A port comprehensive energy system optimization method based on quantum computing

Abstract

The application discloses a port comprehensive energy system optimization method based on quantum calculation, and steps are as follows: 1) a binary quadratic unconstrained optimization model of a port comprehensive energy system objective function is established; 2) a binary quadratic unconstrained optimization model penalty term of a port comprehensive energy system equation constraint is established; 3) a binary quadratic unconstrained optimization model penalty term of a port comprehensive energy system inequality constraint is established; 4) the binary quadratic unconstrained optimization model penalty term of the inequality constraint established in the step (4) is combined with the objective function established in the step (2) and the equation constraint established in the step (3) to obtain a binary quadratic unconstrained optimization model of the port comprehensive energy system; and 5) a quantum computer is used to solve the binary quadratic unconstrained optimization model of the port comprehensive energy system to obtain a port comprehensive energy system scheduling scheme.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy technology, and specifically relates to an optimization method for a port integrated energy system based on quantum computing. Background Technology

[0002] With the rapid growth of global trade, port energy demand has surged. The traditional reliance on fossil fuels and grid power leads to high carbon emissions, necessitating the construction of a green integrated energy system that combines wind / solar power, energy storage, shore power, and multi-energy conversion. However, the diversification of energy sources has dramatically increased 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 coupling of multiple energy flows (electricity and hydrogen) is difficult, forming 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 programming models based on traditional computers, which have the following key drawbacks: Insufficient computational efficiency: When dealing with optimization problems involving multiple energy sources and multiple time scales (day-ahead and intraday coordination), traditional algorithms are prone to getting trapped in local optima or having excessively slow convergence speeds due to high dimensionality of dependent variables and complex constraints. Poor dynamic adaptability: Port loads are significantly affected by dynamic factors such as ship arrivals and loading / unloading operations, while existing methods are mostly based on static prediction data, which cannot effectively cope with real-time changes such as fluctuations in wind and solar power output and sudden load changes, leading to optimization results deviating from actual operating conditions. Insufficient multi-objective balance: When dealing with economic, technical, and environmental objectives, traditional multi-objective optimization algorithms struggle to balance solution diversity and convergence, often resulting in unreasonable weight allocation among objectives and uneven distribution of solution sets, affecting the scientific nature of decision-making.

[0004] In recent years, the rapid development of optical quantum computing technology has provided new ideas for solving the above problems. Compared with traditional computers, optical quantum computers have the following significant advantages: parallel computing capability; low power consumption and high scalability; noise suppression and stability.

[0005] In summary, existing technologies are insufficient to meet the demands of integrated port energy systems in terms of efficiency, dynamism, and multi-objective equilibrium. Quantum computing offers a revolutionary tool for addressing this challenge. This patent proposes a quantum computing-based coordinated optimization method. Through a quantum-classical hybrid algorithm design, multi-source data quantum encoding, 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: To address the technical problems mentioned in the background section, this invention proposes a method for optimizing a port integrated energy system based on quantum computing, comprising the following steps:

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

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

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

[0010] (4) Combine the objective function established in step (2), the equality constraints established in step (3), and the inequality constraints established in step (4) to form a penalty term for the bivariate quadratic unconstrained optimization model, and obtain the bivariate quadratic unconstrained optimization model of the port integrated energy system.

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

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

[0013]

[0014] In the formula, H obj Let N be the objective function; s be the ship; N be the number of ships. S N represents the total number of ships; t represents the scheduling period; t-1 represents the previous scheduling period t; N T This represents the total number of scheduling periods; The cost of the delay in berthing for vessel s; T ord Counting by time period; To characterize whether ship s berths at time t, when ship s berths at time t, otherwise, To characterize whether ship s berths at time t-1, when ship s berths at time t-1, otherwise, T S,A This refers to the actual arrival time of vessel s; The cost per unit time for a ship to berth; Let P be the electricity market transaction price at time t; t EM Let t represent the electricity market transaction volume at time t.

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

[0016] (201) Establish a penalty term for a two-variable quadratic unconstrained optimization model with constraints on the electrical energy balance equation of the port integrated energy system:

[0017]

[0018] In the formula, P is the penalty term for a bivariate quadratic unconstrained optimization model constrained by the energy balance equation; EEB The penalty coefficient for a bivariate quadratic unconstrained optimization model with constraints based on the energy balance equation is denoted as i; i represents distributed energy resources; N represents the energy source. I This represents the total number of distributed energy resources. P represents the active power output of distributed energy source i at time t; t ED P represents the electrolytic power of the electrolytic device at time t. t EES,dis Let t be the discharge power of the power storage device; P represents the active power demand per unit time during the berthing period of a ship. t EES,ch P represents the charging power of the energy storage device at time t. t load Let t be the electrical load of the port's integrated energy system.

[0019] (202) Establish a penalty term for a binary quadratic unconstrained optimization model with hydrogen energy balance equation constraint for a port integrated energy system:

[0020]

[0021] In the formula, P is the penalty term for a bivariate quadratic unconstrained optimization model constrained by the hydrogen energy balance equation; HEB ξ is the penalty coefficient for a bivariate quadratic unconstrained optimization model constrained by the hydrogen energy balance equation; ED The electrolysis efficiency of the electrolysis device; Let t be the hydrogen release power of the hydrogen storage device at time t; The hydrogen demand of a ship per unit time; Let t be the hydrogen storage power of the hydrogen storage device at time t; The hydrogen demand of the port's integrated energy system at time t;

[0022] (03) Establish a penalty term for a binary quadratic unconstrained optimization model with constraints on the energy balance equation of the port integrated energy system:

[0023]

[0024] In the formula, P is the penalty term in a bivariate quadratic unconstrained optimization model constrained by the energy balance equation for energy storage; EESB The penalty coefficient is the binary quadratic unconstrained optimization model constrained by the energy balance equation of electric energy storage. Let ξ be the amount of electricity stored in the power storage device at time t; EES,dis The discharge efficiency of power storage devices; Let ξ be the amount of electricity stored in the power storage device at time t-1; EES,ch The charging efficiency of power storage devices; The penalty term for the bivariate quadratic unconstrained optimization model constrained by the hydrogen energy storage energy balance equation; P HESB The penalty coefficient is the binary quadratic unconstrained optimization model constrained by the hydrogen energy storage energy balance equation. Let ξ be the amount of hydrogen stored in the hydrogen storage device at time t; HES,dis The hydrogen release efficiency of hydrogen storage equipment; ξ represents the amount of hydrogen stored in the hydrogen storage device at time t-1; HES,ch The hydrogen storage efficiency of hydrogen storage equipment;

[0025] (204) Establish a penal term for a binary quadratic unconstrained optimization model with constraints on the equation of ship berthing time in a port integrated energy system:

[0026]

[0027] In the formula, The penalty term for the bivariate quadratic unconstrained optimization model constrained by the ship berthing time equation; P SBT The penalty coefficient is used for the bivariate quadratic unconstrained optimization model constrained by the equation of ship berthing time. This refers to the continuous berthing time requirement for vessel S.

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

[0029] (301) Establish a penal term for a bivariate quadratic unconstrained optimization model with constraints on the ship berthing status inequality of a port integrated energy system:

[0030]

[0031] In the formula, The penalty term for the bivariate quadratic unconstrained optimization model constrained by the ship berthing state inequality; P SBS τ is the penalty coefficient for the bivariate quadratic unconstrained optimization model constrained by the ship berthing state inequality; τ represents the initial scheduling period for the ship to begin berthing. To characterize whether ship s berths at time τ as a binary variable, when ship s berths at time τ, otherwise, n is the number of qubits; The number of qubits required to relax the constraints of the ship's berthing state inequality at time t; This represents the maximum number of qubits required. To characterize whether ship s berths in time period τ-1 as a binary variable, when ship s berths in time period τ-1, otherwise,

[0032] (302) Establish a penal term for a bivariate quadratic unconstrained optimization model with constraints on the ship berthing inequality in a port integrated energy system:

[0033]

[0034] In the formula, The penalty term for the bivariate quadratic unconstrained optimization model with constraints on ship berthing inequalities; P SBB The penalty coefficient is the binary quadratic unconstrained optimization model constrained by the ship berthing inequality. The number of qubits required to relax the constraints of the ship berthing inequality at time t; N is the maximum number of qubits required; B This represents the total number of berths.

[0035] (303) Establish a penal term for a binary quadratic unconstrained optimization model with inequality constraints on electricity market power trading and energy storage charging / discharging in a port integrated energy system:

[0036]

[0037] In the formula, P is the penalty term in the bivariate quadratic unconstrained optimization model on the left-hand side of the electricity market electricity trading inequality constraint; EM,L P represents the penalty coefficient for the bivariate quadratic unconstrained optimization model on the left-hand side of the electricity market electricity trading inequality constraint; t EM,min Let t be the minimum electricity transaction volume in the electricity market. The number of qubits required to relax the left-hand side of the electricity trading inequality constraint at time t; Let be the maximum number of qubits required for the relaxation variables on the left side of the electricity trading inequality constraint at time t; P is the penalty term in the bivariate quadratic unconstrained optimization model on the right-hand side of the electricity market electricity trading inequality constraint; EM,U P represents the penalty coefficient for the bivariate quadratic unconstrained optimization model on the right-hand side of the electricity market electricity trading inequality constraint; t EM,max Let t be the maximum electricity trading volume in the electricity market. The number of qubits required for the relaxation variables on the right-hand side of the electricity trading inequality constraint at time t; Let be the maximum number of qubits required for the relaxation variables on the right side of the electricity trading inequality constraint at time t; The penalty term for the bivariate quadratic unconstrained optimization model on the left-hand side of the charging power inequality constraint for power storage devices; P EESC,L The penalty coefficient for the bivariate quadratic unconstrained optimization model on the left-hand side of the charging power inequality constraint for power storage devices; P t EES,ch,min Let t be the minimum charging power of the power storage device at time t; The number of qubits required to relax the left-hand side of the inequality constraint on the charging power inequality of the power storage device at time t; The maximum number of qubits required to relax the left-hand side of the charging power inequality constraint for the power storage device at time t; H t EESC,U The penalty term for the bivariate quadratic unconstrained optimization model on the right-hand side of the charging power inequality constraint for power storage devices; P EESC,U The penalty coefficient for the bivariate quadratic unconstrained optimization model on the right-hand side of the charging power inequality constraint for power storage devices; P t EES,ch,max Let t be the maximum charging power of the power storage device at time t; The number of qubits required to relax the right-hand side of the inequality constraint on the charging power inequality of the power storage device at time t; H represents the maximum number of qubits required to relax the right-hand side of the inequality constraint on the charging power of the power storage device at time t; t EESD,L P is the penalty term for the bivariate quadratic unconstrained optimization model on the left-hand side of the discharge power inequality constraint for power storage devices; EESD,L P is the penalty coefficient for the bivariate quadratic unconstrained optimization model on the left-hand side of the discharge power inequality constraint of the power storage device; t EES,dis,min Let t be the minimum discharge power of the power storage device at time t; Let be the number of qubits required to relax the left-hand side of the inequality constraint on the discharge power of the power storage device at time t. H represents the maximum number of qubits required to relax the left-hand side of the discharge power inequality constraint of the power storage device at time t; t EESD,U P is the penalty term in the bivariate quadratic unconstrained optimization model on the right-hand side of the discharge power inequality constraint for power storage devices; EESD,U P is the penalty coefficient for the bivariate quadratic unconstrained optimization model on the right-hand side of the discharge power inequality constraint for power storage devices; t EES,dis,max Let t be the maximum discharge power of the power storage device. Let be the number of qubits required to relax the right-hand side of the inequality constraint on the discharge power inequality of the power storage device at time t. H represents the maximum number of qubits required to relax the right-hand side of the discharge power inequality constraint for the power storage device at time t; tHESC,L The penalty term for the binary quadratic unconstrained optimization model on the left-hand side of the hydrogen storage power inequality constraint for hydrogen storage devices; P HESC,L The penalty coefficient for the binary quadratic unconstrained optimization model on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device; Let t be the minimum hydrogen storage power of the hydrogen storage device at time t; Let be the number of qubits required to relax the left-hand side of the hydrogen storage power inequality constraint for the hydrogen storage device at time t. H represents the maximum number of qubits required to relax the left-hand side of the hydrogen storage power inequality constraint for the hydrogen storage device at time t. t HESC,U The penalty term for the binary quadratic unconstrained optimization model on the right-hand side of the hydrogen storage power inequality constraint for hydrogen storage devices; P HESC,U The penalty coefficient for the binary quadratic unconstrained optimization model on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device; Let t be the maximum hydrogen storage capacity of the hydrogen storage device at time t; Let be the number of qubits required to relax the right-hand side of the hydrogen storage power inequality constraint for the hydrogen storage device at time t. Let t be the maximum number of qubits required for the relaxation variables on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device at time t; The penalty term for the binary quadratic unconstrained optimization model on the left-hand side of the hydrogen release power inequality constraint for hydrogen storage devices; P HESD,L The penalty coefficient for the binary quadratic unconstrained optimization model on the left side of the hydrogen release power inequality constraint of the hydrogen storage device; Let t be the minimum hydrogen release power of the hydrogen storage device at time t; Let be the number of qubits required to relax the left-hand side of the hydrogen release power inequality constraint of the hydrogen storage device at time t. Let t be the maximum number of qubits required to relax the left-hand side of the hydrogen release power inequality constraint of the hydrogen storage device at time t. The penalty term for the binary quadratic unconstrained optimization model on the right-hand side of the hydrogen release power inequality constraint for hydrogen storage devices; P HESD,U The penalty coefficient for the binary quadratic unconstrained optimization model on the right side of the hydrogen release power inequality constraint of the hydrogen storage device; Let t be the maximum hydrogen release power of the hydrogen storage device at time t; Let be the number of qubits required to relax the right-hand side of the hydrogen release power inequality constraint for the hydrogen storage device at time t. Let t be the maximum number of qubits required to relax the right-hand side of the hydrogen release power inequality constraint of the hydrogen storage device at time t.

[0038] Furthermore, in step (4), the penalty term of the bivariate quadratic unconstrained optimization model is simultaneously established with the objective function, equality constraints, and inequality constraints to obtain the bivariate quadratic unconstrained optimization model of the port integrated energy system, 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 invention proposes to introduce quantum computing technology into the optimization of integrated port energy systems, overcoming the efficiency bottleneck of traditional optimization algorithms when dealing with high-dimensional nonlinear constraints. By establishing a multi-temporal-spatial coupling model of energy equipment operation and ship scheduling needs, coordinated scheduling of port energy and ships is achieved. Compared with heuristic algorithms of classical computers, the quantum parallel computing advantage of quantum computers can achieve an exponential increase in solution speed and obtain the global optimal solution. Attached Figure Description

[0042] Figure 1 This is a flowchart of the method of the present invention;

[0043] Figure 2 A schedule for ship berthing times;

[0044] Figure 3 A distributed energy dispatch planning map;

[0045] Figure 4 Line graph showing electricity purchases for different time periods. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

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

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

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

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

[0051] (4) Combine the objective function established in step (2), the equality constraints established in step (3), and the inequality constraints established in step (4) to form a penalty term for the bivariate quadratic unconstrained optimization model, and obtain the bivariate quadratic unconstrained optimization model of the port integrated energy system.

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

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

[0054]

[0055] In the formula, H obj Let N be the objective function; s be the ship; N be the number of ships. S N represents the total number of ships; t represents the scheduling period; t-1 represents the previous scheduling period t; N T This represents the total number of scheduling periods; The cost of the delay in berthing for vessel s; T ord Counting by time period; To characterize whether ship s berths at time t, when ship s berths at time t, otherwise, To characterize whether ship s berths at time t-1, when ship s berths at time t-1, otherwise, T S,A This refers to the actual arrival time of vessel s; The cost per unit time for a ship to berth; Let P be the electricity market transaction price at time t; t EM Let t represent the electricity market transaction volume at time t.

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

[0057] (201) Establish a penalty term for a two-variable quadratic unconstrained optimization model with constraints on the electrical energy balance equation of the port integrated energy system:

[0058]

[0059] In the formula, P is the penalty term for a bivariate quadratic unconstrained optimization model constrained by the energy balance equation; EEB The penalty coefficient for a bivariate quadratic unconstrained optimization model with constraints based on the energy balance equation is denoted as i; i represents distributed energy resources; N represents the energy source. I This represents the total number of distributed energy resources. P represents the active power output of distributed energy source i at time t; t ED P represents the electrolytic power of the electrolytic device at time t. t EES,dis Let t be the discharge power of the power storage device; P represents the active power demand per unit time during the berthing period of a ship. t EES,ch P represents the charging power of the energy storage device at time t. t load Let t be the electrical load of the port's integrated energy system.

[0060] (202) Establish a penalty term for a binary quadratic unconstrained optimization model with hydrogen energy balance equation constraint for a port integrated energy system:

[0061]

[0062] In the formula, P is the penalty term for a bivariate quadratic unconstrained optimization model constrained by the hydrogen energy balance equation; HEB ξ is the penalty coefficient for a bivariate quadratic unconstrained optimization model constrained by the hydrogen energy balance equation; ED The electrolysis efficiency of the electrolysis device; H represents the hydrogen release power of the hydrogen storage device at time t. s S The hydrogen demand of a ship per unit time; Let t be the hydrogen storage power of the hydrogen storage device at time t; The hydrogen demand of the port's integrated energy system at time t;

[0063] (03) Establish a penalty term for a binary quadratic unconstrained optimization model with constraints on the energy balance equation of the port integrated energy system:

[0064]

[0065] In the formula, P is the penalty term in a bivariate quadratic unconstrained optimization model constrained by the energy balance equation for energy storage; EESB The penalty coefficient is the binary quadratic unconstrained optimization model constrained by the energy balance equation of electric energy storage. Let ξ be the amount of electricity stored in the power storage device at time t; EES,dis The discharge efficiency of power storage devices; Let ξ be the amount of electricity stored in the power storage device at time t-1; EES,ch The charging efficiency of power storage devices; P is the penalty term in a bivariate quadratic unconstrained optimization model constrained by the hydrogen energy storage energy balance equation; HESBThe penalty coefficient is the binary quadratic unconstrained optimization model constrained by the hydrogen energy storage energy balance equation. Let ξ be the amount of hydrogen stored in the hydrogen storage device at time t; HES,dis The hydrogen release efficiency of hydrogen storage equipment; ξ represents the amount of hydrogen stored in the hydrogen storage device at time t-1; HES,ch The hydrogen storage efficiency of hydrogen storage equipment;

[0066] (204) Establish a penal term for a binary quadratic unconstrained optimization model with constraints on the equation of ship berthing time in a port integrated energy system:

[0067]

[0068] In the formula, The penalty term for the bivariate quadratic unconstrained optimization model constrained by the equation of ship berthing time; P SBT T is the penalty coefficient for a bivariate quadratic unconstrained optimization model with constraints based on the ship berthing time equation; s S,B This refers to the continuous berthing time requirement for vessel S.

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

[0070] (301) Establish a penal term for a bivariate quadratic unconstrained optimization model constrained by the ship berthing state inequality in a port integrated energy system:

[0071]

[0072] In the formula, The penalty term for the bivariate quadratic unconstrained optimization model constrained by the ship berthing state inequality; P SBS τ is the penalty coefficient for the bivariate quadratic unconstrained optimization model constrained by the ship berthing state inequality; τ represents the initial scheduling period for the ship to begin berthing. To characterize whether ship s berths at time τ as a binary variable, when ship s berths at time τ, otherwise, n is the number of qubits; The number of qubits required to relax the constraints of the ship's berthing state inequality at time t; This represents the maximum number of qubits required. To characterize whether ship s berths in time period τ-1 as a binary variable, when ship s berths in time period τ-1, otherwise,

[0073] (302) Establish a penal term for a bivariate quadratic unconstrained optimization model with constraints on the ship berthing inequality in a port integrated energy system:

[0074]

[0075] In the formula, The penalty term for the bivariate quadratic unconstrained optimization model with constraints on ship berthing inequalities; P SBB The penalty coefficient is the binary quadratic unconstrained optimization model constrained by the ship berthing inequality. The number of qubits required to relax the constraints of the ship berthing inequality at time t; N is the maximum number of qubits required; B This represents the total number of berths.

[0076] (303) Establish a penal term for a binary quadratic unconstrained optimization model with inequality constraints on electricity market power trading and energy storage charging / discharging in a port integrated energy system:

[0077]

[0078] In the formula, P is the penalty term in the bivariate quadratic unconstrained optimization model on the left-hand side of the electricity market electricity trading inequality constraint; EM,L P represents the penalty coefficient for the bivariate quadratic unconstrained optimization model on the left-hand side of the electricity market electricity trading inequality constraint; t EM,min Let t be the minimum electricity transaction volume in the electricity market. The number of qubits required to relax the left-hand side of the electricity trading inequality constraint at time t; Let be the maximum number of qubits required for the relaxation variables on the left side of the electricity trading inequality constraint at time t; P is the penalty term in the bivariate quadratic unconstrained optimization model on the right-hand side of the electricity market electricity trading inequality constraint; EM,U P represents the penalty coefficient for the bivariate quadratic unconstrained optimization model on the right-hand side of the electricity market electricity trading inequality constraint; t EM,max Let t be the maximum electricity trading volume in the electricity market. The number of qubits required for the relaxation variables on the right-hand side of the electricity trading inequality constraint at time t; Let be the maximum number of qubits required for the relaxation variables on the right side of the electricity trading inequality constraint at time t; The penalty term for the bivariate quadratic unconstrained optimization model on the left-hand side of the charging power inequality constraint for power storage devices; P EESC,L The penalty coefficient for the bivariate quadratic unconstrained optimization model on the left-hand side of the charging power inequality constraint for power storage devices; P t EES,ch,min Let t be the minimum charging power of the power storage device at time t; The number of qubits required to relax the left-hand side of the inequality constraint on the charging power inequality of the power storage device at time t; The maximum number of qubits required to relax the left-hand side of the inequality constraint on the charging power of the power storage device at time t; The penalty term for the bivariate quadratic unconstrained optimization model on the right-hand side of the charging power inequality constraint for power storage devices; P EESC,U The penalty coefficient for the bivariate quadratic unconstrained optimization model on the right-hand side of the charging power inequality constraint for power storage devices; P t EES,ch,max Let t be the maximum charging power of the power storage device at time t; The number of qubits required to relax the right-hand side of the inequality constraint on the charging power inequality of the power storage device at time t; H represents the maximum number of qubits required to relax the right-hand side of the inequality constraint on the charging power of the power storage device at time t; t EESD,L P is the penalty term for the bivariate quadratic unconstrained optimization model on the left-hand side of the discharge power inequality constraint for power storage devices; EESD,L P is the penalty coefficient for the bivariate quadratic unconstrained optimization model on the left-hand side of the discharge power inequality constraint of the power storage device; t EES,dis,min Let t be the minimum discharge power of the power storage device at time t; Let be the number of qubits required to relax the left-hand side of the inequality constraint on the discharge power of the power storage device at time t. H represents the maximum number of qubits required to relax the left-hand side of the discharge power inequality constraint of the power storage device at time t; t EESD,U P is the penalty term in the bivariate quadratic unconstrained optimization model on the right-hand side of the discharge power inequality constraint for power storage devices; EESD,U P is the penalty coefficient for the bivariate quadratic unconstrained optimization model on the right-hand side of the discharge power inequality constraint for power storage devices; t EES,dis,max Let t be the maximum discharge power of the power storage device. Let be the number of qubits required to relax the right-hand side of the inequality constraint on the discharge power inequality of the power storage device at time t. Let be the maximum number of qubits required for the relaxation variables on the right-hand side of the discharge power inequality constraint of the power storage device at time t; The penalty term for the binary quadratic unconstrained optimization model on the left-hand side of the hydrogen storage power inequality constraint for hydrogen storage devices; P HESC,L The penalty coefficient for the binary quadratic unconstrained optimization model on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device; Let t be the minimum hydrogen storage power of the hydrogen storage device at time t; Let be the number of qubits required to relax the left-hand side of the hydrogen storage power inequality constraint for the hydrogen storage device at time t. Let t be the maximum number of qubits required to relax the left-hand side of the hydrogen storage power inequality constraint of the hydrogen storage device at time t. The penalty term for the binary quadratic unconstrained optimization model on the right-hand side of the hydrogen storage power inequality constraint for hydrogen storage devices; P HESC,U The penalty coefficient for the binary quadratic unconstrained optimization model on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device; Let t be the maximum hydrogen storage capacity of the hydrogen storage device at time t; Let be the number of qubits required to relax the right-hand side of the hydrogen storage power inequality constraint for the hydrogen storage device at time t. Let t be the maximum number of qubits required for the relaxation variables on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device at time t; The penalty term for the binary quadratic unconstrained optimization model on the left-hand side of the hydrogen release power inequality constraint for hydrogen storage devices; P HESD,L The penalty coefficient for the binary quadratic unconstrained optimization model on the left side of the hydrogen release power inequality constraint of the hydrogen storage device; Let t be the minimum hydrogen release power of the hydrogen storage device at time t; Let be the number of qubits required to relax the left-hand side of the hydrogen release power inequality constraint of the hydrogen storage device at time t. Let t be the maximum number of qubits required to relax the left-hand side of the hydrogen release power inequality constraint of the hydrogen storage device at time t. The penalty term for the binary quadratic unconstrained optimization model on the right-hand side of the hydrogen release power inequality constraint for hydrogen storage devices; P HESD,U The penalty coefficient for the binary quadratic unconstrained optimization model on the right side of the hydrogen release power inequality constraint of the hydrogen storage device; Let t be the maximum hydrogen release power of the hydrogen storage device at time t; Let be the number of qubits required to relax the right-hand side of the hydrogen release power inequality constraint for the hydrogen storage device at time t. Let t be the maximum number of qubits required to relax the right-hand side of the hydrogen release power inequality constraint of the hydrogen storage device at time t.

[0079] Furthermore, in step (4), the penalty term of the bivariate quadratic unconstrained optimization model is simultaneously established with the objective function, equality constraints, and inequality constraints to obtain the bivariate quadratic unconstrained optimization model of the port integrated energy system, as follows:

[0080]

[0081] A port integrated energy system is used as an example. A daily ship berthing schedule is implemented, with a scheduling time of 24 hours. The case study is implemented on PyCharm, using a Bose Quantum coherent Ising machine to solve the constructed mixed-integer binary programming problem.

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

[0083] Table 1 Electricity Market Prices ($ / MWh)

[0084]

[0085] Figure 2 The actual berthing status of the vessels is presented. All vessels completed berthing and departure operations within the scheduled arrival-departure time, and the berthing time was flexibly adjusted according to the time-of-use electricity price. Most vessels berthed during off-peak and shallow peak periods (electricity price ≤ $8 / MWh), which reduced the overall electricity cost, made full use of the peak-valley electricity price difference, and achieved refined control of energy costs by dispatching.

[0086] Figure 3 , Figure 4 The distributed energy resource deployment plan and the electricity purchase volume for each time period are presented. The fixed adjustable power of distributed energy sources i1, i2, and i3 are 5kW, 10kW, and 10kW, respectively. The diagram clearly shows that during the periods with the highest electricity prices (11–12 and 13–15), i1, i2, and i3 are operational throughout the entire period, providing a total peak power of 25kW, directly replacing most of the purchased electricity load. Compared to the scenario without distributed energy resources, this effectively suppresses the amount of electricity purchased from the grid during peak periods, avoiding large expenditures on electricity bills during high-price periods.

[0087] The embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A method for optimizing a port integrated energy system based on quantum computing, characterized in that, Includes the following steps: (1) Establish a bivariate quadratic unconstrained optimization model for the objective function of the port integrated energy system; (2) Establish a penalty term for a binary quadratic unconstrained optimization model with equality constraints for the port integrated energy system, including: (201) Establish a penalty term for a binary quadratic unconstrained optimization model with constraints on the electrical energy balance equation of the port integrated energy system; (202) Establish a penalty term for a binary quadratic unconstrained optimization model with hydrogen energy balance equation constraint for a port integrated energy system; (203) Establish a penalty term for a binary quadratic unconstrained optimization model with constraints on the energy balance equation of the port integrated energy system; (204) Establish a penalty term for a binary quadratic unconstrained optimization model with constraints on the equation of ship berthing time in a port integrated energy system; (3) Establish a penalty term for a bivariate quadratic unconstrained optimization model with inequality constraints for the port integrated energy system, including: (301) Establish a penalty term for a binary quadratic unconstrained optimization model with constraints on the ship berthing status inequality of the port integrated energy system; (302) Establish a penal term for a binary quadratic unconstrained optimization model with constraints on the inequality of ship berthing positions in a port integrated energy system; (303) Establish a penal term for a binary quadratic unconstrained optimization model with inequality constraints on electricity trading and energy storage charging and discharging in the port integrated energy system electricity market; (4) Combine the objective function established in step (1), the equality constraints established in step (2), and the inequality constraints established in step (3) to form a penalty term for the bivariate quadratic unconstrained optimization model, and obtain the bivariate quadratic unconstrained optimization model of the port integrated energy system. (5) Use a quantum computer to solve the binary quadratic unconstrained optimization model of the port integrated energy system and obtain the scheduling scheme of the port integrated energy system; In step (1), a bivariate quadratic unconstrained optimization model of the objective function of the port integrated energy system is established, as follows: （1） In the formula, The objective function is... For ships; The total number of ships; t is the scheduling period; t-1 is the previous scheduling period of scheduling period t; This represents the total number of scheduling periods; For ships The cost of penalties for delayed docking; Counting by time period; To characterize ships During the period Whether or not to berth is a binary variable, when the ship During the period When berthing, ,otherwise, ; To characterize ships During the period Whether or not to berth is a binary variable, when the ship During the period When berthing, ,otherwise, ; For ships The actual arrival time; The cost per unit time for a ship to berth; for Real-time electricity market transaction prices; for Real-time electricity market transaction volume.

2. The method for optimizing a port integrated energy system based on quantum computing according to claim 1, characterized in that, The specific process of step (2) is as follows: (201) Establish a penalty term for a two-variable quadratic unconstrained optimization model with constraints on the electrical energy balance equation of the port integrated energy system: （2） In the formula, The penalty term for a binary quadratic unconstrained optimization model constrained by the energy balance equation; The penalty coefficient is the coefficient for a bivariate quadratic unconstrained optimization model constrained by the energy balance equation. It is a distributed energy source; This represents the total number of distributed energy resources. for Distributed energy at all times Those who have made meritorious contributions; for The electrolysis power of the electrolysis device at any given time; for Discharge power of the power storage device at any given time; For ships Active power demand per unit time during berthing period; for The charging power of the power storage device at any given time; for The electrical load of the port's integrated energy system at all times; (202) Establish a penalty term for a binary quadratic unconstrained optimization model with hydrogen energy balance equation constraint for a port integrated energy system: （3） In the formula, The penalty term for the binary quadratic unconstrained optimization model constrained by the hydrogen energy balance equation; The penalty coefficient is the binary quadratic unconstrained optimization model constrained by the hydrogen energy balance equation. The electrolysis efficiency of the electrolysis device; for Hydrogen release power of hydrogen storage equipment at all times; For ships Hydrogen demand per unit time; for Hydrogen storage capacity of hydrogen storage equipment at any given time; for Hydrogen demand of the port's integrated energy system at all times; (203) Establish a penalty term for a binary quadratic unconstrained optimization model with constraints on the energy balance equation of the port integrated energy system: （4） （5） In the formula, The penalty term for the binary quadratic unconstrained optimization model constrained by the energy balance equation of electric energy storage; The penalty coefficient is the binary quadratic unconstrained optimization model constrained by the energy balance equation of electric energy storage. for The amount of electricity stored in the power storage device at any time; The discharge efficiency of power storage devices; for The amount of electricity stored in the power storage device at any time; The charging efficiency of power storage devices; The penalty term for the binary quadratic unconstrained optimization model constrained by the hydrogen energy storage energy balance equation; The penalty coefficient is the binary quadratic unconstrained optimization model constrained by the hydrogen energy storage energy balance equation. for The hydrogen storage capacity of the hydrogen storage device at any given time; The hydrogen release efficiency of hydrogen storage equipment; for The hydrogen storage capacity of the hydrogen storage device at any given time; The hydrogen storage efficiency of hydrogen storage equipment; (204) Establish a penal term for a binary quadratic unconstrained optimization model with constraints on the equation of ship berthing time in a port integrated energy system: （6） In the formula, The penalty term for a binary quadratic unconstrained optimization model constrained by the equation of ship berthing time; The penalty coefficient is used for the bivariate quadratic unconstrained optimization model constrained by the equation of ship berthing time. For ships The continuous docking time requirement.

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 (3) is as follows: (301) Establish a penal term for a bivariate quadratic unconstrained optimization model with constraints on the ship berthing state inequality of a port integrated energy system: （7） In the formula, The penalty term for the binary quadratic unconstrained optimization model constrained by the ship berthing state inequality; The penalty coefficient is the two-variable quadratic unconstrained optimization model constrained by the ship berthing state inequality. This indicates the initial scheduling period for a ship to begin berthing; To characterize ships During the period Whether or not to berth is a binary variable, when the ship During the period When berthing, ,otherwise, ; Counting qubits; for The number of qubits required to relax the constraints of the ship berthing state inequality at any given time; This represents the maximum number of qubits required. To characterize ships During the period Whether or not to berth is a binary variable, when the ship During the period When berthing, ,otherwise, ; (302) Establish a penal term for a bivariate quadratic unconstrained optimization model with inequality constraints on ship berthing positions in a port integrated energy system: （8） In the formula, The penalty term for a binary quadratic unconstrained optimization model constrained by the ship berthing inequality; The penalty coefficient is the binary quadratic unconstrained optimization model constrained by the ship berthing inequality. for The number of qubits required to relax the constraints of the ship berthing inequality at any given time; This represents the maximum number of qubits required. This represents the total number of berths. (303) Establish a penal term for a binary quadratic unconstrained optimization model with inequality constraints on electricity trading and energy storage charging / discharging in the port integrated energy system's electricity market: （9） （10） （11） （12） （13） （14） （15） （16） （17） （18） In the formula, This is the penalty term for the bivariate quadratic unconstrained optimization model on the left-hand side of the electricity market electricity trading inequality constraint. The penalty coefficient is the left-hand side of the bivariate quadratic unconstrained optimization model constrained by the electricity market electricity trading inequality. for Minimum electricity trading volume in the electricity market at any given moment; for The number of qubits required to relax the left-hand slack variables of the instantaneous electricity trading inequality constraint; for The maximum number of qubits required to relax the left-hand side of the time-to-time electricity trading inequality constraint; This is the penalty term for the bivariate quadratic unconstrained optimization model on the right-hand side of the electricity market electricity trading inequality constraint. The penalty coefficient is the right-hand side of the bivariate quadratic unconstrained optimization model for the electricity market electricity trading inequality constraint. for The maximum electricity trading volume in the electricity market at any given moment; for The number of qubits required to constrain the right-hand slack variable of the instantaneous electricity trading inequality; for The maximum number of qubits required to relax the right-hand side of the electricity trading inequality constraint at any given time. The penalty term for the binary quadratic unconstrained optimization model on the left-hand side of the inequality constraint on the charging power of power storage devices; The penalty coefficient for the bivariate quadratic unconstrained optimization model on the left side of the inequality constraint on the charging power of power storage devices; for Minimum charging power of the power storage device at any given time; for The number of qubits required to relax the left-hand side of the charging power inequality constraint for the instantaneous power storage device. for The maximum number of qubits required for the relaxation variables on the left side of the charging power inequality constraint of the power storage device at any given time; The penalty term for the binary quadratic unconstrained optimization model on the right-hand side of the inequality constraint on the charging power of power storage devices; The penalty coefficient for the bivariate quadratic unconstrained optimization model on the right-hand side of the charging power inequality constraint for power storage devices; for The maximum charging power of the power storage device at any given time; for The number of qubits required for the relaxation variables on the right-hand side of the charging power inequality constraint of the instantaneous power storage device. for The maximum number of qubits required for the relaxation variables on the right side of the charging power inequality constraint of the power storage device at any given time; The penalty term for the binary quadratic unconstrained optimization model on the left-hand side of the discharge power inequality constraint for power storage devices; The penalty coefficient is given to the left-hand side of the discharge power inequality constraint of the power storage device in the bivariate quadratic unconstrained optimization model. for Minimum discharge power of the power storage device at any given time; for The number of qubits required to relax the left-hand side of the discharge power inequality constraint for the instantaneous power storage device. for The maximum number of qubits required to relax the left-hand side of the discharge power inequality constraint of the power storage device at any given time; This is the penalty term for the binary quadratic unconstrained optimization model on the right-hand side of the discharge power inequality constraint for power storage devices. The penalty coefficient is given to the right-hand side of the discharge power inequality constraint of the power storage device in the bivariate quadratic unconstrained optimization model. for The maximum discharge power of the power storage device at any given time; for The number of qubits required to relax the right-hand side of the discharge power inequality constraint for the instantaneous power storage device. for The maximum number of qubits required to relax the right-hand side of the discharge power inequality constraint of the power storage device at any given time; The penalty term for the binary quadratic unconstrained optimization model on the left-hand side of the hydrogen storage power inequality constraint for hydrogen storage devices is used. The penalty coefficient for the binary quadratic unconstrained optimization model on the left side of the hydrogen storage power inequality constraint of the hydrogen storage device; for Minimum hydrogen storage capacity of the hydrogen storage device at any given time; for The number of qubits required to relax the left-hand side of the hydrogen storage power inequality constraint for a hydrogen storage device at any given time. for The maximum number of qubits required to relax the left-hand side of the hydrogen storage power inequality constraint for a hydrogen storage device at any given time. The penalty term for the binary quadratic unconstrained optimization model on the right-hand side of the hydrogen storage power inequality constraint for hydrogen storage devices; The penalty coefficient for the binary quadratic unconstrained optimization model on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device; for The maximum hydrogen storage capacity of the hydrogen storage device at any given time; for The number of qubits required to relax the right-hand side of the hydrogen storage power inequality constraint for a hydrogen storage device at any given time. for The maximum number of qubits required for the relaxation variables on the right side of the hydrogen storage power inequality constraint of the hydrogen storage device at any given time; The penalty term for the binary quadratic unconstrained optimization model on the left-hand side of the hydrogen release power inequality of hydrogen storage equipment is constrained. The penalty coefficient for the binary quadratic unconstrained optimization model on the left side of the hydrogen release power inequality constraint of the hydrogen storage device; for Minimum hydrogen release power of the hydrogen storage device at any given time; for The number of qubits required to relax the left-hand side of the hydrogen release power inequality constraint for a hydrogen storage device at any given moment. for The maximum number of qubits required to relax the left-hand side of the hydrogen release power inequality constraint for the hydrogen storage device at any given time; The penalty term for the binary quadratic unconstrained optimization model on the right-hand side of the hydrogen release power inequality of hydrogen storage equipment is constrained. The penalty coefficient for the binary quadratic unconstrained optimization model on the right side of the hydrogen release power inequality constraint of the hydrogen storage device; for The maximum hydrogen release power of the hydrogen storage device at any given time; for The number of qubits required to relax the right-hand side of the hydrogen release power inequality constraint for a hydrogen storage device at any given moment. for The maximum number of qubits required to relax the right-hand side of the hydrogen release power inequality constraint for the hydrogen storage device at any given time.

4. The method for optimizing a port integrated energy system based on quantum computing according to claim 3, characterized in that, In step (4), the penalty term of the bivariate quadratic unconstrained optimization model is formed by simultaneously establishing the objective function, equality constraints, and inequality constraints, resulting in the bivariate quadratic unconstrained optimization model of the port integrated energy system, as shown below: （19）。

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