A General Modeling and Optimization Method and System for LNG Cold Energy Cascade Utilization for Multi-Energy Microgrid Energy Management

Abstract

This invention discloses a general modeling and optimization method and system for LNG cold energy cascade utilization in multi-energy microgrid energy management. The method includes: establishing a general utilization model for each stage of the cold energy cascade utilization process based on the purpose of cascade utilization and equipment parameters, considering the transmission coupling and loss of cold energy; establishing a multi-energy microgrid energy optimization model incorporating cold energy cascade utilization, with the optimization objective of economical and carbon-reduced system operation, and coordinating energy management between the cold energy cascade utilization stage and the multi-energy microgrid based on the multi-energy microgrid architecture and the characteristics of its distributed energy sources; and transforming the energy optimization model into a two-stage stochastic optimization model based on multiple scenarios and solving it, based on source-load uncertainty and the operating characteristics of multi-energy microgrid equipment, to obtain the multi-energy microgrid energy management decision for cold energy cascade utilization. This invention integrates and coordinates a general model for cold energy cascade utilization with the energy management of multi-energy microgrids to optimize and obtain energy management decisions that combine economic efficiency, low carbon emissions, and high energy efficiency.

Description

Technical Field

[0001] This invention relates to a multi-energy system energy optimization method, and in particular to a general modeling and optimization method and system for the cascade utilization of liquefied natural gas (LNG) cold energy for multi-energy microgrid energy management. Background Technology

[0002] Natural gas can be stored and transported in the form of liquefied natural gas (LNG). Upon arrival at an LNG receiving terminal, it is regasified before being delivered to users. During LNG regasification, a large amount of high-quality cold energy is released. If this cold energy is not fully utilized, it will not only result in a huge waste of energy but may also cause cold pollution to nearby sea areas. Efficiently utilizing LNG cold energy can save energy, reduce carbon emissions, and bring significant social, economic, and environmental benefits.

[0003] Because the LNG vaporization process involves a wide temperature range, a single method cannot fully utilize the cold energy of different grades. Cold energy cascade utilization technology can fully utilize the cold energy in each temperature range according to the needs of different temperature zones, thereby reducing waste. Furthermore, fully coordinating cold energy cascade utilization with the LNG receiving terminal's multi-energy microgrid system can effectively improve energy utilization efficiency and enhance the economic and low-carbon characteristics of the multi-energy microgrid system.

[0004] Despite significant progress in research on the cascade utilization of LNG cold energy, limitations remain. Current modeling and simulation of LNG cold energy utilization primarily focus on chemical thermodynamics, with limited application in energy management. Furthermore, a universally applicable model for LNG cold energy cascade utilization has not been developed, and simulations are confined to the equipment level. Therefore, there is an urgent need to propose a universal modeling and optimization method for the cascade utilization of liquefied natural gas (LNG) cold energy, geared towards multi-energy microgrid energy management, to address the aforementioned issues in current LNG cold energy cascade utilization research. Summary of the Invention

[0005] Purpose of the invention: To address the problems existing in the prior art, the purpose of this invention is to provide a general modeling and optimization method and system for the cascaded utilization of liquefied natural gas (LNG) cold energy for multi-energy microgrid energy management. By coupling a general model for cascaded cold energy utilization with the energy demand of multi-energy microgrids, the invention optimizes energy management decisions that are both economical and low-carbon, as well as highly energy efficient, providing a new solution for LNG cold energy cascaded utilization research and the operation optimization of multi-energy microgrid systems.

[0006] Technical Solution: The present invention provides a general modeling and optimization method for LNG cold energy cascade utilization in multi-energy microgrid energy management, comprising the following steps:

[0007] (1) Based on the purpose and equipment parameters of LNG cold energy cascade utilization, establish a general model for the utilization of LNG cold energy at each stage, including: dividing LNG cold energy utilization into stages and determining the purpose of cold energy utilization at each stage; based on the equipment parameters of cold energy utilization at each stage, determining the upper and lower limits of the input cold energy required for equipment operation and establishing an equipment operation constraint model for cold energy utilization at each stage; based on the start-up and shutdown time limits of cold energy utilization equipment, establishing a time constraint model for cold energy utilization at each stage; based on the cold energy loss caused by heat transfer during cold energy utilization and the power consumption of cold energy utilization equipment, establishing a loss model for cold energy utilization at each stage; based on the actual cold energy power and the cold energy heat transfer loss power, establishing a coupling model between the cold energy of cold energy utilization at each stage and the output of the corresponding purpose.

[0008] (2) Establish a transmission coupling model for the cascade utilization of LNG cold energy based on the LNG gasification model. The LNG gasification model includes the power limit of the LNG gasification station and the start-up and shutdown time constraint of the LNG gasification station. The transmission coupling model for the cascade utilization of LNG cold energy includes: the recoverable cold energy power during the LNG gasification process is greater than or equal to the sum of the actual cold energy power of each stage, and the total power consumption in the cold energy utilization process is the sum of the power consumption of the cold energy utilization equipment in each stage.

[0009] (3) Based on the multi-energy microgrid architecture and the distributed energy contained therein, establish a multi-energy microgrid energy optimization model with cascade utilization. The multi-energy microgrid energy optimization model takes minimizing the operating cost and carbon emissions of the multi-energy microgrid system during a specified period as the optimization objective function, and includes the operating and multi-energy conversion constraints of distributed energy in the multi-energy microgrid system, energy balance constraints, and the power purchase and sale constraints between the multi-energy microgrid system and the electricity market. The operating cost of the multi-energy microgrid system in the objective function includes the transaction cost in the electricity market, the operating cost of the cold energy utilization equipment, and the start-up cost. The operating cost is determined by the output of the cold energy utilization stage and the unit operating cost of the equipment.

[0010] (4) Based on the uncertainty of source load and the operating characteristics of multi-energy microgrid equipment, the energy optimization model of the multi-energy microgrid is transformed into a two-stage stochastic optimization model based on multiple scenarios and solved to obtain the energy management decision of the multi-energy microgrid for the cascade utilization of LNG cold energy.

[0011] Furthermore, in step (1), the equipment operation constraint model for cold energy utilization in each stage is specifically as follows: the actual cold energy power used in stage m during time period t does not exceed the upper limit of cold energy input power in that stage and is not lower than the lower limit of cold energy input power in that stage.

[0012] The specific time constraint model for cold energy utilization at each stage is as follows: the start-up and shutdown times of the cold energy utilization equipment must comply with the specified start-up and shutdown time limits;

[0013] The loss model for each stage of cold energy utilization is established based on the relationship between cold energy loss and the cold energy utilized, as well as the relationship between the power consumption of cold energy utilization equipment and the cold energy utilized.

[0014] The coupling model of the cold energy utilized in each stage and the corresponding output is expressed as follows: In the formula, Indicates the output quantity used in stage m of time period t; η m For utilization rate; This represents the actual cooling power used in stage m during time period t; This represents the heat transfer loss power during the time period t.

[0015] Furthermore, in the LNG gasification model, the power limit of the LNG gasification station is specifically as follows: the gasification power of the LNG gasification station during time period t shall not exceed the upper limit of the LNG gasification power and shall not be lower than the lower limit of the LNG gasification power.

[0016] The specific constraints on the start-up and shutdown times of LNG regasification stations are as follows: the start-up and shutdown times of LNG regasification stations must comply with the specified start-up and shutdown time limits.

[0017] In the transmission coupling model for the cascade utilization of LNG cold energy, the recoverable cold energy power is expressed as: In the formula, η represents the recoverable cold energy power during the LNG vaporization process in time period t; lng q represents the LNG cold energy recovery efficiency coefficient; ng This refers to the calorific value of natural gas.

[0018] Furthermore, in step (3), the constraints on the operation and multi-energy conversion of distributed energy in the multi-energy microgrid system include the operation, multi-energy conversion and carbon emission constraints of micro gas turbines, electric chillers, ambient temperature high-pressure gas storage tanks, battery energy storage equipment, as well as the constraints on renewable energy power generation.

[0019] Energy balance constraints include electrical, cooling, gas, and carbon energy balance constraints;

[0020] The electricity purchase and sale constraints of multi-energy microgrid systems in the electricity market include electricity prices set based on day-ahead and intraday electricity market transaction history and forecast data, as well as corresponding upper and lower limits on electricity purchase and sale transmission.

[0021] Furthermore, in step (3), the transaction costs in the electricity market are calculated using the following formula: In the formula, The transaction cost for time period t. These represent the purchase and sale prices of electricity in the day-ahead electricity market during time period t, respectively. These represent the power purchased and sold during time period t in the day-ahead electricity market, respectively. These represent the purchase and sale prices of electricity in the electricity market during time period t within the day. These represent the power purchased and sold during the t-hour period in the daily electricity market, respectively, where τ is the length of the trading period.

[0022] The operating cost of cold energy utilization equipment is calculated using the following formula: In the formula, c represents the operating cost of the cold energy utilization equipment during time period t. lng_m This represents the unit cost of operating the cold energy utilization equipment in stage m. The output quantity for the purpose of stage m in time period t;

[0023] The start-up cost of cold energy utilization equipment is calculated using the following formula: In the formula, The equipment startup cost for time period t. This indicates the start-up cost of the LNG regasification equipment; This indicates the start-up cost of a micro gas turbine; This represents the start-up cost of the cold energy utilization equipment in stage m; It is a 0-1 variable used to indicate the start-up indication of LNG gasification, micro gas turbine and m-stage cold energy utilization.

[0024] Furthermore, step (4) includes:

[0025] (4.1) Based on the predicted expected values ​​of renewable energy output and load and the uncertainty probability distribution, multiple sets of scenarios are randomly generated to simulate the possible actual situation of source load;

[0026] (4.2) Based on the operating characteristics of devices within the multi-energy microgrid, the model described in step (3) is transformed into a two-stage stochastic optimization model based on multiple scenarios, and its corresponding mathematical matrix expression is:

[0027]

[0028] In the formula: ρ n Let u represent the probability of an uncertain scenario, where n represents the uncertain scenario. n y represents uncertain variables, including renewable energy output and electrical cooling load power; x represents day-ahead stage variables, including binary variables controlling the start-up and shutdown of cooling energy utilization equipment, LNG gasification start-up and shutdown, and micro gas turbine start-up and shutdown, as well as the power purchased and sold in the day-ahead electricity market, which should be kept as a one-dimensional variable related to the dispatch period; nThe variables representing the intraday optimization stage mainly include the operation of cold energy utilization equipment, LNG gasification, micro gas turbine operation, electric refrigeration, storage in ambient temperature high-pressure gas storage tanks, and variables related to intraday electricity market transactions. These should be converted into two-dimensional variables related to the scenario and time. C is the coefficient matrix of the day-ahead objective function, mainly consisting of the day-ahead transaction price and equipment start-up cost. E is the coefficient matrix of the intraday objective function, mainly consisting of the intraday transaction price, carbon tax price, and unit cost of cold energy utilization equipment. g(x,y) n ,u n ) represents all the equality constraints included in the above model, h(x,y) n ) represents all inequality constraints;

[0029] (4.3) Based on the above two-stage stochastic optimization model, call the mathematical programming solver to optimize the model and obtain the optimal values ​​of the energy management decision variables of the multi-energy microgrid for the cascade utilization of LNG cold energy.

[0030] Furthermore, step (4) also includes:

[0031] (4.4) Based on the more accurate predicted values ​​of the source and load during the day, the energy optimization model of the multi-energy microgrid with cascade utilization in step (3) is used to fix the optimal values ​​of the day-ahead variables obtained by optimization in (4.3), and the operation variables of each scheduling period during the day are further optimized to obtain the optimal values ​​of the microgrid operation decision during the day.

[0032] A general modeling and optimization system for LNG cold energy cascade utilization for multi-energy microgrid energy management includes:

[0033] The general model building module for cold energy utilization is used to establish a general model for each stage of LNG cold energy utilization based on the purpose and equipment parameters of LNG cold energy cascade utilization. This includes: dividing LNG cold energy utilization into stages and determining the purpose of cold energy utilization in each stage; determining the upper and lower limits of the input cold energy required for equipment operation based on the equipment parameters of each stage, and establishing an equipment operation constraint model for each stage; establishing a time constraint model for each stage based on the start-up and shutdown time limits of the cold energy utilization equipment; establishing a loss model for each stage based on the cold energy loss caused by heat transfer during cold energy utilization and the power consumption of the cold energy utilization equipment; and establishing a coupling model between the cold energy of each stage and the corresponding output for each purpose based on the actual cold energy power and the cold energy heat transfer loss power.

[0034] The module for constructing a transmission coupling model for cascaded utilization of LNG cold energy is used to establish a transmission coupling model for cascaded utilization of LNG cold energy based on the LNG gasification model. The LNG gasification model includes the power limit of the LNG gasification station and the start-up and shutdown time constraints of the LNG gasification station. The transmission coupling model for cascaded utilization of LNG cold energy includes: the recoverable cold energy power during the LNG gasification process is greater than or equal to the sum of the actual cold energy power of each stage, and the total power consumption in the cold energy utilization process is the sum of the power consumption of the cold energy utilization equipment in each stage.

[0035] A module for constructing a multi-energy microgrid energy optimization model with cascade utilization is used to establish a multi-energy microgrid energy optimization model with cascade utilization based on the multi-energy microgrid architecture and its distributed energy resources. The multi-energy microgrid energy optimization model takes minimizing the operating cost and carbon emissions of the multi-energy microgrid system during a specified period as the optimization objective function, and includes constraints on the operation of distributed energy resources and multi-energy conversion in the multi-energy microgrid system, energy balance constraints, and power purchase and sale constraints between the multi-energy microgrid system and the electricity market. The operating cost of the multi-energy microgrid system in the objective function includes transaction costs in the electricity market, operating costs of cold energy utilization equipment, and start-up costs. The operating cost is determined by the output of the cold energy utilization stage and the unit operating cost of the equipment.

[0036] The optimization model transformation and solution module is used to transform the multi-energy microgrid energy optimization model into a two-stage stochastic optimization model based on multiple scenarios and solve it, based on the uncertainty of source load and the operating characteristics of multi-energy microgrid equipment, to obtain the energy management decision of multi-energy microgrid for LNG cold energy cascade utilization.

[0037] 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 steps of the general modeling and optimization method for LNG cold energy cascade utilization for multi-energy microgrid energy management as described above.

[0038] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the general modeling and optimization method for LNG cold energy cascade utilization for multi-energy microgrid energy management as described above.

[0039] Beneficial effects: This invention proposes a general model for the utilization of LNG cold energy at each stage, simplifying the modeling process for the cascade utilization of cold energy; when considering the coupling relationship of cold energy utilization at each stage, it does not strictly limit the proportion of cold energy at each stage, making the coupling relationship more flexible and close; introducing the cascade utilization of LNG cold energy into a multi-energy microgrid for optimization improves the energy utilization rate of the system, reduces the overall operating cost of the system, and reduces carbon emissions. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0041] Figure 2 Typical daily power generation curves for renewable energy and load;

[0042] Figure 3 This is a schematic diagram of energy flow according to an embodiment of the present invention;

[0043] Figure 4 This invention provides the optimized gas load supply and demand results.

[0044] Figure 5 This is the optimized carbon load supply and demand result of this invention;

[0045] Figure 6 This is the optimized electrical load supply and demand result of this invention;

[0046] Figure 7 This invention optimizes the trading situation in the day-ahead and intraday electricity markets.

[0047] Figure 8 This is the optimized cooling load supply and demand result of this invention. Detailed Implementation

[0048] The technical solution of the present invention will be explained and described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] The general modeling and optimization method for cascaded utilization of liquefied natural gas (LNG) cold energy for multi-energy microgrid energy management, as described in this invention, includes the following steps:

[0050] Step 1: Based on the purpose and equipment parameters of LNG cold energy cascade utilization, establish a general model for the utilization of LNG cold energy at each stage.

[0051] The cold energy released during LNG vaporization can be divided into multiple stages. The applications of LNG cold energy in different temperature zones can be summarized as follows: Cryogenic stage, which can be used for air separation (suitable for large receiving stations), cryogenic carbon capture, and cryogenic pulverization (requires specific site selection); Intermediate stage, which can be used for cold energy power generation; and Shallow stage, which can be used for cryogenic cold storage, air conditioning systems, data center cooling, etc. This invention first determines the applications of LNG cold energy in the system, and then divides the LNG cold energy utilization stages according to the different temperature zones corresponding to the applications, using m to represent each stage; and M to represent the output of each stage.

[0052] To make the specific implementation methods described in this article clear and easy to understand, the following example is a multi-energy microgrid system for the cascade utilization of LNG cold energy, which consists of cryogenic carbon capture (deep cryogenic) – cold energy power generation (intermediate cryogenic) – direct cooling (shallow cryogenic). The cryogenic carbon capture stage is represented by c, and its output is the carbon flow rate obtained from carbon capture, denoted by C; the cold energy power generation stage is represented by p, and its output is the power generation output, denoted by P; the direct cooling stage is represented by co, and its output is the direct cooling power output, denoted by CO.

[0053] Based on the equipment parameters for cold energy utilization at each stage, the upper and lower limits of the required cold energy input for equipment operation are determined, and an equipment operation constraint model for cold energy utilization at each stage of LNG is established, namely:

[0054]

[0055] In the formula, m represents the stage of cold energy utilization. These represent the lower and upper limits of the cold energy input power for this stage, respectively. It is a 0-1 variable used to represent the start-up or stop status of the cold energy utilization process at this stage;

[0056] Cold energy utilization equipment cannot be turned on or off at will; its minimum operating time is [not specified]. The minimum shutdown time is The mathematical expression for the constraint is:

[0057]

[0058] During the utilization of cold energy, heat transfer causes a loss of cold energy, and the cold energy utilization equipment also consumes electricity. We can consider the relationship between cold energy loss and the cold energy utilized as linear, and the relationship between electricity consumption and the cold energy utilized as linear, to establish a loss model for cold energy utilization at each stage. The corresponding mathematical expression is as follows:

[0059]

[0060] In the formula: V represents the heat transfer loss power during the time period t. m The loss coefficient is... This represents the actual cooling power used in stage m during time period t; k represents the power consumption of the equipment during time period t; m This represents the power consumption coefficient; it is worth noting that other functional forms can also be used to represent the relationship between cooling loss and the cooling capacity utilized, and between power consumption and the cooling capacity utilized.

[0061] A coupling model is established between the cold energy utilized at each stage and the corresponding output for each purpose. The corresponding mathematical expression is as follows:

[0062]

[0063] In the formula: η represents the output of the cold energy utilization phase during time period t; m For utilization rate;

[0064] It is worth noting that the general model for cold energy utilization in step 1 is applicable to common cold energy utilization scenarios and tiered classifications.

[0065] Step 2: Establish a transmission coupling model for the cascade utilization of LNG cold energy.

[0066] The transmission coupling model for the cascade utilization of LNG cold energy includes:

[0067] (1) LNG vaporization model:

[0068] The power limit of an LNG regasification station is expressed mathematically as follows:

[0069]

[0070] In the formula, This represents the vaporization power of the LNG vaporization station during time period t; It is a 0-1 variable used to represent the on / off state of the LNG vaporization unit; This indicates the upper and lower limits of the LNG regasification power of the LNG regasification station.

[0071] LNG regasification stations cannot be flexibly started or stopped; their minimum operating time is [not specified]. The minimum shutdown time is The mathematical expression for its operating time limit is similar to that of the time constraint (2) of the cold energy utilization equipment, and will not be repeated here.

[0072] (2) Transmission and coupling model for the cascade utilization of cold energy:

[0073] During LNG vaporization, the relationship between recoverable cold energy and vaporization power is as follows:

[0074]

[0075] In the formula, η represents the recoverable cold energy power during the LNG vaporization process in time period t, in kW; lng q represents the LNG cold energy recovery efficiency coefficient; ng This refers to the calorific value of natural gas.

[0076] The mathematical expression for the released cold energy through each stage of transmission and coupling is:

[0077]

[0078] The mathematical expression for the total power consumption of the cold energy cascade utilization equipment at each stage is:

[0079]

[0080] In the formula, This represents the total power consumption of equipment in all stages of the cold energy utilization process.

[0081] Step 3: Based on the multi-energy microgrid architecture and the distributed energy sources it contains, establish a multi-energy microgrid energy optimization model that includes cascade utilization.

[0082] Energy optimization models for multi-energy microgrids that incorporate cascaded utilization include:

[0083] (1) Distributed energy operation and multi-energy conversion constraints: Based on the multi-energy microgrid architecture, establish operation constraints and energy conversion constraints for distributed energy sources, energy-consuming equipment, loads, and cooling energy cascade utilization equipment within the system, specifically including:

[0084] Operating constraints of micro gas turbines:

[0085]

[0086] In the formula: η represents the electrical and gas power of the micro gas turbine during time period t. gt_p For electrical efficiency; Indicates the upper and lower limits of electrical power; It is a 0-1 variable used to represent the micro gas turbine switch; λ represents the carbon emissions generated by a micro gas turbine during time period t, in kg / h; g_co2 The carbon emission factor per unit gas power, h ng This represents the calorific value of natural gas per unit volume.

[0087] Gas turbine equipment cannot be started or stopped at will; the minimum start-up time is [not specified]. The minimum shutdown time is The mathematical expression for the constraint is shown in equation (2), which will not be repeated here.

[0088] Operating constraints of the electric chiller in the system:

[0089]

[0090]

[0091] In the formula: This represents the cooling power of the electric chiller during time period t; η represents the power consumption of the electric chiller during time period t; ec Indicates the refrigeration efficiency of an electric refrigeration machine; This indicates the upper limit of the cooling power of the electric refrigeration system.

[0092] A high-pressure gas storage tank is added to the multi-energy system to meet the supply and demand balance of gas load by storing natural gas. The power consumption of the storage tank is considered to have a linear relationship with the gas storage volume, and its operating constraints are as follows:

[0093]

[0094] In the formula: V represents the state of charge (SOC) of the natural gas storage tank during time period t; gs For the gas storage tank capacity; η gs Δt is the natural gas compression ratio; Δt is the scheduling step size. These represent the lower and upper limits of the State of Charge (SOC) for natural gas storage tanks, respectively. It is a 0-1 variable used to represent the filling and discharging state of the gas storage tank; These represent the storage and venting power of the gas storage tank, respectively; k gs_p The linearity coefficient representing power consumption. This indicates the power consumption of the ambient temperature gas storage tank.

[0095] The operating constraints for renewable energy power generation are:

[0096]

[0097] In the formula: This represents the renewable energy power generation capacity during time period t. This indicates the upper limit of renewable energy power generation.

[0098] (2) Establish a trading model for multi-energy microgrid systems in the electricity market:

[0099] Electricity prices and corresponding upper and lower limits for electricity trading are set based on historical and forecast data of day-ahead and intraday electricity market transactions.

[0100] The current market trading constraints are:

[0101]

[0102] In the formula: These represent the power purchased / sold by the port's MEMG in the electricity market during the day-ahead period t; These are 0-1 variables used to represent the purchase / sale status of electricity in the day-ahead electricity market; This indicates the upper limit of the electricity that can be purchased / sold in the current electricity market.

[0103] The constraints for intraday market trading are:

[0104]

[0105] In the formula: These represent the power purchased / sold by the port's MEMG in the electricity market during time period t within the day; These are 0-1 variables used to represent the purchase / sale status of electricity in the daily electricity market; This indicates the upper limit of the electricity that can be purchased / sold in the electricity market within a day.

[0106] (3) Establish energy balance constraints. The overall energy balance constraints include energy balance constraints for electricity, cooling, gas, and carbon:

[0107]

[0108] In the formula: Indicates the power generation capacity of cold energy. Indicates electrical load power; Indicates gas load power; This indicates the direct cooling power of cold energy. Indicates direct cooling loss. Indicates cooling load power; This represents carbon emissions during time period t, expressed in kg / h. This indicates the carbon flow rate obtained from carbon capture, in kg / h.

[0109] (4) Establish the optimization objective function for low-carbon economy:

[0110] The objective function for optimizing economic efficiency and low carbon emissions is to minimize the daily operating cost and carbon emissions of the multi-energy microgrid system.

[0111]

[0112] The total cost of the system in time period t

[0113]

[0114] System transaction costs in the electricity market during time period t for:

[0115]

[0116] In the formula: These represent the purchase and sale prices of electricity in the day-ahead electricity market during time period t, respectively. These represent the purchase and sale prices of electricity in the electricity market during time period t, respectively, in yuan / kWh; τ is the length of the trading period, such as 15 minutes, 1 hour, etc.

[0117] carbon emission cost of system during time period t for:

[0118]

[0119] In the formula: c co2 This indicates the carbon tax price, in yuan / kg; C outThis indicates carbon emissions, expressed in kg / h.

[0120] Operating cost of cold energy utilization equipment in the system during time period t for:

[0121]

[0122] In the formula: c lng_m The unit cost of operating the cold energy utilization equipment in stage m. The output of the cold energy utilization stage during the time period t, as expressed in equation (5);

[0123] Operating costs This can be specifically expressed as:

[0124]

[0125] In the formula: c lng_c c lng_p c lng_co These represent the unit costs of carbon capture, cold energy power generation, and direct cooling equipment operation, respectively, in yuan / (kg / h), yuan / kW, and yuan / kW. In the above formula, the actual output cold energy in the third stage of LNG cold energy cascade utilization, the direct cooling stage, is equal to the input cold energy of the third stage. Subtracting cold energy loss

[0126] Startup cost of devices in the system during time period t for:

[0127]

[0128] In the formula: This indicates the unit startup cost of the LNG regasification equipment, expressed in yuan / h. This indicates the unit startup cost of a micro gas turbine, expressed in yuan / h. This indicates the unit startup cost of cold energy utilization equipment at each stage, expressed in yuan / h.

[0129] In the system, the start-up indicator variables for LNG vaporization, micro gas turbine, and cold energy utilization equipment during time period t are... for:

[0130]

[0131] Step 4: Transform the above model into a two-stage stochastic optimization model based on multiple scenarios and solve it to obtain the energy management decision of the multi-energy microgrid for the cascade utilization of LNG cold energy.

[0132] (4.1) Generate uncertain scenarios for renewable energy and load based on stochastic optimization principles:

[0133] First, based on the predicted expected values ​​of renewable energy output and load, as well as the uncertainty probability distribution, multiple sets of scenarios are randomly generated to simulate the possible actual situations of the source load.

[0134] (4.2) Based on the operating characteristics of devices within the multi-energy microgrid, the model described in step 3 is transformed into a two-stage stochastic optimization model based on multiple scenarios, and its corresponding mathematical matrix expression is:

[0135]

[0136] In the formula: ρ n Let u represent the probability of an uncertain scenario, where n represents the uncertain scenario. n The variable 'x' represents the uncertain variable, which in this system includes renewable energy output and electrical cooling load power; 'x' represents the day-ahead stage variable, including binary variables controlling the start-up and shutdown of cooling energy utilization equipment, LNG gasification start-up and shutdown, and micro gas turbine start-up and shutdown, as well as the power purchased and sold in the day-ahead electricity market, which should be kept as a one-dimensional variable related to the dispatch period; 'y' represents the day-ahead stage variable. n The variables representing the intraday optimization phase mainly include variables related to the operation of cold energy utilization equipment, LNG gasification, micro gas turbine operation, electric refrigeration, storage in ambient temperature high-pressure gas storage tanks, and intraday electricity market transactions. These should be converted into two-dimensional variables related to the scenario and time. C is the coefficient matrix of the day-ahead objective function, mainly consisting of day-ahead transaction electricity prices and equipment start-up costs. E is the coefficient matrix of the intraday objective function, mainly consisting of intraday transaction electricity prices, carbon tax prices, and unit costs of cold energy utilization equipment. g(x,y) n ,u n ) represents all the equality constraints included in the above model, h(x,y) n ) represents all inequality constraints.

[0137] (4.3) Solving the stochastic optimization model:

[0138] Based on the above two-stage stochastic optimization method, solvers such as Gurobi or Cplex are called to optimize and solve the model, and obtain the optimal values ​​of the energy management decision variables of the multi-energy microgrid for the cascade utilization of LNG cold energy.

[0139] (4.4) Intraday rescheduling:

[0140] Based on the more accurate intraday forecast of source and load, the energy optimization model of multi-energy microgrid with cascade utilization in step 3 is used to fix the optimal value of the daily variable obtained by optimization (4.3), and the operation variables of each scheduling period during the day are further optimized to obtain the optimal value of intraday microgrid operation decision.

[0141] The annual operating data of a multi-energy microgrid system with an LNG gasification station in a port in East China and the operating data of LNG cold energy utilization equipment already in operation were selected for analysis. The basic parameters include: (1) the typical daily power generation curves of renewable energy and load, such as Figure 2 As shown, the average output of renewable energy and the average load of electric cooling are obtained for each hour. The renewable energy fluctuation is set to ±20%, the load fluctuation is set to ±10%, and the upper limit of renewable energy power generation is limited to 1000kW; (2) The operating parameters of the cooling energy cascade utilization equipment and the microgrid energy consumption equipment are shown in Table 1; (3) The daily electricity market transaction power limit is 5000kW, the intraday electricity market transaction power limit is 2000kW, and the reference value of the electricity price is: peak purchase / sale electricity price is 0.76 yuan / kWh, normal purchase / sale electricity price is 0.56 yuan / kWh, and valley purchase / sale electricity price is 0.38 yuan / kWh. The market prices were 1.1 times and 0.9 times the reference value the day before yesterday, and 1.3 times and 0.7 times the reference value the intraday market prices. (4) The unit operating cost of cold energy utilization equipment is RMB 0.3 / (kg / h) for carbon capture equipment, RMB 0.1 / kW for cold energy power generation equipment, and RMB 0.2 / kW for direct cooling devices. (5) The unit start-up cost of system equipment is RMB 2 / h for LNG gasification equipment, RMB 5 / h for micro gas turbines, RMB 5 / h for carbon capture equipment, RMB 10 / h for cold energy power generation equipment, and RMB 2 / h for direct cooling devices. The parameters of cold energy cascade utilization equipment and microgrid energy consumption equipment are shown in Table 1.

[0142] Table 1. Parameters of cold energy cascade utilization equipment and microgrid energy consumption equipment

[0143]

[0144] Depend on Figure 3 It can be seen that, based on the multi-energy microgrid architecture and the LNG cold energy utilization equipment in the system, a simple energy flow analysis of the multi-energy microgrid system containing cold energy cascade utilization is conducted, mainly considering the four major energy flows of electricity, cold, gas and carbon, reflecting the coupling relationship between distributed energy, multi-energy conversion equipment and cold energy utilization equipment in the system.

[0145] Using the aforementioned two-stage stochastic optimization model based on multiple scenarios, 50 sets of uncertain scenarios are generated here, and it is assumed that they are uniformly distributed, with the probability ρ of each scenario set being... n That is

[0146] Built on the Yalmip platform and solved using the Gurobi solver, this study optimizes the cascade utilization of LNG cold energy for multi-energy microgrid energy management. It obtains the optimal values ​​of the decision variables for multi-energy microgrid energy management related to LNG cold energy cascade utilization. The optimization results are as follows: Figures 4-8 As shown.

[0147] Depend on Figure 4 It can be seen that the gas load 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 requirements, 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.

[0148] Depend on Figure 5 It can be seen that the carbon load supply and demand situation after system optimization is as follows: (1) From 1 to 6 o'clock, the carbon load is significantly reduced. This is due to the significant decrease in nighttime load, the system increases renewable energy power generation and cold energy power generation, and reduces the use of micro gas turbines. This reflects the system's strategy of prioritizing the dispatch of low-carbon energy during low-load periods. At the same time, due to the role of carbon capture, carbon emissions are effectively reduced, showing the system's emission reduction potential during low-load periods; (2) From 7 to 24 o'clock, the carbon load remains at a high level. This is due to the increase in electricity demand during the daytime. The system needs to mobilize micro gas turbines and other power generation methods to meet the load demand, resulting in an increase in carbon emissions. This reflects that although the system has a certain amount of low-carbon energy participating in power supply under high load conditions, it still faces certain carbon emission pressure. Therefore, emission reduction measures can be further optimized. (3) Overall, during the period from 1 to 24 o'clock, the cumulative carbon capture amount accounts for about 50% of the total carbon emissions of the system's micro gas turbines, which greatly reduces the system's carbon emissions and shows a significant green carbon reduction optimization effect.

[0149] Depend on Figures 6-7 It can be seen that the power load supply and demand situation after system optimization is as follows: (1) During the periods of 6-10, 12-16, 18-20 and 23-24, due to the synergistic effect of micro gas turbines, renewable energy and cold energy power generation, not only is the power load demand fully met, but the system can also obtain revenue by selling electricity to the power market, thereby reducing the system operating cost and improving economic efficiency; (2) During the periods of 1-2 and 4-5, the system reduces the power generation of micro gas turbines during the low load period at night and relies more on the power generation of low carbon emission renewable energy and cold energy, thereby reducing the carbon emissions generated during operation; (3) Overall, the effect of LNG cold energy power generation is significant. Most of the time, the power generation of cold energy can reach 1600kW, which exceeds 50% of the rated operating power of gas turbines, greatly alleviating the power consumption pressure of the system and reducing the carbon emissions of the system.

[0150] Depend on Figure 8It is evident that the LNG direct cooling process supplements the cooling load demand that electric chillers cannot meet. When the amount of LNG directly cooled is small, the cooling power is approximately 200kW, which can meet about 10% of the cooling load demand. When the amount of LNG directly cooled is large, the cooling power can reach 1100kW, exceeding the cooling load demand by more than 30%. This demonstrates that the LNG direct cooling process effectively supplements the cooling demand. Furthermore, through reasonable system energy allocation, it increases the supply of LNG cooling energy during peak cooling load periods, reducing the need for electric cooling (i.e., additional electricity consumption) and further optimizing energy utilization.

[0151] Based on the same technical concept as the method embodiments, the present invention also provides a general modeling and optimization system for LNG cold energy cascade utilization for multi-energy microgrid energy management, including:

[0152] The general model building module for cold energy utilization is used to establish a general model for each stage of LNG cold energy utilization based on the purpose and equipment parameters of LNG cold energy cascade utilization. This includes: dividing LNG cold energy utilization into stages and determining the purpose of cold energy utilization in each stage; determining the upper and lower limits of the input cold energy required for equipment operation based on the equipment parameters of each stage, and establishing an equipment operation constraint model for each stage; establishing a time constraint model for each stage based on the start-up and shutdown time limits of the cold energy utilization equipment; establishing a loss model for each stage based on the cold energy loss caused by heat transfer during cold energy utilization and the power consumption of the cold energy utilization equipment; and establishing a coupling model between the cold energy of each stage and the corresponding output for each purpose based on the actual cold energy power and the cold energy heat transfer loss power.

[0153] The module for constructing a transmission coupling model for cascaded utilization of LNG cold energy is used to establish a transmission coupling model for cascaded utilization of LNG cold energy based on the LNG gasification model. The LNG gasification model includes the power limit of the LNG gasification station and the start-up and shutdown time constraints of the LNG gasification station. The transmission coupling model for cascaded utilization of LNG cold energy includes: the recoverable cold energy power during the LNG gasification process is greater than or equal to the sum of the actual cold energy power of each stage, and the total power consumption in the cold energy utilization process is the sum of the power consumption of the cold energy utilization equipment in each stage.

[0154] A module for constructing a multi-energy microgrid energy optimization model with cascade utilization is used to establish a multi-energy microgrid energy optimization model with cascade utilization based on the multi-energy microgrid architecture and its distributed energy resources. The multi-energy microgrid energy optimization model takes minimizing the operating cost and carbon emissions of the multi-energy microgrid system during a specified period as the optimization objective function, and includes constraints on the operation of distributed energy resources and multi-energy conversion in the multi-energy microgrid system, energy balance constraints, and power purchase and sale constraints between the multi-energy microgrid system and the electricity market. The operating cost of the multi-energy microgrid system in the objective function includes transaction costs in the electricity market, operating costs of cold energy utilization equipment, and start-up costs. The operating cost is determined by the output of the cold energy utilization stage and the unit operating cost of the equipment.

[0155] The optimization model transformation and solution module is used to transform the multi-energy microgrid energy optimization model into a two-stage stochastic optimization model based on multiple scenarios and solve it, based on the uncertainty of source load and the operating characteristics of multi-energy microgrid equipment, to obtain the energy management decision of multi-energy microgrid for LNG cold energy cascade utilization.

[0156] 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 steps of the general modeling and optimization method for LNG cold energy cascade utilization for multi-energy microgrid energy management.

[0157] 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 steps of the general modeling and optimization method for LNG cold energy cascade utilization for multi-energy microgrid energy management.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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 general modeling and optimization method for LNG cold energy cascade utilization in multi-energy microgrid energy management, characterized in that, Includes the following steps: (1) Based on the purpose and equipment parameters of LNG cold energy cascade utilization, establish a general model for the utilization of LNG cold energy at each stage, including: dividing LNG cold energy utilization into stages and determining the purpose of cold energy utilization at each stage; based on the equipment parameters of cold energy utilization at each stage, determining the upper and lower limits of the input cold energy required for equipment operation and establishing an equipment operation constraint model for cold energy utilization at each stage; based on the start-up and shutdown time limits of cold energy utilization equipment, establishing a time constraint model for cold energy utilization at each stage; based on the cold energy loss caused by heat transfer during cold energy utilization and the power consumption of cold energy utilization equipment, establishing a loss model for cold energy utilization at each stage; based on the actual cold energy power and the cold energy heat transfer loss power, establishing a coupling model between the cold energy of cold energy utilization at each stage and the output of the corresponding purpose. (2) Establish a transmission coupling model for the cascade utilization of LNG cold energy based on the LNG gasification model. The LNG gasification model includes the power limit of the LNG gasification station and the start-up and shutdown time constraint of the LNG gasification station. The transmission coupling model for the cascade utilization of LNG cold energy includes: the recoverable cold energy power during the LNG gasification process is greater than or equal to the sum of the actual cold energy power of each stage, and the total power consumption in the cold energy utilization process is the sum of the power consumption of the cold energy utilization equipment in each stage. (3) Based on the multi-energy microgrid architecture and the distributed energy contained therein, establish a multi-energy microgrid energy optimization model with cascade utilization. The multi-energy microgrid energy optimization model takes minimizing the operating cost and carbon emissions of the multi-energy microgrid system during a specified period as the optimization objective function, and includes the operating and multi-energy conversion constraints of distributed energy in the multi-energy microgrid system, energy balance constraints, and the power purchase and sale constraints between the multi-energy microgrid system and the electricity market. The operating cost of the multi-energy microgrid system in the objective function includes the transaction cost in the electricity market, the operating cost of the cold energy utilization equipment, and the start-up cost. The operating cost is determined by the output of the cold energy utilization stage and the unit operating cost of the equipment. (4) Based on the uncertainty of source load and the operating characteristics of multi-energy microgrid equipment, the energy optimization model of the multi-energy microgrid is transformed into a two-stage stochastic optimization model based on multiple scenarios and solved to obtain the energy management decision of the multi-energy microgrid for the cascade utilization of LNG cold energy.

2. The method according to claim 1, characterized in that, In step (1), the specific equipment operation constraint model for cold energy utilization in each stage is as follows: the actual cold energy power used in stage m during time period t shall not exceed the upper limit of cold energy input power in that stage and shall not be lower than the lower limit of cold energy input power in that stage. The specific time constraint model for cold energy utilization at each stage is as follows: the start-up and shutdown times of the cold energy utilization equipment must comply with the specified start-up and shutdown time limits; The loss model for each stage of cold energy utilization is established based on the relationship between cold energy loss and the cold energy utilized, as well as the relationship between the power consumption of cold energy utilization equipment and the cold energy utilized. The coupling model of the cold energy utilized in each stage and the corresponding output is expressed as follows: In the formula, This represents the output quantity used in stage m of time period t. η m For utilization rate; This represents the actual cooling power used in stage m during time period t; This represents the heat transfer loss power during the time period t.

3. The method according to claim 1, characterized in that, In the LNG gasification model, the power limit of the LNG gasification station is specifically as follows: the gasification power of the LNG gasification station during time period t shall not exceed the upper limit of the gasification power of the LNG gasification station and shall not be lower than the lower limit of the gasification power of the LNG gasification station. The specific constraints on the start-up and shutdown times of LNG regasification stations are as follows: the start-up and shutdown times of LNG regasification stations must comply with the specified start-up and shutdown time limits. In the transmission coupling model for the cascade utilization of LNG cold energy, the recoverable cold energy power is expressed as: In the formula, This represents the recoverable cold energy power during the LNG vaporization process in time period t; η lng q represents the LNG cold energy recovery efficiency coefficient; ng This refers to the calorific value of natural gas.

4. The method according to claim 1, characterized in that, In step (3), the constraints on the operation and multi-energy conversion of distributed energy in the multi-energy microgrid system include the operation, multi-energy conversion and carbon emission constraints of micro gas turbines, electric chillers, ambient temperature high-pressure gas storage tanks, battery energy storage equipment, as well as the constraints on renewable energy power generation. Energy balance constraints include electrical, cooling, gas, and carbon energy balance constraints; The electricity purchase and sale constraints of multi-energy microgrid systems in the electricity market include electricity prices set based on day-ahead and intraday electricity market transaction history and forecast data, as well as corresponding upper and lower limits on electricity purchase and sale transmission.

5. The method according to claim 1, characterized in that, In step (3), the transaction costs in the electricity market are calculated using the following formula: In the formula, The transaction cost for time period t. These represent the purchase and sale prices of electricity in the day-ahead electricity market during time period t, respectively. These represent the power purchased and sold during time period t in the day-ahead electricity market, respectively. These represent the purchase and sale prices of electricity in the electricity market during time period t within the day. These represent the power purchased and sold during the t-hour period in the daily electricity market, respectively, where τ is the length of the trading period. The operating cost of cold energy utilization equipment is calculated using the following formula: In the formula, c represents the operating cost of the cold energy utilization equipment during time period t. lng_m This represents the unit cost of operating the cold energy utilization equipment in stage m. The output quantity for the purpose of stage m in time period t; The start-up cost of cold energy utilization equipment is calculated using the following formula: In the formula, The equipment startup cost for time period t. This indicates the start-up cost of the LNG regasification equipment; This indicates the start-up cost of a micro gas turbine; This represents the start-up cost of the cold energy utilization equipment in stage m; It is a 0-1 variable used to indicate the start-up indication of LNG gasification, micro gas turbine and m-stage cold energy utilization.

6. The method according to claim 1, characterized in that, Step (4) includes: (4.1) Based on the predicted expected values ​​of renewable energy output and load and the uncertainty probability distribution, multiple sets of scenarios are randomly generated to simulate the possible actual situation of source load; (4.2) Based on the operating characteristics of devices within the multi-energy microgrid, the model described in step (3) is transformed into a two-stage stochastic optimization model based on multiple scenarios, and its corresponding mathematical matrix expression is: In the formula: ρ n Let u represent the probability of an uncertain scenario, where n represents the uncertain scenario. n y represents uncertain variables, including renewable energy output and electrical cooling load power; x represents day-ahead stage variables, including binary variables controlling the start-up and shutdown of cooling energy utilization equipment, LNG gasification start-up and shutdown, and micro gas turbine start-up and shutdown, as well as the power purchased and sold in the day-ahead electricity market, which should be kept as a one-dimensional variable related to the dispatch period; n The variables representing intraday optimization, including the operation of cold energy utilization equipment, LNG gasification, micro gas turbine operation, electric refrigeration, storage in ambient temperature high-pressure gas storage tanks, and intraday electricity market trading, should be converted into two-dimensional variables related to the scenario and time; C is the coefficient matrix of the day-ahead objective function, representing the day-ahead trading price and equipment start-up cost; E is the coefficient matrix of the intraday objective function, representing the intraday trading price, carbon tax price, and unit cost of cold energy utilization equipment; g(x,y) n ,u n ) represents all the equality constraints included in the above model, h(x,y) n ) represents all inequality constraints; (4.3) Based on the above two-stage stochastic optimization model, call the mathematical programming solver to optimize the model and obtain the optimal values ​​of the energy management decision variables of the multi-energy microgrid for the cascade utilization of LNG cold energy.

7. The method according to claim 6, characterized in that, Step (4) further includes: (4.4) Based on the more accurate predicted values ​​of the source and load during the day, the energy optimization model of the multi-energy microgrid with cascade utilization in step (3) is used to fix the optimal values ​​of the day-ahead variables obtained by optimization in (4.3), and the operation variables of each scheduling period during the day are further optimized to obtain the optimal values ​​of the microgrid operation decision during the day.

8. A general modeling and optimization system for LNG cold energy cascade utilization for multi-energy microgrid energy management, characterized in that, include: The general model building module for cold energy utilization is used to establish a general model for each stage of LNG cold energy utilization based on the purpose and equipment parameters of LNG cold energy cascade utilization. This includes: dividing LNG cold energy utilization into stages and determining the purpose of cold energy utilization in each stage; determining the upper and lower limits of the input cold energy required for equipment operation based on the equipment parameters of each stage, and establishing an equipment operation constraint model for each stage; establishing a time constraint model for each stage based on the start-up and shutdown time limits of the cold energy utilization equipment; establishing a loss model for each stage based on the cold energy loss caused by heat transfer during cold energy utilization and the power consumption of the cold energy utilization equipment; and establishing a coupling model between the cold energy of each stage and the corresponding output for each purpose based on the actual cold energy power and the cold energy heat transfer loss power. The module for constructing a transmission coupling model for cascaded utilization of LNG cold energy is used to establish a transmission coupling model for cascaded utilization of LNG cold energy based on the LNG gasification model. The LNG gasification model includes the power limit of the LNG gasification station and the start-up and shutdown time constraints of the LNG gasification station. The transmission coupling model for cascaded utilization of LNG cold energy includes: the recoverable cold energy power during the LNG gasification process is greater than or equal to the sum of the actual cold energy power of each stage, and the total power consumption in the cold energy utilization process is the sum of the power consumption of the cold energy utilization equipment in each stage. A module for constructing a multi-energy microgrid energy optimization model with cascade utilization is used to establish a multi-energy microgrid energy optimization model with cascade utilization based on the multi-energy microgrid architecture and its distributed energy resources. The multi-energy microgrid energy optimization model takes minimizing the operating cost and carbon emissions of the multi-energy microgrid system during a specified period as the optimization objective function, and includes constraints on the operation of distributed energy resources and multi-energy conversion in the multi-energy microgrid system, energy balance constraints, and power purchase and sale constraints between the multi-energy microgrid system and the electricity market. The operating cost of the multi-energy microgrid system in the objective function includes transaction costs in the electricity market, operating costs of cold energy utilization equipment, and start-up costs. The operating cost is determined by the output of the cold energy utilization stage and the unit operating cost of the equipment. The optimization model transformation and solution module is used to transform the multi-energy microgrid energy optimization model into a two-stage stochastic optimization model based on multiple scenarios and solve it, based on the uncertainty of source load and the operating characteristics of multi-energy microgrid equipment, to obtain the energy management decision of multi-energy microgrid for LNG cold energy cascade utilization.

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 steps of the general modeling and optimization method for LNG cold energy cascade utilization for multi-energy microgrid energy management 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 steps of the general modeling and optimization method for LNG cold energy cascade utilization for energy management of multi-energy microgrids as described in any one of claims 1-7.

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