculate the thermal power output of the gas turbine based on the electrical power output, reflecting the cogeneration characteristics; is the output power of GT at time t; is the amount of natural gas consumed by GT at time t; H r Indicates the calorific value of natural gas; represents the gas-to-electricity efficiency of GT; is the thermal output power of GT; is the ratio of the thermal output power to the electrical power of the GT;

[0154] The cost model for building GT is as follows:

[0155] Construct the annual investment cost model as Calculate the average annual investment cost of GT equipment, using the equal annuity method to consider the time value of money;

[0156] The operation and maintenance cost model is constructed as Calculate the operation and maintenance cost of GT at time t, which is proportional to the current output power;

[0157] Construct the fuel cost model as Calculate the natural gas fuel consumption cost of GT at time t based on the actual natural gas consumption; C in,gt represents the investment cost of GT; μ gt represents the unit investment cost of GT; P gt Indicates the rated electric power of GT; R gt Indicates the installed capacity of GT; represents the maintenance cost of GT at time t; represents the output power of GT at time t; λ gt represents the unit maintenance cost of GT, is the fuel cost of natural gas consumed by GT at time t; is the unit natural gas consumption cost.

[0158] The upper and lower limits of GT output are constructed as follows: Indicates the upper and lower limits of the GT output power.

[0159] The output model and constraints of the photovoltaic power generation PV model are as follows:

[0160] The PV output model is constructed as is the output power of PV at time t; P pv is the installed capacity of PV; G t , G stc is the light intensity of PV in actual environment and standard working conditions; β c Indicates the PV power temperature coefficient; Indicates the actual ambient temperature of the solar photovoltaic panel; T c-ref is the surface temperature of the solar photovoltaic panel under standard conditions.

[0161] The cost model for building a photovoltaic power generation system (PV) is as follows:

[0162] Construct the annual investment cost model as Calculate the average annual investment cost of the photovoltaic system and convert it into an equivalent annuity using the capital recovery factor method;

[0163] The operation and maintenance cost model is constructed as Calculate the operation and maintenance cost of the photovoltaic system in period i, which is linearly related to the actual output power of the system; C in,pv is the investment cost of PV; μ pv is the unit investment cost of PV; R pv is the installed capacity of PV; T pv is the life of PV; is the maintenance cost of PV at time t; is the output power of PV at time t; pv is the unit maintenance cost of PV.

[0164] When the PV is working in the maximum power tracking mode, the power consumption during the period t cannot exceed the maximum output power during the period, that is, Indicates the maximum output power of the PV.

[0165] The output model of the waste heat recovery device is: is the heating power of HB at time t; is the waste heat recovery efficiency of HB;

[0166] A cost model for waste heat recovery (HB) in a ship's integrated energy system is constructed. This model reflects the fact that ship heating mainly relies on HB:

[0167] Construct the annual investment cost model as Based on the capacity of the heat recovery system, the capital recovery factor method is used to calculate the average annual investment cost over the entire life cycle;

[0168] The annual investment cost model is constructed as Based on the capacity of the heat recovery system, the capital recovery factor method is used to calculate the average annual investment cost over the entire life cycle; C in,hb represents the investment cost of HB; η hb represents the unit investment cost of HB; R hb Indicates the installed capacity of HB; T hb is the life span of HB; represents the maintenance cost of HB at time t; represents the output power of HB at time t; λ hb Represents the unit maintenance cost of HB.

[0169] The upper and lower output constraints of HB are constructed as follows: Indicates the upper limit of HB output power.

[0170] The output model of the absorption chiller (AC) is COP AC It represents the ratio of output cooling power to input heating power and is defined as the thermal coefficient; They are AC output cooling power and input heating power respectively.

[0171] Construct the annual investment cost model as Use the capital recovery factor method to calculate the average annual investment cost of AC equipment, taking into account the time value of money and the equipment life cycle.

[0172] The operation and maintenance cost model is constructed as Calculate the maintenance cost of AC equipment based on real-time output power, reflecting operating losses; C in,ac represents the investment cost of AC; μ ac represents the unit investment cost of AC; Q ac Indicates the rated power of AC; T ac Indicates the life of AC; represents the maintenance cost of AC at time t; represents the output power of AC at time t; λ ac represents the unit maintenance cost of AC;

[0173] Set the upper and lower limits of AC output to Indicates the upper limit of the AC output power.

[0174] The output model of the electric cooling (EC) is is the EC cooling power during period t; is the electric power consumed by EC during period t; is the cooling efficiency of EC.

[0175] Constructing an EC cost model mainly includes:

[0176] Construct the annual investment cost model as The capital recovery factor method is used to calculate the average annual investment cost of EC equipment, based on the installed capacity and the full life cycle;

[0177] The operation and maintenance cost model is constructed as Calculate the maintenance cost of EC equipment based on real-time cooling power, reflecting the correlation of operating load; C in,ec represents the investment cost of EC; μ ec Represents the unit investment cost of EC; R ec represents the installed capacity of EC; T ec represents the lifespan of EC; represents the maintenance cost of EC at time t; represents the output power of EC at time t; λ ec Represents the unit maintenance cost of EC.

[0178] Set the upper and lower limits of EC output as Indicates the upper limit of the EC output power.

[0179] For the energy storage model, a unified model suitable for three types of energy storage devices is constructed as SOC t is the state of charge of ES during period t; SOC t-1 is the state of charge of ES at time t-1; are the charge and discharge power of ES in period t respectively; η cha ES charging efficiency; η dis is the energy release efficiency of ES; k is the self-consumption rate of ES;

[0180] The cost model for building ES is as follows:

[0181] Construct the annual investment cost model as Adopt the dynamic capacity-life conversion method and make differentiated calculations based on energy storage types;

[0182] The operation and maintenance cost model is constructed as z∈{EES, TES, CES}, a linear calculation model based on real-time output power; C in,es represents the investment cost of type z in ES; μ es,z represents the unit investment cost of type z in ES; R es Indicates the installed capacity of type z in ES; T es,z Represents the lifetime of type z in ES; represents the maintenance cost of type z in ES at time t; Output power of type z in ES at time t; λ es,z The maintenance cost of a unit of type z in ES at time t.

[0183] Set the upper and lower limits of ES output to Indicates the upper limit of ES output power.

[0184] Considering the direct impact of ship navigation conditions on energy demand, an accurate ship navigation model needs to be constructed.

[0185] In ship voyage analysis, the relationship between sailing distance and sailing speed is established as D t =D t-1 +v t Δt, D t D is the distance the ship travels at time t; t is the distance the ship travels at time t-1; v t Indicates the speed of the ship at time t. s represents the distance from the initial port to the next port; τ m Indicates the allowable error in the distance between ports.

[0186] When a ship is sailing at sea, speed constraints are set to ensure that the sailing speed is between the specified upper and lower limits. The specific constraints are: Indicates the maximum and minimum speeds of a ship.

[0187] The propulsion load required by the ship at different times can be calculated using the following formula based on the ship's sailing speed. Different ship speeds correspond to different propulsion powers. c1, c2 are corresponding coefficients, generally taken as 0.003, 3; is the propulsion power of the ship at time t; different propulsion power formulas corresponding to different ship speeds It can be seen that the speed of the ship can represent different navigation states, such as v t =0, the propulsion load of the ship is 0, and the ship is in a moored state.

[0188] Compared with the prior art, the present invention has the following technical effects:

[0189] 1) This paper proposes a joint planning method for SMES based on a two-layer optimization framework. By comprehensively optimizing the coupling relationship between cooling, heating, and electronic systems, it significantly improves energy efficiency and economy. At the same time, it introduces carbon emission targets to enhance the environmental benefits of the system, providing an effective solution for the low-carbon planning of multi-energy systems on ships.

[0190] 2) This paper considers the tightly coupled multi-energy system of SMES and establishes its output model and energy conversion model, solving the problem of fragmented optimization of multi-energy systems in traditional planning and providing accurate mathematical model support for collaborative planning;

[0191] 3) This paper establishes a two-layer joint planning model, in which the upper layer minimizes total cost as the objective function, and the lower layer minimizes total operation and maintenance cost and carbon emissions as the goal. Compared with traditional single-layer optimization, the two-layer model achieves the coordination of long-term planning and short-term scheduling, reduces total cost and significantly reduces carbon emissions, and takes into account both economic and environmental benefits.

[0192] 4) The present invention converts the model into a mixed integer linear model for solution, which solves the problem of difficulty in solving nonlinear models in traditional planning, improves computational efficiency, and is suitable for practical engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0193] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0194] Figure 1 Schematic diagram of the SMES system structure in the present invention;

[0195] Figure 2 This is a diagram of a classic ship's navigation pattern;

[0196] Figure 3 This is the flow chart of the double-layer optimization MILP solution for ship SMES in the present invention;

[0197] Figure 4 This is the navigation route map between Port A in Liaoning Province and Port B in Shandong Province.

[0198] Figure 5 This is a map of solar irradiance and outdoor temperature in the navigation area.

[0199] Figure 6 This is the ship's cooling, heating and electricity load demand diagram. DETAILED DESCRIPTION

[0200] A SMES joint planning method considering carbon emissions includes the following steps:

[0201] Step 1: Based on the multi-energy coupling principle, a ship integrated energy system model is constructed. The model includes an AC / DC hybrid ship power system architecture model, a ship energy equipment model, and a ship navigation model.

[0202] Step 2: Use the ship integrated energy system model obtained in Step 1 to establish a two-level optimization planning model that considers carbon emissions. The upper level of the model optimizes equipment configuration with the goal of minimizing total investment cost, while the lower level optimizes equipment scheduling strategy with the dual objectives of operating cost and carbon emissions.

[0203] Step 3: Solve the two-level optimization planning model considering carbon emissions obtained in Step 2 based on the mixed integer linear programming method, and use the CPLEX solver to achieve collaborative optimization of the system's optimal capacity configuration and operation strategy.

[0204] The energy equipment in the ship energy equipment model is divided into power supply equipment, heating equipment, cooling equipment and energy storage equipment; the power supply equipment includes diesel generator sets, gas turbines in CCHP units and solar photovoltaic power generation; the heating equipment is the waste heat recovery device in the CCHP unit; the cooling equipment includes electric refrigerators and absorption refrigerators in CCHP units; the energy storage equipment includes marine batteries, marine heat storage tanks and marine cold storage tank models.

[0205] The output model and constraints of the diesel generator set are:

[0206] Establishing a diesel generator output model is the output power of DG at time t; k b is the diesel combustion efficiency, generally 40%; η is the diesel engine power generation efficiency; h f is the lower calorific value of diesel, generally taken as 49kJ / g; F0 is the no-load fuel consumption of DG unit power; F is the fuel consumption of DG.

[0207] Establishing the diesel generator set DG model is the output power of DG type n at time t; represents the fuel cost coefficient of DG; The switch variable representing the DG type n, 1 means DG is on, 0 means DG is off;

[0208] Construct a DG cost model that covers annual investment cost, maintenance cost, fuel cost, and unit start-up and shutdown cost, as follows:

[0209] The annual investment cost is

[0210] Maintenance cost is

[0211] Fuel cost is

[0212] The unit start-up and shutdown cost is

[0213] Among them, C in,dg is the investment cost of DG; n represents the model of diesel generator; P dg,n is the rated power of DG; μ dg,n is the unit investment cost of DG; T n Indicates the lifespan of DG of type n; is the maintenance cost of DG at time t; is the output power of DG type n at time t; dg,n is the unit maintenance cost of DG; represents the fuel cost required by DG type n at time t; represents the fuel cost coefficient of DG; The switch variable representing the DG type n, 1 means DG is on, 0 means DG is off; is the start-stop cost of DG at time t; and are the start and stop status of the DG of type n at time t and time t-1, respectively, 1 means on and 0 means off; is the unit start-stop cost of DG;

[0214] Set operational constraints, including:

[0215] ① Set the output power constraint to It is necessary to ensure that the output power of the diesel generator set is within the allowable range to avoid overload or underload operation;

[0216] ②Set the climbing rate constraint to Limit the power variation of diesel generator sets per unit time to ensure smooth regulation; Indicates the upper and lower limits of the output power of DG type n; Indicates the upper and lower limits of the climbing of DG type n;

[0217] ③Set the minimum continuous running time constraint to Prevent diesel generator sets from frequent starts and stops and meet the minimum continuous operation time requirement;

[0218] ④ Set the minimum downtime constraint to Ensure that the diesel generator set needs enough time to cool down before it can be restarted after shutdown; where, They represent the startup time and shutdown time of DG type n respectively; They represent the minimum contact startup and downtime of DG type n, respectively.

[0219] The output model and constraints of the gas turbine (GT) are:

[0220] The electric power output model is established as Calculate the electrical power output of the gas turbine at time t, which is proportional to the natural gas consumption and power generation efficiency;

[0221] The thermal power output model is established as Calculate the thermal power output of the gas turbine based on the electrical power output, reflecting the cogeneration characteristics; is the output power of GT at time t; is the amount of natural gas consumed by GT at time t; H r Indicates the calorific value of natural gas; represents the gas-to-electricity efficiency of GT; is the thermal output power of GT; is the ratio of the thermal output power to the electrical power of the GT;

[0222] The cost model for building GT is as follows:

[0223] Construct the annual investment cost model as Calculate the average annual investment cost of GT equipment, using the equal annuity method to consider the time value of money;

[0224] The operation and maintenance cost model is constructed as Calculate the operation and maintenance cost of GT at time t, which is proportional to the current output power;

[0225] Construct the fuel cost model as Calculate the natural gas fuel consumption cost of GT at time t based on the actual natural gas consumption; C in,gt represents the investment cost of GT; μ gt represents the unit investment cost of GT; P gt Indicates the rated electric power of GT; R gt Indicates the installed capacity of GT; represents the maintenance cost of GT at time t; represents the output power of GT at time t; λ gt represents the unit maintenance cost of GT, is the fuel cost of natural gas consumed by GT at time t; is the unit natural gas consumption cost.

[0226] The upper and lower limits of GT output are constructed as follows: Indicates the upper and lower limits of the GT output power.

[0227] The output model and constraints of the photovoltaic power generation PV model are as follows:

[0228] The PV output model is constructed as is the output power of PV at time t; P pv is the installed capacity of PV; G t , G stc is the light intensity of PV in actual environment and standard working conditions; β c Indicates the PV power temperature coefficient; T ct Indicates the actual ambient temperature of the solar photovoltaic panel; T c-ref is the surface temperature of the solar photovoltaic panel under standard conditions.

[0229] The cost model for building a photovoltaic power generation system (PV) is as follows:

[0230] Construct the annual investment cost model as Calculate the average annual investment cost of the photovoltaic system and convert it into an equivalent annuity using the capital recovery factor method;

[0231] The operation and maintenance cost model is constructed as Calculate the operation and maintenance cost of the photovoltaic system in period i, which is linearly related to the actual output power of the system; C in,pv is the investment cost of PV; μ pv is the unit investment cost of PV; R pv is the installed capacity of PV; T pv is the life of PV; is the maintenance cost of PV at time t; is the output power of PV at time t; pv is the unit maintenance cost of PV.

[0232] When the PV is working in the maximum power tracking mode, the power consumption during the period t cannot exceed the maximum output power during the period, that is, Indicates the maximum output power of the PV.

[0233] The output model of the waste heat recovery device is: is the heating power of HB at time t; is the waste heat recovery efficiency of HB;

[0234] A cost model for waste heat recovery (HB) in a ship's integrated energy system is constructed. This model reflects the fact that ship heating mainly relies on HB:

[0235] Construct the annual investment cost model as Based on the capacity of the heat recovery system, the capital recovery factor method is used to calculate the average annual investment cost over the entire life cycle;

[0236] The annual investment cost model is constructed as Based on the capacity of the heat recovery system, the capital recovery factor method is used to calculate the average annual investment cost over the entire life cycle; C in,hb represents the investment cost of HB; η hb represents the unit investment cost of HB; R hb Indicates the installed capacity of HB; T hb is the life span of HB; represents the maintenance cost of HB at time t; represents the output power of HB at time t; λ hb Represents the unit maintenance cost of HB.

[0237] The upper and lower output constraints of HB are constructed as follows: Indicates the upper limit of HB output power.

[0238] The output model of the absorption chiller (AC) is COP AC It represents the ratio of output cooling power to input heating power and is defined as the thermal coefficient; They are AC output cooling power and input heating power respectively.

[0239] Construct the annual investment cost model as Use the capital recovery factor method to calculate the average annual investment cost of AC equipment, taking into account the time value of money and the equipment life cycle.

[0240] The operation and maintenance cost model is constructed as Calculate the maintenance cost of AC equipment based on real-time output power, reflecting operating losses; C in,ac represents the investment cost of AC; μ ac represents the unit investment cost of AC; Q ac Indicates the rated power of AC; T ac Indicates the life of AC; represents the maintenance cost of AC at time t; represents the output power of AC at time t; λ ac represents the unit maintenance cost of AC;

[0241] Set the upper and lower limits of AC output to Indicates the upper limit of the AC output power.

[0242] The output model of the electric cooling (EC) is is the EC cooling power during period t; is the electric power consumed by EC during period t; is the cooling efficiency of EC.

[0243] Constructing an EC cost model mainly includes:

[0244] Construct the annual investment cost model as The capital recovery factor method is used to calculate the average annual investment cost of EC equipment, based on the installed capacity and the full life cycle;

[0245] The operation and maintenance cost model is constructed as Calculate the maintenance cost of EC equipment based on real-time cooling power, reflecting the correlation of operating load; C in,ec represents the investment cost of EC; η ec Represents the unit investment cost of EC; R ec represents the installed capacity of EC; T ec represents the lifespan of EC; represents the maintenance cost of EC at time t; represents the output power of EC at time t; λ ec Represents the unit maintenance cost of EC.

[0246] Set the upper and lower limits of EC output as Indicates the upper limit of the EC output power.

[0247] For the energy storage model, a unified model suitable for three types of energy storage devices is constructed as SOC t is the state of charge of ES during period t; SOC t-1 is the state of charge of ES at time t-1; are the charge and discharge power of ES in period t respectively; η cha ES charging efficiency; η dis is the energy release efficiency of ES; k is the self-consumption rate of ES;

[0248] The cost model for building ES is as follows:

[0249] Construct the annual investment cost model as Adopt the dynamic capacity-life conversion method and make differentiated calculations based on energy storage types;

[0250] The operation and maintenance cost model is constructed as z∈{EES, TES, CES}, a linear calculation model based on real-time output power; C in,es represents the investment cost of type z in ES; μ es,z represents the unit investment cost of type z in ES; P es Indicates the installed capacity of type z in ES; T es,z Represents the lifetime of type z in ES; represents the maintenance cost of type z in ES at time t; Output power of type z in ES at time t; λ es,z The maintenance cost of a unit of type z in ES at time t.

[0251] Set the upper and lower limits of ES output to Indicates the upper limit of ES output power.

[0252] Considering the direct impact of ship navigation conditions on energy demand, an accurate ship navigation model needs to be constructed.

[0253] In ship voyage analysis, the relationship between sailing distance and sailing speed is established as D t =D t-1 +v t Δt, D t D is the distance the ship travels at time t; t is the distance the ship travels at time t-1; v t Indicates the speed of the ship at time t. s represents the distance from the initial port to the next port; τ m Indicates the allowable error in the distance between ports.

[0254] When a ship is sailing at sea, speed constraints are set to ensure that the sailing speed is between the specified upper and lower limits. The specific constraints are: Indicates the maximum and minimum speeds of a ship.

[0255] The propulsion load required by the ship at different times can be calculated using the following formula based on the ship's sailing speed. Different ship speeds correspond to different propulsion powers. c1, c2 are corresponding coefficients, generally taken as 0.003, 3; is the propulsion power of the ship at time t; different propulsion power formulas corresponding to different ship speeds It can be seen that the speed of the ship can represent different navigation states, such as v t =0, the propulsion load of the ship is 0, and the ship is in a moored state.

[0256] In step 2, after completing the basic modeling of the ship's integrated energy system, a scientific optimization framework needs to be constructed to achieve a balance between economy and environmental protection. This step adopts a two-layer optimization structure to decompose the complex planning problem into two levels, mainly including the following steps:

[0257] Step ① Construct an upper-level investment decision optimization model;

[0258] Step ②: Build the lower-level operation optimization model;

[0259] Step ③: Establish coupling constraints;

[0260] When constructing the upper-level investment decision optimization model, the planning period (20 years) is used as the time scale to solve the equipment capacity configuration problem. The upper-level investment decision optimization model is constructed as follows:

[0261] The upper investment decision optimization model serves as the long-term planning layer, focusing on solving the equipment capacity configuration problem. Its objective function is expressed as C P is the total cost of the system; C IN is the annual investment cost of the system; is the maintenance cost of the system during period t; is the fuel cost of diesel and natural gas required for the system during period t; is the system's unit start-up and shutdown cost at time t; is the shore power cost of the system purchased electricity during period t; x D represents the number of sailing days of a ship in a year; Δt represents the scheduling period of the ship;

[0262] Among them, the annual investment cost is C IN =C in,pv +C in,dg +C in,cchp +C in,ec +C in,es , C in,cchp =C in,gt +C in,hb +C in,ac The maintenance cost is C om,cchp =C om,gt +C om,hb +C om,ac The cost of shore power is Carbon emissions are is the carbon emission of the diesel generator of type n at time t; are the carbon emissions of the gas turbine at time t; Fuel cost coefficient of diesel generator; κ gt are the unit carbon emission coefficients of gas turbines respectively.

[0263] In step ②, when constructing the lower-level operation optimization model, the lower-level model uses the scheduling cycle (24 hours) as the time scale to optimize the daily operation strategy and construct the lower-level operation optimization model:

[0264] The dual objective function for constructing the lower layer operation is

[0265] Carbon emissions are C OP is the total operation and maintenance cost of the system; G EN is the total carbon emissions of the system; is the sum of the gas emissions generated by the equipment at time t; It represents the gas emission of diesel engine type n at time t; represents the carbon dioxide emissions of GT at time t.

[0266] In step ③, when establishing coupling constraints, the two-layer model collaborative optimization is achieved:

[0267] The system energy balance constraints are set as follows:

[0268] The power balance constraint is Ensure real-time balance between power generation, energy storage and electricity load;

[0269] The thermodynamic balance constraint is Maintain a dynamic balance between waste heat recovery, heat storage and heat load;

[0270] The cooling balance constraint is Coordinate the supply and demand of refrigeration equipment, cold storage and cooling load;

[0271] in, represents the discharge power of EES at time t; represents the charging power of ESS at time t; represents the power consumption of EC at time t; represents the propulsion load of the ship at time t; It represents the ship's life electricity load at time t; represents the heat release power of TES at time t; represents the thermal storage power of TES at time t; represents the thermal power consumed by AC at time t; represents the heat load of the ship at time t; represents the cooling power of AC at time t; represents the cooling power of CES at time t, represents the cold storage power of CES at time t; represents the cooling load of the ship at time t;

[0272] Construct the capacity-output correlation model as P i (t)≤x i ·u i (t); P i (t) represents the actual output of equipment i in period t; x i represents the configuration capacity of device i (upper-level decision variable); u i (t) represents the start / stop status of device i in period t (0 / 1 variable).

[0273] In step 3, when using the mixed integer linear programming method to solve the two-level optimization planning model considering carbon emissions, there are nonlinear equations. The specific solution includes the following steps:

[0274] Step 3.1: Linearize the nonlinear equation;

[0275] Step 3.2: Construct a mixed integer programming model;

[0276] Step 3.3: Use the solution algorithm to optimize the model.

[0277] In step 3.1, the speed-propulsion power relationship is converted and the fuel cost formula is corrected using piecewise linearization of nonlinear terms. Unified model formula applicable to three types of energy storage devices Formulas for different propulsion powers corresponding to the same ship speed And the dual objective function formula of the lower layer operation The nonlinear terms in are piecewise linearized.

[0278] When performing piecewise linearization, the following steps are taken:

[0279] Step ① Approximate the nonlinear function by piecewise linear combination:

[0280] Step ② is to calculate the slope.

[0281] Step ③ is to calculate the intercept:

[0282] Step ④ Set the segment interval activation constraint to

[0283] Step ⑤ Set the unique constraint to

[0284] in, and is the origin and the approximate linearization function; m is the index of the block; N R is the total number of blocks; k m and l m are the slope and intercept of the linear function; x m is the starting point of segment m; is the binary state (0-1 variable) of the mth segment.

[0285] Cost of starting and stopping the unit The formula line is personalized and the maximum function is separated into two separate parts, and we get

[0286] Formula for minimum continuous running time constraint Perform linearization:

[0287] Initial state processing, the remaining minimum running time at the beginning of the scheduling cycle is compensated and calculated to obtain

[0288] Set the continuous operation constraint to

[0289] Perform terminal period processing to obtain

[0290] Where w is the cycle index marking the on / off time of the generator, is the DG opening time before scheduling; G n It is the start time of the generator reset at the beginning of ship operation.

[0291] Formula for minimum downtime constraint Perform linearization:

[0292] Initial state compensation, calculate the number of downtime periods that need to be supplemented in the initial scheduling period, and get

[0293] Set the main runtime constraints to

[0294] Set the terminal constraint to

[0295] in, They represent the startup time and shutdown time of DG type n before operation; L n This is the shutdown time when the generator is reset to zero at the start of ship operation.

[0296] In step 3, after linearization, the ship coordinated operation model becomes a MILP problem, which can be effectively solved by industrially proven efficient solvers such as CPLEX. The specific process is shown in the attached figure. Figure 3 As shown, a brief description is as follows:

[0297] Step 1: Input equipment parameters, constraints, cooling, heating and electricity loads and other information;

[0298] Step ② generates a set of candidate equipment capacity configuration solutions;

[0299] Step 3: Calculate the annual equipment investment cost corresponding to each configuration plan;

[0300] Step ④ brings the upper-layer configuration plan into the lower-layer operation model;

[0301] Step 5: Establish operation and maintenance models for various types of equipment and consider operation constraints;

[0302] Step 6: Use the CPLEX solver to solve the minimum operating cost and the minimum carbon emissions respectively;

[0303] Step 7: Obtain the Pareto non-inferior solution set of multi-objective optimization and select the comprehensive optimal solution using the normalization method;

[0304] Step ⑧ Add the operating cost and investment cost to obtain the total annual cost;

[0305] Step 9: Determine whether the current solution is the minimum annual total cost. If not, iterate;

[0306] Step ⑩ outputs the final optimal equipment configuration plan and operation results.

[0307] The present invention also includes a method for establishing a two-level optimization planning model considering carbon emissions, comprising the following steps:

[0308] Step ① Construct an upper-level investment decision optimization model;

[0309] Step ②: Build the lower-level operation optimization model;

[0310] Step ③: Establish coupling constraints;

[0311] This planning model, in which the upper layer takes minimizing total cost as the objective function, and the lower layer takes minimizing total operation and maintenance cost and carbon emissions as the goal, achieves the coordination of long-term planning and short-term scheduling compared to traditional single-layer optimization, reduces total cost, significantly reduces carbon emissions, and takes into account both economic and environmental performance.

[0312] When constructing the upper-level investment decision optimization model, the planning period (20 years) is used as the time scale to solve the equipment capacity configuration problem. The upper-level investment decision optimization model is constructed as follows:

[0313] The upper investment decision optimization model serves as the long-term planning layer, focusing on solving the equipment capacity configuration problem. Its objective function is expressed as C P is the total cost of the system; C IN is the annual investment cost of the system; is the maintenance cost of the system during period t; is the fuel cost of diesel and natural gas required for the system during period t; is the system's unit start-up and shutdown cost at time t; is the shore power cost of the system purchased electricity during period t; x D represents the number of sailing days of a ship in a year; Δt represents the scheduling period of the ship;

[0314] Among them, the annual investment cost is C IN =C in,pv +C in,dg +C in,cchp +C in,ec +C in,es , C in,cchp =C in,gt +C in,hb +C in,ac The maintenance cost is Com,cchp =C om,gt +C om,hb +C om,ac The cost of shore power is Carbon emissions are is the carbon emission of the diesel generator of type n at time t; are the carbon emissions of the gas turbine at time t; Fuel cost coefficient of diesel generator; κ gt are the unit carbon emission coefficients of gas turbines respectively.

[0315] In step ②, when constructing the lower-level operation optimization model, the lower-level model uses the scheduling cycle (24 hours) as the time scale to optimize the daily operation strategy and construct the lower-level operation optimization model:

[0316] The dual objective function for constructing the lower layer operation is

[0317] Carbon emissions are C OP is the total operation and maintenance cost of the system; G EN is the total carbon emissions of the system; is the sum of the gas emissions generated by the equipment at time t; It represents the gas emission of diesel engine type n at time t; represents the carbon dioxide emissions of GT at time t.

[0318] In step ③, when establishing coupling constraints, the two-layer model collaborative optimization is achieved:

[0319] The system energy balance constraints are set as follows:

[0320] The power balance constraint is Ensure real-time balance between power generation, energy storage and electricity load;

[0321] The thermodynamic balance constraint is Maintain a dynamic balance between waste heat recovery, heat storage and heat load;

[0322] The cooling balance constraint is Coordinate the supply and demand of refrigeration equipment, cold storage and cooling load;

[0323] in, represents the discharge power of EES at time t; represents the charging power of ESS at time t; represents the power consumption of EC at time t; represents the propulsion load of the ship at time t; It represents the ship's life electricity load at time t; represents the heat release power of TES at time t; represents the thermal storage power of TES at time t; represents the thermal power consumed by AC at time t; represents the heat load of the ship at time t; represents the cooling power of AC at time t; represents the cooling power of CES at time t, represents the cold storage power of CES at time t; represents the cooling load of the ship at time t;

[0324] Construct the capacity-output correlation model as P i (t)≤x i ·u i (t); P i (t) represents the actual output of equipment i in period t; x i represents the configuration capacity of device i (upper-level decision variable); u i (t) represents the start / stop status of device i in period t (0 / 1 variable).

[0325] The two-level optimization planning model considering carbon emissions is based on the ship integrated energy system model, wherein the ship integrated energy system model is specifically:

[0326] The energy equipment in the ship energy equipment model is divided into power supply equipment, heating equipment, cooling equipment and energy storage equipment; the power supply equipment includes diesel generator sets, gas turbines in CCHP units and solar photovoltaic power generation; the heating equipment is the waste heat recovery device in the CCHP unit; the cooling equipment includes electric refrigerators and absorption refrigerators in CCHP units; the energy storage equipment includes marine batteries, marine heat storage tanks and marine cold storage tank models.

[0327] The output model and constraints of the diesel generator set are:

[0328] Establishing a diesel generator output model is the output power of DG at time t; k b is the diesel combustion efficiency, generally 40%; η is the diesel engine power generation efficiency; h f is the lower calorific value of diesel, generally taken as 49kJ / g; F0 is the no-load fuel consumption of DG unit power; F is the fuel consumption of DG.

[0329] Establishing the diesel generator set DG model is the output power of DG type n at time t; represents the fuel cost coefficient of DG; The switch variable representing the DG type n, 1 means DG is on, 0 means DG is off;

[0330] Construct a DG cost model that covers annual investment cost, maintenance cost, fuel cost, and unit start-up and shutdown cost, as follows:

[0331] The annual investment cost is

[0332] Maintenance cost is

[0333] Fuel cost is

[0334] The unit start-up and shutdown cost is

[0335] Among them, C in,dg is the investment cost of DG; n represents the model of diesel generator; P dg,n is the rated power of DG; μ dg,n is the unit investment cost of DG; T n Indicates the lifespan of DG of type n; is the maintenance cost of DG at time t; is the output power of DG type n at time t; dg,n is the unit maintenance cost of DG; represents the fuel cost required by DG type n at time t; represents the fuel cost coefficient of DG; The switch variable representing the DG type n, 1 means DG is on, 0 means DG is off; is the start-stop cost of DG at time t; and are the start and stop status of the DG of type n at time t and time t-1, respectively, 1 means on and 0 means off; is the unit start-stop cost of DG;

[0336] Set operational constraints, including:

[0337] ① Set the output power constraint to It is necessary to ensure that the output power of the diesel generator set is within the allowable range to avoid overload or underload operation;

[0338] ②Set the climbing rate constraint to Limit the power variation of diesel generator sets per unit time to ensure smooth regulation; Indicates the upper and lower limits of the output power of DG type n; Indicates the upper and lower limits of the climbing of DG type n;

[0339] ③Set the minimum continuous running time constraint to Prevent diesel generator sets from frequent starts and stops and meet the minimum continuous operation time requirement;

[0340] ④ Set the minimum downtime constraint to Ensure that the diesel generator set needs enough time to cool down before it can be restarted after shutdown; where, They represent the startup time and shutdown time of DG type n respectively; They represent the minimum contact startup and downtime of DG type n, respectively.

[0341] The output model and constraints of the gas turbine (GT) are:

[0342] The electric power output model is established as Calculate the electrical power output of the gas turbine at time t, which is proportional to the natural gas consumption and power generation efficiency;

[0343] The thermal power output model is established as Calculate the thermal power output of the gas turbine based on the electrical power output, reflecting the cogeneration characteristics; is the output power of GT at time t; is the amount of natural gas consumed by GT at time t; H r Indicates the calorific value of natural gas; represents the gas-to-electricity efficiency of GT; is the thermal output power of GT; is the ratio of the thermal output power to the electrical power of the GT;

[0344] The cost model for building GT is as follows:

[0345] Construct the annual investment cost model as Calculate the average annual investment cost of GT equipment, using the equal annuity method to consider the time value of money;

[0346] The operation and maintenance cost model is constructed as Calculate the operation and maintenance cost of GT at time t, which is proportional to the current output power;

[0347] Construct the fuel cost model as Calculate the natural gas fuel consumption cost of GT at time t based on the actual natural gas consumption; C in,gt represents the investment cost of GT; μ gt represents the unit investment cost of GT; P gt Indicates the rated electric power of GT; R gt Indicates the installed capacity of GT; represents the maintenance cost of GT at time t; represents the output power of GT at time t; λgt represents the unit maintenance cost of GT, is the fuel cost of natural gas consumed by GT at time t; is the unit natural gas consumption cost.

[0348] The upper and lower limits of GT output are constructed as follows: Indicates the upper and lower limits of the GT output power.

[0349] The output model and constraints of the photovoltaic power generation PV model are as follows:

[0350] The PV output model is constructed as is the output power of PV at time t; P pv is the installed capacity of PV; G t , G stc is the light intensity of PV in actual environment and standard working conditions; β c Indicates the PV power temperature coefficient; Indicates the actual ambient temperature of the solar photovoltaic panel; T c-ref is the surface temperature of the solar photovoltaic panel under standard conditions.

[0351] The cost model for building a photovoltaic power generation system (PV) is as follows:

[0352] Construct the annual investment cost model as Calculate the average annual investment cost of the photovoltaic system and convert it into an equivalent annuity using the capital recovery factor method;

[0353] The operation and maintenance cost model is constructed as Calculate the operation and maintenance cost of the photovoltaic system in period i, which is linearly related to the actual output power of the system; C in,pv is the investment cost of PV; μ pv is the unit investment cost of PV; R pv is the installed capacity of PV; T pv is the life of PV; is the maintenance cost of PV at time t; is the output power of PV at time t; pv is the unit maintenance cost of PV.

[0354] When the PV is working in the maximum power tracking mode, the power consumption during the period t cannot exceed the maximum output power during the period, that is, Indicates the maximum output power of the PV.

[0355] The output model of the waste heat recovery device is: is the heating power of HB at time t; is the waste heat recovery efficiency of HB;

[0356] A cost model for waste heat recovery (HB) in a ship's integrated energy system is constructed. This model reflects the fact that ship heating mainly relies on HB:

[0357] Construct the annual investment cost model as Based on the capacity of the heat recovery system, the capital recovery factor method is used to calculate the average annual investment cost over the entire life cycle;

[0358] The annual investment cost model is constructed as Based on the capacity of the heat recovery system, the capital recovery factor method is used to calculate the average annual investment cost over the entire life cycle; C in,hb represents the investment cost of HB; η hb represents the unit investment cost of HB; R hb Indicates the installed capacity of HB; T hb is the life span of HB; represents the maintenance cost of HB at time t; represents the output power of HB at time t; λ hb Represents the unit maintenance cost of HB.

[0359] The upper and lower output constraints of HB are constructed as follows: Indicates the upper limit of HB output power.

[0360] The output model of the absorption chiller (AC) is COP AC It represents the ratio of output cooling power to input heating power and is defined as the thermal coefficient; They are AC output cooling power and input heating power respectively.

[0361] Construct the annual investment cost model as Use the capital recovery factor method to calculate the average annual investment cost of AC equipment, taking into account the time value of money and the equipment life cycle.

[0362] The operation and maintenance cost model is constructed as Calculate the maintenance cost of AC equipment based on real-time output power, reflecting operating losses; C in,ac represents the investment cost of AC; μ ac represents the unit investment cost of AC; Q ac Indicates the rated power of AC; T ac Indicates the life of AC; represents the maintenance cost of AC at time t; represents the output power of AC at time t; λ ac represents the unit maintenance cost of AC;

[0363] Set the upper and lower limits of AC output to Indicates the upper limit of the AC output power.

[0364] The output model of the electric cooling (EC) is is the EC cooling power during period t; is the electric power consumed by EC during period t; is the cooling efficiency of EC.

[0365] Constructing an EC cost model mainly includes:

[0366] Construct the annual investment cost model as The capital recovery factor method is used to calculate the average annual investment cost of EC equipment, based on the installed capacity and the full life cycle;

[0367] The operation and maintenance cost model is constructed as Calculate the maintenance cost of EC equipment based on real-time cooling power, reflecting the correlation of operating load; C in,ec represents the investment cost of EC; μ ec Represents the unit investment cost of EC; R ec represents the installed capacity of EC; T ec represents the lifespan of EC; represents the maintenance cost of EC at time t; represents the output power of EC at time t; λ ec Represents the unit maintenance cost of EC.

[0368] Set the upper and lower limits of EC output as Indicates the upper limit of the EC output power.

[0369] For the energy storage model, a unified model suitable for three types of energy storage devices is constructed as SOC t is the state of charge of ES during period t; SOC t-1 is the state of charge of ES at time t-1; are the charge and discharge power of ES in period t respectively; η cha ES charging efficiency; η dis is the energy release efficiency of ES; k is the self-consumption rate of ES;

[0370] The cost model for building ES is as follows:

[0371] Construct the annual investment cost model as Adopt the dynamic capacity-life conversion method and make differentiated calculations based on energy storage types;

[0372] The operation and maintenance cost model is constructed as z∈{EES, TES, CES}, a linear calculation model based on real-time output power; C in,es represents the investment cost of type z in ES; μ es,z represents the unit investment cost of type z in ES; R es Indicates the installed capacity of type z in ES; T es,z Represents the lifetime of type z in ES; represents the maintenance cost of type z in ES at time t; Output power of type z in ES at time t; λ es,z The maintenance cost of a unit of type z in ES at time t.

[0373] Set the upper and lower limits of ES output to Indicates the upper limit of ES output power.

[0374] Considering the direct impact of ship navigation conditions on energy demand, an accurate ship navigation model needs to be constructed.

[0375] In ship voyage analysis, the relationship between sailing distance and sailing speed is established as D t =D t-1 +v t Δt, D t D is the distance the ship travels at time t; t is the distance the ship travels at time t-1; v t Indicates the speed of the ship at time t. s represents the distance from the initial port to the next port; τ m Indicates the allowable error in the distance between ports.

[0376] When a ship is sailing at sea, speed constraints are set to ensure that the sailing speed is between the specified upper and lower limits. The specific constraints are: Indicates the maximum and minimum speeds of a ship.

[0377] The propulsion load required by the ship at different times can be calculated using the following formula based on the ship's sailing speed. Different ship speeds correspond to different propulsion powers. c1, c2 are corresponding coefficients, generally taken as 0.003, 3; is the propulsion power of the ship at time t; different propulsion power formulas corresponding to different ship speeds It can be seen that the speed of the ship can represent different navigation states, such as v t=0, the propulsion load of the ship is 0, and the ship is in a moored state.

[0378] Example:

[0379] The present invention takes a large tourist cruise ship as the research object. The ship cruises back and forth between two ports and is mainly used for summer sightseeing tours. The total voyage is divided into 2 sections: the first section is from Port A in Liaoning Province to Port B in Shandong Province. The standard voyage is 184 nautical miles and the sailing time is 6:00-16:00 (10 hours); the second section is from Port B back to Port A, and the sailing time is 20:00-6:00 (the next day). Among them, during the period of 16:00-20:00, the ship arrives at Port B and berths, and the passengers will travel to the city near the port for 4 hours. There are a total of 48 scheduling periods (24 hours), which are scheduled every half hour. The planning period is 20 years. The sailing route and sailing distance of the ship are shown in the attached figure. Figure 4 shown.

[0380] In order to verify the superiority of this patented method, four comparison schemes are set up:

[0381] Table 1 Comparison of 4 methods

[0382]

[0383]

[0384] The planning results of the above four methods are shown in Tables 2 and 3 below.

[0385] Table 2 Comparison of planning costs

[0386]

[0387] Table 3 Comparison of equipment capacity configuration results

[0388]

[0389] Comparative analysis of method 1 and method 2:

[0390] To verify the necessity of the joint planning in this paper, the planning results of Method 1 and Method 2 in terms of equipment investment, operation and maintenance costs, fuel costs, and carbon emissions are compared, as shown in Tables 2 and 3.

[0391] As shown in Table 2, the annual investment cost for Method 2 increased by 3.6667 million RMB compared to Method 1. This is primarily due to the fact that, while the capacity allocation for DGs and ECs was reduced through joint planning, the integration of CCHP units, particularly GTs, resulted in a significant increase in initial investment. CCHP equipment has a high investment cost, but this increased cost can be offset by improved energy efficiency during later operation. Specifically, by providing both electricity and heat, CCHP units reduce the demand for diesel generators and electric chillers, thereby reducing the capacity allocation for these devices.

[0392] Compared to Method 1, Method 2 reduced operation and maintenance costs by 81,570 yuan and fuel costs by 84,660 yuan, respectively, and reduced carbon emissions by 15.84 tons. This is primarily due to the improved energy cascade utilization efficiency brought about by joint planning. The GT in the CCHP unit can provide additional power during the ship's acceleration and constant-speed navigation phases, replacing some of the load demand of the DGs, reducing dependence on DGs and effectively reducing its fuel consumption and carbon emissions. In addition, the waste heat recovery function of the CCHP also improves energy utilization efficiency, further reducing the ship's overall energy consumption and emissions.

[0393] Method 2 increased unit startup and shutdown costs by 4,820 yuan. This is because, during joint planning, GTs and DGs provide power in conjunction. During periods of low load demand, the system shuts down some diesel generators to reduce fuel consumption. However, the high number of unit startups and shutdowns results in higher startup and shutdown costs. While this cost increase is significant, reducing reliance on traditional diesel generators effectively reduces the system's long-term fuel consumption, thereby lowering operational and maintenance costs.

[0394] From a capacity perspective, joint planning reduces the system's reliance on DGs and ECs. CCHP units utilize the principle of cascaded cooling, heating, and electricity energy to effectively meet both the ship's thermal and electrical loads, further improving the system's energy efficiency. While the introduction of CCHP equipment increases investment costs, by reducing the need for other equipment, the overall system capacity configuration is optimized, reducing over-allocation of energy equipment.

[0395] In general, the joint planning approach effectively reduces diesel consumption, fuel costs, and significantly reduces operation and maintenance costs through the coordinated cooperation of GTs and DGs, thereby reducing the total cost of the overall system. It also shows obvious advantages in achieving carbon emission reductions.

[0396] Comparative analysis of method 2 and method 3:

[0397] To verify the effectiveness of the planning-running two-level optimization method in this paper, we compared the planning results of Method 2 and Method 3, as shown in Tables 2 and 3.

[0398] As shown in Table 3, compared with Method 2, the addition of EES, CES, and PV in Method 3 results in a significant increase in annual investment costs. This is because Method 3 introduces joint planning and operation optimization, making the system more flexible during navigation and able to dynamically adjust the output of various equipment. During the deceleration phase of navigation, when load demand is low, the system can combine DGs with EES for power supply and simultaneously connect to CES for load regulation. At this time, the AC and EC in the CCHP can also cooperate to provide cooling load, making full use of the bidirectional energy flow characteristics of EES and CES, improving the system's operational flexibility and energy efficiency under different load conditions, thereby enabling ships to achieve more efficient energy scheduling during navigation.

[0399] In terms of fuel costs and shore power costs, Method 3 is cheaper than Method 2, reducing them by 39,500 yuan and 331,600 yuan, respectively. Although the introduction of PV, EES, and CES equipment increases the annual investment cost, the introduction of these devices can significantly optimize the energy supply structure and reduce the operating time of DGs under non-optimal loads. By balancing the load, the system can keep the diesel engine operating in a higher fuel efficiency range, avoiding frequent starting and stopping of the diesel engine at low loads, thereby reducing fuel consumption and carbon emissions. At the same time, EES and CES can also reduce the demand for shore power, further saving costs.

[0400] Overall, Method 3 reduced total costs by 3.5728 million yuan compared to Method 2. This is because Method 3 employs a two-tiered optimization process: planning and operation. This ensures optimal system economics by precisely adjusting equipment output. While maintaining a balance between electricity, heat, and cooling, the system can adjust according to the temporal characteristics of various loads, optimizing the output strategy of each device and minimizing the total operating cost of the ship during navigation. This two-tiered optimization not only improves the overall operational efficiency of the system, but also reduces energy waste, optimizing the system's energy efficiency and economic benefits.

[0401] Comparative analysis of method three and method four:

[0402] To verify the necessity of considering carbon emissions in this paper, we compared the differences in planning results between Method 3 and Method 4, as shown in Tables 2 and 3.

[0403] As shown in Table 2, the annual investment cost of Method 4 increased by RMB 20.3498 million compared to Method 3. This is because Method 4 increases the capacity of CCHP, EES and CES by 2MW, 5MW and 10MW respectively. Although the capacity of DG3 and PV is reduced by 2.5MW and 500m3 respectively, 2 Overall, the unit investment costs of CCHP, EES, and CES are relatively high, leading to increased investment costs. Specifically, Method 4, when employing a two-tiered planning-operation optimization approach, fully considers the goal of minimizing carbon emissions, thereby increasing the capacity allocation of the EES. By increasing EES capacity, large-scale charging can be performed while the ship is docked, thereby reducing the ship's reliance on DGs and GTs during navigation. As a result, the capacity allocation of DG3 has been reduced, and the optimized system has reduced the demand for traditional diesel generators.

[0404] Compared to Method 3, Method 4 increases maintenance costs by RMB 140 and shore power costs by RMB 11,300, respectively. This change is due to the fact that Method 4 takes carbon emission targets into consideration and increases the configuration capacity of EES and CES. The increase in EES allows the system to store more electricity for subsequent use. To achieve this goal, the system needs to purchase more electricity to meet the EES charging needs, which leads to an increase in shore power costs. This increased cost is mainly due to the increased use of shore power, and this expenditure is reasonable during operation because it helps further reduce the ship's dependence on DGs and GT.

[0405] Compared to Method 3, Method 4 reduces fuel costs by 22,400 yuan and carbon emissions by 563 tons, respectively. This change stems from the fact that Method 4 reduces the system's reliance on DGs for power by increasing the capacity of CCHP, EES, and CES. During navigation, equipment like CCHP and EES can provide more clean energy, replacing diesel generators and reducing DG fuel consumption. This not only reduces the ship's fuel costs but also reduces carbon emissions during operation, significantly improving the system's environmental benefits.

[0406] In summary, although Method 4 increased its total cost by 1,048,580 yuan compared to Method 3, carbon emissions decreased by 5.63 tons due to the optimized energy structure and the introduction of clean energy equipment. This demonstrates that, despite a short-term decrease in the economic efficiency of the system after considering carbon emissions, the environmental benefits of the SMES are improved in the long term and its sustainable development potential is enhanced. This shows that, in the context of the current green transformation facing the global shipping industry, considering carbon emissions is of great significance for the optimization of ship integrated energy systems.

Claims

1. A SMES joint planning method considering carbon emissions, characterized by: The following steps are involved: Step 1: Based on the multi-energy coupling principle, a ship integrated energy system model is constructed. The model includes an AC / DC hybrid ship power system architecture model, a ship energy equipment model, and a ship navigation model. Step 2: Use the ship integrated energy system model obtained in Step 1 to establish a two-level optimization planning model that considers carbon emissions. The upper level of the model optimizes equipment configuration with the goal of minimizing total investment cost, while the lower level optimizes equipment scheduling strategy with the dual objectives of operating cost and carbon emissions. Step 3: Solve the two-level optimization planning model considering carbon emissions obtained in Step 2 based on the mixed integer linear programming method, and use the CPLEX solver to achieve collaborative optimization of the system's optimal capacity configuration and operation strategy.

2. The method according to claim 1, characterized in that The energy equipment in the ship energy equipment model is divided into power supply equipment, heating equipment, cooling equipment and energy storage equipment; the power supply equipment includes diesel generator sets, gas turbines in CCHP units and solar photovoltaic power generation; the heating equipment is the waste heat recovery device in the CCHP unit; the cooling equipment includes electric refrigerators and absorption refrigerators in CCHP units; the energy storage equipment includes marine batteries, marine heat storage tanks and marine cold storage tank models.

3. The method according to claim 2, characterized in that The output model and constraints of the diesel generator set are: Establishing a diesel generator output model is the output power of DG at time t; k b For diesel combustion efficiency; η is the diesel engine power generation efficiency; h f is the lower calorific value of diesel; F0 is the no-load fuel consumption per unit power of DG; F is the fuel consumption of DG; Establishing the diesel generator set DG model is the output power of DG type n at time t; represents the fuel cost coefficient of DG; The switch variable representing the DG type n, 1 means DG is on, 0 means DG is off; Construct a DG cost model that covers annual investment cost, maintenance cost, fuel cost, and unit start-up and shutdown cost, as follows: The annual investment cost is Maintenance cost is Fuel cost is The unit start-up and shutdown cost is Among them, C in,dg is the investment cost of DG; n represents the model of diesel generator; P dg,n is the rated power of DG; μ dg,n is the unit investment cost of DG; T n Indicates the lifespan of DG of type n; is the maintenance cost of DG at time t; is the output power of DG type n at time t; dg,n is the unit maintenance cost of DG; represents the fuel cost required by DG type n at time t; represents the fuel cost coefficient of DG; The switch variable representing the DG type n, 1 means DG is on, 0 means DG is off; is the start-stop cost of DG at time t; and are the start and stop status of the DG of type n at time t and time t-1, respectively, 1 means on and 0 means off; is the unit start-stop cost of DG; Set operational constraints, including: ① Set the output power constraint to It is necessary to ensure that the output power of the diesel generator set is within the allowable range to avoid overload or underload operation; ②Set the climbing rate constraint to Limit the power variation of diesel generator sets per unit time to ensure smooth regulation; Indicates the upper and lower limits of the output power of DG type n; Indicates the upper and lower limits of the climbing of DG type n; ③Set the minimum continuous running time constraint to Prevent diesel generator sets from frequent starts and stops and meet the minimum continuous operation time requirement; ④ Set the minimum downtime constraint to Ensure that the diesel generator set needs enough time to cool down before it can be restarted after shutdown; where, They represent the startup time and shutdown time of DG type n respectively; They represent the minimum contact startup and downtime of DG type n, respectively.

4. The method according to claim 2, characterized in that The output model and constraints of the gas turbine GT are: The electric power output model is established as Calculate the electrical power output of the gas turbine at time t, which is proportional to the natural gas consumption and power generation efficiency; The thermal power output model is established as Calculate the thermal power output of the gas turbine based on the electrical power output, reflecting the cogeneration characteristics; is the output power of GT at time t; is the amount of natural gas consumed by GT at time t; H r Indicates the calorific value of natural gas; represents the gas-to-electricity efficiency of GT; is the thermal output power of GT; is the ratio of the thermal output power to the electrical power of the GT; The cost model for building GT is as follows: Construct the annual investment cost model as Calculate the average annual investment cost of GT equipment, using the equal annuity method to consider the time value of money; Constructing the operation and maintenance cost model Calculate the operation and maintenance cost of GT at time t, which is proportional to the current output power; Construct the fuel cost model as Calculate the natural gas fuel consumption cost of GT at time t based on the actual natural gas consumption; C in,gt represents the investment cost of GT; μ gt represents the unit investment cost of GT; P gt Indicates the rated electric power of GT; R gt Indicates the installed capacity of GT; represents the maintenance cost of GT at time t; represents the output power of GT at time t; λ gt represents the unit maintenance cost of GT, is the fuel cost of natural gas consumed by GT at time t; is the unit natural gas consumption cost; The upper and lower limits of GT output are constructed as follows: Indicates the upper and lower limits of the GT output power.

5. The method according to claim 2, characterized in that The output model and constraints of the photovoltaic power generation PV model are as follows: The PV output model is constructed as is the output power of PV at time t; P pv is the installed capacity of PV; G t , G stc is the light intensity of PV in actual environment and standard working conditions; β c Indicates the PV power temperature coefficient; Indicates the actual ambient temperature of the solar photovoltaic panel; T c-ref is the surface temperature of the solar photovoltaic panel under standard conditions; The cost model for building a photovoltaic power generation system PV is as follows: Construct the annual investment cost model as Calculate the average annual investment cost of the photovoltaic system and convert it into an equivalent annuity using the capital recovery factor method; Constructing the operation and maintenance cost model Calculate the operation and maintenance cost of the photovoltaic system in period i, which is linearly related to the actual output power of the system; C in,pv is the investment cost of PV; μ pv is the unit investment cost of PV; R pv is the installed capacity of PV; T pv is the life of PV; is the maintenance cost of PV at time t; is the output power of PV at time t; pv is the unit maintenance cost of PV; When the PV is working in the maximum power tracking mode, the power consumption during the period t cannot exceed the maximum output power during the period, that is, Indicates the maximum output power of the PV.

6. The method according to claim 2, characterized in that The output model of the waste heat recovery device is: is the heating power of HB at time t; is the waste heat recovery efficiency of HB; A cost model for the waste heat recovery device HB in the ship's integrated energy system is constructed. This model reflects the characteristics that ship heating mainly relies on HB: Construct the annual investment cost model as Based on the capacity of the heat recovery system, the capital recovery factor method is used to calculate the average annual investment cost over the entire life cycle; The annual investment cost model is constructed as Based on the capacity of the heat recovery system, the capital recovery factor method is used to calculate the average annual investment cost over the entire life cycle; C in,hb represents the investment cost of HB; η hb represents the unit investment cost of HB; R hb Indicates the installed capacity of HB; T hb is the life span of HB; represents the maintenance cost of HB at time t; represents the output power of HB at time t; λ hb represents the unit maintenance cost of HB; The upper and lower output constraints of HB are constructed as follows: Indicates the upper limit of HB output power.

7. The method according to claim 2, characterized in that The output model of the absorption chiller AC is: COP AC It represents the ratio of output cooling power to input heating power and is defined as the thermal coefficient; are AC output cooling power and input heating power respectively; Construct the annual investment cost model as The capital recovery factor method is used to calculate the average annual investment cost of AC equipment, taking into account the time value of money and the equipment life cycle; Constructing the operation and maintenance cost model Calculate the maintenance cost of AC equipment based on real-time output power, reflecting operating losses; C in,ac represents the investment cost of AC; μ ac represents the unit investment cost of AC; Q ac Indicates the rated power of AC; T ac Indicates the life of AC; represents the maintenance cost of AC at time t; represents the output power of AC at time t; λ ac represents the unit maintenance cost of AC; Set the upper and lower limits of AC output to Indicates the upper limit of the AC output power.

8. The method according to claim 2, characterized in that The output model of the electric refrigerator EC is: is the EC cooling power during period t; is the electric power consumed by EC during period t; is the cooling efficiency of EC; Constructing an EC cost model mainly includes: Construct the annual investment cost model as The capital recovery factor method is used to calculate the average annual investment cost of EC equipment, based on the installed capacity and the full life cycle; Constructing the operation and maintenance cost model Calculate the maintenance cost of EC equipment based on real-time cooling power, reflecting the correlation of operating load; C in,ec represents the investment cost of EC; μ ec Represents the unit investment cost of EC; R ec represents the installed capacity of EC; T ec represents the lifespan of EC; represents the maintenance cost of EC at time t; represents the output power of EC at time t; λ ec represents the unit maintenance cost of EC; Set the upper and lower limits of EC output as Indicates the upper limit of the EC output power.

9. The method according to claim 2, characterized in that For the energy storage model, a unified model suitable for three types of energy storage devices is constructed as SOC t is the state of charge of ES during period t; SOC t-1 is the state of charge of ES at time t-1; are the charge and discharge power of ES in period t respectively; η cha ES charging efficiency; η dis is the energy release efficiency of ES; k is the self-consumption rate of ES; The cost model for building ES is as follows: Construct the annual investment cost model as Adopt the dynamic capacity-life conversion method and make differentiated calculations based on energy storage types; Constructing the operation and maintenance cost model z∈{EES, TES, CES}, a linear calculation model based on real-time output power; C in,es represents the investment cost of type z in ES; μ es,z represents the unit investment cost of type z in ES; R es Indicates the installed capacity of type z in ES; T es,z Represents the lifetime of type z in ES; represents the maintenance cost of type z in ES at time t; Output power of type z in ES at time t; λ es,z The unit maintenance cost of type z in ES at time t; Set the upper and lower limits of ES output to Indicates the upper limit of ES output power.

10. The method according to claim 1, characterized in that Considering the direct impact of ship navigation conditions on energy demand, an accurate ship navigation model needs to be constructed; In ship voyage analysis, the relationship between sailing distance and sailing speed is established as D t =D t-1 +v t Δt, D t D is the distance the ship travels at time t; t is the distance the ship travels at time t-1; v t Indicates the speed of the ship at time t; D s represents the distance from the initial port to the next port; τ m Indicates the permissible error in the distance between ports; When a ship is sailing at sea, speed constraints are set to ensure that the sailing speed is between the specified upper and lower limits. The specific constraints are: Indicates the maximum and minimum speeds of a ship; The propulsion load required by the ship at different times can be calculated using the following formula based on the ship's sailing speed. Different ship speeds correspond to different propulsion powers. c1, c2 are corresponding coefficients; is the propulsion power of the ship at time t; different propulsion power formulas corresponding to different ship speeds It can be seen that the speed of the ship can represent different navigation states, such as v t =0, the propulsion load of the ship is 0, and the ship is in a moored state.

Images