Port integrated energy system optimization method based on quantum computing

Abstract

The invention discloses a port integrated energy system optimization method based on quantum computing. The method comprises the following steps: 1) establishing a binary quadratic unconstrained optimization model of a port integrated energy system objective function; 2) establishing a binary quadratic unconstrained optimization model penalty term of equality constraint of the port integrated energy system; 3) establishing a binary quadratic unconstrained optimization model penalty term of the inequality constraint of the port integrated energy system; (4) obtaining a binary quadratic unconstrained optimization model of the port integrated energy system by combining the objective function established in the step (2), the equality constraint established in the step (3) and the penalty term of the binary quadratic unconstrained optimization model of the inequality constraint established in the step (4); and 5) solving the binary quadratic unconstrained optimization model of the port integrated energy system by using a quantum computer to obtain a port integrated energy system scheduling scheme.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy technology, and in particular relates to a method for optimizing a port integrated energy system based on quantum computing. Background Art

[0002] With the rapid growth of global trade, port energy demand has surged. The traditional model of relying on fossil fuels and grid power generation has resulted in high carbon emissions, necessitating the development of a green, integrated energy system that integrates wind power / photovoltaic power, energy storage, shore power, and multi-energy conversion. However, the diversification of energy sources has led to a dramatic increase in the complexity of system coordination: wind power output is affected by wind speed fluctuations, energy storage needs to dynamically match load changes, and modeling the coupled energy flows of electricity and hydrogen is difficult, resulting in a high-dimensional, nonlinear, multi-objective optimization problem.

[0003] Current optimization methods for port energy systems mainly rely on classical algorithms (such as particle swarm optimization and genetic algorithms) or multi-objective planning models based on traditional computers, which have the following key defects: Insufficient computational efficiency: When dealing with optimization problems involving multi-energy coupling and multiple time scales (day-ahead and intraday coordination), traditional algorithms have high dependent variable dimensions and complex constraints, making them prone to falling into local optimality or slow convergence. Poor dynamic adaptability: Port loads are significantly affected by dynamic factors such as ship arrivals and loading and unloading operations, while existing methods are mostly based on static forecast data and cannot effectively cope with real-time changes such as wind and solar power output fluctuations and load mutations, resulting in optimization results that deviate from actual operating conditions. Insufficient multi-objective balance: When dealing with economic, technical, and environmental goals, traditional multi-objective optimization algorithms have difficulty balancing solution set diversity and convergence. Problems such as unreasonable weight distribution between goals and uneven distribution of solution sets often occur, affecting the scientific nature of decision-making.

[0004] In recent years, the rapid development of optical quantum computing technology has provided new solutions to these problems. Compared with traditional computers, optical quantum computers have the following significant advantages: parallel computing capabilities; low energy consumption and high scalability; noise suppression and stability.

[0005] In summary, existing technologies struggle to meet the efficiency, dynamism, and multi-objective balance requirements of integrated port energy systems. Optical quantum computing provides a revolutionary tool for addressing this challenge. This patent proposes a coordinated optimization method based on quantum computing. Through the design of a quantum-classical hybrid algorithm, quantum encoding of multi-source data, and a dynamic weight adjustment mechanism, it achieves real-time optimization and multi-objective coordination of port energy systems, providing technical support for the green transformation of global ports. Summary of the Invention

[0006] Technical solution: In order to solve the technical problems mentioned in the above background technology, the present invention proposes a method for optimizing the integrated energy system of a port based on quantum computing, comprising the following steps:

[0007] (1) Establish a binary quadratic unconstrained optimization model for the objective function of the port integrated energy system;

[0008] (2) Establish the penalty term of the binary quadratic unconstrained optimization model with equality constraints for the port integrated energy system;

[0009] (3) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints for the port integrated energy system;

[0010] (4) The objective function established in step (2), the equality constraint established in step (3), and the penalty term of the binary quadratic unconstrained optimization model established in step (4) are combined to obtain the binary quadratic unconstrained optimization model of the port integrated energy system;

[0011] (5) Use quantum computers to solve the binary quadratic unconstrained optimization model of the port integrated energy system and obtain the scheduling plan of the port integrated energy system.

[0012] Furthermore, in step (1), a binary quadratic unconstrained optimization model of the objective function of the port integrated energy system is established, which is expressed as follows:

[0013]

[0014] Where H obj is the objective function; s is the ship; N S is the total number of ships; t is the scheduling period; t-1 is the previous scheduling period of scheduling period t; N T is the total number of scheduling periods; is the penalty cost of delayed docking for vessel s; T ord Count the time periods; is a binary variable that represents whether ship s berths at time period t. When ship s berths at time period t, otherwise, is a binary variable representing whether ship s berths in period t-1. When ship s berths in period t-1, otherwise, T S,A is the actual arrival time of vessel s; The unit time docking cost of the ship after berthing; is the electricity market transaction price at time t; P t EM is the electricity market transaction volume at time t.

[0015] Furthermore, the specific process of step (2) is as follows:

[0016] (201) Establish the penalty term of the binary quadratic unconstrained optimization model with the equal constraint of the electric energy balance of the port integrated energy system:

[0017]

[0018] Where, P is the penalty term of the binary quadratic unconstrained optimization model constrained by the electric energy balance equation; EEB is the penalty coefficient of the binary quadratic unconstrained optimization model with electric energy balance equality constraint; i is the distributed energy; N I is the total number of distributed energy resources; is the active power output of distributed energy i at time t; P t ED is the electrolysis power of the electrolysis device at time t; P t EES,dis is the discharge power of the power storage device at time t; is the active power demand per unit time during the berthing period of ship s; P t EES,ch is the charging power of the power storage device at time t; P t load is the power load of the port integrated energy system at time t;

[0019] (202) Establish the penalty term of the binary quadratic unconstrained optimization model with equality constraints on the hydrogen energy balance of the port integrated energy system:

[0020]

[0021] Where, is the penalty term of the binary quadratic unconstrained optimization model constrained by the hydrogen energy balance equation; P HEB is the penalty coefficient of the binary quadratic unconstrained optimization model constrained by the hydrogen energy balance equation; ξ ED is the electrolysis efficiency of the electrolysis device; is the hydrogen discharge power of the hydrogen storage device at time t; is the hydrogen demand of ship s per unit time; is the hydrogen storage power of the hydrogen storage device at time t; is the hydrogen demand of the port integrated energy system at time t;

[0022] (203) Establish the penalty term of the binary quadratic unconstrained optimization model with energy balance equality constraint for the port integrated energy system:

[0023]

[0024] Where, is the penalty term of the binary quadratic unconstrained optimization model constrained by the energy balance equation of the electric energy storage; EESB is the penalty coefficient of the two-variable quadratic unconstrained optimization model constrained by the energy balance equation of the electric energy storage; is the storage capacity of the power storage device at time t; EES,dis is the discharge efficiency of the power storage device; is the storage capacity of the power storage device at time t-1; EES,ch Charging efficiency of power storage devices; is the penalty term of the binary quadratic unconstrained optimization model constrained by the energy balance equation of hydrogen energy storage; HESB is the penalty coefficient of the binary quadratic unconstrained optimization model constrained by the energy balance equation of hydrogen energy storage; is the hydrogen storage capacity of the hydrogen storage device at time t; ξ HES,dis The hydrogen discharge efficiency of the hydrogen storage device; is the hydrogen storage capacity of the hydrogen storage device at time t-1; ξ HES,ch The hydrogen storage efficiency of the hydrogen storage device;

[0025] (204) Establish the penalty term of the binary quadratic unconstrained optimization model with equality constraint of ship docking time in the port integrated energy system:

[0026]

[0027] Where, is the penalty term of the binary quadratic unconstrained optimization model with equality constraint on ship docking time; P SBT is the penalty coefficient of the two-variable quadratic unconstrained optimization model with equality constraints on ship docking time; is the continuous docking time requirement of ship s.

[0028] Furthermore, the specific process of step (3) is as follows:

[0029] (301) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on the ship docking status of the port integrated energy system:

[0030]

[0031] Where, is the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on the ship docking status; P SBS is the penalty coefficient of the binary quadratic unconstrained optimization model with inequality constraints on the ship's docking status; τ is the starting scheduling period for the ship to start docking; is a binary variable that represents whether ship s berths in time period τ. When ship s berths in time period τ, otherwise, n is the quantum bit count; The quantum bits required for the relaxation of the inequality constraint on the docking state of the ship at time t; is the maximum number of qubits required; is a binary variable that represents whether ship s berths in time period τ-1. When ship s berths in time period τ-1, otherwise,

[0032] (302) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on ship berthing space in the port integrated energy system:

[0033]

[0034] Where, is the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on ship berths; SBB is the penalty coefficient of the two-variable quadratic unconstrained optimization model with inequality constraints on ship berths; The quantum bits required to relax the inequality constraint of the ship's berth at time t; is the maximum number of quantum bits required; N B is the total number of berths;

[0035] (303) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on power market transaction and energy storage charging and discharging in the port integrated energy system:

[0036]

[0037] Where, is the penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint of electricity market electricity transaction; P EM,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint of electricity market electricity transaction; P t EM,min is the minimum electricity transaction volume in the electricity market at time t; The quantum bits required to relax the variables on the left side of the electricity transaction inequality constraint at time t; is the maximum number of quantum bits required for the slack variables on the left side of the electricity trading inequality constraint at time t; is the penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint of electricity market electricity transaction; P EM,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the electricity market electricity transaction inequality constraint; P t EM,max is the maximum electricity transaction volume in the electricity market at time t; The quantum bits required for the relaxation variables on the right side of the electricity trading inequality constraint at time t; is the maximum number of quantum bits required for the slack variables on the right side of the electricity trading inequality constraint at time t; The penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the charging power of the power storage device; P EESC,L The penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the charging power of the power storage device; P t EES,ch,min is the minimum charging power of the power storage device at time t; The quantum bits required to relax the variables on the left side of the inequality constraint for charging the power storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the left side of the inequality constraint on the charging power of the power storage device at time t; H t EESC,U The penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the charging power of the power storage device; P EESC,U The penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the charging power of the power storage device; P t EES,ch,max is the maximum charging power of the power storage device at time t; The quantum bits required to relax the variables on the right side of the inequality constraint for the charging power of the power storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the right side of the inequality constraint on the charging power of the power storage device at time t; H t EESD,L is the penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the discharge power of the power storage device; P EESD,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the discharge power of the power storage device; P t EES,dis,min is the minimum discharge power of the power storage device at time t; is the quantum bit required to relax the variables on the left side of the inequality constraint for the discharge power of the power storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the left side of the inequality constraint for the discharge power of the power storage device at time t; H t EESD,U is the penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the discharge power of the power storage device; P EESD,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the discharge power of the power storage device; P t EES,dis,max is the maximum discharge power of the power storage device at time t; is the quantum bit required for the relaxation variable on the right side of the inequality constraint for the discharge power of the power storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the right side of the inequality constraint on the discharge power of the power storage device at time t; H tHESC,L is the penalty term of the binary quadratic unconstrained optimization model on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device; P HESC,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device; is the minimum hydrogen storage power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the left side of the inequality constraint for the hydrogen storage power of the hydrogen storage device at time t; is the maximum number of quantum bits required by the relaxed variables on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device at time t; H t HESC,U is the penalty term of the binary quadratic unconstrained optimization model on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device; P HESC,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device; is the maximum hydrogen storage power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the right side of the inequality constraint for the hydrogen storage power of the hydrogen storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device at time t; is the penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for hydrogen storage equipment discharge power; P HESD,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for hydrogen storage device discharge power; is the minimum hydrogen discharge power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the left side of the inequality constraint for the hydrogen storage device's hydrogen discharge power at time t; is the maximum number of quantum bits required to relax the variables on the left side of the inequality constraint for the hydrogen storage device's hydrogen discharge power at time t; is the penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for hydrogen storage device discharge power; P HESD,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for hydrogen storage device discharge power; is the maximum hydrogen discharge power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the right side of the inequality constraint for the hydrogen storage device's hydrogen discharge power at time t; is the maximum number of quantum bits required for the relaxation variable on the right side of the inequality constraint on the hydrogen storage device's hydrogen discharge power at time t.

[0038] Furthermore, in step (4), the objective function, equality constraint, and inequality constraint penalty terms of the binary quadratic unconstrained optimization model are combined to obtain the binary quadratic unconstrained optimization model of the port integrated energy system, which is expressed as follows:

[0039]

[0040] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0041] This paper considers introducing quantum computing technology into the field of port integrated energy system optimization, overcoming the efficiency bottleneck of traditional optimization algorithms when dealing with high-dimensional nonlinear constraints. By establishing a multi-temporal and spatial coupling model of energy equipment operation and ship scheduling requirements, the coordinated scheduling of port energy and ships is achieved. Compared with the heuristic algorithms of classical computers, the quantum parallel computing advantages of quantum computers can achieve exponential improvements in solution speed and obtain the global optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of the method of the present invention;

[0043] Figure 2 Plan the berthing time for ships;

[0044] Figure 3 Provide planning maps for distributed energy mobilization;

[0045] Figure 4 This is a line chart of electricity purchases for each time period. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0047] like Figure 1 As shown, the present invention proposes a method for optimizing a port integrated energy system based on quantum computing, comprising the following steps:

[0048] (1) Establish a binary quadratic unconstrained optimization model for the objective function of the port integrated energy system;

[0049] (2) Establish the penalty term of the binary quadratic unconstrained optimization model with equality constraints for the port integrated energy system;

[0050] (3) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints for the port integrated energy system;

[0051] (4) The objective function established in step (2), the equality constraint established in step (3), and the penalty term of the binary quadratic unconstrained optimization model established in step (4) are combined to obtain the binary quadratic unconstrained optimization model of the port integrated energy system;

[0052] (5) Use quantum computers to solve the binary quadratic unconstrained optimization model of the port integrated energy system and obtain the scheduling plan of the port integrated energy system.

[0053] Furthermore, in step (1), a binary quadratic unconstrained optimization model of the objective function of the port integrated energy system is established, which is expressed as follows:

[0054]

[0055] Where H obj is the objective function; s is the ship; N S is the total number of ships; t is the scheduling period; t-1 is the previous scheduling period of scheduling period t; N T is the total number of scheduling periods; is the penalty cost of delayed docking for vessel s; T ord Count the time periods; is a binary variable that represents whether ship s berths at time period t. When ship s berths at time period t, otherwise, is a binary variable representing whether ship s berths in period t-1. When ship s berths in period t-1, otherwise, T S,A is the actual arrival time of vessel s; The unit time docking cost of the ship after berthing; is the electricity market transaction price at time t; P t EM is the electricity market transaction volume at time t.

[0056] Furthermore, the specific process of step (2) is as follows:

[0057] (201) Establish the penalty term of the binary quadratic unconstrained optimization model with the equal constraint of the electric energy balance of the port integrated energy system:

[0058]

[0059] Where, P is the penalty term of the binary quadratic unconstrained optimization model constrained by the electric energy balance equation; EEB is the penalty coefficient of the binary quadratic unconstrained optimization model with electric energy balance equality constraint; i is the distributed energy; N I is the total number of distributed energy resources; is the active power output of distributed energy i at time t; P t ED is the electrolysis power of the electrolysis device at time t; P t EES,dis is the discharge power of the power storage device at time t; is the active power demand per unit time during the berthing period of ship s; P t EES,ch is the charging power of the power storage device at time t; P t load is the power load of the port integrated energy system at time t;

[0060] (202) Establish the penalty term of the binary quadratic unconstrained optimization model with equality constraints on the hydrogen energy balance of the port integrated energy system:

[0061]

[0062] Where, is the penalty term of the binary quadratic unconstrained optimization model constrained by the hydrogen energy balance equation; P HEB is the penalty coefficient of the binary quadratic unconstrained optimization model constrained by the hydrogen energy balance equation; ξ ED is the electrolysis efficiency of the electrolysis device; is the hydrogen discharge power of the hydrogen storage device at time t; H s S is the hydrogen demand of ship s per unit time; is the hydrogen storage power of the hydrogen storage device at time t; is the hydrogen demand of the port integrated energy system at time t;

[0063] (203) Establish the penalty term of the binary quadratic unconstrained optimization model with energy balance equality constraint for the port integrated energy system:

[0064]

[0065] Where, is the penalty term of the binary quadratic unconstrained optimization model constrained by the energy balance equation of the electric energy storage; EESB is the penalty coefficient of the two-variable quadratic unconstrained optimization model constrained by the energy balance equation of the electric energy storage; is the storage capacity of the power storage device at time t; EES,dis is the discharge efficiency of the power storage device; is the storage capacity of the power storage device at time t-1; EES,ch Charging efficiency of power storage devices; is the penalty term of the binary quadratic unconstrained optimization model constrained by the energy balance equation of hydrogen energy storage; HESBis the penalty coefficient of the binary quadratic unconstrained optimization model constrained by the energy balance equation of hydrogen energy storage; is the hydrogen storage capacity of the hydrogen storage device at time t; ξ HES,dis The hydrogen discharge efficiency of the hydrogen storage device; is the hydrogen storage capacity of the hydrogen storage device at time t-1; ξ HES,ch The hydrogen storage efficiency of the hydrogen storage device;

[0066] (204) Establish the penalty term of the binary quadratic unconstrained optimization model with equality constraint of ship docking time in the port integrated energy system:

[0067]

[0068] Where, is the penalty term of the binary quadratic unconstrained optimization model with equality constraint on ship docking time; P SBT is the penalty coefficient of the binary quadratic unconstrained optimization model with equality constraints on ship docking time; T s S,B is the continuous docking time requirement of ship s.

[0069] Furthermore, the specific process of step (3) is as follows:

[0070] (301) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on the ship docking status of the port integrated energy system:

[0071]

[0072] Where, is the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on the ship docking status; P SBS is the penalty coefficient of the binary quadratic unconstrained optimization model with inequality constraints on the ship's docking status; τ is the starting scheduling period for the ship to start docking; is a binary variable that represents whether ship s berths in time period τ. When ship s berths in time period τ, otherwise, n is the quantum bit count; The quantum bits required for the relaxation of the inequality constraint on the docking state of the ship at time t; is the maximum number of qubits required; is a binary variable that represents whether ship s berths in time period τ-1. When ship s berths in time period τ-1, otherwise,

[0073] (302) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on ship berthing space in the port integrated energy system:

[0074]

[0075] Where, is the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on ship berths; SBB is the penalty coefficient of the two-variable quadratic unconstrained optimization model with inequality constraints on ship berths; The quantum bits required to relax the inequality constraint of the ship's berth at time t; is the maximum number of quantum bits required; N B is the total number of berths;

[0076] (303) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on power market transaction and energy storage charging and discharging in the port integrated energy system:

[0077]

[0078] Where, is the penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint of electricity market electricity transaction; P EM,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint of electricity market electricity transaction; P t EM,min is the minimum electricity transaction volume in the electricity market at time t; The quantum bits required to relax the variables on the left side of the electricity transaction inequality constraint at time t; is the maximum number of quantum bits required for the slack variables on the left side of the electricity trading inequality constraint at time t; is the penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint of electricity market electricity transaction; P EM,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the electricity market electricity transaction inequality constraint; P t EM,max is the maximum electricity transaction volume in the electricity market at time t; The quantum bits required for the relaxation variables on the right side of the electricity trading inequality constraint at time t; is the maximum number of quantum bits required for the slack variables on the right side of the electricity trading inequality constraint at time t; The penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the charging power of the power storage device; P EESC,L The penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the charging power of the power storage device; P t EES,ch,min is the minimum charging power of the power storage device at time t; The quantum bits required to relax the variables on the left side of the inequality constraint for charging the power storage device at time t; is the maximum number of quantum bits required for the slack variable on the left side of the inequality constraint on the charging power of the power storage device at time t; The penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the charging power of the power storage device; P EESC,U The penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the charging power of the power storage device; P t EES,ch,max is the maximum charging power of the power storage device at time t; The quantum bits required to relax the variables on the right side of the inequality constraint for the charging power of the power storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the right side of the inequality constraint on the charging power of the power storage device at time t; H t EESD,L is the penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the discharge power of the power storage device; P EESD,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the discharge power of the power storage device; P t EES,dis,min is the minimum discharge power of the power storage device at time t; is the quantum bit required to relax the variables on the left side of the inequality constraint for the discharge power of the power storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the left side of the inequality constraint for the discharge power of the power storage device at time t; H t EESD,U is the penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the discharge power of the power storage device; P EESD,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the discharge power of the power storage device; P t EES,dis,max is the maximum discharge power of the power storage device at time t; is the quantum bit required for the relaxation variable on the right side of the inequality constraint for the discharge power of the power storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the right side of the inequality constraint on the discharge power of the power storage device at time t; is the penalty term of the binary quadratic unconstrained optimization model on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device; P HESC,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device; is the minimum hydrogen storage power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the left side of the inequality constraint for the hydrogen storage power of the hydrogen storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device at time t; is the penalty term of the binary quadratic unconstrained optimization model on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device; P HESC,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device; is the maximum hydrogen storage power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the right side of the inequality constraint for the hydrogen storage power of the hydrogen storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device at time t; is the penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for hydrogen storage equipment discharge power; P HESD,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for hydrogen storage device discharge power; is the minimum hydrogen discharge power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the left side of the inequality constraint for the hydrogen storage device's hydrogen discharge power at time t; is the maximum number of quantum bits required to relax the variables on the left side of the inequality constraint for the hydrogen storage device's hydrogen discharge power at time t; is the penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for hydrogen storage device discharge power; P HESD,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for hydrogen storage device discharge power; is the maximum hydrogen discharge power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the right side of the inequality constraint for the hydrogen storage device's hydrogen discharge power at time t; is the maximum number of quantum bits required for the relaxation variable on the right side of the inequality constraint on the hydrogen storage device's hydrogen discharge power at time t.

[0079] Furthermore, in step (4), the objective function, equality constraint, and inequality constraint penalty terms of the binary quadratic unconstrained optimization model are combined to obtain the binary quadratic unconstrained optimization model of the port integrated energy system, which is expressed as follows:

[0080]

[0081] This example uses a port integrated energy system as an example. Consider a ship berthing schedule for a particular day, with a 24-hour scheduling period. The example is tested on PyCharm, using a coherent Ising machine from Bose Quantum to solve a mixed-integer binary programming problem.

[0082] In this embodiment, port energy and ships are coordinated based on time-of-use electricity prices in the electricity market and ship berthing plans. Energy is supplied to the port by providing distributed energy with fixed, adjustable power and purchasing electricity from the grid. The electricity prices for each time period are shown in Table 1.

[0083] Table 1 Electricity market price ($ / MWh)

[0084]

[0085] Figure 2 The actual berthing status of ships is presented. All ships completed berthing and departure operations within the scheduled arrival and departure times. Berthing times were flexibly adjusted based on time-of-use electricity prices. Most ships berthed during off-peak and off-peak periods (electricity prices ≤ $8 / MWh). This reduced overall electricity costs, fully utilized the difference between peak and off-peak electricity prices, and achieved refined control of energy costs through dispatch.

[0086] Figure 3 、 Figure 4 The distributed energy resource deployment plan and power purchases for each time period are shown. Distributed energy resources i1, i2, and i3 have fixed adjustable powers of 5 kW, 10 kW, and 10 kW, respectively. The figure clearly shows that during the November–December and November–May periods, when electricity prices are highest, i1, i2, and i3 are fully operational, providing a combined 25 kW of peak power, directly replacing the majority of the purchased power load. Compared to a scenario without distributed energy resources, this effectively reduces grid-side power purchases during peak periods, avoiding significant electricity bills during high-price periods.

[0087] The embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for optimizing a port integrated energy system based on quantum computing, characterized in that: The following steps are involved: (1) Establish a binary quadratic unconstrained optimization model for the objective function of the port integrated energy system; (2) Establish the penalty term of the binary quadratic unconstrained optimization model with equality constraints for the port integrated energy system; (3) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints for the port integrated energy system; (4) The objective function established in step (2), the equality constraint established in step (3), and the penalty term of the binary quadratic unconstrained optimization model established in step (4) are combined to obtain the binary quadratic unconstrained optimization model of the port integrated energy system; (5) Use quantum computers to solve the binary quadratic unconstrained optimization model of the port integrated energy system and obtain the scheduling plan of the port integrated energy system.

2. The method for optimizing a port integrated energy system based on quantum computing according to claim 1, characterized in that: In step (1), a binary quadratic unconstrained optimization model of the objective function of the port integrated energy system is established, which is expressed as follows: Where H obj is the objective function; s is the ship; N S is the total number of ships; t is the scheduling period; t-1 is the previous scheduling period of scheduling period t; N T is the total number of scheduling periods; is the penalty cost of delayed docking for vessel s; T ord Count the time periods; is a binary variable that represents whether ship s berths at time period t. When ship s berths at time period t, otherwise, is a binary variable representing whether ship s berths in period t-1. When ship s berths in period t-1, otherwise, T S,A is the actual arrival time of vessel s; The unit time docking cost of the ship after berthing; is the electricity market transaction price at time t; P t EM is the electricity market transaction volume at time t.

3. The method for optimizing a port integrated energy system based on quantum computing according to claim 2, characterized in that: The specific process of step (2) is as follows: (201) Establish the penalty term of the binary quadratic unconstrained optimization model with the equal constraint of the electric energy balance of the port integrated energy system: Where H t EEB P is the penalty term of the binary quadratic unconstrained optimization model constrained by the electric energy balance equation; EEB is the penalty coefficient of the binary quadratic unconstrained optimization model with electric energy balance equality constraint; i is the distributed energy; N I is the total number of distributed energy resources; is the active power output of distributed energy i at time t; P t ED is the electrolysis power of the electrolysis device at time t; P t EES,dis is the discharge power of the power storage device at time t; P s S is the active power demand per unit time during the berthing period of ship s; P t EES,ch is the charging power of the power storage device at time t; P t load is the power load of the port integrated energy system at time t; (202) Establish the penalty term of the binary quadratic unconstrained optimization model with equality constraints on the hydrogen energy balance of the port integrated energy system: Where H t HEB is the penalty term of the binary quadratic unconstrained optimization model constrained by the hydrogen energy balance equation; P HEB is the penalty coefficient of the binary quadratic unconstrained optimization model constrained by the hydrogen energy balance equation; ξ ED is the electrolysis efficiency of the electrolysis device; is the hydrogen discharge power of the hydrogen storage device at time t; is the hydrogen demand of ship s per unit time; is the hydrogen storage power of the hydrogen storage device at time t; is the hydrogen demand of the port integrated energy system at time t; (203) Establish the penalty term of the binary quadratic unconstrained optimization model with energy balance equality constraint for the port integrated energy system: Where H t EESB is the penalty term of the binary quadratic unconstrained optimization model constrained by the energy balance equation of the electric energy storage; EESB is the penalty coefficient of the two-variable quadratic unconstrained optimization model constrained by the energy balance equation of the electric energy storage; is the storage capacity of the power storage device at time t; EES,dis is the discharge efficiency of the power storage device; is the storage capacity of the power storage device at time t-1; EES,ch is the charging efficiency of the power storage device; H t HESB is the penalty term of the binary quadratic unconstrained optimization model constrained by the energy balance equation of hydrogen energy storage; HESB is the penalty coefficient of the binary quadratic unconstrained optimization model constrained by the energy balance equation of hydrogen energy storage; is the hydrogen storage capacity of the hydrogen storage device at time t; ξ HES,dis The hydrogen discharge efficiency of the hydrogen storage device; is the hydrogen storage capacity of the hydrogen storage device at time t-1; ξ HES,ch The hydrogen storage efficiency of the hydrogen storage device; (204) Establish the penalty term of the binary quadratic unconstrained optimization model with equality constraint of ship docking time in the port integrated energy system: Where, is the penalty term of the binary quadratic unconstrained optimization model with equality constraint on ship docking time; P SBT is the penalty coefficient of the two-variable quadratic unconstrained optimization model with equality constraints on ship docking time; is the continuous docking time requirement of ship s.

4. The method for optimizing a port integrated energy system based on quantum computing according to claim 3 is characterized in that: The specific process of step (3) is as follows: (301) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on the ship docking status of the port integrated energy system: Where, is the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on the ship docking status; P SBS is the penalty coefficient of the binary quadratic unconstrained optimization model with inequality constraints on the ship's docking status; τ is the starting scheduling period for the ship to start docking; is a binary variable that represents whether ship s berths in time period τ. When ship s berths in time period τ, otherwise, n is the quantum bit count; The quantum bits required for the relaxation of the inequality constraint on the docking state of the ship at time t; is the maximum number of qubits required; is a binary variable that represents whether ship s berths in time period τ-1. When ship s berths in time period τ-1, otherwise, (302) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on ship berthing space in the port integrated energy system: Where H t SBB is the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on ship berths; SBB is the penalty coefficient of the two-variable quadratic unconstrained optimization model with inequality constraints on ship berths; The quantum bits required to relax the inequality constraint of the ship's berth at time t; is the maximum number of quantum bits required; N B is the total number of berths; (303) Establish the penalty term of the binary quadratic unconstrained optimization model with inequality constraints on power market transaction and energy storage charging and discharging in the port integrated energy system: Where, is the penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint of electricity market electricity transaction; P EM,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint of electricity market electricity transaction; P t EM,min is the minimum electricity transaction volume in the electricity market at time t; The quantum bits required to relax the variables on the left side of the electricity transaction inequality constraint at time t; is the maximum number of quantum bits required for the slack variables on the left side of the electricity transaction inequality constraint at time t; H t EM,U is the penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint of electricity market electricity transaction; P EM,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the electricity market electricity transaction inequality constraint; P t EM,max is the maximum electricity trading volume in the electricity market at time t; The quantum bits required for the relaxation variables on the right side of the electricity trading inequality constraint at time t; is the maximum number of quantum bits required for the slack variables on the right side of the electricity trading inequality constraint at time t; The penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the charging power of the power storage device; P EESC,L The penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the charging power of the power storage device; P t EES,ch,min is the minimum charging power of the power storage device at time t; The quantum bits required to relax the variables on the left side of the inequality constraint for charging the power storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the left side of the inequality constraint on the charging power of the power storage device at time t; H t EESC,U The penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the charging power of the power storage device; P EESC,U The penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the charging power of the power storage device; P t EES,ch,max is the maximum charging power of the power storage device at time t; The quantum bits required to relax the variables on the right side of the inequality constraint for the charging power of the power storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the right side of the inequality constraint on the charging power of the power storage device at time t; H t EESD,L is the penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the discharge power of the power storage device; P EESD,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for the discharge power of the power storage device; P t EES,dis,min is the minimum discharge power of the power storage device at time t; is the quantum bit required to relax the variables on the left side of the inequality constraint for the discharge power of the power storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the left side of the inequality constraint for the discharge power of the power storage device at time t; H t EESD,U is the penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the discharge power of the power storage device; P EESD,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for the discharge power of the power storage device; P t EES,dis,max is the maximum discharge power of the power storage device at time t; is the quantum bit required for the relaxation variable on the right side of the inequality constraint for the discharge power of the power storage device at time t; is the maximum number of quantum bits required for the relaxation variable on the right side of the inequality constraint on the discharge power of the power storage device at time t; H t HESC,L is the penalty term of the binary quadratic unconstrained optimization model on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device; P HESC,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device; is the minimum hydrogen storage power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the left side of the inequality constraint for the hydrogen storage power of the hydrogen storage device at time t; is the maximum number of quantum bits required by the relaxed variables on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device at time t; H t HESC,U is the penalty term of the binary quadratic unconstrained optimization model on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device; P HESC,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device; is the maximum hydrogen storage power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the right side of the inequality constraint for the hydrogen storage power of the hydrogen storage device at time t; is the maximum number of quantum bits required by the relaxation variable on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device at time t; H t HESD,L is the penalty term of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for hydrogen storage equipment discharge power; P HESD,L is the penalty coefficient of the binary quadratic unconstrained optimization model on the left side of the inequality constraint for hydrogen storage device discharge power; is the minimum hydrogen discharge power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the left side of the inequality constraint for the hydrogen storage device's hydrogen discharge power at time t; is the maximum number of quantum bits required to relax the variables on the left side of the inequality constraint for the hydrogen storage device's hydrogen discharge power at time t; is the penalty term of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for hydrogen storage device discharge power; P HESD,U is the penalty coefficient of the binary quadratic unconstrained optimization model on the right side of the inequality constraint for hydrogen storage device discharge power; is the maximum hydrogen discharge power of the hydrogen storage device at time t; The quantum bits required to relax the variables on the right side of the inequality constraint for the hydrogen storage device's hydrogen discharge power at time t; is the maximum number of quantum bits required for the relaxation variable on the right side of the inequality constraint on the hydrogen storage device's hydrogen discharge power at time t.

5. The method for optimizing a port integrated energy system based on quantum computing according to claim 4 is characterized in that: In step (4), the objective function, equality constraint, and inequality constraint penalty terms of the binary quadratic unconstrained optimization model are combined to obtain the binary quadratic unconstrained optimization model of the port integrated energy system, which is expressed as follows:

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