LNG (Liquefied Natural Gas) cold energy gradient utilization equipment and high-pressure gas storage tank capacity collaborative configuration optimization method and system

Abstract

The invention discloses an LNG (Liquefied Natural Gas) cold energy gradient utilization equipment and high-pressure gas storage tank capacity collaborative configuration optimization method and system. The method comprises the steps that an LNG cold energy cascade power generation-refrigeration capacity configuration and operation model is established; according to the selectable model parameters of the high-pressure gas storage tank, establishing a capacity configuration and operation model of the high-pressure gas storage tank; according to the method, distributed resources in a multi-energy micro-grid system, power purchase and sale of a superior power grid and power-cold-gas multi-energy flow balance constraints are considered, and a multi-energy micro-grid energy management optimization model containing LNG cold energy gradient utilization is established by taking the total configuration investment cost and operation cost in a minimum planning period as a target; and according to prediction data of new energy output and electricity-cold-gas loads in the planning period, a fuzzy set is constructed based on the Wasserstein distance, the model is converted into a two-stage distribution robust collaborative configuration optimization model and solved, and the optimal configuration capacity of the LNG cold energy gradient utilization equipment and the high-pressure gas storage tank is obtained.

Description

Technical Field

[0001] The present invention relates to a capacity configuration optimization method for equipment, in particular to a coordinated configuration optimization method and system for LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity distribution rods for multi-energy microgrids. Background Art

[0002] Natural gas (NG), a clean energy source, is experiencing a significant increase in demand. For ease of storage, NG is often transported as liquefied natural gas (LNG). However, before use, LNG must be vaporized. During this vaporization process, each kilogram of LNG releases approximately 830 kJ of cold energy. Currently, LNG receiving stations are mostly located along coastal areas, utilizing seawater or air for vaporization. This not only wastes cold energy but also potentially pollutes the environment. Consequently, the recovery and utilization of LNG cold energy is attracting increasing attention from scholars.

[0003] To maximize the utilization of LNG cold energy, current mainstream technologies employ a cascaded approach. This involves dividing LNG cold energy into different cooling segments based on temperature zones and applying the energy from each segment to different applications. For example, air separation, carbon capture, and pulverization technologies primarily utilize cold energy from the deep cooling zone; power generation primarily utilizes cold energy from the deep and intermediate cooling zones; and air conditioning, refrigeration, and cold storage primarily utilize cold energy from the shallow cooling zone.

[0004] Although research on the cascade utilization of LNG cold energy has made great progress, there are still limitations. On the one hand, current research on the cascade utilization of LNG cold energy tends to focus on the design and simulation levels, and lacks research on capacity configuration and energy management optimization. On the other hand, the cascade utilization of LNG cold energy is limited by the demand for gas load. When the demand for gas load is high or low, it may lead to waste or shortage of cold energy, resulting in a decrease in the utilization rate of cold energy. Therefore, there is an urgent need for a method and system for the coordinated configuration optimization of LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity distribution for multi-energy microgrids to improve the above-mentioned series of problems existing in the current cascade utilization of LNG cold energy. Summary of the Invention

[0005] Purpose of the invention: In response to the problems existing in the prior art, the present invention proposes a method and system for optimizing the coordinated configuration of LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity. By optimizing the capacity configuration of LNG cold energy cascade utilization equipment and high-pressure gas storage tanks and coupling the energy optimization management of multi-energy microgrids, the LNG cold energy rate is improved, and the LNG cold energy cascade utilization is made more flexible and economical.

[0006] Technical solution: To achieve the above-mentioned purpose, a method for optimizing the coordinated configuration of LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity is proposed, which is characterized by comprising the following steps:

[0007] (1) Based on the gasification characteristics of liquefied natural gas (LNG), an LNG gasification and cold energy release model is established, including the upper and lower limit constraints of the NG output flow of the gasification station, the cold energy that can be recovered and utilized during the LNG gasification process, and the constraints between the cold energy utilization amount at each stage and the total cold energy that can be recovered and utilized during the LNG gasification process; based on the characteristics of the LNG cold energy cascade utilization equipment, an LNG cold energy cascade power generation and cooling capacity configuration and operation model is established, including the capacity configuration constraints of the LNG cold energy power generation equipment, the operation model of the LNG cold energy power generation, the configuration constraints of the LNG cold energy cooling equipment, the operation model of the LNG cold energy cooling, and the total power consumption of the LNG cold energy utilization equipment, and the bilinear terms are linearized;

[0008] (2) According to the optional model parameters of the high-pressure gas storage tank, a capacity configuration and operation model of the high-pressure gas storage tank is established. The selection capacity configuration model of the high-pressure gas storage tank includes: the upper and lower limit constraints of the configuration capacity of the high-pressure gas storage tank, the total capacity of the selected configuration of different models of high-pressure gas storage tanks, the constraint that the number of selected models of all models is non-negative, and the construction area constraint of the high-pressure gas storage tank; the operation model of the high-pressure gas storage tank includes: the upper and lower limit constraints of the gas storage volume in the high-pressure gas storage tank, the upper and lower limit constraints of gas charging and discharging, the gas storage capacity of the high-pressure gas storage tank in each time period, the constraint that the gas storage volume in the high-pressure gas storage tank is the same as the initial value at the end of the day, and the power consumption of the high-pressure gas storage tank when charging and discharging;

[0009] (3) Establish a multi-energy microgrid energy management optimization model that includes LNG cold energy cascade utilization, including distributed resource operation constraints in the multi-energy microgrid system, power purchase and sales constraints between the multi-energy microgrid system and the upper power grid, and electricity-cooling-gas multi-energy flow balance constraints. The objective function is to minimize the total investment cost of cold energy cascade power generation-refrigeration and high-pressure gas storage tanks and the total operation cost of the microgrid within the planning period, and form an optimization model for the coordinated configuration of LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity for multi-energy microgrids;

[0010] (4) According to the forecast data of new energy output and electricity-cooling-gas load during the planning period, the original configuration optimization model is transformed and reconstructed into a two-stage distributed blue-rod collaborative configuration optimization model based on the Wasserstein distance and solved to obtain the optimal configuration capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tanks.

[0011] Furthermore, in step (1), the LNG gasification and cold energy release model has the following expression:

[0012]

[0013] Where y represents the planning year index; i represents the typical day scenario index; t represents the scheduling period index in a day; τ represents the length of the scheduling period; Indicates the natural gas NG output flow rate of the gasification station; Gng_max Indicates the upper limit of NG output flow rate; Represents the total cooling power that can be recovered during the LNG gasification process; ρ ng 、c ng With h ng represents NG density, NG specific heat capacity and LNG latent heat respectively; ΔT represents the temperature difference of NG after gasification; r u Indicates the unit conversion ratio between kJ and kW·h; and Represents the cooling power used for power generation and cooling operation respectively;

[0014] Formula (1) represents the upper and lower limit constraints of the NG output flow rate of the gasification station; Formula (2) represents the cold energy that can be recovered and utilized during the LNG gasification process; Formula (3) represents the constraint between the cold energy utilization in each stage and the total cold energy that can be recovered and utilized during the LNG gasification process;

[0015] The capacity configuration and operation model of LNG cold energy power generation and refrigeration cascade utilization is expressed as follows:

[0016] 0≤L lng_Pmax ≤L Pmax (4)

[0017]

[0018] 0≤L lng_Lmax ≤L Lmax (9)

[0019]

[0020] Where, L Pmax With L Lmax Respectively represent the upper limit of the configuration capacity of cold energy power generation and refrigeration equipment; L lng_Pmax With L lng _Lmax Respectively represent the configuration capacity of cold energy power generation and refrigeration equipment; and are binary variables, representing the start and stop of the cooling energy utilization equipment; and Respectively represent the cooling power consumed in LNG cooling power generation and refrigeration; l p With l l They represent the ratio of the lower limit to the upper limit of cooling energy for power generation and cooling energy for refrigeration respectively; and Respectively represent the operating power consumption of LNG cold energy power generation and refrigeration equipment; k p With k l Respectively represent the operating power consumption rates of LNG cold energy power generation and refrigeration equipment; and They represent the cooling losses of LNG cooling energy cascade utilization for power generation and refrigeration respectively; and They represent the power generation power and refrigeration power of LNG cold energy cascade utilization respectively; n p With n l is the heat transfer loss rate at each stage; u p with u l is the cooling energy utilization rate at each stage; Indicates the total power consumption of LNG cooling energy utilization;

[0021] Formula (4) is the capacity configuration constraint of LNG cold energy power generation equipment; Formulas (5)-(8) are the operation models of LNG cold energy power generation, where Formula (5) represents the start and stop of LNG cold energy power generation equipment and the upper and lower limits of cold energy consumption; Formula (6) represents the cold loss of LNG cold energy power generation; Formula (7) represents the constraint of LNG cold energy power generation power and cold energy consumption power; Formula (8) is the power consumption of LNG cold energy power generation equipment; Formula (9) is the capacity configuration constraint of LNG cold energy refrigeration equipment; Formulas (10)-(13) are the LNG cold energy refrigeration operation models; Formula (14) represents the total power consumption of LNG cold energy utilization equipment.

[0022] Furthermore, in formula (5) is a bilinear term and can be transformed into linear constraints (15)-(17); in formula (10) is a bilinear term and can be transformed into linear constraints (18)-(20) as follows:

[0023]

[0024] In the formula, the new variable after linearization is introduced and Represents the upper limit of the cooling power consumed in the LNG cooling power generation and refrigeration startup state, and is combined with the binary variable and Indicates the start and stop of LNG cold energy utilization equipment.

[0025] Furthermore, in step (2), the high-pressure gas storage tank capacity configuration and operation model, the corresponding expression is:

[0026] 0≤V ng ≤V ng_max (twenty one)

[0027] V ng =X n T V n (twenty two)

[0028] X n ≥0 (23)

[0029] 0≤X n T S n ≤S max (twenty four)

[0030]

[0031] Formulas (21)-(24) are the selection and capacity configuration models for high-pressure gas storage tanks, where V ng Indicates the total configuration capacity of the high-pressure gas storage tank; V ng_max Indicates the upper limit of the total configuration capacity of the high-pressure gas storage tank; X n is an n×1 integer decision variable array, which represents the number of configurations of n high-pressure gas storage tanks of different models. The superscript T represents the transpose of the matrix. V n With S n is a one-dimensional array of n×1, representing the capacity and floor space of n high-pressure gas storage tanks of different models; r v Indicates the gas volume compression ratio of the high-pressure gas storage tank; S max Indicates the upper limit of the total floor space occupied by high-pressure gas storage tanks;

[0032] Formula (21) represents the upper and lower limits of the configuration capacity of the high-pressure gas storage tank; Formula (22) represents the total capacity of the selected configuration of different types of high-pressure gas storage tanks; Formula (23) limits the number of selected models to be non-negative; Formula (24) constrains the construction area of ​​the high-pressure gas storage tank;

[0033] Equations (25)-(30) are the operation models of high-pressure gas storage tanks, where: Indicates the gas storage capacity of the high-pressure gas tank; and Respectively represent the gas charging and discharging volume of the high-pressure gas tank; S gt_max With S gt_min Respectively represent the upper and lower limits of the proportion of the gas storage capacity of the high-pressure gas storage tank to the maximum capacity; l gt Indicates the ratio of the upper limit of gas filling and discharging of the high-pressure gas storage tank to the maximum capacity per unit time; S gt_0 Indicates the initial value of the high-pressure gas tank within one day; Indicates the power consumption of charging and discharging high-pressure gas tank; k gt Indicates the power consumption rate of charging and discharging high-pressure gas tanks;

[0034] Formula (25) represents the upper and lower limit constraints of the gas storage volume in the high-pressure gas storage tank; Formulas (26)-(27) represent the upper and lower limit constraints of charging and discharging; Formula (28) represents the gas storage capacity of the high-pressure gas storage tank in each time period; Formula (29) limits the gas storage volume in the high-pressure gas storage tank to be the same as the initial value at the end of the day; Formula (30) represents the power consumption of charging and discharging the high-pressure gas storage tank.

[0035] Furthermore, in step (3), the distributed energy operation constraints in the multi-energy microgrid system specifically include:

[0036] Operating constraints of microturbines:

[0037]

[0038] Where, represents the output power of the micro gas turbine; represents the NG consumed by the micro gas turbine for power generation; η mt Indicates power generation efficiency; P mt_max With P mt_min Respectively represent the upper and lower limits of the micro gas turbine power generation; P r_mt represents the ramp rate limit of the microturbine;

[0039] Operating constraints of electric refrigerators:

[0040]

[0041] Where, Indicates the cooling power of the electric refrigerator; Indicates the power consumption of the electric refrigerator; η ec Indicates the refrigeration coefficient of the electric refrigerator; L ec_max Indicates the upper limit of cooling power;

[0042] The power purchase and sales constraints between multi-energy microgrids and the upper-level power grid include:

[0043]

[0044] Where, and They represent the electric power bought and sold between the multi-energy microgrid system and the upper power grid; is a binary variable, indicating the buying / selling state of the system; P tra_max Indicates the upper limit of the system's buying and selling electricity;

[0045] Electricity-cooling-air multi-energy flow balance constraints, including:

[0046]

[0047] Where, Indicates new energy output; and Represents electricity, cooling and gas loads respectively;

[0048] The objective function is to minimize the equipment capacity configuration cost and operation cost of the multi-energy microgrid system in a specified period of time:

[0049] min(Cinv +C total ) (40)

[0050] Where C inv Represents the total equipment configuration investment cost; C total represents the total operating cost of the multi-energy microgrid during the planning period;

[0051] C inv Including the capacity configuration cost of LNG cold energy cascade utilization equipment and the capacity configuration cost of high-pressure gas storage tanks, the calculation formula is:

[0052] C inv =c lng_p L lng_Pmax +c lng_l L lng_Lmax +X n T C n (41)

[0053] Where c lng_p with c lng_l Represents the unit capacity configuration cost of LNG cold energy power generation and refrigeration equipment respectively; C n It is a one-dimensional array of n×1, representing the configuration cost unit price of different models of high-pressure gas storage tanks;

[0054] The total operating cost during the system planning period is:

[0055]

[0056] Where, Y represents the total planning period; I represents the number of typical daily scenarios in each year of the planning period; T represents the total time period in a day; D represents the number of days in a year; N y is the annualized net present value;

[0057] System and grid transaction costs for:

[0058]

[0059] Where: and Respectively represent the price of purchasing / selling electricity from the main power grid; and Respectively represent the power purchased / sold from the main grid;

[0060] Operating costs of LNG cold energy cascade utilization equipment for:

[0061]

[0062] Where c lng_p with c lng_lThey represent the unit costs of LNG cold energy power generation and refrigeration equipment operation respectively.

[0063] Furthermore, in step (4), based on the uncertainty scenario data of renewable energy output and load of the multi-energy microgrid during the planning period, the Wasserstein distance is defined as:

[0064]

[0065] Ξ={ξ∈R m |Dξ≤d} (45b)

[0066] Where ξ is a random variable including the renewable energy output and electricity-cooling-gas load of the multi-energy microgrid; is the predicted data sample of the random variable ξ; For P and P N The joint probability distribution of P and P N They are On the random variables ξ and The marginal distribution of ; ‖·‖ is an arbitrary norm; inf represents the infimum function; Ξ represents the polyhedron support set of the random variable; m is the dimension of the random variable ξ; D, d represent the corresponding constant matrix and column vector respectively;

[0067] The fuzzy set of probability distribution is constructed based on Wasserstein distance, and its corresponding expression is:

[0068]

[0069] Where, F ε (P N ) is expressed as the empirical distribution P N The Wasserstein sphere with the center and radius ε reflects the conservativeness of the model. is the set of all probability distributions with support Ξ.

[0070] Furthermore, in step (4), the original configuration optimization model is converted and reconstructed into a two-stage distributed robust collaborative configuration optimization model, the corresponding mathematical expression of which is:

[0071]

[0072] stAx≤b (47b)

[0073]

[0074] stEx+Fy+Gξ≤h (47d)

[0075] Where x is the decision variable in the capacity configuration stage, i.e., the first stage; c1T x is the objective function for minimizing the investment cost in the first stage; y is the decision variable in the operation stage, i.e. the second stage; c2 T y is the objective function for minimizing the typical daily operating cost during the two-stage planning period; f y,i (x,ξ) represents the objective function for a typical day considering the annualized net present value; E, F, and G are the coefficient matrices of the constraints related to x, y, and ξ within a single typical day, respectively;

[0076] Formula (47a) represents the overall objective function; Formula (47b) represents the constraints related only to the first-stage decision variable x; Formula (47c) represents the second-stage objective function for a typical day within the planning period; Formula (47d) represents the relevant constraints coupled between the two stages;

[0077] Restrict the second-stage variable y to an affine function that depends on the random variable ξ, as follows:

[0078] y=Y0+Y ξ ξ (48)

[0079] Where, the linear coefficients Y0 and Y ξ is the decision variable that determines the affine relationship between y and ξ, where Y0 is a column vector and Y ξ is the corresponding dimension matrix;

[0080] According to the dual problem and formula (48), the dual variable λ is introduced y ,θ y,i and The original two-stage distributed blue-rod optimization problem is transformed into an easy-to-solve form, and the existing solver is called to optimize and solve the model to obtain the optimal values ​​of the decision variables for the coordinated configuration optimization of distributed blue-rods for the LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity in a multi-energy microgrid.

[0081] A system for coordinating and optimizing the capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tanks, comprising:

[0082] The LNG cold energy utilization equipment capacity configuration and operation model construction module is used to establish an LNG gasification and cold energy release model based on the liquefied natural gas (LNG) gasification characteristics, including upper and lower limit constraints on the NG output flow of the gasification station, the cold energy that can be recovered and utilized during the LNG gasification process, and the constraints between the cold energy utilization amount at each stage and the total cold energy that can be recovered and utilized during the LNG gasification process. Based on the characteristics of the LNG cold energy cascade utilization equipment, an LNG cold energy cascade power generation and cooling capacity configuration and operation model is established, including capacity configuration constraints of the LNG cold energy power generation equipment, the operation model of the LNG cold energy power generation, configuration constraints of the LNG cold energy cooling equipment, the operation model of the LNG cold energy cooling, and the total power consumption of the LNG cold energy utilization equipment, and the linearization of bilinear terms.

[0083] The high-pressure gas storage tank capacity configuration and operation model construction module is used to establish the high-pressure gas storage tank capacity configuration and operation model based on the optional model parameters of the high-pressure gas storage tank. The high-pressure gas storage tank selection capacity configuration model includes: upper and lower limit constraints on the configuration capacity of the high-pressure gas storage tank, the total capacity of the selected configuration of different models of high-pressure gas storage tanks, the constraint that the number of selected models of all models is non-negative, and the construction area constraint of the high-pressure gas storage tank; the high-pressure gas storage tank operation model includes: upper and lower limit constraints on the gas storage volume in the high-pressure gas storage tank, upper and lower limit constraints on charging and discharging, the gas storage capacity of the high-pressure gas storage tank in each time period, the constraint that the gas storage volume in the high-pressure gas storage tank is the same as the initial value at the end of the day, and the power consumption of charging and discharging the high-pressure gas storage tank;

[0084] A multi-energy microgrid energy management optimization model construction module is used to establish a multi-energy microgrid energy management optimization model that includes LNG cold energy cascade utilization. This includes distributed resource operation constraints in the multi-energy microgrid system, power purchase and sales constraints between the multi-energy microgrid system and the upper power grid, and electricity-cooling-gas multi-energy flow balance constraints. The objective function is to minimize the total investment cost of cold energy cascade power generation-refrigeration and high-pressure gas storage tanks and the total operating cost of the microgrid within the planning period, forming an optimization model for the coordinated configuration of LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity in the multi-energy microgrid.

[0085] The optimization model solving module is used to convert and reconstruct the original configuration optimization model into a two-stage distributed blue-rod collaborative configuration optimization model based on the distributed blue-rod optimization strategy of Wasserstein distance according to the predicted data of new energy output and electricity-cooling-gas load during the planning period, and solve it to obtain the optimal configuration capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tanks.

[0086] The present invention also provides a computer device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the method for optimizing the coordinated configuration of the LNG cold energy cascade utilization equipment and the high-pressure gas storage tank capacity as described above is implemented.

[0087] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for optimizing the coordinated configuration of the LNG cold energy cascade utilization equipment and the high-pressure gas storage tank capacity.

[0088] Beneficial effects: (1) The present invention optimizes the capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tanks and couples them with the energy optimization management of multi-energy microgrids, thereby improving the utilization rate of LNG cold energy and increasing the flexibility and economy of LNG cold energy cascade utilization. (2) The present invention considers the energy flow of electricity, cold energy, and gas, couples the LNG cold energy cascade utilization with the energy management of multi-energy microgrids, and utilizes the coupling relationship between the cold energy utilization equipment and high-pressure gas storage tanks, distributed energy, and multi-energy conversion equipment to perform coordinated configuration optimization, thereby improving the overall energy utilization rate of the system and reducing the total operating cost of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 Schematic diagram of the process of the present invention;

[0090] Figure 2 This is a schematic diagram of energy flow in an embodiment of the present invention;

[0091] Figure 3 is the typical daily power curve of renewable energy and load;

[0092] Figure 4 This is a schematic diagram of the electricity supply and demand balance result after optimization of the present invention;

[0093] Figure 5 This is a schematic diagram of the gas supply and demand balance result after optimization of the present invention;

[0094] Figure 6 This is a schematic diagram of the cooling supply and demand balance results after optimization of the present invention. DETAILED DESCRIPTION

[0095] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0096] This embodiment discloses a method for optimizing the coordinated configuration of LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity. Figure 1 , the method comprises the following steps:

[0097] (1) Based on the gasification characteristics of liquefied natural gas (LNG), a model of LNG gasification and cold energy release is established; based on the characteristics of LNG cold energy cascade utilization equipment, a model of LNG cold energy cascade power generation-refrigeration capacity configuration and operation is established and the bilinear terms are linearized;

[0098] (2) Establish the capacity configuration and operation model of the high-pressure gas storage tank based on the optional model parameters of the high-pressure gas storage tank;

[0099] (3) Establish a multi-energy microgrid energy management optimization model that includes LNG cold energy cascade utilization, including distributed resource operation constraints in the multi-energy microgrid system, power purchase and sales constraints between the system and the upper power grid, and electricity-cooling-gas multi-energy flow balance constraints; minimize the total investment cost of cold energy cascade power generation-refrigeration and high-pressure gas storage tanks and the total operation cost of the microgrid within the planning period as the objective function; form an optimization model for the coordinated configuration of LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity for multi-energy microgrids;

[0100] (4) According to the forecast data of new energy output and electricity-cooling-gas load during the planning period, the above configuration optimization model is converted and reconstructed into a two-stage distributed blue-rod collaborative configuration optimization model based on the Wasserstein distance and solved to obtain the optimal configuration capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tanks.

[0101] According to a preferred embodiment of the present invention, the LNG cold energy cascade utilization and gas tank capacity configuration model in step (1) is specifically as follows:

[0102] (1.1) LNG gasification and cold energy release model, the corresponding expression is:

[0103]

[0104] Where y represents the planning year index; i represents the typical day scenario index; t represents the scheduling period index in a day; τ represents the length of the scheduling period; Indicates the natural gas (NG) output flow of the gasification station; G ng_max Indicates the upper limit of NG output flow rate; Represents the total cooling power that can be recovered during the LNG gasification process; ρ ng 、c ng With h ng represents NG density, NG specific heat capacity and LNG latent heat respectively; ΔT represents the temperature difference of NG after gasification; r u Indicates the unit conversion ratio between kJ and kW·h; and Represents the cooling energy power used for power generation and cooling operation respectively.

[0105] Formula (1) represents the upper and lower limit constraints of the NG output flow rate of the gasification station; Formula (2) represents the cold energy that can be recovered and utilized during the LNG gasification process; Formula (3) represents the constraint between the cold energy utilization at each stage and the total cold energy that can be recovered and utilized during the LNG gasification process.

[0106] (1.2) LNG cold energy power generation-refrigeration cascade utilization capacity configuration and operation model, the corresponding expression is:

[0107] 0≤L lng_Pmax ≤LPmax (4)

[0108]

[0109] 0≤L lng_Lmax ≤L Lmax (9)

[0110]

[0111]

[0112] Where, L Pmax With L Lmax Respectively represent the upper limit of the configuration capacity of cold energy power generation and refrigeration equipment; L lng_Pmax With L lng _Lmax Respectively represent the configuration capacity of cold energy power generation and refrigeration equipment; and are binary variables, representing the start and stop of the cooling energy utilization equipment; and Respectively represent the cooling power consumed in LNG cooling power generation and refrigeration; l p With l l They represent the ratio of the lower limit to the upper limit of cooling energy for power generation and cooling energy for refrigeration respectively; and Respectively represent the operating power consumption of LNG cold energy power generation and refrigeration equipment; k p With k l Respectively represent the operating power consumption rates of LNG cold energy power generation and refrigeration equipment; and They represent the cooling losses of LNG cooling energy cascade utilization for power generation and refrigeration respectively; and They represent the power generation power and refrigeration power of LNG cold energy cascade utilization respectively; n p With n l is the heat transfer loss rate at each stage; u p with u l is the cooling energy utilization rate at each stage; Indicates the total power consumption of LNG cooling energy utilization.

[0113] Formula (4) is the capacity configuration constraint of LNG cold energy power generation equipment; Formulas (5)-(8) are the operation models of LNG cold energy power generation, where Formula (5) represents the start and stop of LNG cold energy power generation equipment and the upper and lower limits of cold energy consumption; Formula (6) represents the cold loss of LNG cold energy power generation; Formula (7) represents the constraint of LNG cold energy power generation power and cold energy consumption power; Formula (8) is the power consumption of LNG cold energy power generation equipment; Formulas (9)-(13) are the configuration and operation models of LNG cold energy refrigeration, which are similar to Formulas (4)-(8) and will not be repeated here; Formula (14) represents the total power consumption of LNG cold energy utilization equipment; In addition, the formula in Formula (5) is a bilinear term and needs to be linearized. Formula (5) can be transformed into linear constraints (15)-(17). Similarly, in formula (10) It is also a bilinear term and can be transformed into linear constraints (18)-(20) as follows:

[0114]

[0115]

[0116] In the formula, the new variable after linearization is introduced Represents the upper limit of the cooling power consumed in the LNG cooling power generation and refrigeration startup state, and is combined with the binary variable and Indicates the start and stop of LNG cold energy utilization equipment.

[0117] According to a preferred embodiment of the present invention, the high-pressure gas storage tank capacity configuration and operation model in step (2) corresponds to the expression:

[0118] 0≤V ng ≤V ng_max (twenty one)

[0119] V ng =X n T V n (twenty two)

[0120] X n ≥0 (23)

[0121] 0≤X n T S n ≤S max (twenty four)

[0122]

[0123] Formulas (21)-(24) are the selection and capacity configuration models for high-pressure gas storage tanks, where Vng Indicates the total configuration capacity of the high-pressure gas storage tank; V ng_max Indicates the upper limit of the total configuration capacity of the high-pressure gas storage tank; X n is an n×1 integer decision variable array, which represents the number of configurations of n high-pressure gas storage tanks of different models. The superscript T represents the transpose of the matrix. V n With S n is a one-dimensional array of n×1, representing the capacity and floor space of n high-pressure gas storage tanks of different models; r v Indicates the gas volume compression ratio of the high-pressure gas storage tank; S max Indicates the upper limit of the total floor space of the high-pressure gas storage tank.

[0124] Formula (21) represents the upper and lower limit constraints of the configuration capacity of the high-pressure gas storage tank; Formula (22) represents the total capacity of the selected configuration of different types of high-pressure gas storage tanks; Formula (23) limits the number of selected models to be non-negative; Formula (24) constrains the construction area of ​​the high-pressure gas storage tank.

[0125] Equations (25)-(30) are the operation models of high-pressure gas storage tanks, where: Indicates the gas storage capacity of the high-pressure gas tank; and Respectively represent the gas charging and discharging volume of the high-pressure gas tank; S gt_max With s gt_min Respectively represent the upper and lower limits of the proportion of the gas storage capacity of the high-pressure gas storage tank to the maximum capacity; l gt Indicates the ratio of the upper limit of gas filling and discharging of the high-pressure gas storage tank to the maximum capacity per unit time; S gt_0 Indicates the initial value of the high-pressure gas tank within one day; Indicates the power consumption of charging and discharging high-pressure gas tank; k gt Indicates the power consumption rate of charging and deflating the high-pressure gas tank.

[0126] Formula (25) represents the upper and lower limit constraints of the gas storage volume in the high-pressure gas storage tank; Formulas (26)-(27) represent the upper and lower limit constraints of charging and discharging; Formula (28) represents the gas storage capacity of the high-pressure gas storage tank in each time period; Formula (29) limits the gas storage volume in the high-pressure gas storage tank to be the same as the initial value at the end of the day; Formula (30) represents the power consumption of charging and discharging the high-pressure gas storage tank.

[0127] According to a preferred embodiment of the present invention, the multi-energy microgrid energy optimization model in step (3) is specifically:

[0128] (3.1) Distributed energy operation constraints in multi-energy microgrid systems include:

[0129] Operating constraints of microturbines:

[0130]

[0131] Where, represents the output power of the micro gas turbine; represents the NG consumed by the micro gas turbine for power generation; η mt Indicates power generation efficiency; P mt_max With P mt_min Respectively represent the upper and lower limits of the micro gas turbine power generation; P r_mt Indicates the ramp rate limit of the microturbine.

[0132] Operating constraints of electric refrigerators:

[0133]

[0134] Where, Indicates the cooling power of the electric refrigerator; Indicates the power consumption of the electric refrigerator; η ec Indicates the refrigeration coefficient of the electric refrigerator; L ec_max Indicates the upper limit of cooling power.

[0135] (3.2) Constraints on power purchase and sales between multi-energy microgrids and upper-level power grids:

[0136]

[0137]

[0138] Where, and They represent the electric power bought and sold between the multi-energy microgrid system and the upper power grid; is a binary variable, indicating the buying / selling state of the system; P tra_max Indicates the upper limit of the system's buying and selling electricity.

[0139] (3.3) Electricity-cooling-air multi-energy flow balance constraints:

[0140]

[0141] Where, Indicates new energy output; and Represents electricity, cooling and gas loads respectively.

[0142] (3.4) The objective function of minimizing the equipment capacity configuration cost and operation cost of the multi-energy microgrid system in a specified period of time is as follows:

[0143] min(C inv +C total )(40)

[0144] Where C inv Represents the total equipment configuration investment cost; Ctotal represents the total operating cost of the multi-energy microgrid during the planning period;

[0145] C inv Including the capacity configuration cost of LNG cold energy cascade utilization equipment and the capacity configuration cost of high-pressure gas storage tanks, the calculation formula is:

[0146] C inv =c lng_p L lng_Pmax +c lng_l L lng_Lmax +X n T C n (41)

[0147] Where c lng_p with c lng_l Represents the unit capacity configuration cost of LNG cold energy power generation and refrigeration equipment respectively; C n It is an n×1 one-dimensional array, representing the configuration cost unit price of different models of high-pressure gas storage tanks.

[0148] The total operating cost during the system planning period is:

[0149]

[0150] Where, Y represents the total planning period; I represents the number of typical daily scenarios in each year of the planning period; T represents the total time period in a day; D represents the number of days in a year, which is 365; N y is the annualized net present value, expressed as:

[0151]

[0152] Where, d r is the discount rate;

[0153] System and grid transaction costs for:

[0154]

[0155] Where: and Respectively represent the price of purchasing / selling electricity from the main power grid; and Respectively represent the power purchased / sold from the main grid;

[0156] Operating costs of LNG cold energy cascade utilization equipment for:

[0157]

[0158] Where c lng_p with clng_l They represent the unit costs of LNG cold energy power generation and refrigeration equipment operation respectively.

[0159] According to a preferred embodiment of the present invention, the two-stage distributed robust collaborative optimization model in step (4) is specifically:

[0160] (4.1) Based on the uncertainty scenario data of renewable energy output and load of the multi-energy microgrid during the planning period, the Wasserstein distance is defined as:

[0161]

[0162] Ξ={ξ∈R m |Dξ≤d} (45b)

[0163] Where ξ is a random variable including the renewable energy output and electricity-cooling-gas load of the multi-energy microgrid; is the predicted data sample of the random variable ξ; For P and P N The joint probability distribution of P and P N They are On the random variables ξ and is the marginal distribution of ; ‖·‖ is an arbitrary norm; inf represents the infimum function; Ξ represents the polyhedral support set of the random variable; m is the dimension of the random variable ξ; D and d represent the corresponding constant matrix and column vector, respectively.

[0164] The fuzzy set of probability distribution is constructed based on Wasserstein distance, and its corresponding expression is:

[0165]

[0166] Where, F ε (P N ) is expressed as the empirical distribution P N The Wasserstein sphere with the center and radius ε reflects the conservativeness of the model. is the set of all probability distributions with support Ξ.

[0167] (4.2) The original optimization model of step (3) is transformed into a two-stage distributed robust collaborative configuration optimization model, and its corresponding mathematical expression is:

[0168]

[0169] stAx≤b (47b)

[0170]

[0171] stEx+Fy+Gξ≤h (47d)

[0172] Where x is the decision variable in the capacity configuration stage (first stage); c1 T x is the objective function for minimizing the investment cost in the first stage; y is the decision variable in the operation stage (the second stage); c2 T y is the objective function for minimizing the typical daily operating cost during the two-stage planning period; f y,i (x,ξ) represents the objective function for a typical day considering the annualized net present value; E, F, and G are the coefficient matrices of the constraints related to x, y, and ξ within a single typical day, respectively.

[0173] Formula (47a) represents the overall objective function; Formula (47b) represents the constraints related only to the first-stage decision variable x; Formula (47c) represents the second-stage objective function for a typical day within the planning period; Formula (47d) represents the relevant constraints coupled between the two stages.

[0174] Restrict the second-stage variable y to an affine function that depends on the random variable ξ, as follows:

[0175] y=Y0+Y ξ ξ (48)

[0176] Where, the linear coefficients Y0 and Y ξ is the decision variable that determines the affine relationship between y and ξ, where Y0 is a column vector and Y ξ is the matrix of corresponding dimension.

[0177] According to the dual problem and formula (48), the dual variable λ is introduced y ,θ y,i and The original two-stage distributed robust optimization problem is transformed into the following form:

[0178]

[0179] stAx≤b (49b)

[0180]

[0181]

[0182] Where s y,i With z y,i is an auxiliary variable; is the i-th predicted data sample of the random variable ξ in the y-th year.

[0183] (4.3) According to the above two-stage distributed blue rod collaborative configuration optimization method, the model is optimized and solved by calling Gurobi or Cplex solvers to obtain the optimal values ​​of decision variables for the distributed blue rod collaborative configuration optimization of multi-energy microgrid LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity.

[0184] In order to verify the performance of the method of the present invention, a simulation experiment was conducted. The basic parameters selected include: (1) combining the typical daily output curve of renewable energy and load to obtain the average output of renewable energy and the average load of electric cooling in each hour. The typical daily output curve of renewable energy and load is as follows: Figure 3 As shown in Table 1, the renewable energy fluctuation is set to ±30% and the load fluctuation is set to ±10%; (2) the relevant parameters of the cold energy cascade utilization equipment, high-pressure gas storage tanks and microgrid energy consumption equipment are shown in Table 1; (3) the model parameters of the high-pressure gas storage tanks are shown in Table 2; (4) the transaction limit between the system and the main power grid is 10,000kW, and the reference value of the power grid electricity price is: the peak purchase price is 1.10 yuan / kWh, the normal purchase price is 0.70 yuan / kWh, the valley purchase price is 0.40 yuan / kWh, and the electricity sales price is 0.3 yuan / kWh; (5) the configuration and operation cost of the cold energy utilization equipment, the configuration cost of the cold energy power generation equipment is 7,000 yuan / kW, the operation cost is 0.4 yuan / kW, the configuration cost of the direct cooling equipment is 2,000 yuan / kW, and the operation cost is 0.2 yuan / kW.

[0185] Table 1 Parameters of cold energy cascade utilization equipment, high-pressure gas storage tanks, and microgrid energy-consuming equipment

[0186]

[0187]

[0188] Table 2 High-pressure gas storage tank model parameters

[0189] model <![CDATA[Capacity (m 3 )]]> <![CDATA[Floor area (m 2 )]]> Configuration cost (10,000 yuan) 1 5 10.5 2 2 10 14 4 3 7.8 12.6 3.2

[0190] Depend on Figure 2 It can be seen that coupling the cascade utilization of LNG cold energy with the energy management of multi-energy microgrids, considering the energy flow of electricity, cold and gas, reflects the coupling relationship between the cold energy utilization equipment and high-pressure gas storage tanks, distributed energy, and multi-energy conversion equipment in the system.

[0191] The Yalmip platform is used to build and solve the problem with Gurobi solver to obtain the optimal values ​​of decision variables for the coordinated configuration optimization of LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity distribution. The energy balance is as follows: Figure 4-6 shown.

[0192] Depend on Figure 4It can be seen that the power flow after capacity optimization configuration is as follows: during the periods of 7-12 and 14-22, due to the higher electricity purchase price at this time, the LNG cold energy power generation power increases, which is equivalent to the rated operating power generation power of the gas turbine, greatly alleviating the system's power consumption pressure and reducing the system's carbon emissions.

[0193] Depend on Figure 5 It can be seen that the cooling power flow after capacity optimization configuration is as follows: during the periods of 7-10, 12-13, and 15-20, the cooling load demand increases. At this time, the LNG cooling power cooperates with the electric refrigeration machine to supply the cooling load demand. When the LNG cooling capacity is small, the cooling power is about 200kW, accounting for about 10% of the cooling load demand; when the LNG cooling capacity is large, the cooling power can reach 1000kW, which is a maximum of more than 50% of the cooling load demand. This shows that the LNG cooling link has the effect of supplementing the supply of cooling load demand, reducing the demand for electric cooling, that is, additional electricity consumption, and further optimizing energy utilization.

[0194] Depend on Figure 6 As can be seen, NG production fluctuates throughout the day, with higher daytime demand likely due to increased cooling demand or for micro-gas turbines. LNG gasification can largely meet the system's gas load. Furthermore, gas storage equipment stores gas during low-load periods and releases it during peak periods, balancing the system's natural gas supply.

[0195] The optimal capacity configuration of the high-pressure gas storage tank in the optimization result is 40m 3 The selection results indicate that eight No. 1 gas tanks will be constructed; the optimal capacity configuration for the LNG cold energy power generation equipment is 1790 kW; and the optimal capacity configuration for the LNG cold energy refrigeration equipment is 2000 kW. The total capacity configuration investment cost is 17.04 million yuan, and the total cost over the planning period is 255.29 million yuan. The total cost over the same planning period for a multi-energy microgrid system without LNG cold energy utilization equipment and high-pressure gas storage is 260.09 million yuan. This shows that the LNG cold energy cascade power generation and refrigeration system and high-pressure gas storage proposed in this method can improve the economic operation and energy efficiency of the multi-energy microgrid.

[0196] The present invention also provides a system for coordinating and optimizing the capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tanks, comprising:

[0197] The LNG cold energy utilization equipment capacity configuration and operation model construction module is used to establish an LNG gasification and cold energy release model based on the liquefied natural gas (LNG) gasification characteristics, including upper and lower limit constraints on the NG output flow of the gasification station, the cold energy that can be recovered and utilized during the LNG gasification process, and the constraints between the cold energy utilization amount at each stage and the total cold energy that can be recovered and utilized during the LNG gasification process. Based on the characteristics of the LNG cold energy cascade utilization equipment, an LNG cold energy cascade power generation and cooling capacity configuration and operation model is established, including capacity configuration constraints of the LNG cold energy power generation equipment, the operation model of the LNG cold energy power generation, configuration constraints of the LNG cold energy cooling equipment, the operation model of the LNG cold energy cooling, and the total power consumption of the LNG cold energy utilization equipment, and the linearization of bilinear terms.

[0198] The high-pressure gas storage tank capacity configuration and operation model construction module is used to establish the high-pressure gas storage tank capacity configuration and operation model based on the optional model parameters of the high-pressure gas storage tank. The high-pressure gas storage tank selection capacity configuration model includes: upper and lower limit constraints on the configuration capacity of the high-pressure gas storage tank, the total capacity of the selected configuration of different models of high-pressure gas storage tanks, the constraint that the number of selected models of all models is non-negative, and the construction area constraint of the high-pressure gas storage tank; the high-pressure gas storage tank operation model includes: upper and lower limit constraints on the gas storage volume in the high-pressure gas storage tank, upper and lower limit constraints on charging and discharging, the gas storage capacity of the high-pressure gas storage tank in each time period, the constraint that the gas storage volume in the high-pressure gas storage tank is the same as the initial value at the end of the day, and the power consumption of charging and discharging the high-pressure gas storage tank;

[0199] A multi-energy microgrid energy management optimization model construction module is used to establish a multi-energy microgrid energy management optimization model that includes LNG cold energy cascade utilization. This includes distributed resource operation constraints in the multi-energy microgrid system, power purchase and sales constraints between the multi-energy microgrid system and the upper power grid, and electricity-cooling-gas multi-energy flow balance constraints. The objective function is to minimize the total investment cost of cold energy cascade power generation-refrigeration and high-pressure gas storage tanks and the total operating cost of the microgrid within the planning period, forming an optimization model for the coordinated configuration of LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity in the multi-energy microgrid.

[0200] The optimization model solving module is used to convert and reconstruct the original configuration optimization model into a two-stage distributed blue-rod collaborative configuration optimization model based on the distributed blue-rod optimization strategy of Wasserstein distance according to the predicted data of new energy output and electricity-cooling-gas load during the planning period, and solve it to obtain the optimal configuration capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tanks.

[0201] It should be understood that the LNG cold energy cascade utilization equipment and the high-pressure gas storage tank capacity collaborative configuration optimization system in this embodiment can implement all the technical solutions in the above-mentioned method embodiments, and the functions of its various functional modules can be specifically implemented according to the methods in the above-mentioned method embodiments. The specific implementation process can refer to the relevant description in the above-mentioned method embodiments, and will not be repeated here.

[0202] The present invention also provides a computer device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the method for optimizing the coordinated configuration of the LNG cold energy cascade utilization equipment and the high-pressure gas storage tank capacity as described above is implemented.

[0203] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for optimizing the coordinated configuration of the LNG cold energy cascade utilization equipment and the high-pressure gas storage tank capacity.

[0204] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus (systems), computer devices, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0205] The present invention is described with reference to flowcharts of methods according to embodiments of the present invention. It should be understood that each process in the flowcharts and combinations of processes in the flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 A device that specifies functions in a process or multiple processes.

[0206] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A function specified in a process or multiple processes.

[0207] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 The steps of a specified function in a process or multiple processes.

Claims

1. A method for optimizing the coordinated configuration of LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity, characterized in that: The following steps are involved: (1) Based on the gasification characteristics of liquefied natural gas (LNG), an LNG gasification and cold energy release model is established, including the upper and lower limit constraints of the NG output flow of the gasification station, the cold energy that can be recovered and utilized during the LNG gasification process, and the constraints between the cold energy utilization amount at each stage and the total cold energy that can be recovered and utilized during the LNG gasification process; based on the characteristics of the LNG cold energy cascade utilization equipment, an LNG cold energy cascade power generation and cooling capacity configuration and operation model is established, including the capacity configuration constraints of the LNG cold energy power generation equipment, the operation model of the LNG cold energy power generation, the configuration constraints of the LNG cold energy cooling equipment, the operation model of the LNG cold energy cooling, and the total power consumption of the LNG cold energy utilization equipment, and the bilinear terms are linearized; (2) According to the optional model parameters of the high-pressure gas storage tank, a capacity configuration and operation model of the high-pressure gas storage tank is established. The selection capacity configuration model of the high-pressure gas storage tank includes: the upper and lower limit constraints of the configuration capacity of the high-pressure gas storage tank, the total capacity of the selected configuration of different models of high-pressure gas storage tanks, the constraint that the number of selected models of all models is non-negative, and the construction area constraint of the high-pressure gas storage tank; the operation model of the high-pressure gas storage tank includes: the upper and lower limit constraints of the gas storage volume in the high-pressure gas storage tank, the upper and lower limit constraints of gas charging and discharging, the gas storage capacity of the high-pressure gas storage tank in each time period, the constraint that the gas storage volume in the high-pressure gas storage tank is the same as the initial value at the end of the day, and the power consumption of the high-pressure gas storage tank when charging and discharging; (3) Establish a multi-energy microgrid energy management optimization model that includes LNG cold energy cascade utilization, including distributed resource operation constraints in the multi-energy microgrid system, power purchase and sales constraints between the multi-energy microgrid system and the upper power grid, and electricity-cooling-gas multi-energy flow balance constraints. The objective function is to minimize the total investment cost of cold energy cascade power generation-refrigeration and high-pressure gas storage tanks and the total operation cost of the microgrid within the planning period, and form an optimization model for the coordinated configuration of LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity for multi-energy microgrids; (4) According to the forecast data of new energy output and electricity-cooling-gas load during the planning period, the original configuration optimization model is transformed and reconstructed into a two-stage distributed blue-rod collaborative configuration optimization model based on the Wasserstein distance and solved to obtain the optimal configuration capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tanks.

2. The method according to claim 1, characterized in that In step (1), the LNG gasification and cold energy release model has the following expression: Where y represents the planning year index; i represents the typical day scenario index; t represents the scheduling period index in a day; τ represents the length of the scheduling period; Indicates the natural gas NG output flow rate of the gasification station; G ng_max Indicates the upper limit of NG output flow rate; Represents the total cooling power that can be recovered during the LNG gasification process; ρ ng 、c ng With h ng represents NG density, NG specific heat capacity and LNG latent heat respectively; ΔT represents the temperature difference of NG after gasification; r u Indicates the unit conversion ratio between kJ and kW·h; and Represents the cooling power used for power generation and cooling operation respectively; Formula (1) represents the upper and lower limit constraints of the NG output flow rate of the gasification station; Formula (2) represents the cold energy that can be recovered and utilized during the LNG gasification process; Formula (3) represents the constraint between the cold energy utilization in each stage and the total cold energy that can be recovered and utilized during the LNG gasification process; The capacity configuration and operation model of LNG cold energy power generation and refrigeration cascade utilization is expressed as follows: 0≤L lng_Pmax ≤L Pmax (4) Where, L Pmax With L Lmax Respectively represent the upper limit of the configuration capacity of cold energy power generation and refrigeration equipment; L lng_Pmax With L lng_nmax Respectively represent the configuration capacity of cold energy power generation and refrigeration equipment; and are binary variables, representing the start and stop of the cooling energy utilization equipment; and Respectively represent the cooling power consumed in LNG cooling power generation and refrigeration; l p With l l They represent the ratio of the lower limit to the upper limit of cooling energy for power generation and cooling energy for refrigeration respectively; and Respectively represent the operating power consumption of LNG cold energy power generation and refrigeration equipment; k p With k l Respectively represent the operating power consumption rates of LNG cold energy power generation and refrigeration equipment; and They represent the cooling losses of LNG cooling energy cascade utilization for power generation and refrigeration respectively; and They represent the power generation power and refrigeration power of LNG cold energy cascade utilization respectively; n p With n l is the heat transfer loss rate at each stage; u p with u l is the cooling energy utilization rate at each stage; Indicates the total power consumption of LNG cooling energy utilization; Formula (4) is the capacity configuration constraint of LNG cold energy power generation equipment; Formulas (5)-(8) are the operation models of LNG cold energy power generation, where Formula (5) represents the start and stop of LNG cold energy power generation equipment and the upper and lower limits of cold energy consumption; Formula (6) represents the cold loss of LNG cold energy power generation; Formula (7) represents the constraint of LNG cold energy power generation power and cold energy consumption power; Formula (8) is the power consumption of LNG cold energy power generation equipment; Formula (9) is the capacity configuration constraint of LNG cold energy refrigeration equipment; Formulas (10)-(13) are the LNG cold energy refrigeration operation models; Formula (14) represents the total power consumption of LNG cold energy utilization equipment.

3. The method according to claim 2, characterized in that In formula (5) is a bilinear term and can be transformed into linear constraints (15)-(17); in formula (10) is a bilinear term and can be transformed into linear constraints (18)-(20) as follows: In the formula, the new variable after linearization is introduced and Represents the upper limit of the cooling power consumed in the LNG cooling power generation and refrigeration startup state, and is combined with the binary variable and Indicates the start and stop of LNG cold energy utilization equipment.

4. The method according to claim 3, characterized in that In step (2), the high-pressure gas storage tank capacity configuration and operation model, the corresponding expression is: 0≤V ng ≤V ng_max (21) V ng =X n T V n (22) X n ≥0 (23) 0≤X n T S n ≤S max (24) Formulas (21)-(24) are the selection and capacity configuration models for high-pressure gas storage tanks, where V ng Indicates the total configuration capacity of the high-pressure gas storage tank; V ng_max Indicates the upper limit of the total configuration capacity of the high-pressure gas storage tank; X n is an n×1 integer decision variable array, which represents the number of configurations of n high-pressure gas storage tanks of different models. The superscript T represents the transpose of the matrix. V n With S n is a one-dimensional array of n×1, representing the capacity and floor space of n high-pressure gas storage tanks of different models; r v Indicates the gas volume compression ratio of the high-pressure gas storage tank; S max Indicates the upper limit of the total floor space occupied by high-pressure gas storage tanks; Formula (21) represents the upper and lower limits of the configuration capacity of the high-pressure gas storage tank; Formula (22) represents the total capacity of the selected configuration of different types of high-pressure gas storage tanks; Formula (23) limits the number of selected models to be non-negative; Formula (24) constrains the construction area of ​​the high-pressure gas storage tank; Equations (25)-(30) are the operation models of high-pressure gas storage tanks, where: Indicates the gas storage capacity of the high-pressure gas tank; and Respectively represent the gas charging and discharging volume of the high-pressure gas tank; S gt_max With S gt_min Respectively represent the upper and lower limits of the proportion of the gas storage capacity of the high-pressure gas storage tank to the maximum capacity; l gt Indicates the ratio of the upper limit of gas filling and discharging of the high-pressure gas storage tank to the maximum capacity per unit time; S gt_0 Indicates the initial value of the high-pressure gas tank within one day; Indicates the power consumption of charging and discharging high-pressure gas tank; k gt Indicates the power consumption rate of charging and discharging high-pressure gas tanks; Formula (25) represents the upper and lower limit constraints of the gas storage volume in the high-pressure gas storage tank; Formulas (26)-(27) represent the upper and lower limit constraints of charging and discharging; Formula (28) represents the gas storage capacity of the high-pressure gas storage tank in each time period; Formula (29) limits the gas storage volume in the high-pressure gas storage tank to be the same as the initial value at the end of the day; Formula (30) represents the power consumption of charging and discharging the high-pressure gas storage tank.

5. The method according to claim 4, characterized in that In step (3), the distributed energy operation constraints in the multi-energy microgrid system specifically include: Operating constraints of microturbines: Where, represents the output power of the micro gas turbine; represents the NG consumed by the micro gas turbine for power generation; η mt Indicates power generation efficiency; P mt_max With P mt_min Respectively represent the upper and lower limits of the micro gas turbine power generation; P r_mt represents the ramp rate limit of the microturbine; Operating constraints of electric refrigerators: Where, Indicates the cooling power of the electric refrigerator; Indicates the power consumption of the electric refrigerator; η ec Indicates the refrigeration coefficient of the electric refrigerator; L ec_max Indicates the upper limit of cooling power; The power purchase and sales constraints between multi-energy microgrids and the upper-level power grid include: Where, and They represent the electric power bought and sold between the multi-energy microgrid system and the upper power grid; is a binary variable, indicating the buying / selling state of the system; P tra_max Indicates the upper limit of the system's buying and selling electricity; Electricity-cooling-air multi-energy flow balance constraints, including: Where, Indicates new energy output; and Represents electricity, cooling and gas loads respectively; The objective function is to minimize the equipment capacity configuration cost and operation cost of the multi-energy microgrid system in a specified period of time: min(C inv +C total ) (40) Where C inv Represents the total equipment configuration investment cost; C total represents the total operating cost of the multi-energy microgrid during the planning period; C inv Including the capacity configuration cost of LNG cold energy cascade utilization equipment and the capacity configuration cost of high-pressure gas storage tanks, the calculation formula is: C inv =c lng_p L lng_Pmax +c lng_l L lng_Lmax +X n T C n (41) Where c lng_p with c lng_l Represents the unit capacity configuration cost of LNG cold energy power generation and refrigeration equipment respectively; C n It is a one-dimensional array of n×1, representing the configuration cost unit price of different models of high-pressure gas storage tanks; The total operating cost during the system planning period is: Where, Y represents the total planning period; I represents the number of typical daily scenarios in each year of the planning period; T represents the total time period in a day; D represents the number of days in a year; N y is the annualized net present value; System and grid transaction costs for: Where: and Respectively represent the price of purchasing / selling electricity from the main power grid; and Respectively represent the power purchased / sold from the main grid; Operating costs of LNG cold energy cascade utilization equipment for: Where c lng_p with c lng_l They represent the unit costs of LNG cold energy power generation and refrigeration equipment operation respectively.

6. The method according to claim 5, characterized in that In step (4), based on the uncertainty scenario data of renewable energy output and load of the multi-energy microgrid during the planning period, the Wasserstein distance is defined as: Ξ={ξ∈R m |Dξ≤d} (45b) Where ξ is a random variable including the renewable energy output and electricity-cooling-gas load of the multi-energy microgrid; is the predicted data sample of the random variable ξ; For P and P N The joint probability distribution of P and P N They are On the random variables ξ and The marginal distribution of ; ‖·‖ is an arbitrary norm; inf represents the infimum function; Ξ represents the polyhedron support set of the random variable; m is the dimension of the random variable ξ; D, d represent the corresponding constant matrix and column vector respectively; The fuzzy set of probability distribution is constructed based on Wasserstein distance, and its corresponding expression is: Where, F ε (P N ) is expressed as the empirical distribution P N The Wasserstein sphere with the center and radius ε reflects the conservativeness of the model. is the set of all probability distributions with support Ξ.

7. The method according to claim 6, characterized in that In step (4), the original configuration optimization model is converted and reconstructed into a two-stage distributed robust collaborative configuration optimization model, the corresponding mathematical expression of which is: Where x is the decision variable in the capacity configuration stage, i.e., the first stage; c1 T x is the objective function for minimizing the investment cost in the first stage; y is the decision variable in the operation stage, i.e. the second stage; c2 T y is the objective function for minimizing the typical daily operating cost during the two-stage planning period; f y,i (x,ξ) represents the objective function for a typical day considering the annualized net present value; E, F, and G are the coefficient matrices of the constraints related to x, y, and ξ within a single typical day, respectively; Formula (47a) represents the overall objective function; Formula (47b) represents the constraints related only to the first-stage decision variable x; Formula (47c) represents the second-stage objective function for a typical day within the planning period; Formula (47d) represents the relevant constraints coupled between the two stages; Restrict the second-stage variable y to an affine function that depends on the random variable ξ, as follows: y=Y0+Y ξ ξ (48) Where, the linear coefficients Y0 and Y ξ is the decision variable that determines the affine relationship between y and ξ, where Y0 is a column vector and Y ξ is the corresponding dimension matrix; According to the dual problem and formula (48), the dual variable λ is introduced y ,θ y,i and The original two-stage distributed blue-rod optimization problem is transformed into an easy-to-solve form, and the existing solver is called to optimize and solve the model to obtain the optimal values ​​of the decision variables for the coordinated configuration optimization of distributed blue-rods for the LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity in a multi-energy microgrid.

8. A system for optimizing the coordinated configuration of LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity, characterized in that: include: The LNG cold energy utilization equipment capacity configuration and operation model construction module is used to establish an LNG gasification and cold energy release model based on the liquefied natural gas (LNG) gasification characteristics, including upper and lower limit constraints on the NG output flow of the gasification station, the cold energy that can be recovered and utilized during the LNG gasification process, and the constraints between the cold energy utilization amount at each stage and the total cold energy that can be recovered and utilized during the LNG gasification process. Based on the characteristics of the LNG cold energy cascade utilization equipment, an LNG cold energy cascade power generation and cooling capacity configuration and operation model is established, including capacity configuration constraints of the LNG cold energy power generation equipment, the operation model of the LNG cold energy power generation, configuration constraints of the LNG cold energy cooling equipment, the operation model of the LNG cold energy cooling, and the total power consumption of the LNG cold energy utilization equipment, and the linearization of bilinear terms. The high-pressure gas storage tank capacity configuration and operation model construction module is used to establish the high-pressure gas storage tank capacity configuration and operation model based on the optional model parameters of the high-pressure gas storage tank. The high-pressure gas storage tank selection capacity configuration model includes: upper and lower limit constraints on the configuration capacity of the high-pressure gas storage tank, the total capacity of the selected configuration of different models of high-pressure gas storage tanks, the constraint that the number of selected models of all models is non-negative, and the construction area constraint of the high-pressure gas storage tank; the high-pressure gas storage tank operation model includes: upper and lower limit constraints on the gas storage volume in the high-pressure gas storage tank, upper and lower limit constraints on charging and discharging, the gas storage capacity of the high-pressure gas storage tank in each time period, the constraint that the gas storage volume in the high-pressure gas storage tank is the same as the initial value at the end of the day, and the power consumption of charging and discharging the high-pressure gas storage tank; A multi-energy microgrid energy management optimization model construction module is used to establish a multi-energy microgrid energy management optimization model that includes LNG cold energy cascade utilization. This includes distributed resource operation constraints in the multi-energy microgrid system, power purchase and sales constraints between the multi-energy microgrid system and the upper power grid, and electricity-cooling-gas multi-energy flow balance constraints. The objective function is to minimize the total investment cost of cold energy cascade power generation-refrigeration and high-pressure gas storage tanks and the total operating cost of the microgrid within the planning period, forming an optimization model for the coordinated configuration of LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity in the multi-energy microgrid. The optimization model solving module is used to convert and reconstruct the original configuration optimization model into a two-stage distributed blue-rod collaborative configuration optimization model based on the distributed blue-rod optimization strategy of Wasserstein distance according to the predicted data of new energy output and electricity-cooling-gas load during the planning period, and solve it to obtain the optimal configuration capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tanks.

9. A computer device, characterized in that: include: one or more processors; Memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the method for optimizing the coordinated configuration of the LNG cold energy cascade utilization equipment and the high-pressure gas storage tank capacity as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the coordinated configuration of LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity is implemented as described in any one of claims 1 to 7.

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