LNG (Liquefied Natural Gas) cold energy gradient utilization system optimization model considering peak regulation refrigeration house and solving method

Abstract

The invention discloses an LNG (Liquefied Natural Gas) cold energy gradient utilization system optimization model considering a peak regulation refrigeration house and a solving method. The model comprises a target function with system operation economic benefit maximization as a target, decision variables covering cold energy power of each module and cold storage charging and discharging power, and multiple types of constraint conditions integrating cold energy supply and demand balance, energy storage operation, gradient utilization and conservation. According to the method, a peak regulation refrigeration house unit module and a cold energy gradient utilization mechanism are innovatively introduced, and an optimization model for efficient and comprehensive utilization of LNG cold energy is established; according to the method, an artificial travel mouse optimization algorithm is adopted for solving, cold energy distribution of power generation, refrigeration and liquefaction modules can be effectively coordinated, a cold storage energy charging and discharging strategy is optimized, the cold energy utilization economy and peak regulation flexibility are remarkably improved on the premise that stable operation of the system is guaranteed, and effective decision support is provided for comprehensive utilization of cold energy of the LNG receiving station.

Description

Technical Field

[0001] This invention relates to the field of LNG cold energy utilization system optimization, specifically to an optimization model and solution method for an LNG cold energy cascade utilization system considering peak-shaving cold storage. Background Technology

[0002] With the continuous expansion of liquefied natural gas (LNG) imports, the large amount of cold energy released during its regasification process has gradually become a key focus in the field of energy conservation and emission reduction. Traditional LNG cold energy utilization systems mostly employ a direct and singular utilization method, failing to achieve tiered matching of cold energy at different temperatures and grades. This results in inefficient utilization of high-grade cold energy and overall system performance issues. The efficiency is low. Especially in scenarios with large fluctuations in gas load, fixed-structure cooling systems struggle to adapt to dynamically changing demands, often resulting in instantaneous imbalances in cooling supply and demand and insufficient regulation capabilities, thus limiting the improvement of the system's economic operation level.

[0003] While some studies have addressed the optimization of individual modules such as LNG cold energy power generation and refrigeration, or proposed simple cascade utilization frameworks, a systematic modeling method that can coordinate the collaborative operation of multiple modules and integrate cold energy storage and peak-shaving functions is still lacking. Existing models often neglect the strict grading constraints of cold energy quality and the dynamic scheduling requirements, and do not fully consider the potential for improved system flexibility and economic efficiency with the participation of cold energy storage devices. Therefore, it is necessary to construct a complete model and method that integrates cold energy cascade matching, energy storage regulation, and operational optimization to achieve efficient, stable, and value-added utilization of LNG cold energy. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes an optimization model and solution method for an LNG cold energy cascade utilization system that considers peak-shaving cold storage. By introducing cold energy grade grading constraints and dynamic energy storage strategies, an optimization model is established with the goal of maximizing the economic benefits of system operation. An intelligent optimization algorithm is then used for efficient solution, providing theoretical basis and decision support for the design and operation of LNG receiving terminal cold energy systems.

[0005] The technical solution of this invention is as follows:

[0006] An optimization model and solution method for an LNG cold energy cascade utilization system considering peak-shaving cold storage are characterized by including an objective function, decision variables, and constraints. The objective function is established by considering the cold energy utilization benefits, natural gas sales benefits, and cold energy system operating costs, with the minimum system operating economic benefits as the optimization objective.

[0007] Preferably, the objective function expression is:

[0008]

[0009] In the formula, Bm,t For time period t, module m generates economic benefits per unit of cooling energy consumed; O m,t For time period t, module m consumes a unit of cooling energy, incurring operating costs; L m,t For time period t, the cooling power of module m; Y m,t η represents the cooling energy consumption of module m during time period t. m For module m, production efficiency; V t,LNG V represents the LNG storage capacity in the tank during time period t; t-1,LNG P represents the LNG storage capacity in the tank during the time period t-1. r For natural gas sales price; E m,t For time period t, the operating efficiency of module m; H m,t Let m be the operating cost of module m during time period t. The efficiency coefficient of module m; Let m be the operating cost coefficient for module m.

[0010] Preferably, the decision variable is:

[0011] X={G m,t C m,t ,L m1,t ,L m2,t ,L m3,t ,L in,t ,L out,t ,σ m1,t ,v m2,t ,σ m3,t}

[0012] In the formula, G t,NG LNG vaporization power during time period t; L m1,t L represents the cold energy power of the power generation module during time period t. m2,t L represents the cooling power of the refrigeration module during time period t. m2,t L represents the cooling power of the gas liquefaction module during time period t. in,t L represents the energy output of the cold storage unit during time period t. in,t σ represents the charging power of the cold storage unit during time period t; m1,t The percentage of cold energy power generation modules during time period t; σ m2,t The percentage of cooling modules within time period t; σ m3,t The percentage of gas liquefaction modules during time period t.

[0013] Preferably, the constraints include:

[0014] (1) LNG storage constraints

[0015]

[0016] In the formula, S t,LNGThe cold energy stored in the LNG storage tank during time period t; S t-1,LNG α represents the cold energy stored in the LNG storage tank during the time period t-1; α is the volume ratio of LNG to NG; Q NG The calorific value of natural gas;

[0017] (2) LNG vaporization power constraint

[0018]

[0019] In the formula, This represents the lower limit of LNG vaporization power. This represents the upper limit of LNG vaporization power.

[0020] (3) Cold energy charging and discharging power constraint

[0021]

[0022] In the formula, This represents the lower limit of cold energy charging power. This is the upper limit of cold energy charging power; This represents the lower limit of cold energy release power. This represents the upper limit of cold energy release power.

[0023] (4) Cold energy supply and demand balance constraints

[0024]

[0025] In the formula, Y sup,t Y represents the system's cooling energy supply during time period t; out,t Y represents the energy released by the cold storage unit during time period t; in,t During time period t, the cold storage unit is charged with energy; Y m1,t The energy consumed by the power generation module during time period t; Y m2,t The energy consumed by the cooling module during time period t; Y m3,t The energy consumed by the gas liquefaction module during time period t;

[0026] (5) Cold storage unit operation constraints

[0027]

[0028] In the formula, E t+1,stor Energy stored in the cold storage unit during the t+1 time period; E t,stor η represents the energy stored in the cold storage unit during time period t. cha For cold storage unit energy storage efficiency; η discha Energy release efficiency of cold storage units; This represents the minimum energy storage capacity of the cold storage unit. This represents the maximum energy storage capacity of the cold storage unit.

[0029] (6) Initial constraints of energy storage

[0030] E 0,stor =0

[0031] In the formula, E 0,stor Initial energy storage for the cold storage unit;

[0032] (7) Cascade utilization constraints

[0033]

[0034] 0≤L m1,t ≤L t,LNG σ m1,t

[0035]

[0036] 0≤L m2,t ≤(L t,LNG- L m1 )σ m2,t

[0037]

[0038] 0≤L m3,t ≤(L t,LNG- L m1 -L m2 )σ m3,t

[0039] In the formula, P m1,t σ represents the power generated by cold energy during time period t; m1,t The percentage of cold energy power generation modules during time period t; For cold energy power generation efficiency; D m2,t The cooling capacity during time period t; For cooling efficiency; σ m2,t B represents the percentage of cooling modules within time period t. m3,t The liquefaction power of cold gas during time period t; For gas liquefaction efficiency; σ m3,t The percentage of gas liquefaction modules within time period t;

[0040] (8) Cold Conservation equations

[0041]

[0042] In the formula, For power generation module process Efficiency coefficient; For refrigeration module process Efficiency coefficient; For gas liquefaction module process Efficiency coefficient; m LNG h represents the LNG mass flow rate. env,NG h represents the enthalpy of NG at ambient temperature. LNG ΔS represents the enthalpy of LNG; T0 represents the ambient temperature; ΔS represents the enthalpy of LNG. env,NG ΔS represents the entropy of NG at ambient temperature. LNG The entropy value of LNG;

[0043] (9) Operational constraints of the power generation module

[0044]

[0045] In the formula, This represents the minimum operating power of the power generation module; This represents the maximum operating power of the power generation module;

[0046] (10) Operating constraints of the cooling module

[0047]

[0048] In the formula, This represents the minimum operating power of the cooling module. This represents the maximum operating power of the cooling module.

[0049] (11) Operational constraints of the gas liquefaction module

[0050]

[0051] In the formula, This represents the minimum operating power for gas liquefaction. This represents the maximum operating power for gas liquefaction.

[0052] On the other hand, a solution method for the optimization model and solution method of the LNG cold energy cascade utilization system considering peak-shaving cold storage as described in any of the above is also provided, including the following steps:

[0053] S1: Set the initial decision variable values ​​for each time period within the optimization cycle, and read the system's basic parameters, including the calorific value of natural gas, the enthalpy of natural gas, the enthalpy of LNG, the volume ratio of LNG to natural gas, the efficiency coefficient of the power generation module, the efficiency coefficient of the refrigeration module, the efficiency coefficient of the gas liquefaction module, the energy storage efficiency and energy release efficiency of the cold storage unit, the maximum operating power of the power generation module, the minimum operating power of the power generation module, the maximum operating power of the refrigeration module, the minimum operating power of the refrigeration module, the minimum operating power of the gas liquefaction module, the maximum operating power of the gas liquefaction module, and the power generation module process. Efficiency coefficient, refrigeration module process Efficiency coefficient, gas liquefaction module process Efficiency coefficient, lower limit of LNG vaporization power, upper limit of LNG vaporization power, lower limit of cold energy charging power, upper limit of cold energy charging power, lower limit of cold energy releasing power, upper limit of cold energy releasing power, energy storage efficiency of cold storage unit, energy release efficiency of cold storage unit, minimum energy storage capacity of cold storage unit and maximum energy storage capacity of cold storage unit;

[0054] S2: Based on the LNG vaporization power during the current period, calculate the cold energy power released by LNG vaporization during that period and determine the cold energy supply and demand relationship of the system;

[0055] S3: Based on the charging and discharging power of the cold storage unit in the current period, and combined with its energy storage efficiency and energy discharging efficiency, update the energy storage capacity of the cold storage unit in the next period; verify whether the updated energy storage capacity meets its upper and lower operating limits. If not, adjust the charging and discharging strategy and recalculate until all energy storage system operating constraints are met.

[0056] S4: Verify whether the cooling power of the power generation module, cooling module, and gas liquefaction module meets the module operation constraints; if not, return to S1 to regenerate the decision variables;

[0057] S5: Based on the cold energy consumption of the power generation module, refrigeration module, and gas liquefaction module, the operating status of the cold storage, and the LNG sales volume, calculate the total economic benefit of the system under the current decision variables according to the objective function formula;

[0058] S6: Under the premise of satisfying all constraints, change the values ​​of decision variables through optimization algorithm and output the total economic benefits of the objective function;

[0059] S7: Compare the objective function values ​​of the new solution with the current optimal solution. If the number of optimization iterations has reached the preset maximum number, the optimization process is determined to have converged, and the process proceeds to the final output step, outputting the current optimal solution as the optimization result. Otherwise, return to step S2 and continue iterative calculation with the newly generated decision variables.

[0060] Preferably, the cold energy power released by LNG vaporization during the current period is calculated based on the LNG vaporization power during that period. The formula for calculating the cold energy power released by LNG vaporization is as follows:

[0061]

[0062] Determine the system's cold energy supply and demand relationship. If the cold energy supply exceeds the sum of the consumption of each module and the cold storage's storage requirements, then initiate the cold storage charging process. The expression is:

[0063] L in =L t,LNG -L m1,t +L m2,t +L m3,t if L t,LNG ≥Lm1,t +L m2,t +L m3,t

[0064] If the cold energy supply is insufficient, the cold storage energy release process will be activated to supplement the system's cold energy gap. The expression is:

[0065] L out =L m1,t +L m2,t +L m3,t -L t,LNG if L t,LNG ≤L m1,t +L m2,t +L m3,t .

[0066] Preferably, the optimization algorithm adopts the artificial lemming algorithm, and the algorithm flow is as follows:

[0067] S601: Set the algorithm population size N and the maximum number of iterations ω max and behavioral factor r ω Randomly initialize the positions of all lemming individuals in the population. The random initialization expression is:

[0068] z ω =R×(ub-lb)+lb

[0069] In the formula, z i R represents the population location; ub represents the random parameter; lb represents the upper bound of the problem space; and lb represents the lower bound of the problem space.

[0070] S602: Calculate the fitness value of the current position of all lemming individuals in the population according to the objective function of the model, compare the fitness values ​​of the current positions of all lemming individuals, and determine and record the current global optimal solution Z. best ;

[0071] S603: Calculation of energy factor e ω If e ω >1, the algorithm iteration enters the exploration phase, if e ω ≤1, the algorithm iteration enters the development stage;

[0072] S604: In the exploration phase, if the behavioral factor r ω If the value is less than 0.3, the individual's location will be updated to a long-distance migration mode. The formula for updating the location of a lemming individual is:

[0073] Z ω+1 =Z best +F×BM×(R ω ×(Z best -Z ω )+(1-R ω )×(Zω -Z a ))

[0074] In the formula, Z ω+1 Z represents the position of the lemming individual at iteration ω+1; best The current global optimal solution; F is the direction factor; BM is the Brownian motion vector; R ω Z is the circumferential factor; a Location of individual lemmings during long-distance migration;

[0075] If the behavioral factor r ω If the value is ≥0.3, then the individual's position is updated in digging mode. The formula for updating the individual position of a lemming is:

[0076] Z ω+1 =Z ω +F×L×(Z best -Z b )

[0077]

[0078] In the formula, Z b ω represents the location of the burrowing lemming; J is the burrowing direction factor; ω is the current iteration number.

[0079] S605: During the development phase, if behavioral factor r ω If the value is less than 0.5, the individual lemming enters foraging mode to update its location. The formula for updating the lemming's individual location is:

[0080] Z ω+1 =Z best (t)+F×s p ×R ω ×Z ω

[0081] s p =R ω ×(sin(2π×R ω )+cos(2π×R ω ))

[0082] In the formula, s p The spiral shape factor;

[0083] If the behavioral factor r ω If the value is ≥0.5, the individual lemming will enter predator avoidance mode and update its position. The formula for updating the individual lemming position is:

[0084] Z ω+1 =Z best +F×φ×(Z best -Z v )

[0085] In the formula, φ is the escape coefficient;

[0086] S606: Evaluate the fitness values ​​of all lemming individuals in the population. If a better solution is found, update the current global optimum Z. best ;

[0087] S607: Determine if the current iteration number satisfies ω≥ω max If the condition is met, the algorithm terminates the iteration and outputs the optimal solution as the optimization result; otherwise, it returns to S603 to continue the iteration.

[0088] The beneficial effects of this invention are:

[0089] This invention effectively improves the economy and flexibility of LNG cold energy utilization by constructing an optimization model for the cascade utilization of integrated peak-shaving cold storage. It introduces cold energy grade-based constraints to achieve precise matching of cold energy across multiple temperature zones, including cryogenic, meso-cooled, and general-cooled systems, significantly reducing [the impact of cold energy consumption]. Losses; by coupling the cold energy storage module, the system's adaptability to load fluctuations is enhanced, overcoming the shortcomings of insufficient regulation capacity in traditional systems. Using intelligent optimization algorithms, the system can coordinate the cold energy distribution and cold storage filling / discharging strategies of multiple modules such as power generation, liquefaction, and refrigeration, while meeting various operational constraints. Ultimately, this significantly improves the comprehensive utilization efficiency of cold energy and overall economic benefits while ensuring stable operation. Attached Figure Description

[0090] Figure 1 This is a flowchart illustrating the solution method.

[0091] Figure 2 Flowchart for solving the artificial lemming algorithm;

[0092] Figure 3 This describes the distribution of economic benefits within the system.

[0093] Figure 4 This refers to the system's cooling energy supply and demand balance.

[0094] Figure 5 This is the operational status of the cold storage unit. Detailed Implementation

[0095] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0096] An optimization model and solution method for an LNG cold energy cascade utilization system considering peak-shaving cold storage are characterized by including an objective function, decision variables, and constraints. The objective function is established by considering the cold energy utilization benefits, natural gas sales benefits, and cold energy system operating costs, with the minimum system operating economic benefits as the optimization objective.

[0097] In one embodiment, the objective function expression is:

[0098]

[0099] In the formula, B m,t For time period t, module m generates economic benefits per unit of cooling energy consumed; O m,t For time period t, module m consumes a unit of cooling energy, incurring operating costs; L m,t For time period t, the cooling power of module m; Y m,t η represents the cooling energy consumption of module m during time period t. m For module m, production efficiency; V t,LNG V represents the LNG storage capacity in the tank during time period t; t-1,LNG P represents the LNG storage capacity in the tank during the time period t-1. r For natural gas sales price; E m,t For time period t, the operating efficiency of module m; H m,t Let m be the operating cost of module m during time period t. The efficiency coefficient of module m; Let m be the operating cost coefficient for module m.

[0100] In one embodiment, the decision variable is:

[0101] X={G m,t C m,t ,L m1,t ,L m2,t ,L m3,t ,L in,t ,L out,t ,σ m1,t ,σ m2,t ,σ m3,t}

[0102] In the formula, G t,NG LNG vaporization power during time period t; L m1,t L represents the cold energy power of the power generation module during time period t. m2,t L represents the cooling power of the refrigeration module during time period t. m2,t L represents the cooling power of the gas liquefaction module during time period t. in,t L represents the energy output of the cold storage unit during time period t. in,t σ represents the charging power of the cold storage unit during time period t; m1,tThe percentage of cold energy power generation modules during time period t; σ m2,t The percentage of cooling modules within time period t; σ m3,t The percentage of gas liquefaction modules during time period t.

[0103] In one embodiment, the constraints include:

[0104] (1) LNG storage constraints

[0105]

[0106] In the formula, S t,LNG The cold energy stored in the LNG storage tank during time period t; S t-1,LNG α represents the cold energy stored in the LNG storage tank during the time period t-1; α is the volume ratio of LNG to NG; Q NG The calorific value of natural gas;

[0107] (2) LNG vaporization power constraint

[0108]

[0109] In the formula, This represents the lower limit of LNG vaporization power. This represents the upper limit of LNG vaporization power.

[0110] (3) Cold energy charging and discharging power constraint

[0111]

[0112] In the formula, This represents the lower limit of cold energy charging power. This is the upper limit of cold energy charging power; This represents the lower limit of cold energy release power. This represents the upper limit of cold energy release power.

[0113] (4) Cold energy supply and demand balance constraints

[0114]

[0115] In the formula, Y sup,t Y represents the system's cooling energy supply during time period t; out,t Y represents the energy released by the cold storage unit during time period t; in,t During time period t, the cold storage unit is charged with energy; Y m1,t The energy consumed by the power generation module during time period t; Y m2,t The energy consumed by the cooling module during time period t; Y m3,t The energy consumed by the gas liquefaction module during time period t;

[0116] (5) Cold storage unit operation constraints

[0117]

[0118] In the formula, E t+1,stor Energy stored in the cold storage unit during the t+1 time period; E t,stor η represents the energy stored in the cold storage unit during time period t. cha For cold storage unit energy storage efficiency; η discha Energy release efficiency of cold storage units; This represents the minimum energy storage capacity of the cold storage unit. This represents the maximum energy storage capacity of the cold storage unit.

[0119] (6) Initial constraints of energy storage

[0120] E 0,stor =0

[0121] In the formula, E 0,stor Initial energy storage for the cold storage unit;

[0122] (7) Cascade utilization constraints

[0123]

[0124] 0≤L m1,t ≤L t,LNG σ m1,t

[0125]

[0126] 0≤L m2,t ≤(L t,LNG- L m1 )σ m2,t

[0127]

[0128] 0≤L m3,t ≤(L t,LNG- L m1 -L m2 )σ m3,t

[0129] In the formula, P m1,t σ represents the power generated by cold energy during time period t; m1,t The percentage of cold energy power generation modules during time period t; For cold energy power generation efficiency; D m2,t The cooling capacity during time period t; For cooling efficiency; σ m2,t B represents the percentage of cooling modules within time period t. m3,t The liquefaction power of cold gas during time period t; For gas liquefaction efficiency; σ m3,t The percentage of gas liquefaction modules within time period t;

[0130] (8) Cold Conservation equations

[0131]

[0132] In the formula, For power generation module process Efficiency coefficient; For refrigeration module process Efficiency coefficient; For gas liquefaction module process Efficiency coefficient; m LNG h represents the LNG mass flow rate. env,NG h represents the enthalpy of NG at ambient temperature. LNG ΔS represents the enthalpy of LNG; T0 represents the ambient temperature; ΔS represents the enthalpy of LNG. env,NG ΔS represents the entropy of NG at ambient temperature. LNG The entropy value of LNG;

[0133] (9) Operational constraints of the power generation module

[0134]

[0135] In the formula, This represents the minimum operating power of the power generation module; This represents the maximum operating power of the power generation module;

[0136] (10) Operating constraints of the cooling module

[0137]

[0138] In the formula, This represents the minimum operating power of the cooling module. This represents the maximum operating power of the cooling module.

[0139] (11) Operational constraints of the gas liquefaction module

[0140]

[0141] In the formula, This represents the minimum operating power for gas liquefaction. This represents the maximum operating power for gas liquefaction.

[0142] On the other hand, a solution method is also provided for the optimization model and solution method of the LNG cold energy cascade utilization system considering peak-shaving cold storage as described in any of the above-mentioned embodiments, such as... Figure 1 As shown, it includes the following steps:

[0143] S1: Set the initial decision variable values ​​for each time period within the optimization cycle, and read the system's basic parameters, including the calorific value of natural gas, the enthalpy of natural gas, the enthalpy of LNG, the volume ratio of LNG to natural gas, the efficiency coefficient of the power generation module, the efficiency coefficient of the refrigeration module, the efficiency coefficient of the gas liquefaction module, the energy storage efficiency and energy release efficiency of the cold storage unit, the maximum operating power of the power generation module, the minimum operating power of the power generation module, the maximum operating power of the refrigeration module, the minimum operating power of the refrigeration module, the minimum operating power of the gas liquefaction module, the maximum operating power of the gas liquefaction module, and the power generation module process. Efficiency coefficient, refrigeration module process Efficiency coefficient, gas liquefaction module process Efficiency coefficient, lower limit of LNG vaporization power, upper limit of LNG vaporization power, lower limit of cold energy charging power, upper limit of cold energy charging power, lower limit of cold energy releasing power, upper limit of cold energy releasing power, energy storage efficiency of cold storage unit, energy release efficiency of cold storage unit, minimum energy storage capacity of cold storage unit and maximum energy storage capacity of cold storage unit;

[0144] S2: Based on the LNG vaporization power during the current period, calculate the cold energy power released by LNG vaporization during that period and determine the cold energy supply and demand relationship of the system;

[0145] S3: Based on the charging and discharging power of the cold storage unit in the current period, and combined with its energy storage efficiency and energy discharging efficiency, update the energy storage capacity of the cold storage unit in the next period; verify whether the updated energy storage capacity meets its upper and lower operating limits. If not, adjust the charging and discharging strategy and recalculate until all energy storage system operating constraints are met.

[0146] S4: Verify whether the cooling power of the power generation module, cooling module, and gas liquefaction module meets the module operation constraints; if not, return to S1 to regenerate the decision variables;

[0147] S5: Based on the cold energy consumption of the power generation module, refrigeration module, and gas liquefaction module, the operating status of the cold storage, and the LNG sales volume, calculate the total economic benefit of the system under the current decision variables according to the objective function formula;

[0148] S6: Under the premise of satisfying all constraints, change the values ​​of decision variables through optimization algorithm and output the total economic benefits of the objective function;

[0149] S7: Compare the objective function values ​​of the new solution with the current optimal solution. If the number of optimization iterations has reached the preset maximum number, the optimization process is determined to have converged, and the process proceeds to the final output step, outputting the current optimal solution as the optimization result. Otherwise, return to step S2 and continue iterative calculation with the newly generated decision variables.

[0150] In one embodiment, the cold energy power released by LNG vaporization during the current period is calculated based on the LNG vaporization power during that period. The formula for calculating the cold energy power released by LNG vaporization is as follows:

[0151]

[0152] Determine the system's cold energy supply and demand relationship. If the cold energy supply exceeds the sum of the consumption of each module and the cold storage's storage requirements, then initiate the cold storage charging process. The expression is:

[0153] L in =L t,LNG -L m1,t +L m2,t +L m3,t if L t,LNG ≥L m1,t +L m2,t +L m3,t

[0154] If the cold energy supply is insufficient, the cold storage energy release process will be activated to supplement the system's cold energy gap. The expression is:

[0155] L out =L m1,t +L m2,t +L m3,t -L t,LNG if L t,LNG ≤L m1,t +L m2,t +L m3,t .

[0156] In one embodiment, the optimization algorithm employs the artificial lemming algorithm, such as... Figure 2 As shown, the algorithm flow is as follows:

[0157] S601: Set the algorithm population size N and the maximum number of iterations ω max and behavioral factor r ω Randomly initialize the positions of all lemming individuals in the population. The random initialization expression is:

[0158] z ω =R×(ub-lb)+lb

[0159] In the formula, z i R represents the population location; ub represents the random parameter; lb represents the upper bound of the problem space; and lb represents the lower bound of the problem space.

[0160] S602: Calculate the fitness value of the current position of all lemming individuals in the population according to the objective function of the model, compare the fitness values ​​of the current positions of all lemming individuals, and determine and record the current global optimal solution Z. best ;

[0161] S603: Calculation of energy factor e ω If e ω >1, the algorithm iteration enters the exploration phase, if e ω ≤1, the algorithm iteration enters the development stage;

[0162] S604: In the exploration phase, if the behavioral factor r ω If the value is less than 0.3, the individual's location will be updated to a long-distance migration mode. The formula for updating the location of a lemming individual is:

[0163] Z ω+1 =Z best +F×BM×(R ω ×(Z best -Z ω )+(1-R ω )×(Z ω -Z a ))

[0164] In the formula, Z ω+1 Z represents the position of the lemming individual at iteration ω+1; best The current global optimal solution; F is the direction factor; BM is the Brownian motion vector; R ω Z is the circumferential factor; a Location of individual lemmings during long-distance migration;

[0165] If the behavioral factor r ω If the value is ≥0.3, then the individual's position is updated in digging mode. The formula for updating the individual position of a lemming is:

[0166] Z ω+1 =Z ω +F×L×(Z best -Z b )

[0167]

[0168] In the formula, Z b ω represents the location of the burrowing lemming; J is the burrowing direction factor; ω is the current iteration number.

[0169] S605: During the development phase, if behavioral factor r ω If the value is less than 0.5, the individual lemming enters foraging mode to update its location. The formula for updating the lemming's individual location is:

[0170] Z ω+1 =Z best (t)+F×s p ×R ω ×Z ω

[0171] s p =R ω×(sin(2π×R ω )+cos(2π×R ω ))

[0172] In the formula, s p The spiral shape factor;

[0173] If the behavioral factor r ω If the value is ≥0.5, the individual lemming will enter predator avoidance mode and update its position. The formula for updating the individual lemming position is:

[0174] Z ω+1 =Z best +F×φ×(Z best -Z ω )

[0175] In the formula, φ is the escape coefficient;

[0176] S606: Evaluate the fitness values ​​of all lemming individuals in the population. If a better solution is found, update the current global optimum Z. best ;

[0177] S607: Determine if the current iteration number satisfies ω≥ω max If the condition is met, the algorithm terminates the iteration and outputs the optimal solution as the optimization result; otherwise, it returns to S603 to continue the iteration.

[0178] In one specific embodiment, the optimization model and solution method of the LNG cold energy cascade utilization system considering peak-shaving cold storage proposed in this invention are used to optimize the operation scheme of a domestic LNG cold energy system.

[0179] The system maintains an LNG storage temperature of -162℃ and a natural gas temperature of 14.85℃, with a daily gasification capacity of 1000 tons / day. The power generation module's cooling energy requirement ranges from 100-800 GJ / h, fluctuating with electricity load. The refrigeration module's cooling energy requirement ranges from 100-600 GJ / h, increasing by 40% during peak daytime hours (10:00-18:00). The gas liquefaction module has a constant cooling energy requirement of 200 GJ / h, requiring a continuous supply unaffected by time fluctuations.

[0180] The energy storage system has a maximum capacity of 2000 GJ, an energy storage efficiency of 0.85, and an energy release efficiency of 0.78. The LNG sales price is 3.5 yuan / kg. This is the market price. The revenue from cold energy power generation is 0.20 yuan / MJ, the refrigeration service price is 0.22 yuan / MJ, and the gas liquefaction revenue is 0.25 yuan / MJ.

[0181] The system was optimized based on the artificial lemming algorithm, and the optimization results are as follows: Figures 3 to 5 As shown. Figure 3This indicates that natural gas sales revenue fluctuates significantly, with substantial revenue during periods of high demand; the efficiency of process modules gradually increases with rising demand, contributing significantly during medium- to high demand periods; operating costs are always present and increase accordingly when cooling energy demand is high. Overall, revenue increases significantly during periods of high demand, while operating costs account for a relatively high proportion of total revenue during periods of low demand.

[0182] Further analysis was conducted on the supply and demand balance of cold energy and the peak-shaving situation of energy storage. From Figure 4 It can be seen that the supply and demand of cold energy in the system can be divided into three stages: (1) From 1:00 to 9:00 is the basic energy supply stage, the basic energy supply fully meets the demand, no energy storage is needed to supplement, and the supply and demand deviation is zero; (2) From 10:00 to 14:00 is the energy storage supply stage. As the demand increases, the energy storage system releases cold energy to make up for the insufficient basic energy supply. The amount of cold energy released increases from 423.1 GJ / h to 482.7 GJ / h, and the supply and demand remain balanced; (3) From 15:00 to 24:00, the supply and demand are balanced again, the basic energy supply fully covers the demand, and the energy storage system does not participate in the energy supply.

[0183] Figure 5 The peak-shaving behavior of energy storage was demonstrated, specifically as follows: (1) The stored energy gradually increased from 412.1 GJ to 1672.5 GJ between 1:00 and 9:00, completing the charging; during the peak period (10:00–14:00), the stored energy remained at a high level and gradually released energy; after 14:00, it dropped rapidly and remained at zero between 15:00 and 21:00, without any energy storage or release; after 22:00, charging resumed, but at a low level. (2) The utilization rate gradually increased during the charging phase (1:00–9:00), reaching a maximum of 83.6%; during the energy release phase (10:00–14:00), it remained at a high level; it dropped to zero between 15:00 and 21:00; and it gradually rebounded between 22:00 and 24:00, ranging from 19.7% to 62.1%.

[0184] The above descriptions are merely embodiments of this specification and are not intended to limit the scope of this specification. Various modifications and variations can be made to the embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of this specification should be included within the scope of the claims of the embodiments of this specification.

Claims

1. An optimization model and solution method for an LNG cold energy cascade utilization system considering peak-shaving cold storage, characterized in that, It includes an objective function, decision variables, and constraints. The objective function is established by considering the benefits of cold energy utilization, the benefits of natural gas sales, and the operating costs of the cold energy system, with the goal of minimizing the economic benefits of system operation.

2. The optimization model and solution method for the LNG cold energy cascade utilization system considering peak-shaving cold storage as described in claim 1, characterized in that, The objective function expression is: In the formula, B m,t For time period t, module m generates economic benefits per unit of cooling energy consumed; O m,t For time period t, module m consumes a unit of cooling energy, incurring operating costs; L m,t For time period t, the cooling power of module m; Y m,t The amount of cooling energy consumed by module m during time period t; η m For module m, production efficiency; V t,LNG V represents the LNG storage capacity in the tank during time period t; t-1,LNG P represents the LNG storage capacity in the tank during the time period t-1. r For natural gas sales price; E m,t For time period t, the operating efficiency of module m; H m,t Let m be the operating cost of module m during time period t. The efficiency coefficient of module m; Let m be the operating cost coefficient for module m.

3. The optimization model and solution method for the LNG cold energy cascade utilization system considering peak-shaving cold storage as described in claim 1, characterized in that, The decision variables are: X={G m,t C m,t ,L m1,t ,L m2,t ,L m3,t ,L in,t ,L out,t ,s m1,t ,s m2,t ,s m3,t } In the formula, G t,NG LNG vaporization power during time period t; L m1,t L represents the cold energy power of the power generation module during time period t. m2,t L represents the cooling power of the refrigeration module during time period t. m2,t L represents the cooling power of the gas liquefaction module during time period t. in,t L represents the energy output of the cold storage unit during time period t. in,t σ represents the charging power of the cold storage unit during time period t; m1,t The percentage of cold energy power generation modules during time period t; σ m2,t The percentage of cooling modules within time period t; σ m3,t The percentage of gas liquefaction modules during time period t.

4. The optimization model and solution method for the LNG cold energy cascade utilization system considering peak-shaving cold storage as described in claim 1, characterized in that, The constraints include: (1) LNG storage constraints In the formula, S t,LNG The cold energy stored in the LNG storage tank during time period t; S t-1,LNG α represents the cold energy stored in the LNG storage tank during the time period t-1; α is the volume ratio of LNG to NG; Q NG The calorific value of natural gas; (2) LNG vaporization power constraint In the formula, This represents the lower limit of LNG vaporization power. This represents the upper limit of LNG vaporization power. (3) Cold energy charging and discharging power constraint In the formula, This represents the lower limit of cold energy charging power. This is the upper limit of cold energy charging power; This represents the lower limit of cold energy release power. This represents the upper limit of cold energy release power. (4) Cold energy supply and demand balance constraints In the formula, Y sup,t Y represents the system's cooling energy supply during time period t; out,t Y represents the energy released by the cold storage unit during time period t; in,t During time period t, the cold storage unit is charged with energy; Y m1,t The energy consumed by the power generation module during time period t; Y m2,t The energy consumed by the cooling module during time period t; Y m3,t The energy consumed by the gas liquefaction module during time period t; (5) Cold storage unit operation constraints In the formula, E t+1,stor Energy stored in the cold storage unit during the t+1 time period; E t,stor η represents the energy stored in the cold storage unit during time period t. cha For cold storage unit energy storage efficiency; η discha Energy release efficiency of cold storage units; This represents the minimum energy storage capacity of the cold storage unit. This represents the maximum energy storage capacity of the cold storage unit. (6) Initial constraints of energy storage E 0,stor ＝0 In the formula, E 0,stor Initial energy storage for the cold storage unit; (7) Cascade utilization constraints 0≤L m1,t ≤L t,LNG s m1,t 0≤L m2,t ≤(L t,LNG- L m1 )s m2,t 0≤L m3,t ≤(L t,LNG- L m1 -L m2 )σ m3,t In the formula, P m1,t σ represents the power generated by cold energy during time period t; m1,t The percentage of cold energy power generation modules during time period t; For cold energy power generation efficiency; D m2,t The cooling capacity during time period t; For cooling efficiency; σ m2,t B represents the percentage of cooling modules within time period t. m3,t The liquefaction power of cold gas during time period t; For gas liquefaction efficiency; σ m3,t The percentage of gas liquefaction modules within time period t; (8) Cold Conservation equations In the formula, For power generation module process Efficiency coefficient; For refrigeration module process Efficiency coefficient; For gas liquefaction module process Efficiency coefficient; m LNG h represents the LNG mass flow rate. env,NG h represents the enthalpy of NG at ambient temperature. LNG ΔS represents the enthalpy of LNG; T0 represents the ambient temperature; ΔS represents the enthalpy of LNG. env,NG ΔS represents the entropy of NG at ambient temperature. LNG The entropy value of LNG; (9) Operational constraints of the power generation module In the formula, This represents the minimum operating power of the power generation module; This represents the maximum operating power of the power generation module; (10) Operating constraints of the cooling module In the formula, This represents the minimum operating power of the cooling module. This represents the maximum operating power of the cooling module. (11) Operational constraints of the gas liquefaction module In the formula, This represents the minimum operating power for gas liquefaction. This represents the maximum operating power for gas liquefaction.

5. A solution method for an optimization model and solution method of an LNG cold energy cascade utilization system considering peak-shaving cold storage, as described in any one of claims 1-4, characterized in that, Includes the following steps: S1: Set the initial decision variable values ​​for each time period within the optimization cycle, and read the system's basic parameters, including natural gas physical properties, LNG physical properties, module efficiency coefficient, cold storage unit energy storage efficiency and energy release efficiency, module maximum operating power, module minimum operating power, and module process parameters. Efficiency coefficient, lower limit of LNG vaporization power, upper limit of LNG vaporization power, boundary of cold energy charging power, boundary of cold energy releasing power, energy storage efficiency of cold storage unit, energy releasing efficiency of cold storage unit, minimum energy storage capacity of cold storage unit, and maximum energy storage capacity of cold storage unit; S2: Based on the LNG vaporization power during the current period, calculate the cold energy power released by LNG vaporization during that period and determine the cold energy supply and demand relationship of the system; S3: Based on the charging and discharging power of the cold storage unit in the current period, and combined with its energy storage efficiency and energy discharging efficiency, update the energy storage capacity of the cold storage unit in the next period; verify whether the updated energy storage capacity meets its upper and lower operating limits. If not, adjust the charging and discharging strategy and recalculate until all energy storage system operating constraints are met. S4: Verify whether the cooling power of the power generation module, cooling module, and gas liquefaction module meets the module operation constraints; if not, return to S1 to regenerate the decision variables; S5: Based on the cold energy consumption of the power generation module, refrigeration module, and gas liquefaction module, the operating status of the cold storage, and the LNG sales volume, calculate the total economic benefit of the system under the current decision variables according to the objective function formula; S6: Under the premise of satisfying all constraints, change the values ​​of decision variables through optimization algorithm and output the total economic benefits of the objective function; S7: Compare the objective function values ​​of the new solution with the current optimal solution. If the number of optimization iterations has reached the preset maximum number, the optimization process is determined to have converged. Proceed to the final output step and output the current optimal solution as the optimization result. Otherwise, return to step S2 and continue the iterative calculation with the newly generated decision variables.

6. The solution method for the optimization model of the LNG cold energy cascade utilization system considering peak-shaving cold storage as described in claim 5, characterized in that, Based on the LNG vaporization power during the current period, calculate the cooling energy released by LNG vaporization during that period. The formula for calculating the cooling energy released by LNG vaporization is as follows: Determine the system's cold energy supply and demand relationship. If the cold energy supply exceeds the sum of the consumption of each module and the cold storage's storage requirements, then initiate the cold storage charging process. The expression is: L in =L t,LNG -L m1,t +L m2,t +L m3,t if L t,LNG ≥L m1,t +L m2,t +L m3,t If the cold energy supply is insufficient, the cold storage energy release process will be activated to supplement the system's cold energy gap. The expression is: L out =L m1,t +L m2,t +L m3,t -L t,LNG if L t,LNG ≤L m1,t +L m2,t +L m3,t 。 7. The solution method for the optimization model of the LNG cold energy cascade utilization system considering peak-shaving cold storage as described in claim 5, characterized in that, The optimization algorithm uses the artificial lemming algorithm, and the algorithm flow is as follows: S601: Set the algorithm population size N and the maximum number of iterations ω max and behavioral factor r ω Randomly initialize the positions of all lemming individuals in the population. The random initialization expression is: z ω =R×(ub-lb)+lb In the formula, z i R represents the population location; ub represents the random parameter; lb represents the upper bound of the problem space; and lb represents the lower bound of the problem space. S602: Calculate the fitness value of the current position of all lemming individuals in the population according to the objective function of the model, compare the fitness values ​​of the current positions of all lemming individuals, and determine and record the current global optimal solution Z. best ; S603: Calculation of energy factor e ω If e ω >1, the algorithm iteration enters the exploration phase, if e ω ≤1, the algorithm iteration enters the development stage; S604: In the exploration phase, if the behavioral factor r ω If the value is less than 0.3, the individual's location will be updated to a long-distance migration mode. The formula for updating the location of a lemming individual is: WITH ω+1 =Z best +F×BM×(R ω ×(Z best -WITH ω )+(1-R ω )×(Z ω -WITH a )) In the formula, Z ω+1 Z represents the position of the lemming individual at iteration ω+1; best The current global optimal solution; F is the direction factor; BM is the Brownian motion vector; R ω Z is the circumferential factor; a Location of individual lemmings during long-distance migration; If the behavioral factor r ω If the value is ≥0.3, then the individual's position is updated in digging mode. The formula for updating the individual position of a lemming is: WITH ω+1 =Z ω +F×L×(Z best -WITH b ) In the formula, Z b ω represents the location of the burrowing lemming; J is the burrowing direction factor; ω is the current iteration number. S605: During the development phase, if behavioral factor r ω If the value is less than 0.5, the individual lemming enters foraging mode to update its location. The formula for updating the lemming's individual location is: Z ω+1 =Z best (t)+F×s p ×R ω ×Z ω s p =R ω ×(sin(2π×R ω )+cos(2π×R ω )) In the formula, s p The spiral shape factor; If the behavioral factor r ω If the value is ≥0.5, the individual lemming will enter predator avoidance mode and update its position. The formula for updating the individual lemming position is: WITH ω+1 =Z best +F×φ×(Z best -WITH v ) In the formula, φ is the escape coefficient; S606: Evaluate the fitness values ​​of all lemming individuals in the population. If a better solution is found, update the current global optimum Z. best ; S607: Determine if the current iteration number satisfies ω≥ω max If the condition is met, the algorithm terminates the iteration and outputs the optimal solution as the optimization result; otherwise, it returns to S603 to continue the iteration.

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