Cooperative scheduling method and device for port integrated energy system

Abstract

The invention discloses a port integrated energy system cooperative scheduling method and device, and relates to the technical field of power system optimization scheduling, and the method comprises the steps: receiving equipment operation parameters of a port logistics system and an integrated energy system and ship operation related data, the method comprises the following steps of: inputting equipment operation parameters of a port logistics system and an integrated energy system and ship operation related data into a pre-established port integrated energy system collaborative scheduling model fusing hydrogen energy and electric energy, and modeling uncertainty by utilizing a Markov chain according to a multi-stage stochastic optimization theory; based on a random dual dynamic integer programming algorithm, solving the port integrated energy system collaborative scheduling model fusing the hydrogen energy and the electric energy, and outputting to obtain an optimal collaborative scheduling result of the port integrated energy system under the multi-source uncertainty condition; and the operation flexibility and stability of the energy system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and dispatching, and in particular to a method and device for coordinated dispatching of a port integrated energy system. Background Art

[0002] As the global energy structure undergoes profound adjustments, ports, as crucial nodes connecting international trade and regional economies, face the dual pressures of energy system transformation and operational efficiency improvement. In recent years, electrified equipment such as electric container trucks, shore power facilities, and electric ships have gradually replaced traditional fuel-powered equipment, contributing to a certain degree of cleaner energy use in ports. However, the large-scale integration of electric equipment has significantly increased the demands on the power system's real-time scheduling and flexible peak-shaving capabilities. Furthermore, hydrogen, a new generation of zero-carbon energy, offers advantages such as high energy density and rapid recharge. Its demonstration application in port transportation equipment is gradually expanding, giving rise to a new energy system characterized by coupled electricity and hydrogen operation. Against this backdrop, the coordinated scheduling of port logistics systems and integrated energy systems has become increasingly prominent. In particular, given the intermittent fluctuations in output from renewable energy sources such as wind and photovoltaic power, as well as logistics-side uncertainties such as ship arrival times and reefer container temperatures, there is an urgent need to develop an integrated port scheduling method that considers the characteristics of coupled electricity and hydrogen and adapts to multiple sources of uncertainty to achieve efficient green energy utilization and stable operational execution. Summary of the Invention

[0003] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a coordinated scheduling method and device for a port integrated energy system.

[0004] In a first aspect, the purpose of the present invention can be achieved by the following technical solution: a method for coordinated scheduling of a port integrated energy system, the method comprising the following steps:

[0005] Receive equipment operating parameters of the port logistics system and integrated energy system, as well as ship operation-related data. The equipment operating parameters of the port logistics system and integrated energy system include status information of electric container trucks, hydrogen container trucks, quay cranes, yard cranes, electrolyzers, fuel cells, and hydrogen energy storage systems. The ship operation data includes estimated arrival time, loading and unloading workload, latest departure time, and initial temperature of refrigerated containers.

[0006] The equipment operating parameters of the port logistics system and integrated energy system, as well as the ship operation-related data, are input into a pre-established coordinated scheduling model of the port integrated energy system that integrates hydrogen energy and electricity. According to the multi-stage stochastic optimization theory, the uncertainty is modeled using Markov chain, and the coordinated scheduling model of the port integrated energy system that integrates hydrogen energy and electricity is solved based on the stochastic dual dynamic integer programming algorithm. The output is the optimal coordinated scheduling result of the port integrated energy system under multi-source uncertainty conditions.

[0007] In combination with the first aspect, in certain implementations of the first aspect, the method also includes: the pre-established collaborative scheduling model of the port integrated energy system that integrates hydrogen energy and electric energy is modeled based on the operating characteristics and multi-source energy demands of different types of equipment, combined with the ship arrival information, and the pre-established collaborative scheduling model of the port integrated energy system that integrates hydrogen energy and electric energy includes the coupling relationship between various logistics equipment and energy systems in the port.

[0008] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the operating characteristics and multi-source energy requirements of the different types of equipment include:

[0009] Day-ahead: berth allocation and scheduling constraints, quay crane scheduling and refrigerated container unloading constraints, and the corresponding day-ahead logistics operation cost model;

[0010] Intraday stage: yard crane and yard allocation and scheduling constraints, electric ship charging and discharging behavior constraints, electric ship energy replenishment constraints, electric container truck scheduling constraints and charge state constraints, electric container truck energy replenishment constraints, hydrogen container truck scheduling constraints and hydrogen charge state constraints, hydrogen container truck energy replenishment constraints, energy system equipment scheduling constraints, hydrogen energy equipment constraints, port integrated energy system power constraints; intraday operation constraints of the port integrated energy system, including operation constraints of gas turbines, waste heat boilers, electric energy storage systems, wind power and photovoltaic power generation, electrolyzers, fuel cells and hydrogen energy storage devices; port intraday energy system operation cost model constructed by comprehensively considering electricity prices, natural gas prices, carbon emission factors, and renewable energy abandonment costs.

[0011] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the berth allocation scheduling constraints in the day-ahead phase include:

[0012]

[0013] Where, ζ s,b,t Indicates the berthing status of the ship. If ship s is docked at berth b at time t, it is 1, otherwise it is 0; is the ship's arrival time, is the ship's berthing start time, is the end time of the ship's berthing, The latest departure time of the ship, M is a positive number;

[0014] The quay crane scheduling constraints include:

[0015]

[0016] Where, δ s,r,q,t Indicates whether the qth quay crane is responsible for loading and unloading the rth group of refrigerated containers on the sth ship at time t. If so, the value is 1, otherwise 0; and The minimum and maximum number of quay cranes that can be carried by ship No. s are respectively;

[0017] Reefer unloading operation constraints include:

[0018]

[0019] Where, T s,r,t represents the internal temperature of the rth group of refrigerated containers on ship s at time t; T t amb is the ambient temperature at time t; c s,r,t 、u s,r,t and p s,r,t are binary state variables representing the refrigerator being in constant temperature, power off, and cooling states, respectively, where c s,r,t =1 means that the refrigerated container is kept at a constant temperature by the ship's power supply system during the voyage; u s,r,t =1 means the refrigerated container has arrived at the port but has not yet been unloaded. During this period, the power outage causes the internal temperature to rise with the environment; s,r,t =1 indicates that the refrigerated container has been unloaded and connected to shore power; A is the heat exchange area of ​​the refrigerated container, k is the thermal conductivity, m is the mass of the contents of the refrigerated container, c is its specific heat capacity, and Δt is the scheduling time step; is the cooling power of the rth group of refrigerated containers on ship No. s at time t; T s and are the lower and upper safety limits of the refrigerator temperature, T s,0 is the initial temperature of the refrigerator, The upper limit of cooling power;

[0020] The expression of the day-ahead logistics operation cost model includes:

[0021]

[0022] Where c w is the berthing cost coefficient of the ship, c d is the berthing cost coefficient of the ship.

[0023] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the electric container truck scheduling constraints and hydrogen container truck scheduling constraints in the intra-day phase include:

[0024]

[0025] Where, Indicates whether the kth electric container truck is assigned to transport the rth group of refrigerated containers of ship s at time t. If so, it is 1, otherwise it is 0; γ s,r,h,t Indicates whether the hth hydrogen container truck is allocated to transport the rth group of refrigerated containers on ship s at time t. If so, it is 1, otherwise it is 0; η q Indicates the loading and unloading efficiency of the quay crane;

[0026] The scheduling constraints for the yard crane and storage yard allocation during the intra-day phase include:

[0027]

[0028] Where μ s,r,y,t Indicates whether the yth gantry crane is assigned to load and unload the rth group of refrigerated containers of ship s at time t; η y Indicates the loading and unloading efficiency of the field crane; C y,t represents the number of refrigerated containers stored in the yth storage yard at time t; The maximum number of refrigerated containers that can be accommodated in a single storage yard;

[0029] The constraints on electric ship recharging during the day include:

[0030]

[0031]

[0032] Where, Indicates the remaining power of ship s at time t; and They represent the charging power and discharging power of ship s at time t respectively; η esc and η esd Represent the charging efficiency and discharging efficiency respectively; Δt represents the time step; and are binary variables indicating whether ship s is in the charging or discharging state at time t; The upper limit of charge and discharge power; The minimum power required for ship S to leave the port;

[0033] The constraints on electric truck recharging during the day include:

[0034]

[0035] Where, represents the remaining power of the kth electric container truck at time t; and Respectively represent the charging power and discharging power at time t; η evc and η evd are charging efficiency and discharging efficiency respectively; and is a binary variable, indicating whether to charge / discharge; c k,t 、w k,t and d k,t They represent the charging, working and idle state variables of the electric container truck respectively. The three are mutually exclusive and their sum is 1; is the total number of charging piles; P is the upper limit of the charging and discharging power of the electric container truck; w With P d Respectively represent the unit energy consumption of electric container trucks in working state and idle state;

[0036] The constraints on hydrogen truck recharging during the daily period include:

[0037]

[0038]

[0039] Where, Indicates the remaining hydrogen amount in the hth hydrogen energy truck at time t; ΔF h,t Indicates the pressure of hydrogen in the hydrogen energy container at time t; c h,t 、w h,t with d h,t Represent the recharging, working and idle state variables of the hydrogen container truck respectively; is the number of hydrogen refueling stations; is the hydrogenation rate of the hth hydrogen truck at time t, is the maximum hydrogenation rate of hydrogen energy truck; η hvc is the hydrogenation efficiency; V w and V d are the hydrogen consumption rates of hydrogen trucks in working and idle states respectively; f hv is the load factor of the hydrogen energy truck; ρ(·,·) is the hydrogen density function, which depends on the hydrogen volume and temperature; F ref With T ref is the hydrogen pressure and temperature under standard operating conditions;

[0040] The energy system equipment scheduling constraints include:

[0041]

[0042] Among them, η gt,e and ηgt,h They represent the power generation efficiency and heat generation efficiency of the gas turbine, η whb is the heat recovery efficiency of the waste heat boiler; P t gt,g is the natural gas input power of the gas turbine at time t, P t gt,e With P t gt,h Represent the electrical power output and thermal power output of the gas turbine respectively; P t whb,h represents the heat power recovered by the waste heat boiler at time t; and P gt,g The upper and lower limits of gas input power. represents the state of charge of the energy storage system at time t, P t essc With P t essd are the charging power and discharging power of the energy storage system, η essc and η essd are the corresponding charge and discharge efficiencies, and P is the charge and discharge status indicator variable of the energy storage system at time t; t wd,l With P t wd They represent the abandoned power and actual generated power of wind power at time t, P t pv,l With P t pv Represent the abandoned power and actual power generation of photovoltaic power, and is the maximum available output of wind power and photovoltaic power at time t;

[0043] The hydrogen energy equipment constraints include:

[0044]

[0045] Among them, P t el represents the hydrogen production power of the electrolyzer at time t, is the hydrogen production at that moment, U el is the cell voltage, is the number of moles of electrons per mole of hydrogen, F is the Faraday constant, η el is the electrolysis efficiency of the electrolytic cell, N el is the number of electrolysis units, is the upper limit of electrolytic cell power. t fc represents the output power of the fuel cell, is the amount of hydrogen consumed by the fuel cell, N A is Avogadro's constant, is the molar mass of hydrogen, C0 is the number of electrons per coulomb, U fc is the fuel cell cell voltage, η fc is the fuel cell efficiency, The upper limit of its power. represents the hydrogen storage capacity in the hydrogen storage tank at time t, V t hssc and V t hssd are the hydrogen filling rate and hydrogen discharge rate of the hydrogen storage tank, η hssc and η hssd For the hydrogen storage tank charging and discharging efficiency, and is the hydrogen storage tank's charging and discharging state variable, The upper limit of the hydrogen filling and discharging rate of the hydrogen storage tank. represents the internal pressure of the hydrogen storage tank at time t, R is the ideal gas constant, is the pressure value in the hydrogen storage tank after standardization, is the rated reference pressure, H hss and are the lower and upper limits of the pressure ratio respectively;

[0046] The power constraints of the port integrated energy system include:

[0047]

[0048] Where, P s is the fixed load of the ship, P t ci is the total shore power supply power at time t; P t q is the total operating power of the quay crane at time t, P q P is the working power of the quay crane; t y is the total operating power of the field crane at time t, P y P is the working power of the field bridge; t es is the net power of the energy storage system at time t; P t ev is the net power of the electric container truck at time t; P t rc P is the refrigeration power of the refrigerator at time t; t ele is the power supply of the grid at time t.

[0049] The expressions for daily port operating costs include:

[0050]

[0051] f t l =Δt(c wd P t wd,l +c pv P t pv,l )

[0052] Where c r is the intraday redispatching cost coefficient, λ t ele and λ gas are the grid time-of-use electricity price and natural gas price, P t ele and V t gas are the electricity and gas purchase volumes at time t, and are the carbon dioxide emission factors of the power grid and gas grid, is the unit carbon price, c wd and c pv are the penalty coefficients for wind and solar power abandonment, P t wd,l With P t pv,l are the abandoned power of wind power and photovoltaic power at time t respectively.

[0053] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the pre-established coordinated scheduling model of the port integrated energy system integrating hydrogen energy and electric energy is as follows:

[0054] minf da (x0,y0)+∑ t f t id (x t ,y t )

[0055] stA0x0+D0y0≥b0

[0056] A t x0+B t x t-1 +C t x t +D t y t ≥b t ,t=1,2,...,T

[0057] Where x0 represents the day-ahead scheduling baseline decision variable; x t represents the intraday decision variables at stage t; y0 and y tis the auxiliary variable for each stage; f da (x0,y0) is the day-ahead scheduling cost function, f t id (x t ,y t ) is the intraday operating cost function of stage t; A t 、B t 、C t 、D t is the constraint coefficient matrix of each stage, b t is the corresponding right end vector.

[0058] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: modeling uncertainty using a Markov chain based on multi-stage stochastic optimization theory, and solving a coordinated scheduling model for a port integrated energy system integrating hydrogen energy and electricity based on a stochastic dual dynamic integer programming algorithm, including:

[0059] According to the multi-stage stochastic optimization theory, the coordinated scheduling model of the port integrated energy system is expressed as follows:

[0060]

[0061] stA0x0+D0y0≥b0

[0062]

[0063] Where, ξ t Represents the uncertainty realization of stage t; adopts the stochastic dual dynamic integer programming algorithm to solve the pre-established coordinated scheduling model of the port integrated energy system integrating hydrogen energy and electricity;

[0064] The stochastic dual dynamic integer programming algorithm first generates S random perturbation paths independently according to the Markov process of uncertainty evolution. Along each uncertainty sample path, the algorithm solves the subproblems of each stage in sequence from t = 0 to t = T. At each stage, the algorithm uses dynamic programming to minimize the sum of the approximate values ​​of the immediate cost function and the future expected cost function. The future expected cost function of each stage is approximated by the lower bound of a piecewise linear convex function constructed from the tangent plane accumulated in the historical iterations. The subproblem of stage t in the k-th iteration is expressed as follows:

[0065]

[0066] stA t x0+B t z t +C t x t +Dt y t ≥b t (ξ t,m )

[0067]

[0068] in,

[0069]

[0070] Where V t k (·) represents the approximate value function at the tth stage of the kth iteration; represents a piecewise linear lower bound approximation of the expected cost function formed by the iteratively constructed tangent plane; is the dual multiplier. t is an auxiliary variable; L t is a constant lower bound; τ(t,m) represents the set of all child nodes of node m under the Markov chain in stage t; p t,m,n is the transition probability from node m in stage t to node n in stage t+1;

[0071] The stochastic dual dynamic integer programming algorithm starts from the last stage T and moves forward step by step to stage 0; for each stage t, the decision obtained in the kth iteration of the given forward stage is and the corresponding uncertainty ξ t,m Based on this, solve the relaxed form of the forward subproblem and approximate the value function of the next stage Treated as a known piecewise linear function; in each perturbation scenario, the solution of the subproblem will produce a dual solution, which can be expressed as a tangent plane in the form of a linear inequality This cutting plane can be used as an effective lower bound of the value function. By aggregating the cutting planes generated by all perturbation samples, the new value function approximation at stage t can be updated. Then, enter stage t-1 and repeat the above process; after completing the backtracking of stage 0, the function obtained is As an effective lower bound for the original multi-stage problem, the following two cutting planes are used in the stochastic dual dynamic integer programming algorithm:

[0072] Benders cut

[0073]

[0074] in, Indicates that in the forward solution The optimal objective value obtained by solving the linear relaxation subproblem;

[0075] Lagrangian cut

[0076]

[0077] in,

[0078]

[0079] stx t ∈{0,1} d ,z t ∈[0,1] d

[0080] θ t ≥L t

[0081]

[0082] Where, In the Lagrange multiplier The objective function value of a given Lagrangian subproblem.

[0083] In a second aspect, in order to achieve the above-mentioned purpose, the present invention discloses a coordinated dispatching system for a port integrated energy system, comprising:

[0084] A data receiving module is configured to receive equipment operating parameters of the port logistics system and the integrated energy system, as well as ship operation-related data. The equipment operating parameters of the port logistics system and the integrated energy system include status information of electric container trucks, hydrogen container trucks, quay cranes, yard cranes, electrolyzers, fuel cells, and hydrogen energy storage systems. The ship operation data includes estimated arrival time, loading and unloading workload, latest departure time, and initial temperature of refrigerated containers.

[0085] The model solving module is used to input the equipment operating parameters of the port logistics system and the integrated energy system as well as the ship operation-related data into the pre-established coordinated scheduling model of the port integrated energy system that integrates hydrogen energy and electricity. According to the multi-stage stochastic optimization theory, the Markov chain is used to model the uncertainty, and the coordinated scheduling model of the port integrated energy system that integrates hydrogen energy and electricity is solved based on the stochastic dual dynamic integer programming algorithm. The output is the optimal coordinated scheduling result of the port integrated energy system under the conditions of multi-source uncertainty.

[0086] In another aspect of the present invention, in order to achieve the above-mentioned purpose, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, it adopts the above-mentioned coordinated scheduling method of the port integrated energy system.

[0087] In another aspect of the present invention, in order to achieve the above-mentioned purpose, a computer-readable storage medium is disclosed, in which a computer program is stored. When the computer program is loaded and executed by a processor, a collaborative scheduling method for a port integrated energy system as described above is adopted.

[0088] Beneficial effects of the present invention:

[0089] The present invention can achieve coordinated scheduling of electric and hydrogen energy equipment, effectively improve the on-site absorption capacity of renewable energy, alleviate peak and valley load fluctuations in the power system, and enhance the operational flexibility and stability of the energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0091] Figure 1 It is a schematic flow chart of the method of the present invention;

[0092] Figure 2 Schematic diagram of the electrical load distribution in this embodiment;

[0093] Figure 3 This is a schematic diagram of the hydrogen equipment scheduling plan of this embodiment;

[0094] Figure 4 It is a schematic diagram of the structure of the device of the present invention. DETAILED DESCRIPTION

[0095] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0096] Example 1:

[0097] like Figure 1 As shown, a coordinated scheduling method for a port integrated energy system includes the following steps:

[0098] S101: Receive equipment operating parameters of the port logistics system and the integrated energy system, as well as ship operation-related data. The equipment operating parameters of the port logistics system and the integrated energy system include status information of electric container trucks, hydrogen container trucks, quay cranes, yard cranes, electrolyzers, fuel cells, and hydrogen energy storage systems. The ship operation data includes estimated arrival time, loading and unloading workload, latest departure time, and initial temperature of refrigerated containers.

[0099] The pre-established coordinated scheduling model for a port integrated energy system integrating hydrogen energy and electric energy is based on the operating characteristics and multi-source energy demands of different types of equipment, and is modeled in combination with the information of ships arriving at the port. The pre-established coordinated scheduling model for a port integrated energy system integrating hydrogen energy and electric energy includes the coupling relationship between various logistics equipment and energy systems in the port.

[0100] The operating characteristics and multi-source energy requirements of different types of equipment include:

[0101] Day-ahead: berth allocation and scheduling constraints, quay crane scheduling and refrigerated container unloading constraints, and the corresponding day-ahead logistics operation cost model;

[0102] Intraday stage: yard crane and yard allocation and scheduling constraints, electric ship charging and discharging behavior constraints, electric ship energy replenishment constraints, electric container truck scheduling constraints and charge state constraints, electric container truck energy replenishment constraints, hydrogen container truck scheduling constraints and hydrogen charge state constraints, hydrogen container truck energy replenishment constraints, energy system equipment scheduling constraints, hydrogen energy equipment constraints, port integrated energy system power constraints; intraday operation constraints of the port integrated energy system, including operation constraints of gas turbines, waste heat boilers, electric energy storage systems, wind power and photovoltaic power generation, electrolyzers, fuel cells and hydrogen energy storage devices; port intraday energy system operation cost model constructed by comprehensively considering electricity prices, natural gas prices, carbon emission factors, and renewable energy abandonment costs.

[0103] The berth allocation scheduling constraints in the day-ahead phase include:

[0104]

[0105] Where, ζ s,b,t Indicates the berthing status of the ship. If ship s is docked at berth b at time t, it is 1, otherwise it is 0; is the ship's arrival time, is the ship's berthing start time, is the end time of the ship's berthing, The latest departure time of the ship, M is a positive number;

[0106] The quay crane scheduling constraints include:

[0107]

[0108] Where, δ s,r,q,t Indicates whether the qth quay crane is responsible for loading and unloading the rth group of refrigerated containers on the sth ship at time t. If so, the value is 1, otherwise 0; and The minimum and maximum number of quay cranes that can be carried by ship No. s are respectively;

[0109] Reefer unloading operation constraints include:

[0110]

[0111] Where, T s,r,t represents the internal temperature of the rth group of refrigerated containers on ship s at time t; T t amb is the ambient temperature at time t; c s,r,t 、u s,r,t and p s,r,t are binary state variables representing the refrigerator being in constant temperature, power off, and cooling states, respectively, where c s,r,t =1 means that the refrigerated container is kept at a constant temperature by the ship's power supply system during the voyage; u s,r,t =1 means the refrigerated container has arrived at the port but has not yet been unloaded. During this period, the power outage causes the internal temperature to rise with the environment; s,r,t =1 indicates that the refrigerated container has been unloaded and connected to shore power; A is the heat exchange area of ​​the refrigerated container, k is the thermal conductivity, m is the mass of the contents of the refrigerated container, c is its specific heat capacity, and Δt is the scheduling time step; is the cooling power of the rth group of refrigerated containers on ship No. s at time t; T s and are the lower and upper safety limits of the refrigerator temperature, T s,0 is the initial temperature of the refrigerator, The upper limit of cooling power;

[0112] The expression of the day-ahead logistics operation cost model includes:

[0113]

[0114] Where c w is the berthing cost coefficient of the ship, c d is the berthing cost coefficient of the ship.

[0115] The scheduling constraints for electric trucks and hydrogen trucks during the intraday period include:

[0116]

[0117] Where, Indicates whether the kth electric container truck is assigned to transport the rth group of refrigerated containers of ship s at time t. If so, it is 1, otherwise it is 0; γ s,r,h,t Indicates whether the hth hydrogen container truck is allocated to transport the rth group of refrigerated containers on ship s at time t. If so, it is 1, otherwise it is 0; η q Indicates the loading and unloading efficiency of the quay crane;

[0118] The scheduling constraints for the yard crane and storage yard allocation during the intra-day phase include:

[0119]

[0120] Where μ s,r,y,t Indicates whether the yth gantry crane is assigned to load and unload the rth group of refrigerated containers of ship s at time t; η y Indicates the loading and unloading efficiency of the field crane; C y,t represents the number of refrigerated containers stored in the yth storage yard at time t; The maximum number of refrigerated containers that can be accommodated in a single storage yard;

[0121] The constraints on electric ship recharging during the day include:

[0122]

[0123]

[0124] Where, Indicates the remaining power of ship s at time t; and They represent the charging power and discharging power of ship s at time t respectively; η esc and η esd Represent the charging efficiency and discharging efficiency respectively; Δt represents the time step; and are binary variables indicating whether ship s is in the charging or discharging state at time t; The upper limit of charge and discharge power; The minimum power required for ship S to leave the port;

[0125] The constraints on electric truck recharging during the day include:

[0126]

[0127] Where, represents the remaining power of the kth electric container truck at time t; and Respectively represent the charging power and discharging power at time t; η evc and η evd are charging efficiency and discharging efficiency respectively; and is a binary variable, indicating whether to charge / discharge; c k,t 、w k,t and d k,t They represent the charging, working and idle state variables of the electric container truck respectively. The three are mutually exclusive and their sum is 1; is the total number of charging piles; P is the upper limit of the charging and discharging power of the electric container truck; w With P d Respectively represent the unit energy consumption of electric container trucks in working state and idle state;

[0128] The constraints on hydrogen truck recharging during the daily period include:

[0129]

[0130]

[0131] Where, Indicates the remaining hydrogen amount in the hth hydrogen energy truck at time t; ΔF h,t Indicates the pressure of hydrogen in the hydrogen energy container at time t; c h,t 、w h,t with d h,t Represent the recharging, working and idle state variables of the hydrogen container truck respectively; is the number of hydrogen refueling stations; is the hydrogenation rate of the hth hydrogen truck at time t, is the maximum hydrogenation rate of hydrogen energy truck; η hvc is the hydrogenation efficiency; V w and V d are the hydrogen consumption rates of hydrogen trucks in working and idle states respectively; f hv is the load factor of the hydrogen energy truck; ρ(·,·) is the hydrogen density function, which depends on the hydrogen volume and temperature; F ref With T ref is the hydrogen pressure and temperature under standard operating conditions;

[0132] The energy system equipment scheduling constraints include:

[0133]

[0134] Among them, η gt,e and η gt,h They represent the power generation efficiency and heat generation efficiency of the gas turbine, η whb is the heat recovery efficiency of the waste heat boiler; P t gt,g is the natural gas input power of the gas turbine at time t, P t gt,e With Pt gt,h Represent the electrical power output and thermal power output of the gas turbine respectively; P t whb,h represents the heat power recovered by the waste heat boiler at time t; and P gt,g The upper and lower limits of gas input power. represents the state of charge of the energy storage system at time t, P t essc With P t essd are the charging power and discharging power of the energy storage system, η essc and η essd are the corresponding charge and discharge efficiencies, and P is the charge and discharge status indicator variable of the energy storage system at time t; t wd,l With P t wd They represent the abandoned power and actual generated power of wind power at time t, P t pv,l With P t pv Represent the abandoned power and actual power generation of photovoltaic power, and is the maximum available output of wind power and photovoltaic power at time t;

[0135] The hydrogen energy equipment constraints include:

[0136]

[0137] Among them, P t el represents the hydrogen production power of the electrolyzer at time t, is the hydrogen production at that moment, U el is the cell voltage, is the number of moles of electrons per mole of hydrogen, F is the Faraday constant, η el is the electrolysis efficiency of the electrolytic cell, N el is the number of electrolysis units, is the upper limit of electrolytic cell power. t fc represents the output power of the fuel cell, is the amount of hydrogen consumed by the fuel cell, N A is Avogadro's constant, is the molar mass of hydrogen, C0 is the number of electrons per coulomb, U fc is the fuel cell cell voltage, η fc is the fuel cell efficiency, The upper limit of its power. represents the hydrogen storage capacity in the hydrogen storage tank at time t, V t hssc and V t hssd are the hydrogen filling rate and hydrogen discharge rate of the hydrogen storage tank, η hssc and η hssd For the hydrogen storage tank charging and discharging efficiency, and is the hydrogen storage tank's charging and discharging state variable, The upper limit of the hydrogen filling and discharging rate of the hydrogen storage tank. represents the internal pressure of the hydrogen storage tank at time t, R is the ideal gas constant, is the pressure value in the hydrogen storage tank after standardization, is the rated reference pressure, H hss and are the lower and upper limits of the pressure ratio respectively;

[0138] The power constraints of the port integrated energy system include:

[0139]

[0140] Where, P s is the fixed load of the ship, P t ci is the total shore power supply power at time t; P t q is the total operating power of the quay crane at time t, P q P is the working power of the quay crane; t y is the total operating power of the field crane at time t, P y P is the working power of the field bridge; t es is the net power of the energy storage system at time t; P t ev is the net power of the electric container truck at time t; P t rc P is the refrigeration power of the refrigerator at time t; t ele is the power supply of the grid at time t.

[0141] The expressions for daily port operating costs include:

[0142]

[0143] f t l =Δt(c wd P t wd,l +c pv P tpv,l )

[0144] Where c r is the intraday redispatching cost coefficient, and λ gas are the grid time-of-use electricity price and natural gas price, P t ele and V t gas are the electricity and gas purchase volumes at time t, and are the carbon dioxide emission factors of the power grid and gas grid, is the unit carbon price, c wd and c pv are the penalty coefficients for wind and solar power abandonment, P t wd,l With P t pv,l are the abandoned power of wind power and photovoltaic power at time t respectively.

[0145] The pre-established coordinated dispatch model of the port integrated energy system integrating hydrogen energy and electric energy is as follows:

[0146] minf da (x0,y0)+∑ t f t id (x t ,y t )

[0147] stA0x0+D0y0≥b0

[0148] A t x0+B t x t-1 +C t x t +D t y t ≥b t ,t=1,2,...,T

[0149] Where x0 represents the day-ahead scheduling baseline decision variable; x t represents the intraday decision variables at stage t; y0 and y t is the auxiliary variable for each stage; f da (x0,y0) is the day-ahead scheduling cost function, f t id (x t ,y t ) is the intraday operating cost function of stage t; A t 、B t 、C t 、D tis the constraint coefficient matrix of each stage, b t is the corresponding right end vector.

[0150] S102: Input the equipment operating parameters of the port logistics system and the integrated energy system as well as the ship operation related data into the pre-established coordinated scheduling model of the port integrated energy system integrating hydrogen energy and electric energy. According to the multi-stage stochastic optimization theory, the uncertainty is modeled using the Markov chain, and the coordinated scheduling model of the port integrated energy system integrating hydrogen energy and electric energy is solved based on the stochastic dual dynamic integer programming algorithm. The optimal coordinated scheduling result of the port integrated energy system under the condition of multi-source uncertainty is output.

[0151] According to the multi-stage stochastic optimization theory, the uncertainty is modeled using Markov chains, and the coordinated scheduling model of the port integrated energy system integrating hydrogen energy and electricity is solved based on the stochastic dual dynamic integer programming algorithm, including:

[0152] According to the multi-stage stochastic optimization theory, the coordinated scheduling model of the port integrated energy system is expressed as follows:

[0153]

[0154] stA0x0+D0y0≥b0

[0155]

[0156] Where, ξ t Represents the uncertainty realization of stage t; adopts the stochastic dual dynamic integer programming algorithm to solve the pre-established coordinated scheduling model of the port integrated energy system integrating hydrogen energy and electricity;

[0157] The stochastic dual dynamic integer programming algorithm first generates S random perturbation paths independently according to the Markov process of uncertainty evolution. Along each uncertainty sample path, the algorithm solves the subproblems of each stage in sequence from t = 0 to t = T. At each stage, the algorithm uses dynamic programming to minimize the sum of the approximate values ​​of the immediate cost function and the future expected cost function. The future expected cost function of each stage is approximated by the lower bound of a piecewise linear convex function constructed from the tangent plane accumulated in the historical iterations. The subproblem of stage t in the k-th iteration is expressed as follows:

[0158]

[0159] stA t x0+B t z t +C t x t +Dt y t ≥b t (ξ t,m )

[0160]

[0161] in,

[0162]

[0163] Where V t k (·) represents the approximate value function at the tth stage of the kth iteration; represents a piecewise linear lower bound approximation of the expected cost function formed by the iteratively constructed tangent plane; is the dual multiplier. t is an auxiliary variable; L t is a constant lower bound; τ(t,m) represents the set of all child nodes of node m under the Markov chain in stage t; p t,m,n is the transition probability from node m in stage t to node n in stage t+1;

[0164] The stochastic dual dynamic integer programming algorithm starts from the last stage T and moves forward step by step to stage 0; for each stage t, the decision obtained in the kth iteration of the given forward stage is and the corresponding uncertainty ξ t,m Based on this, solve the relaxed form of the forward subproblem and approximate the value function of the next stage Treated as a known piecewise linear function; in each perturbation scenario, the solution of the subproblem will produce a dual solution, which can be expressed as a tangent plane in the form of a linear inequality This cutting plane can be used as an effective lower bound of the value function. By aggregating the cutting planes generated by all perturbation samples, the new value function approximation at stage t can be updated. Then, enter stage t-1 and repeat the above process; after completing the backtracking of stage 0, the function obtained is As an effective lower bound for the original multi-stage problem, the following two cutting planes are used in the stochastic dual dynamic integer programming algorithm:

[0165] Benders cut

[0166]

[0167] in, Indicates that in the forward solution The optimal objective value obtained by solving the linear relaxation subproblem;

[0168] Lagrangian cut

[0169]

[0170] in,

[0171]

[0172] stx t ∈{0,1} d ,z t ∈[0,1] d

[0173] θ t ≥L t

[0174]

[0175] Where, In the Lagrange multiplier The objective function value of a given Lagrangian subproblem.

[0176] Specifically, the present invention is further described through examples below: In order to prove the economic efficiency of the electrified port logistics energy coordinated scheduling proposed by the present invention, the examples compare the differences between the separate scheduling and coordinated scheduling of the logistics system and the energy system. Figure 2 The hydrogen equipment scheduling plan is shown as follows: Figure 3 The scheduling costs are shown in Table 1. Because independent scheduling decouples and optimizes the energy and logistics systems, logistics loads cannot be flexibly shifted, resulting in high energy costs. Collaborative scheduling can effectively utilize the flexibility of logistics loads, reduce system energy purchase costs, and help ports absorb renewable energy generation.

[0177] Table 1 Scheduling costs

[0178]

[0179] Example 2: In order to achieve the above purpose, Figure 4 As shown, based on the first embodiment, the present invention discloses a coordinated dispatching system for a port integrated energy system, including:

[0180] The data receiving module 11 is used to receive equipment operating parameters of the port logistics system and the integrated energy system, as well as ship operation-related data. The equipment operating parameters of the port logistics system and the integrated energy system include status information of electric container trucks, hydrogen container trucks, quay cranes, yard cranes, electrolyzers, fuel cells, and hydrogen energy storage systems. The ship operation data includes the estimated arrival time, loading and unloading workload, latest departure time, and the initial temperature of the refrigerated container.

[0181] The model solving module 12 is used to input the equipment operating parameters of the port logistics system and the integrated energy system and the ship operation related data into the pre-established coordinated scheduling model of the port integrated energy system integrating hydrogen energy and electric energy. According to the multi-stage stochastic optimization theory, the Markov chain is used to model the uncertainty, and the coordinated scheduling model of the port integrated energy system integrating hydrogen energy and electric energy is solved based on the stochastic dual dynamic integer programming algorithm, and the optimal coordinated scheduling result of the port integrated energy system under the condition of multi-source uncertainty is output.

[0182] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0183] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which executes the above method when executed by a processor. The storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0184] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0185] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present disclosure. Various changes and improvements may be made to the present disclosure without departing from the spirit and scope of the present disclosure, and such changes and improvements shall fall within the scope of the present disclosure.

Claims

1. A coordinated dispatching method for a port integrated energy system, characterized in that: The method comprises the following steps: Receive equipment operating parameters of the port logistics system and integrated energy system, as well as ship operation-related data. The equipment operating parameters of the port logistics system and integrated energy system include status information of electric container trucks, hydrogen container trucks, quay cranes, yard cranes, electrolyzers, fuel cells, and hydrogen energy storage systems. The ship operation data includes estimated arrival time, loading and unloading workload, latest departure time, and initial temperature of refrigerated containers. The equipment operating parameters of the port logistics system and integrated energy system, as well as the ship operation-related data, are input into a pre-established coordinated scheduling model of the port integrated energy system that integrates hydrogen energy and electricity. According to the multi-stage stochastic optimization theory, the uncertainty is modeled using Markov chain, and the coordinated scheduling model of the port integrated energy system that integrates hydrogen energy and electricity is solved based on the stochastic dual dynamic integer programming algorithm. The output is the optimal coordinated scheduling result of the port integrated energy system under multi-source uncertainty conditions.

2. A coordinated dispatching method for a port integrated energy system according to claim 1, characterized in that: The pre-established coordinated scheduling model for a port integrated energy system integrating hydrogen energy and electric energy is based on the operating characteristics and multi-source energy demands of different types of equipment, and is modeled in combination with the information of ships arriving at the port. The pre-established coordinated scheduling model for a port integrated energy system integrating hydrogen energy and electric energy includes the coupling relationship between various logistics equipment and energy systems in the port.

3. A coordinated dispatching method for a port integrated energy system according to claim 2, characterized in that: The operating characteristics and multi-source energy requirements of different types of equipment include: Day-ahead: berth allocation and scheduling constraints, quay crane scheduling and refrigerated container unloading constraints, and the corresponding day-ahead logistics operation cost model; Intraday stage: yard crane and yard allocation and scheduling constraints, electric ship charging and discharging behavior constraints, electric ship energy replenishment constraints, electric container truck scheduling constraints and charge state constraints, electric container truck energy replenishment constraints, hydrogen container truck scheduling constraints and hydrogen charge state constraints, hydrogen container truck energy replenishment constraints, energy system equipment scheduling constraints, hydrogen energy equipment constraints, port integrated energy system power constraints; intraday operation constraints of the port integrated energy system, including operation constraints of gas turbines, waste heat boilers, electric energy storage systems, wind power and photovoltaic power generation, electrolyzers, fuel cells and hydrogen energy storage devices; port intraday energy system operation cost model constructed by comprehensively considering electricity prices, natural gas prices, carbon emission factors, and renewable energy abandonment costs.

4. A coordinated dispatching method for a port integrated energy system according to claim 3, characterized in that: The berth allocation scheduling constraints in the day-ahead phase include: Where, ζ s,b,t Indicates the berthing status of the ship. If ship s is docked at berth b at time t, it is 1, otherwise it is 0; is the ship's arrival time, is the ship's berthing start time, is the end time of the ship's berthing, The latest departure time of the ship, M is a positive number; The quay crane scheduling constraints include: Where, δ s,r,q,t Indicates whether the qth quay crane is responsible for loading and unloading the rth group of refrigerated containers on the sth ship at time t. If so, the value is 1, otherwise 0; and The minimum and maximum number of quay cranes that can be carried by ship No. s are respectively; Reefer unloading operation constraints include: Where, T s,r,t represents the internal temperature of the rth group of refrigerated containers on ship s at time t; is the ambient temperature at time t; c s,r,t 、u s,r,t and p s,r,t are binary state variables representing the refrigerator being in constant temperature, power off, and cooling states, respectively, where c s,r,t =1 means that the refrigerated container is kept at a constant temperature by the ship's power supply system during the voyage; u s,r,t =1 means the refrigerated container has arrived at the port but has not yet been unloaded. During this period, the power outage causes the internal temperature to rise with the environment; s,r,t =1 indicates that the refrigerated container has been unloaded and connected to shore power; A is the heat exchange area of ​​the refrigerated container, k is the thermal conductivity, m is the mass of the contents of the refrigerated container, c is its specific heat capacity, and Δt is the scheduling time step; is the cooling power of the rth group of refrigerated containers on ship s at time t; T s and are the lower and upper safety limits of the refrigerator temperature, T s,0 is the initial temperature of the refrigerator, The upper limit of cooling power; The expression of the day-ahead logistics operation cost model includes: Where c w is the berthing cost coefficient of the ship, c d is the berthing cost coefficient of the ship.

5. A coordinated dispatching method for a port integrated energy system according to claim 4, characterized in that: The scheduling constraints for electric trucks and hydrogen trucks during the intraday period include: Where, Indicates whether the kth electric container truck is assigned to transport the rth group of refrigerated containers of ship s at time t. If so, it is 1, otherwise it is 0; γ s,r,h,t Indicates whether the hth hydrogen container truck is allocated to transport the rth group of refrigerated containers on ship s at time t. If so, it is 1, otherwise it is 0; η q Indicates the loading and unloading efficiency of the quay crane; The scheduling constraints for the yard crane and storage yard allocation during the intra-day phase include: Where μ s,r,y,t Indicates whether the yth gantry crane is assigned to load and unload the rth group of refrigerated containers of ship s at time t; η y Indicates the loading and unloading efficiency of the field crane; C y,t represents the number of refrigerated containers stored in the yth storage yard at time t; The maximum number of refrigerated containers that can be accommodated in a single storage yard; The constraints on electric ship recharging during the day include: Where, Indicates the remaining power of ship s at time t; and They represent the charging power and discharging power of ship s at time t respectively; η esc and η esd Represent the charging efficiency and discharging efficiency respectively; Δt represents the time step; and are binary variables indicating whether ship s is in the charging or discharging state at time t; The upper limit of charge and discharge power; The minimum power required for ship S to leave the port; The constraints on electric truck recharging during the day include: Where, represents the remaining power of the kth electric container truck at time t; and Respectively represent the charging power and discharging power at time t; η evc and η evd are charging efficiency and discharging efficiency respectively; and is a binary variable, indicating whether to charge / discharge; c k,t 、w k,t and d k,t They represent the charging, working and idle state variables of the electric container truck respectively. The three are mutually exclusive and their sum is 1; is the total number of charging piles; P is the upper limit of the charging and discharging power of the electric container truck; w With P d Respectively represent the unit energy consumption of electric container trucks in working state and idle state; The constraints on hydrogen truck recharging during the daily period include: Where, Indicates the remaining hydrogen amount in the hth hydrogen energy truck at time t; ΔF h,t Indicates the pressure of hydrogen in the hydrogen energy container at time t; c h,t 、w h,t with d h,t Represent the recharging, working and idle state variables of the hydrogen container truck respectively; is the number of hydrogen refueling stations; is the hydrogenation rate of the hth hydrogen truck at time t, is the maximum hydrogenation rate of hydrogen energy truck; η hvc is the hydrogenation efficiency; V w and V d are the hydrogen consumption rates of hydrogen trucks in working and idle states respectively; f hv is the load factor of the hydrogen energy truck; ρ(·,·) is the hydrogen density function, which depends on the hydrogen volume and temperature; F ref With T ref is the hydrogen pressure and temperature under standard operating conditions; The energy system equipment scheduling constraints include: Among them, η gt,e and η gt,h They represent the power generation efficiency and heat generation efficiency of the gas turbine, η whb is the heat recovery efficiency of the waste heat boiler; is the natural gas input power of the gas turbine at time t, and represent the electrical power output and thermal power output of the gas turbine respectively; represents the heat power recovered by the waste heat boiler at time t; and P gt,g The upper and lower limits of gas input power. represents the state of charge of the energy storage system at time t, and are the charging power and discharging power of the energy storage system, η essc and η essd are the corresponding charge and discharge efficiencies, and is the charge and discharge status indicator variable of the energy storage system at time t; and They represent the abandoned power and actual generated power of wind power at time t, and Represent the abandoned power and actual power generation of photovoltaic power, and is the maximum available output of wind power and photovoltaic power at time t; The hydrogen energy equipment constraints include: in, represents the hydrogen production power of the electrolyzer at time t, is the hydrogen production at that moment, U el is the cell voltage, is the number of moles of electrons per mole of hydrogen, F is the Faraday constant, η el is the electrolysis efficiency of the electrolytic cell, N el is the number of electrolysis units, The upper limit of electrolyzer power. represents the output power of the fuel cell, is the amount of hydrogen consumed by the fuel cell, N A is Avogadro's constant, is the molar mass of hydrogen, C0 is the number of electrons per coulomb, U fc is the fuel cell cell voltage, η fc is the fuel cell efficiency, The upper limit of its power. represents the hydrogen storage amount in the hydrogen storage tank at time t, and are the hydrogen filling rate and hydrogen discharge rate of the hydrogen storage tank, η hssc and η hssd For the hydrogen storage tank charging and discharging efficiency, and is the hydrogen storage tank's charging and discharging state variable, The upper limit of the hydrogen filling and discharging rate of the hydrogen storage tank. represents the internal pressure of the hydrogen storage tank at time t, R is the ideal gas constant, is the pressure value in the hydrogen storage tank after standardization, is the rated reference pressure, H hss and are the lower and upper limits of the pressure ratio respectively; The power constraints of the port integrated energy system include: Where, P s To secure the ship's load, is the total shore power supply power at time t; is the total operating power of the quay crane at time t, P q is the working power of the quay crane; is the total operating power of the field crane at time t, P y is the working power of the field bridge; is the net power of the energy storage system at time t; is the net power of the electric container truck at time t; is the refrigeration power of the refrigerator at time t; is the power supply of the grid at time t. The expressions for daily port operating costs include: Where c r is the intraday redispatching cost coefficient, and λ gas are the time-of-use electricity price of the power grid and the natural gas price, and are the electricity and gas purchase volumes at time t, and are the carbon dioxide emission factors of the power grid and gas grid, is the unit carbon price, c wd and c pv are the penalty coefficients for wind and solar power abandonment, and are the abandoned power of wind power and photovoltaic power at time t respectively.

6. A coordinated dispatching method for a port integrated energy system according to claim 5, characterized in that: The pre-established coordinated dispatch model of the port integrated energy system integrating hydrogen energy and electric energy is as follows: Where x0 represents the day-ahead scheduling baseline decision variable; x t represents the intraday decision variables at stage t; y0 and y t is the auxiliary variable for each stage; f da (x0,y0) is the day-ahead scheduling cost function, is the intraday operating cost function of stage t; A t 、B t 、C t 、D t is the constraint coefficient matrix of each stage, b t is the corresponding right end vector.

7. A coordinated dispatching method for a port integrated energy system according to claim 1, characterized in that: According to the multi-stage stochastic optimization theory, the uncertainty is modeled using Markov chains, and the coordinated scheduling model of the port integrated energy system integrating hydrogen energy and electricity is solved based on the stochastic dual dynamic integer programming algorithm, including: According to the multi-stage stochastic optimization theory, the coordinated scheduling model of the port integrated energy system is expressed as follows: Where, ξ t Represents the uncertainty realization of stage t; adopts the stochastic dual dynamic integer programming algorithm to solve the pre-established coordinated scheduling model of the port integrated energy system integrating hydrogen energy and electricity; The stochastic dual dynamic integer programming algorithm first generates S random perturbation paths independently according to the Markov process of uncertainty evolution. Along each uncertainty sample path, the algorithm solves the subproblems of each stage in sequence from t = 0 to t = T. At each stage, the algorithm uses dynamic programming to minimize the sum of the approximate values ​​of the immediate cost function and the future expected cost function. The future expected cost function of each stage is approximated by the lower bound of a piecewise linear convex function constructed from the tangent plane accumulated in the historical iterations. The subproblem of stage t in the k-th iteration is expressed as follows: in, Where, represents the approximate value function at the kth iteration and the tth stage; represents a piecewise linear lower bound approximation of the expected cost function formed by the iteratively constructed tangent plane; is the dual multiplier. t is an auxiliary variable; L t is a constant lower bound; τ(t,m) represents the set of all child nodes of node m under the Markov chain in stage t; p t,m,n is the transition probability from node m in stage t to node n in stage t+1; The stochastic dual dynamic integer programming algorithm starts from the last stage T and moves forward step by step to stage 0; for each stage t, the decision obtained in the kth iteration of the given forward stage is and the corresponding uncertainty ξ t,m Based on this, solve the relaxed form of the forward subproblem and approximate the value function of the next stage Treated as a known piecewise linear function; in each perturbation scenario, the solution of the subproblem will produce a dual solution, which can be expressed as a tangent plane in the form of a linear inequality This cutting plane can be used as an effective lower bound of the value function. By aggregating the cutting planes generated by all perturbation samples, the new value function approximation at stage t can be updated. Then, enter stage t-1 and repeat the above process; after completing the backtracking of stage 0, the function obtained is As an effective lower bound for the original multi-stage problem, the following two cutting planes are used in the stochastic dual dynamic integer programming algorithm: in, Indicates that in the forward solution The optimal objective value obtained by solving the linear relaxation subproblem; in, Where, In the Lagrange multiplier The objective function value of a given Lagrangian subproblem.

8. A coordinated dispatching system for a port integrated energy system, characterized in that: include: A data receiving module is configured to receive equipment operating parameters of the port logistics system and the integrated energy system, as well as ship operation-related data. The equipment operating parameters of the port logistics system and the integrated energy system include status information of electric container trucks, hydrogen container trucks, quay cranes, yard cranes, electrolyzers, fuel cells, and hydrogen energy storage systems. The ship operation data includes estimated arrival time, loading and unloading workload, latest departure time, and initial temperature of refrigerated containers. The model solving module is used to input the equipment operating parameters of the port logistics system and the integrated energy system as well as the ship operation-related data into the pre-established coordinated scheduling model of the port integrated energy system that integrates hydrogen energy and electricity. According to the multi-stage stochastic optimization theory, the Markov chain is used to model the uncertainty, and the coordinated scheduling model of the port integrated energy system that integrates hydrogen energy and electricity is solved based on the stochastic dual dynamic integer programming algorithm. The output is the optimal coordinated scheduling result of the port integrated energy system under the conditions of multi-source uncertainty.

9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, it adopts a port integrated energy system collaborative scheduling method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, wherein: When the computer program is loaded and executed by the processor, a coordinated scheduling method for a port integrated energy system according to any one of claims 1 to 7 is adopted.

Images