LNG cold energy cascade utilization equipment and high-pressure gas tank capacity coordinated configuration optimization method and system

Abstract

The application discloses a LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity collaborative configuration optimization method and system. The method comprises the following steps: establishing an LNG cold energy cascade power generation-refrigeration capacity configuration and operation model; establishing a high-pressure gas storage tank capacity configuration and operation model according to the optional model parameters of the high-pressure gas storage tank; considering the distributed resources in the multi-energy micro-grid system, the purchase and sale of electricity from the upper-level power grid, and the electric-cold-gas multi-energy flow balance constraint, establishing a multi-energy micro-grid energy management optimization model containing LNG cold energy cascade utilization, taking the minimum total configuration investment cost and operation cost in the planning period as the target; according to the predicted data of new energy output and electric-cold-gas load during the planning period, a fuzzy set is constructed based on the Wasserstein distance, the above-mentioned 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 cascade utilization equipment and the high-pressure gas storage tank is obtained.

Description

Technical Field

[0001] This invention relates to a method for optimizing equipment capacity configuration, and in particular to a method and system for optimizing the capacity configuration of LNG cold energy cascade power generation, refrigeration and high-pressure gas storage tanks in multi-energy microgrids. Background Technology

[0002] Natural gas (NG), as a clean energy source, is experiencing a significant increase in demand. For ease of storage, NG is mostly transported in the form of liquefied natural gas (LNG). However, before using LNG, it needs to be vaporized, and during this process, approximately 830 kJ of cold energy is released per kilogram of LNG. Currently, most LNG receiving terminals are built along the coast, utilizing seawater or air for vaporization. This not only wastes cold energy but may also cause some degree of environmental pollution. Therefore, the recovery and utilization of LNG cold energy has received increasing attention from scholars.

[0003] To maximize the utilization of LNG cold energy, most mainstream technologies currently employ a tiered utilization approach, dividing LNG cold energy into different cold segments based on different temperature zones and applying the cold energy from each segment to different applications. For example, air separation, carbon capture, and pulverization technologies primarily utilize deep-cooled zone cold energy; power generation technologies mainly utilize deep-cooled and medium-cooled zone cold energy; while air conditioning and cold storage primarily utilize shallow-cooled zone cold energy.

[0004] Despite significant progress in research on the cascade utilization of LNG cold energy, limitations remain. On one hand, current research on LNG cold energy cascade utilization largely focuses on design and simulation, lacking research on capacity configuration and energy management optimization. On the other hand, the cascade utilization of LNG cold energy is constrained by gas load demand; when gas load demand is high or low, it may lead to cold energy waste or shortage, resulting in decreased cold energy utilization. Therefore, there is an urgent need for a method and system for the coordinated configuration optimization of LNG cold energy cascade power generation, cooling, and high-pressure gas storage tank capacity distribution and operation, specifically for multi-energy microgrids, to address the aforementioned problems in the current LNG cold energy cascade utilization. Summary of the Invention

[0005] Purpose of the invention: To address the problems existing in the prior art, this invention proposes a method and system for optimizing the capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tank. By optimizing the capacity configuration of LNG cold energy cascade utilization equipment and high-pressure gas storage tank, and coupling the energy optimization management of multi-energy microgrid, the LNG cold energy efficiency is improved, and the LNG cold energy cascade utilization is made more flexible and economical.

[0006] Technical Solution: To achieve the above-mentioned objectives, a method for optimizing the coordinated configuration of LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity is proposed, characterized by 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 upper and lower limits of NG output flow of the gasification station, recoverable cold energy during LNG gasification, and constraints between the cold energy utilization at each stage and the total recoverable cold energy during LNG gasification; based on the characteristics of LNG cold energy cascade utilization equipment, an LNG cold energy cascade power generation-cooling capacity configuration and operation model is established, including capacity configuration constraints of LNG cold energy power generation equipment, operation model of LNG cold energy power generation, configuration constraints of LNG cold energy cooling equipment, operation model of LNG cold energy cooling, total power consumption of LNG cold energy utilization equipment, and linearization of bilinear terms;

[0008] (2) Based on the available model parameters of the high-pressure gas storage tank, establish a high-pressure gas storage tank capacity configuration and operation model. The high-pressure gas storage tank selection capacity configuration model includes: upper and lower limit constraints of the configuration capacity of the high-pressure gas storage tank, total capacity of different models of high-pressure gas storage tank selection configuration, constraint that the selection quantity of all models is non-negative, and constraint of the construction area of ​​the high-pressure gas storage tank. The high-pressure gas storage tank operation model includes: upper and lower limit constraints of the gas storage volume in the high-pressure gas storage tank, upper and lower limit constraints of the gas filling and discharging, gas storage capacity of the high-pressure gas storage tank in each time period, 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 power consumption of the high-pressure gas storage tank for filling and discharging.

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

[0010] (4) Based on the predicted data of new energy output and power-cooling-gas load during the planning period, the original configuration optimization model is transformed and reconstructed into a two-stage split-bar collaborative configuration optimization model based on the Wasserstein distance optimization strategy, and the optimal configuration capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tank is obtained.

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

[0012]

[0013] In the formula, y represents the planning year index; i represents the typical daily scenario index; t represents the scheduling period index in a day; and τ represents the scheduling period length. Indicates the NG output flow rate of natural gas at the gasification station; Gng_max Indicates the maximum output flow rate of NG; ρ represents the total recoverable cooling energy power during the LNG vaporization process. ng c ng with h ng These represent NG density, NG specific heat capacity, and LNG latent heat, respectively; ΔT represents the temperature difference of NG after vaporization; r u This indicates the unit conversion ratio between kJ and kW·h; and These represent the cooling energy power used for power generation and cooling operation, respectively.

[0014] Formula (1) represents the upper and lower limits of the NG output flow rate of the gasification station; Formula (2) represents the recoverable cold energy during the LNG gasification process; Formula (3) represents the constraint between the cold energy utilization at each stage and the total recoverable cold energy during the LNG gasification process.

[0015] The capacity configuration and operation model for LNG cold energy power generation-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] In the formula, L Pmax With L Lmax These represent the upper limits of the configuration capacity for cold energy power generation and refrigeration equipment, respectively; L lng_Pmax With L lng _Lmax These represent the configuration capacity of cold energy power generation and refrigeration equipment, respectively. and These are binary variables, representing the start and stop of the cold energy utilization equipment, respectively; and These represent the cooling energy consumed in LNG cold power generation and refrigeration, respectively; p With l l These represent the ratio of the lower limit to the upper limit of cold energy consumption for power generation and cooling, respectively. and These represent the operating power consumption of the LNG cold energy power generation and refrigeration equipment, respectively; k p With k l These represent the operating power consumption rates of LNG cold energy power generation and refrigeration equipment, respectively; and These represent the cold losses from LNG cold energy cascade utilization for power generation and refrigeration, respectively. and These represent the power generation and cooling capacity of LNG cold energy cascade utilization, respectively; n p With n l The heat transfer loss rate at each stage; u p with u l For the utilization rate of cold energy at each stage; This indicates the total power consumption for LNG cold energy utilization;

[0021] Formula (4) represents the capacity configuration constraint of the LNG cold energy power generation equipment; Formulas (5)-(8) represent the operation model of LNG cold energy power generation, wherein Formula (5) represents the start-up and shutdown of the 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) represents the power consumption of LNG cold energy power generation equipment; Formula (9) represents the capacity configuration constraint of LNG cold energy refrigeration equipment; Formulas (10)-(13) represent the operation model of LNG cold energy refrigeration; Formula (14) represents the total power consumption of LNG cold energy utilization equipment.

[0022] Furthermore, in formula (5) The terms are bilinear and can be transformed into linear constraints (15)-(17); in formula (10) Since they are bilinear terms, they can be transformed into linear constraints (18)-(20), as shown below:

[0023]

[0024] In the formula, a new variable after linearization is introduced. and These represent the upper limits of the cold energy power consumed in LNG cold power generation and cooling start-up states, respectively, and are combined with binary variables. and This indicates the start-up and shutdown of the LNG cold energy utilization equipment.

[0025] Furthermore, in step (2), the expression corresponding to the high-pressure gas storage tank capacity configuration and operation model 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] Equations (21)-(24) are the selection capacity configuration models for high-pressure gas storage tanks, where V ng V represents the total configuration capacity of the high-pressure gas storage tank; ng_max Indicates the maximum total capacity of the high-pressure gas storage tank; X n V is an n×1 integer decision variable array, representing the number of configurations of n different types of high-pressure gas storage tanks, with the superscript T indicating the transpose of the matrix; n With S n Let r be an n×1 one-dimensional array, representing the capacity and floor space of n different types of high-pressure gas storage tanks; v S represents the gas volume compression ratio of a high-pressure gas storage tank. max This indicates the upper limit of the total floor space occupied by the high-pressure gas storage tank;

[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 models of high-pressure gas storage tanks; Formula (23) restricts the number of all models to be non-negative; Formula (24) restricts the construction area of ​​the high-pressure gas storage tank.

[0033] Equations (25)-(30) represent the operating model of the high-pressure gas storage tank, where, This indicates the gas storage capacity of the high-pressure gas storage tank; and These represent the filling and discharging volumes of the high-pressure gas storage tank, respectively; S gt_max With S gt_min These represent the upper and lower limits of the proportion of the high-pressure gas storage tank's gas storage capacity to its maximum capacity; gt S represents the ratio of the upper limit of the high-pressure gas tank's filling / discharging capacity to its maximum capacity per unit time. gt_0 This indicates the initial value of the high-pressure gas storage tank within a day; This indicates the power consumption for charging and discharging the high-pressure gas storage tank; k gt This indicates the power consumption rate for charging and discharging the high-pressure gas storage tank.

[0034] Formula (25) represents the upper and lower limits of the gas storage capacity in the high-pressure gas storage tank; Formulas (26)-(27) represent the upper and lower limits of the gas filling and discharging; Formula (28) represents the gas storage capacity of the high-pressure gas storage tank for each period; Formula (29) restricts the gas storage capacity 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 the high-pressure gas storage tank for filling and discharging.

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

[0036] Operating constraints of micro gas turbines:

[0037]

[0038] In the formula, This indicates the output power of the micro gas turbine; NG represents the energy consumed by a micro gas turbine for power generation; η represents the energy consumed by the micro gas turbine for power generation. mt P represents power generation efficiency. mt_max With P mt_min These represent the upper and lower limits of the power generation capacity of the micro gas turbine, respectively; P r_mt This indicates the ramp rate limit for a micro gas turbine;

[0039] Operating constraints of electric chillers:

[0040]

[0041] In the formula, Indicates the cooling capacity of the electric chiller; Indicates the power consumption of the electric chiller; η ec L represents the coefficient of performance (COP) of an electric chiller. ec_max Indicates the upper limit of cooling capacity;

[0042] Constraints on power purchase and sale between multi-energy microgrids and the upper-level power grid, specifically including:

[0043]

[0044] In the formula, and These represent the electricity traded between the multi-energy microgrid system and the upstream power grid, respectively. P is a binary variable representing the system's electricity buying / selling status; tra_max Indicates the upper limit of electricity trading in the system;

[0045] The multi-energy flow balance constraints of electricity, cooling, and gas include:

[0046]

[0047] In the formula, This indicates the contribution of new energy sources; and These represent the electrical, cooling, and gas loads, respectively.

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

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

[0050] In the formula, C inv Indicates the total investment cost of equipment configuration; C total This represents the total operating cost of the multi-energy microgrid within the planning period;

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

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

[0053] In the formula, c lng_p With c lng_l These represent the unit capacity configuration cost of LNG cold power generation and refrigeration equipment, respectively; C n Let n×1 be a one-dimensional array representing the unit cost of different models of high-pressure gas storage tanks;

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

[0055]

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

[0057] System and grid transaction costs for:

[0058]

[0059] In the formula: and These represent the purchase / sale prices of electricity from the main grid, respectively. and These represent the power purchased / sold from the main grid, respectively.

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

[0061]

[0062] In the formula, c lng_p With c lng_lThese 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] In the formula, ξ is a random variable that includes the renewable energy output of multi-energy microgrids and the electricity-cooling-gas load; For the predicted data sample of random variable ξ; For P and P N The joint probability distribution of P and P N They are respectively Regarding random variable ξ and The marginal distribution of ξ; ‖·‖ is an arbitrary norm; inf denotes the infimum function; Ξ denotes the polyhedral support set of the random variable; m is the dimension of the random variable ξ; D and d denote the corresponding constant matrix and column vector, respectively;

[0067] The fuzzy set of probability distributions constructed based on Wasserstein distance is expressed as follows:

[0068]

[0069] In the formula, F ε (P N ) is represented by the empirical distribution P N A Wasserstein sphere centered at ε with radius ε; the size of radius ε reflects the degree of conservatism of the model. It is the set of all probability distributions supported by Ξ.

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

[0071]

[0072] stAx≤b (47b)

[0073]

[0074] stEx+Fy+Gξ≤h (47d)

[0075] In the formula, x is the decision variable for the capacity allocation stage, i.e., stage one; c1T x is the objective function for minimizing the investment cost in the first stage; y is the decision variable for the second stage, i.e., the operational 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;

[0076] Formula (47a) represents the overall objective function; Formula (47b) represents the constraints that are only related to the first-stage decision variable x; Formula (47c) represents the second-stage objective function for a typical day during the planning period; Formula (47d) represents the related constraints that couple the two stages.

[0077] The two-stage variable y is restricted to an affine function that depends on the random variable ξ, as shown below:

[0078] y = Y0 + Y ξ ξ (48)

[0079] In the formula, the linear coefficients Y0 and Y ξ Let Y be the decision variable, determining the affine relationship between y and ξ, where Y0 is a column vector, and Y... ξ This is the corresponding dimension matrix;

[0080] Based on the dual problem and formula (48), we introduce the dual variable λ. y θ y,i and The original two-stage sub-Blule bar optimization problem is transformed into an easier-to-solve form. An existing solver is called to optimize the model and obtain the optimal values ​​of the decision variables for the coordinated configuration optimization of the sub-Blule bar capacity of LNG cold energy cascade power generation, refrigeration and high-pressure gas storage tank in multi-energy microgrids.

[0081] An LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity coordinated configuration optimization system includes:

[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 characteristics of LNG gasification, including upper and lower limits of NG output flow of the gasification station, recoverable cold energy during LNG gasification, and constraints between the cold energy utilization at each stage and the total recoverable cold energy during LNG gasification; based on the characteristics of LNG cold energy cascade utilization equipment, an LNG cold energy cascade power generation-cooling capacity configuration and operation model is established, including capacity configuration constraints of LNG cold energy power generation equipment, operation model of LNG cold energy power generation, configuration constraints of LNG cold energy cooling equipment, operation model of LNG cold energy cooling, total power consumption of LNG cold energy utilization equipment, and linearization of bilinear terms;

[0083] The high-pressure gas storage tank capacity configuration and operation model construction module is used to establish a high-pressure gas storage tank capacity configuration and operation model based on the available high-pressure gas storage tank model parameters. 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 different models of high-pressure gas storage tank selection configuration, the constraint that the selection quantity 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 the gas filling and discharging, the gas storage capacity of the high-pressure gas storage tank for 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 for filling and discharging.

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

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

[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 configured to be executed by the one or more processors, wherein when the programs are executed by the processors, they implement the LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity co-configuration optimization method as described above.

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

[0088] Beneficial effects: (1) This invention improves the utilization rate of LNG cold energy and increases the flexibility and economy of LNG cold energy cascade utilization by optimizing the capacity configuration of LNG cold energy cascade utilization equipment and high-pressure gas storage tank, coupled with the energy optimization management of multi-energy microgrid. (2) This invention considers the energy flow of electricity, cooling, and gas, couples LNG cold energy cascade utilization with multi-energy microgrid energy management, and uses the coupling relationship between cold energy utilization equipment and high-pressure gas storage tank, distributed energy, and multi-energy conversion equipment to carry out collaborative configuration optimization, thereby improving the overall energy utilization rate of the system and reducing the total operating cost of the system. Attached Figure Description

[0089] Figure 1 This is a schematic diagram of the method flow of the present invention;

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

[0091] Figure 3 Typical daily power generation curves for renewable energy and load;

[0092] Figure 4 This is a schematic diagram of the optimized power supply and demand balance result of this invention;

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

[0094] Figure 6 This is a schematic diagram of the optimized cold supply and demand balance result of the present invention. Detailed Implementation

[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, referring to... Figure 1 The method includes the following steps:

[0097] (1) Based on the gasification characteristics of liquefied natural gas (LNG), establish an LNG gasification and cold energy release model; based on the characteristics of LNG cold energy cascade utilization equipment, establish an LNG cold energy cascade power generation-cooling capacity configuration and operation model and linearize the bilinear terms;

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

[0099] (3) Establish an energy management optimization model for a multi-energy microgrid that includes the cascade utilization of LNG cold energy, including distributed resource operation constraints in the multi-energy microgrid system, power purchase and sale constraints between the system and the upper-level power grid, and balance constraints of the multi-energy flow of electricity, cooling and gas; take minimizing the total investment cost of cascade power generation and cooling and the total operating 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 and cooling and high-pressure gas storage capacity for multi-energy microgrids;

[0100] (4) Based on the predicted data of new energy output and power-cooling-gas load during the planning period, the above configuration optimization model is transformed and reconstructed into a two-stage split-bar collaborative configuration optimization model based on the Wasserstein distance optimization strategy, and the optimal configuration capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tank is obtained.

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

[0102] (1.1) The LNG vaporization and cold energy release model, and its corresponding expression is:

[0103]

[0104] In the formula, y represents the planning year index; i represents the typical daily scenario index; t represents the scheduling period index in a day; and τ represents the scheduling period length. Indicates the output flow rate of natural gas (NG) at the gasification station; G ng_max Indicates the maximum output flow rate of NG; ρ represents the total recoverable cooling energy power during the LNG vaporization process. ng c ng with h ng These represent NG density, NG specific heat capacity, and LNG latent heat, respectively; ΔT represents the temperature difference of NG after vaporization; r u This indicates the unit conversion ratio between kJ and kW·h; and These represent the cooling energy power used for power generation and cooling operation, respectively.

[0105] Formula (1) represents the upper and lower limits of the NG output flow of the gasification station; Formula (2) represents the recoverable cold energy during the LNG gasification process; Formula (3) represents the constraint between the amount of cold energy utilized in each stage and the total recoverable cold energy during the LNG gasification process.

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

[0107] 0≤L lng_Pmax ≤LPmax (4)

[0108]

[0109] 0≤L lng_Lmax ≤L Lmax (9)

[0110]

[0111]

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

[0113] Formula (4) represents the capacity configuration constraint of the LNG cold energy power generation equipment; Formulas (5)-(8) represent the operation model of LNG cold energy power generation, wherein Formula (5) represents the start-up and shutdown of the 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) represents the power consumption of the LNG cold energy power generation equipment; Formulas (9)-(13) represent the configuration and operation model 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 the LNG cold energy utilization equipment; in addition, Formula (5) Since it is a bilinear term, it needs to be linearized. Formula (5) can be transformed into linear constraints (15)-(17); similarly, in formula (10) It is also a bilinear term, which can be transformed into linear constraints (18)-(20), as shown below:

[0114]

[0115]

[0116] In the formula, a new variable after linearization is introduced. These represent the upper limits of cold energy power consumed in LNG cold power generation and cooling start-up states, respectively, and are accompanied by binary variables. and This indicates the start-up and shutdown of the LNG cold energy utilization equipment.

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

[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 capacity configuration models for high-pressure gas storage tanks, where Vng V represents the total configuration capacity of the high-pressure gas storage tank; ng_max Indicates the maximum total capacity of the high-pressure gas storage tank; X n V is an n×1 integer decision variable array, representing the number of configurations of n different types of high-pressure gas storage tanks, with the superscript T indicating the transpose of the matrix; n With S n Let r be an n×1 one-dimensional array, representing the capacity and floor space of n different types of high-pressure gas storage tanks; v S represents the gas volume compression ratio of a high-pressure gas storage tank. max This indicates the upper limit of the total floor space occupied by the high-pressure gas storage tank.

[0124] 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 models of high-pressure gas storage tanks; Formula (23) restricts the number of all models to be non-negative; Formula (24) restricts the construction area of ​​the high-pressure gas storage tank.

[0125] Equations (25)-(30) represent the operating model of the high-pressure gas storage tank, where, This indicates the gas storage capacity of the high-pressure gas storage tank; and These represent the filling and discharging volumes of the high-pressure gas storage tank, respectively; S gt_max With s gt_min These represent the upper and lower limits of the proportion of the high-pressure gas storage tank's gas storage capacity to its maximum capacity; gt S represents the ratio of the upper limit of the high-pressure gas tank's filling / discharging capacity to its maximum capacity per unit time. gt_0 This indicates the initial value of the high-pressure gas storage tank within a day; This indicates the power consumption for charging and discharging the high-pressure gas storage tank; k gt This indicates the power consumption rate for filling and discharging the high-pressure gas storage tank.

[0126] Formula (25) represents the upper and lower limits of the gas storage capacity in the high-pressure gas storage tank; Formulas (26)-(27) represent the upper and lower limits of the gas filling and discharging; Formula (28) represents the gas storage capacity of the high-pressure gas storage tank for each period; Formula (29) restricts the gas storage capacity 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 the high-pressure gas storage tank for filling and discharging.

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

[0128] (3.1) Operational constraints of distributed energy resources in multi-energy microgrid systems, specifically including:

[0129] Operating constraints of micro gas turbines:

[0130]

[0131] In the formula, This indicates the output power of the micro gas turbine; NG represents the energy consumed by a micro gas turbine for power generation; η represents the energy consumed by the micro gas turbine for power generation. mt P represents power generation efficiency. mt_max With P mt_min These represent the upper and lower limits of the power generation capacity of the micro gas turbine, respectively; P r_mt This indicates the ramp rate limit for a micro gas turbine.

[0132] Operating constraints of electric chillers:

[0133]

[0134] In the formula, Indicates the cooling capacity of the electric chiller; Indicates the power consumption of the electric chiller; η ec L represents the coefficient of performance (COP) of an electric chiller. ec_max This indicates the upper limit of cooling capacity.

[0135] (3.2) Constraints on power purchase and sale between multi-energy microgrids and the upper-level power grid:

[0136]

[0137]

[0138] In the formula, and These represent the electricity traded between the multi-energy microgrid system and the upstream power grid, respectively. P is a binary variable representing the system's electricity buying / selling status; tra_max This indicates the upper limit for electricity trading in the system.

[0139] (3.3) Balance constraints of electric-cooling-gas multi-energy flow:

[0140]

[0141] In the formula, This indicates the contribution of new energy sources; and These represent the electrical, cooling, and gas loads, respectively.

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

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

[0144] In the formula, C inv Indicates the total investment cost of equipment configuration; Ctotal This represents the total operating cost of the multi-energy microgrid within the planning period;

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

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

[0147] In the formula, c lng_p With c lng_l These represent the unit capacity configuration cost of LNG cold power generation and refrigeration equipment, respectively; C n Let n be a 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] In the formula, Y represents the total planning period; I represents the number of typical daily scenarios each year during the planning period; T represents the total time period within a day; D represents the number of days in a year, with a value of 365; N y The annualized net present value is expressed as:

[0151]

[0152] In the formula, d r The discount rate;

[0153] System and grid transaction costs for:

[0154]

[0155] In the formula: and These represent the purchase / sale prices of electricity from the main grid, respectively. and These represent the power purchased / sold from the main grid, respectively.

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

[0157]

[0158] In the formula, c lng_p With clng_l These 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 sub-Bruker collaborative optimization model in step (4) is specifically as follows:

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

[0161]

[0162] Ξ={ξ∈R m |Dξ≤d} (45b)

[0163] In the formula, ξ is a random variable that includes the renewable energy output of multi-energy microgrids and the electricity-cooling-gas load; For the predicted data sample of random variable ξ; For P and P N The joint probability distribution of P and P N They are respectively Regarding random variable ξ and The marginal distribution of ξ; ‖·‖ 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 distributions constructed based on Wasserstein distance is expressed as follows:

[0165]

[0166] In the formula, F ε (P N ) is represented by the empirical distribution P N A Wasserstein sphere centered at ε with radius ε; the size of radius ε reflects the degree of conservatism of the model. It is the set of all probability distributions supported by Ξ.

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

[0168]

[0169] stAx≤b (47b)

[0170]

[0171] stEx+Fy+Gξ≤h (47d)

[0172] In the formula, x is the decision variable for the capacity allocation phase (phase one); c1 T x is the objective function for minimizing the investment cost in the first stage; y is the decision variable for the second stage (operation phase); 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.

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

[0174] The two-stage variable y is restricted to an affine function that depends on the random variable ξ, as shown below:

[0175] y = Y0 + Y ξ ξ (48)

[0176] In the formula, the linear coefficients Y0 and Y ξ Let Y be the decision variable, determining the affine relationship between y and ξ, where Y0 is a column vector, and Y... ξ This is the corresponding dimension matrix.

[0177] Based on the dual problem and formula (48), we introduce the dual variable λ. y θ y,i and The original two-stage Blue Bar optimization problem is transformed into the following form:

[0178]

[0179] stAx≤b (49b)

[0180]

[0181]

[0182] In the formula, s y,i With z y,i As an auxiliary variable; Let ξ be the i-th predicted data sample for the y-th year.

[0183] (4.3) Based on the above two-stage sub-Bluer bar collaborative configuration optimization method, call solvers such as Gurobi or Cplex to optimize the model and obtain the optimal values ​​of decision variables for the sub-Bluer bar collaborative configuration optimization of LNG cold energy cascade power generation-refrigeration and high-pressure gas storage tank capacity of multi-energy microgrid.

[0184] To verify the performance of the method of the present invention, simulation experiments were conducted. The basic parameters selected included: (1) combining the typical daily power output curves of renewable energy and load to obtain the average power output of renewable energy and the average load of electric cooling system for each hour. The typical daily power output curves of renewable energy and load are as follows: Figure 3 As shown, 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 tank and microgrid energy consumption equipment are shown in Table 1; (3) The high-pressure gas storage tank model parameters are shown in Table 2; (4) The system and the main grid transaction limit is 10000kW, and the reference value of the grid electricity price is: peak purchase price is RMB 1.10 / kWh, normal purchase price is RMB 0.70 / kWh, valley purchase price is RMB 0.40 / kWh, and sales price is RMB 0.3 / kWh; (5) The configuration and operation cost of the cold energy utilization equipment is RMB 7000 / kW for cold energy power generation equipment and RMB 0.4 / kW for operation. The configuration cost of the direct cooling equipment is RMB 2000 / kW and the operation cost is RMB 0.2 / kW.

[0185] Table 1. Relevant parameters of cold energy cascade utilization equipment, high-pressure gas storage tanks, and microgrid energy consumption equipment.

[0186]

[0187]

[0188] Table 2 High-Pressure Gas Storage Tank Model Parameters

[0189] 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 by coupling the cascade utilization of LNG cold energy with the energy management of multi-energy microgrids, and considering the energy flow of electricity, cold, and gas, the coupling relationship between the cold energy utilization equipment and the high-pressure gas storage tank, distributed energy, and multi-energy conversion equipment in the system is reflected.

[0191] The solution was built using the Yalmip platform and solved using the Gurobi solver. This yielded 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-based Bruker rods in a multi-energy microgrid. The energy balance was as follows: Figure 4-6 As shown.

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

[0193] Depend on Figure 5 It can be seen that the cooling power flow after capacity optimization 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 energy refrigeration power works in conjunction with the electric chiller to supply the cooling load demand. When the LNG cooling energy is relatively small, the refrigeration power is about 200kW, accounting for about 10% of the cooling load demand; when the LNG cooling energy is relatively large, the refrigeration power can reach 1000kW, exceeding 50% of the cooling load demand. It can be seen that the LNG cooling energy refrigeration link has the effect of supplementing the cooling load demand, reducing the demand for electric cooling, i.e., additional power consumption, and further optimizing energy utilization.

[0194] Depend on Figure 6 It can be seen that NG volume fluctuates throughout the day, with higher demand during the daytime, possibly due to increased demand for cooling energy or for gas supply to micro gas turbines. LNG regasification can basically meet the system's gas load demand, while the gas storage equipment stores gas during periods of low gas load and releases gas during peak periods, playing a role in balancing the system's natural gas supply.

[0195] The optimal capacity configuration for the high-pressure gas storage tank in the optimization results is 40m³. 3 The selection results indicate that eight LNG storage tanks will be built; the optimal capacity configuration for LNG cold energy power generation equipment is 1790kW; and the optimal capacity configuration for LNG cold energy refrigeration equipment is 2000kW. The total investment cost for the capacity configuration is 17.04 million yuan, and the total cost within the planning period is 255.29 million yuan. The total cost of a multi-energy microgrid system without LNG cold energy utilization equipment and high-pressure storage tanks within the same planning period is 260.09 million yuan. This demonstrates that the LNG cold energy cascade power generation-refrigeration and high-pressure storage proposed in this method can improve the economic efficiency and energy utilization efficiency of multi-energy microgrid operation.

[0196] This invention also provides a system for coordinating and optimizing the capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tank, 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 characteristics of LNG gasification, including upper and lower limits of NG output flow of the gasification station, recoverable cold energy during LNG gasification, and constraints between the cold energy utilization at each stage and the total recoverable cold energy during LNG gasification; based on the characteristics of LNG cold energy cascade utilization equipment, an LNG cold energy cascade power generation-cooling capacity configuration and operation model is established, including capacity configuration constraints of LNG cold energy power generation equipment, operation model of LNG cold energy power generation, configuration constraints of LNG cold energy cooling equipment, operation model of LNG cold energy cooling, total power consumption of LNG cold energy utilization equipment, and linearization of bilinear terms;

[0198] The high-pressure gas storage tank capacity configuration and operation model construction module is used to establish a high-pressure gas storage tank capacity configuration and operation model based on the available high-pressure gas storage tank model parameters. 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 different models of high-pressure gas storage tank selection configuration, the constraint that the selection quantity 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 the gas filling and discharging, the gas storage capacity of the high-pressure gas storage tank for 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 for filling and discharging.

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

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

[0201] It should be understood that the LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity collaborative configuration optimization system in this embodiment can realize all the technical solutions in the above method embodiments. The functions of each functional module can be specifically implemented according to the methods in the above method embodiments. The specific implementation process can be referred to the relevant descriptions in the above method embodiments, which 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 configured to be executed by the one or more processors, wherein when the programs are executed by the processors, they implement the LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity co-configuration optimization method as described above.

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

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

[0205] This invention is described with reference to a flowchart of a method according to embodiments of the invention. It should be understood that each step in the flowchart and combinations thereof can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 A device for a function specified in one or more processes.

[0206] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 The function specified in one or more processes.

[0207] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 Steps of a specified function in one or more 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, Includes the following steps: (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 limits of the NG output flow of the gasification station, the recoverable cold energy during the LNG gasification process, and the constraints between the cold energy utilization at each stage and the total recoverable cold energy during the LNG gasification process; based on the characteristics of LNG cold energy cascade utilization equipment, an LNG cold energy cascade power generation-cooling capacity configuration and operation model is established, including the capacity configuration constraints of LNG cold energy power generation equipment, the operation model of LNG cold energy power generation, the configuration constraints of LNG cold energy cooling equipment, the operation model of LNG cold energy cooling, the total power consumption of LNG cold energy utilization equipment, and the linearization of the bilinear terms; (2) Based on the available model parameters of the high-pressure gas storage tank, establish the capacity configuration and operation model of the high-pressure gas storage tank. The capacity configuration model of the high-pressure gas storage tank includes: upper and lower limit constraints of the configuration capacity of the high-pressure gas storage tank, total capacity of different models of high-pressure gas storage tanks, constraint that the number of all models selected is non-negative, and constraint of the construction area of ​​the high-pressure gas storage tank. The operation model of the high-pressure gas storage tank includes: upper and lower limit constraints of the gas storage volume in the high-pressure gas storage tank, upper and lower limit constraints of the gas filling and discharging, gas storage capacity of the high-pressure gas storage tank in each time period, 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 power consumption of the high-pressure gas storage tank for filling and discharging. (3) Establish an energy management optimization model for multi-energy microgrids that includes the cascade utilization of LNG cold energy, including distributed resource operation constraints in the multi-energy microgrid system, power purchase and sale constraints between the multi-energy microgrid system and the upper-level power grid, and power-cooling-gas multi-energy flow balance constraints. The objective function is to minimize the total investment cost of cascade power generation and cooling and the total operating cost of the microgrid within the planning period, and to form an optimization model for the coordinated configuration of LNG cold energy cascade power generation and cooling and high-pressure gas storage capacity for multi-energy microgrids. (4) Based on the predicted data of new energy output and power-cooling-gas load during the planning period, the original configuration optimization model is transformed and reconstructed into a two-stage split-bar collaborative configuration optimization model based on the Wasserstein distance optimization strategy, and the optimal configuration capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tank is obtained.

2. The method according to claim 1, characterized in that, In step (1), the LNG vaporization and cold energy release model has the following expression: In the formula, Indicates the planning year index; Represents a typical daily scene index; This represents the index of the scheduling period within a day; Indicates the length of the scheduling period; This indicates the NG output flow rate of the gasification station; Indicates the maximum output flow rate of NG; This represents the total recyclable cooling energy power during the LNG vaporization process; , and These represent the density of NG, the specific heat capacity of NG, and the latent heat of LNG, respectively. This indicates the temperature difference of NG after vaporization; This indicates the unit conversion ratio between kJ and kW·h; and These represent the cooling energy power used for power generation and cooling operation, respectively. Formula (1) represents the upper and lower limits 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 amount of cold energy utilized 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 for LNG cold energy power generation-refrigeration cascade utilization is expressed as follows: In the formula, and These represent the upper limits of the configuration capacity for cold energy power generation and refrigeration equipment, respectively. and These represent the configuration capacity of cold energy power generation and refrigeration equipment, respectively. and These are binary variables, representing the start and stop of the cold energy utilization equipment, respectively; and These represent the cooling energy consumed in LNG cold power generation and refrigeration, respectively; and These represent the ratio of the lower limit to the upper limit of cold energy consumption for power generation and cooling, respectively. and These represent the operating power consumption of the LNG cold energy power generation and refrigeration equipment, respectively; and These represent the operating power consumption rates of LNG cold energy power generation and refrigeration equipment, respectively; and These represent the cold losses from LNG cold energy cascade utilization for power generation and refrigeration, respectively. and These represent the power generation and cooling capacity of LNG cold energy cascade utilization, respectively. and The heat transfer loss rate at each stage; and For the utilization rate of cold energy at each stage; This indicates the total power consumption for LNG cold energy utilization; Formula (4) represents the capacity configuration constraint of the LNG cold energy power generation equipment; Formulas (5)-(8) represent the operation model of LNG cold energy power generation, wherein Formula (5) represents the start-up and shutdown of the 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) represents the power consumption of LNG cold energy power generation equipment; Formula (9) represents the capacity configuration constraint of LNG cold energy refrigeration equipment; Formulas (10)-(13) represent the operation model of LNG cold energy refrigeration; 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) As bilinear terms, they can be transformed into linear constraints (15)-(17); in formula (10) Since it is a bilinear term, it can be transformed into linear constraints (18)-(20), as shown below: In the formula, a new variable after linearization is introduced. and These represent the upper limits of the cold energy power consumed in LNG cold power generation and cooling start-up states, respectively, and are combined with binary variables. and This indicates the start-up and shutdown of the LNG cold energy utilization equipment.

4. The method according to claim 3, characterized in that, In step (2), the capacity configuration and operation model of the high-pressure gas storage tank are expressed as follows: Equations (21)-(24) are the selection capacity configuration models for high-pressure gas storage tanks, where, This indicates the total configuration capacity of the high-pressure gas storage tank; This indicates the upper limit of the total configuration capacity of the high-pressure gas storage tank; Let n×1 be an array of integer decision variables, representing the number of configurations of n different types of high-pressure gas storage tanks, with superscripts... Represents the transpose of a matrix; and It is a one-dimensional array of n×1, representing the capacity and floor area of ​​n different types of high-pressure gas storage tanks; Indicates the gas volume compression ratio of the high-pressure gas storage tank; This indicates the upper limit of the total floor space occupied by the high-pressure gas storage tank; 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 models of high-pressure gas storage tanks; Formula (23) restricts the number of all models to be non-negative; Formula (24) restricts the construction area of ​​the high-pressure gas storage tank. Equations (25)-(30) represent the operating model of the high-pressure gas storage tank, where, This indicates the gas storage capacity of the high-pressure gas storage tank; and These represent the filling and releasing volumes of the high-pressure gas storage tank, respectively. and These represent the upper and lower limits of the proportion of the high-pressure gas storage tank's gas storage capacity to its maximum capacity, respectively. This represents the ratio of the upper limit of the high-pressure gas storage tank's filling and discharging capacity to its maximum capacity per unit time. This indicates the initial value of the high-pressure gas storage tank within a day; This indicates the power consumption for filling and discharging the high-pressure gas storage tank; This indicates the power consumption rate for charging and discharging the high-pressure gas storage tank. Formula (25) represents the upper and lower limits of the gas storage capacity in the high-pressure gas storage tank; Formulas (26)-(27) represent the upper and lower limits of the gas filling and discharging; Formula (28) represents the gas storage capacity of the high-pressure gas storage tank for each period; Formula (29) restricts the gas storage capacity 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 the high-pressure gas storage tank for filling and discharging.

5. The method according to claim 4, characterized in that, In step (3), the operational constraints of distributed energy resources in the multi-energy microgrid system specifically include: Operating constraints of micro gas turbines: In the formula, This indicates the output power of the micro gas turbine; NG represents the energy consumed by a micro gas turbine for power generation; Indicates power generation efficiency; and These represent the upper and lower limits of the power generation capacity of a micro gas turbine; This indicates the ramp rate limit for a micro gas turbine; Operating constraints of electric chillers: In the formula, Indicates the cooling capacity of the electric chiller; This indicates the power consumption of the electric chiller; This indicates the coefficient of performance (COP) of an electric refrigeration unit. Indicates the upper limit of cooling capacity; Constraints on power purchase and sale between multi-energy microgrids and the upper-level power grid, specifically including: In the formula, and These represent the electricity traded between the multi-energy microgrid system and the upstream power grid, respectively. This is a binary variable representing the system's electricity buying / selling status; Indicates the upper limit of electricity trading in the system; The multi-energy flow balance constraints of electricity, cooling, and gas include: In the formula, This indicates the contribution of new energy sources; , and These represent the electrical, cooling, and gas loads, respectively. The objective function is to minimize the equipment capacity configuration cost and operating cost of a multi-energy microgrid system over a specified time period. In the formula, This indicates the total investment cost for equipment configuration; This represents the total operating cost of the multi-energy microgrid within the planning period; The calculation formula includes the capacity configuration cost of LNG cold energy cascade utilization equipment and the capacity configuration cost of high-pressure gas storage tanks: In the formula, and These represent the unit capacity configuration costs of LNG cold energy power generation and refrigeration equipment, respectively. Let n×1 be a one-dimensional array representing the unit cost of different models of high-pressure gas storage tanks; The total operating cost during the system planning period is: In the formula, Indicates the total planning period; This indicates the number of typical daily scenarios for each year during the planning period; Indicates the total time period within a day; Indicates the number of days in a year; This is the annualized net present value; System and grid transaction costs for: In the formula: and These represent the purchase / sale prices of electricity from the main grid, respectively. and These represent the power purchased / sold from the main grid, respectively. Operating costs of LNG cold energy cascade utilization equipment for: In the formula, and These 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 multi-energy microgrids during the planning period, the Wasserstein distance is defined as: In the formula, It is a random variable that includes the renewable energy output of multi-energy microgrids and the electricity-cooling-gas load; For random variables The predicted data sample; for and The joint probability distribution of ; and They are respectively Regarding random variables and The marginal distribution; It is any norm; Denotes the infimum function; Represents the polyhedral support set of a random variable; For random variables dimensionality; , These represent the corresponding constant matrix and column vector, respectively. The fuzzy set of probability distributions constructed based on Wasserstein distance is expressed as follows: In the formula, Represented as an empirical distribution Centered on, with radius Wasserstein sphere; radius The size of the value reflects the degree of conservatism in the model; Therefore It is the set of all probability distributions of the support set.

7. The method according to claim 6, characterized in that, In step (4), the original configuration optimization model is transformed and reconstructed into a two-stage split-Brussels collaborative configuration optimization model, the corresponding mathematical expression of which is: In the formula, These are the decision variables for the capacity configuration phase, i.e., the first phase. The objective function is to minimize the investment cost in the first stage. These are the decision variables for the operational phase, i.e., the second phase. The objective function is to minimize the typical daily operating cost during the second-phase planning period. This represents the objective function for a typical day considering annualized net present value; , and Each typical day , and The coefficient matrix of the relevant constraints; Formula (47a) represents the overall objective function; Formula (47b) represents the objective function with respect to only one-stage decision variables. Related constraints; Formula (47c) represents the two-stage objective function for a typical day during the planning period; Formula (47d) represents the related constraints of the coupling between the two stages; Two-stage variables Restricted to depend on random variables The affine function is shown below: In the formula, the linear coefficients and As a decision variable, determine and Affine relations, in which It is a column vector. This is the corresponding dimension matrix; Based on the dual problem and formula (48), dual variables are introduced. , and The original two-stage sub-Blule bar optimization problem is transformed into an easier-to-solve form. An existing solver is called to optimize the model and obtain the optimal values ​​of the decision variables for the coordinated configuration optimization of the sub-Blule bar capacity of LNG cold energy cascade power generation, refrigeration and high-pressure gas storage tank in multi-energy microgrids.

8. A system for coordinating and optimizing the capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tank, 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 characteristics of LNG gasification, including upper and lower limits of NG output flow of the gasification station, recoverable cold energy during LNG gasification, and constraints between the cold energy utilization at each stage and the total recoverable cold energy during LNG gasification; based on the characteristics of LNG cold energy cascade utilization equipment, an LNG cold energy cascade power generation-cooling capacity configuration and operation model is established, including capacity configuration constraints of LNG cold energy power generation equipment, operation model of LNG cold energy power generation, configuration constraints of LNG cold energy cooling equipment, operation model of LNG cold energy cooling, total power consumption of LNG cold energy utilization equipment, and linearization of bilinear terms; The high-pressure gas storage tank capacity configuration and operation model construction module is used to establish a high-pressure gas storage tank capacity configuration and operation model based on the available high-pressure gas storage tank model parameters. 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 different models of high-pressure gas storage tank selection configuration, the constraint that the selection quantity 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 the gas filling and discharging, the gas storage capacity of the high-pressure gas storage tank for 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 for filling and discharging. The multi-energy microgrid energy management optimization model construction module is used to establish a multi-energy microgrid energy management optimization model that includes the cascade utilization of LNG cold energy. This model includes distributed resource operation constraints in the multi-energy microgrid system, power purchase and sale constraints between the multi-energy microgrid system and the upper-level power grid, and power-cooling-gas multi-energy flow balance constraints. The objective function is to minimize the total investment cost of cold energy cascade power generation-cooling and high-pressure gas storage tanks and the total operating cost of the microgrid within the planning period. This forms an optimization model for the coordinated configuration of LNG cold energy cascade power generation-cooling and high-pressure gas storage tank capacity in multi-energy microgrids. The optimization model solving module is used to convert and reconstruct the original configuration optimization model into a two-stage split-Bluerbar collaborative configuration optimization model based on the predicted data of new energy output and power-cooling-gas load during the planning period and the split-Bluerbar optimization strategy based on the Wasserstein distance, and then solve it to obtain the optimal configuration capacity of LNG cold energy cascade utilization equipment and high-pressure gas storage tank.

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 configured to be executed by the one or more processors, wherein when the programs are executed by the processors, they implement the LNG cold energy cascade utilization equipment and high-pressure gas storage tank capacity co-configuration optimization method as described in any one of claims 1-7.

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

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