Multi-energy coupling system optimization scheduling method considering grid-connected safety

Abstract

The invention relates to the technical field of power systems, and particularly discloses a multi-energy coupling system optimal scheduling method considering grid-connected safety, and the method comprises the steps: constructing a multi-energy coupling system architecture model; the multi-energy coupling system architecture model comprises a renewable energy power generation unit, a traditional energy power generation unit, an energy storage unit, an energy conversion unit and a combined cooling heating and power generation unit; constructing a distributed energy storage optimization operation model based on the multi-energy coupling system architecture model; the constraint conditions comprise charging and discharging constraint, charge state constraint and peak clipping constraint of battery energy storage; carrying out output constraint on the energy conversion equipment; electric vehicle charging station operation constraint; new energy power generation constraint; power balance constraint; public network line transmission power constraint; constraining transmission power of an internal distribution network line; and solving the distributed energy storage optimization operation model by adopting an optimization algorithm to obtain an optimization scheduling scheme. The method has the advantage that scientific and reliable technical support is provided for efficient operation of the multi-energy coupling system.

Description

Technical Field

[0001] The invention relates to the technical field of power systems, and in particular to a multi-energy coupling system optimization dispatching method considering grid connection safety. Background Art

[0002] With the widespread application of multi-energy coupling systems in the energy field, how to achieve optimal scheduling while ensuring the safety of system grid connection has become a problem that needs to be solved urgently. The complexity of multi-energy flow coupling makes the dynamic conversion and coordinated optimization between multiple energy sources such as cold, heat, and electricity inefficient, and the traditional "divide and conquer" scheduling strategy is prone to energy waste and equipment redundancy; in addition, the existing methods mostly use static thresholds when dealing with grid-connected safety constraints, and fail to fully consider dynamic safety boundaries such as grid-connected power fluctuations, resulting in increased operating risks or overly conservative scheduling results. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a multi-energy coupling system optimization scheduling method considering grid connection safety.

[0004] The object of the present invention is achieved by the following technical solution: a multi-energy coupling system optimization scheduling method considering grid safety, the method comprising: Constructing a multi-energy coupling system architecture model; the multi-energy coupling system architecture model includes a renewable energy power generation unit, a traditional energy power generation unit, an energy storage unit, an energy conversion unit and a combined cooling, heating and power unit; A distributed energy storage optimization operation model is constructed based on the multi-energy coupling system architecture model; its objective function is: ; In the formula, f n are different evaluation index values; λ n for f n The weight value of N Indicates the number of dispatch targets; evaluation indicators include grid-connected variance volatility f 1. Power fluctuation range ratio f 2. Wind and solar power curtailment rate f 3. Indicators of insufficient power supply f 4 and safety evaluation indicators f 5; The constraints of the distributed energy storage optimization operation model include: Battery energy storage charging and discharging constraints, and state of charge constraints; Output constraints of energy conversion equipment; Operating constraints for electric vehicle charging stations; Constraints on renewable energy generation; Power balance constraints; Transmission power constraints of public network lines; Transmission power constraints of internal distribution network lines; The optimization algorithm is used to solve the distributed energy storage optimization operation model to obtain the optimal scheduling plan.

[0005] Specifically, the grid-connected variance volatility f The calculation formula of 1 is as follows: ; In the formula, is the net load power of the system in the high penetration photovoltaic grid-connected scenario, T For data statistics duration, is the maximum value of net load; A moment in the data statistics duration; Power fluctuation range ratio f The calculation formula of 2 is as follows: ; In the formula, and They are Δ t The maximum and minimum values ​​of net load power within the time interval; M is the number of equally divided time intervals, Δ t i is the length of the unit time interval; Wind and solar power curtailment rate f The calculation formula for 3 is as follows: ; In the formula, The actual output of wind power at each moment; The actual output of photovoltaic power generation at each moment; is the maximum value of wind power generation at each moment; is the maximum value of photovoltaic power generation at each moment; T For data statistics duration, A moment in the data statistics period.

[0006] Power supply shortage indicator f The calculation formula for 4 is as follows: ; In the formula, The amount of power wasted at each moment; T The duration of data statistics; is the electric load after demand response.

[0007] Safety evaluation indicators f The calculation formula for 5 is as follows: ; In the formula, Purchase electricity for the system at each moment; is the technical limit ratio; is the power grid limit power; T The duration of data statistics.

[0008] Specifically, the charge and discharge constraints are as follows: ; In the formula, , It is a collection of electricity storage, heat storage, gas storage, hydrogen storage, water storage and ice storage; For the i Energy storage devices t The capacity of the time slot; For the i Energy storage devices t-1 The capacity of the time slot; , Respectively i Energy storage devices t Charging and discharging power in each time period; , Respectively i The maximum power of a single charge and discharge of an energy storage device; is the ratio of energy storage power to capacity; Respectively i Energy storage devices t The state parameters of charging and discharging in the time period are binary variables. When When , the energy storage device is in the energy discharging state; , They represent the charging and discharging efficiency of the energy storage device respectively.

[0009] The output constraint of the energy conversion equipment is as follows: ; In the formula, , For electrolytic cell, For the methane reactor, For hydrogen fuel cells, For gas boilers, For gas turbines, For electric refrigerator, It is an absorption refrigerator; express t The upper limit of the output power of each energy conversion device in the time period; is the input power of the i-th energy conversion device, is the output power of the i-th energy conversion device; is the conversion efficiency of the i-th energy conversion device; The state of charge constraint is as follows: ; In the formula, For the The lower capacity limit of the energy storage device is For the The upper limit of the capacity of the energy storage device.

[0010] Specifically, the operating constraints of the electric vehicle charging station are as follows: ; Where: The limit of the discharge capacity ratio; The charging capacity ratio limit; Charging station capacity for electric vehicles; , They are the charging and discharging power of the electric vehicle charging station at each moment.

[0011] Specifically, the constraints on renewable energy generation are as follows: ; In the formula, Respectively t The power output of wind power and photovoltaic power during the period; , They represent the upper limits of the predicted output power of wind power and photovoltaic power respectively.

[0012] Specifically, the power balance constraints include: Electric load balancing constraints: ; ; In the formula, Providing power for wind power generation; Contribute to photovoltaic power generation; The power purchased; , , , , They are hydrogen fuel cells, gas turbines, diesel generators, electrolyzers, and electric refrigerators. t The electrical power at the time; for t The power of controllable resources of electric load in time period; and Respectively represent the discharge and charging power of electric vehicles; and Respectively represent the discharge and charge power of the electric storage; To limit the power of purchased electricity; Heat load balancing constraints: ; In the formula, , , , They are gas turbines, gas boilers, hydrogen fuel cells, and absorption chillers. t Thermal power at the moment; express t Heat load power during the period; and Respectively represent the heat storage in t The heat release and charging efficiency at all times.

[0013] Cooling load balancing constraints: ; In the formula, , are the cooling power of the electric refrigerator and absorption refrigerator at time t respectively; , They represent the charging and discharging power of water cooling storage at time t respectively; , They represent the charging and discharging power of ice storage at time t respectively; Indicates t Cooling load power of the time period; Gas power balance constraints: ; ; In the formula, for t Gas purchasing power at the time; for t The output power of the methane reactor at each moment; for t The required gas power of the gas boiler at any time, express t The required gas power of the gas turbine at the moment; for t Gas load at all times; and Respectively t Inflation and deflation power at all times; To limit the power of gas purchase; Hydrogen load balance constraints: ; In the formula, , , They are tThe hydrogen power required for the electrolyzer, methane reactor, and hydrogen fuel cell at the time; and Respectively represent hydrogen storage in t The hydrogen discharge and charging efficiency at all times.

[0014] Specifically, the transmission power constraint of the public network line is as follows: ; In the formula, , They are the upper limits of transmission power of power lines and gas lines at each moment; for t Gas purchasing power at the moment, To purchase electricity.

[0015] Specifically, the transmission power constraint of the internal distribution network line is as follows: ; In the formula, , , , , They are the bus input power of electricity, gas, cold, heat and hydrogen at each moment respectively; , , , , They are the upper limits of bus transmission power for electricity, gas, cold, heat and hydrogen respectively.

[0016] The present invention has the following advantages: The present invention innovatively integrates three types of physical safety indicators: grid-connected variance volatility (reflecting the dynamic stability of grid voltage / frequency), wind and solar power abandonment rate (measuring renewable energy absorption capacity), and power supply shortage index (assessing load power supply reliability), breaking through the limitations of the traditional single safety constraint model and comprehensively covering the core risk dimensions of grid-connected safety. The present invention eliminates the influence of dimensional differences on the optimization results through normalization processing, and realizes the flexible coordination of safety, economy and low carbon goals by combining weight coefficients. The weight value can be adjusted according to the real-time operating conditions. By optimizing the wind and solar power curtailment rate indicators, the energy storage scheduling strategy can be optimized to minimize renewable energy curtailment and improve the absorption capacity. At the same time, low-carbon technologies such as hydrogen fuel cells and electrolyzers can be used to reduce dependence on fossil energy and promote low-carbon operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the multi-energy coupling system architecture model of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0020] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0021] The present invention is further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description.

[0022] like Figure 1 As shown, a multi-energy coupling system optimization scheduling method considering grid-connected safety includes: Construct a multi-energy coupling system architecture model; the multi-energy coupling system architecture model includes a renewable energy power generation unit, a traditional energy power generation unit, an energy storage unit, an energy conversion unit and a combined cooling, heating and power unit; Figure 1As shown in the figure, the multi-energy coupling system architecture integrates a variety of energy production, storage and consumption technologies to achieve efficient energy management and optimized scheduling. Ice storage and water storage technologies are used to meet the cooling load demand, while electric refrigerators and absorption refrigerators are used for refrigeration. In terms of power supply, the system combines renewable energy such as wind power and photovoltaics, as well as traditional gas turbines and diesel generators. Energy storage technologies include electric storage, hydrogen storage and heat storage to balance supply and demand fluctuations. In addition, the system integrates two-stage power-to-gas technology (P2G, Power-to-Gas), converting electrical energy into methane through electrolyzers and methane reactors, which is stored in gas storage and used by the gas grid. The overall architecture achieves efficient use of energy through combined heat and power (CHP, Combined Heat and Power) and combined cooling, heat and power (CCHP, Combined Cooling, Heat and Power) technologies, while using hydrogen fuel cells to further optimize energy conversion efficiency.

[0023] A distributed energy storage optimization operation model is constructed based on the multi-energy coupling system architecture model; its objective function is: ; In the formula, f n are different evaluation index values; λ n for f n The weight value of N Indicates the number of dispatch targets; evaluation indicators include grid-connected variance volatility f 1. Power fluctuation range ratio f 2. Wind and solar power curtailment rate f 3. Indicators of insufficient power supply f 4 and safety evaluation indicators f 5. Since the indicators have different dimensions, they cannot be weighted directly. In order to make different indicators comparable, the indicators need to be normalized. First, multiple target sub-functions are obtained. Secondly, based on the target function indicators f n Based on the definition of , combined with the goal of minimizing the net load power deviation, the energy storage system is dispatched and decided.

[0024] In order to characterize the peak shaving and valley filling capability, the ratio of the variance of the system net load power to the maximum net load value is selected as a quantitative index. The smaller the value, the more uniform the load distribution in each period, the flatter the system net load curve, and the greater the role of the energy storage system in achieving peak load reduction and valley filling. f The calculation formula of 1 is as follows: ; In the formula, is the net load power of the system in the high penetration photovoltaic grid-connected scenario, T For data statistics duration, is the maximum value of net load; A moment in the data statistics period.

[0025] When the output power of high-penetration distributed photovoltaic power sources fluctuates greatly, the voltage fluctuations at key nodes in the power grid are obvious and may even exceed the limit. T Divided into M time intervals of equal length Δ t i The average value of the peak-to-valley difference of the net load power in each period in the sampling interval is used as an indicator to measure the ability of the energy storage system to smooth power fluctuations. The smaller the value, the smoother the net load power and the smaller the fluctuation amplitude; the proportion of power fluctuation range f The calculation formula of 2 is as follows: ; In the formula, and They are Δ t The maximum and minimum values ​​of net load power within the time interval; M is the number of equally divided time intervals, Δ t i is the length of the unit time interval; A moment in the data statistics period.

[0026] The wind and solar power abandonment rate is selected as the preferred indicator to measure the energy storage's ability to improve the renewable energy consumption capacity. The smaller the value, the better the effect of the energy storage system on improving the utilization rate of photovoltaic power generation. f The calculation formula for 3 is as follows: ; In the formula, The actual output of wind power at each moment; The actual output of photovoltaic power generation at each moment; is the maximum value of wind power generation at each moment; is the maximum value of photovoltaic power generation at each moment; T The duration of data statistics; A moment in the data statistics period.

[0027] Power supply reliability refers to the ability of the power system to continuously supply power and is an important indicator for assessing the power quality of the power system. This project uses the proportion of load shedding during system failures as an indicator to measure the power supply shortage index of the power grid, in order to reflect the role of energy storage in supporting power grid failures; f The calculation formula for 4 is as follows: ; In the formula, The amount of power wasted at each moment; T The duration of data statistics; is the electric load after demand response.

[0028] The gap between the actual grid access power and the grid technical limit power is used as a safety assessment indicator; safety assessment indicator f The calculation formula for 5 is as follows: ; In the formula, Purchase electricity for the system at each moment; is the technical limit ratio; is the power grid limit power; T The duration of data statistics.

[0029] The constraints of the distributed energy storage optimization operation model include: Battery energy storage charging and discharging constraints, and state of charge constraints; Output constraints of energy conversion equipment; Operating constraints for electric vehicle charging stations; Constraints on renewable energy generation; Power balance constraints; Transmission power constraints of public network lines; Transmission power constraints of internal distribution network lines; Commercial solvers such as gurobi are used to solve the distributed energy storage optimization operation model to obtain the optimized scheduling plan.

[0030] Furthermore, the charge and discharge constraints are as follows: ; In the formula, , It is a collection of electricity storage, heat storage, gas storage, hydrogen storage, water storage and ice storage; For the i Energy storage devices t The capacity of the time slot; For the i Energy storage devices t-1 The capacity of the time slot; , Respectively i Energy storage devices t Charging and discharging power in each time period; , Respectively i The maximum power of a single charge and discharge of an energy storage device; is the ratio of energy storage power to capacity; Respectively iEnergy storage devices t The state parameters of charging and discharging in the time period are binary variables. When When , the energy storage device is in the energy discharging state; , They represent the charging and discharging efficiency of the energy storage device respectively.

[0031] Since ice storage and water storage have similar functions, when both are present at the same time, spatial constraints are considered to coordinate the planning of the two storage modes. The spatial constraints are as follows: ; Where: , The area occupied per unit power for ice storage and water storage; , It is the total power capacity of ice storage and water storage; This is the maximum space limit for the cold storage device.

[0032] The state of charge constraint is as follows: ; In the formula, For the The lower capacity limit of the energy storage device is For the The upper limit of the capacity of the energy storage device.

[0033] The typical charging and discharging capacity of electric vehicle clusters is considered in the model of electric vehicle charging piles to improve the planning operation efficiency. The charging and discharging capacity model of electric vehicle (EV) clusters can be obtained by summing up the charging and discharging capacity models of single electric vehicles. The operating constraints of electric vehicle charging stations mainly consider the constraints of their charging and discharging power. The operating constraints of electric vehicle charging stations are shown in the following formula: ; Where: The limit of the discharge capacity ratio; The charging capacity ratio limit; Charging station capacity for electric vehicles; , They are the charging and discharging power of the electric vehicle charging station at each moment.

[0034] The cooling and heating equipment of the industrial park comprehensive energy system needs to have power limits to meet the load while not exceeding the maximum power of the equipment. The output limits of each system meet the requirements of the following formula. According to the relationship between the energy input and output of the park comprehensive energy system structure, the input power, output power and conversion efficiency of each energy conversion equipment are expressed as ; The output constraint of the energy conversion equipment is as follows: ; In the formula, , For electrolytic cell, For the methane reactor, For hydrogen fuel cells, For gas boilers, For gas turbines, For electric refrigerator, It is an absorption refrigerator; express t The upper limit of the output power of each energy conversion device in the time period; is the input power of the i-th energy conversion device, is the output power of the i-th energy conversion device; is the conversion efficiency of the i-th energy conversion device.

[0035] Furthermore, the constraints on renewable energy generation are as follows: ; In the formula, Respectively t The power output of wind power and photovoltaic power during the period; , They represent the upper limits of the predicted output power of wind power and photovoltaic power respectively.

[0036] Furthermore, the power balance constraints include: According to historical weather data and historical load data of energy consumption, based on the predetermined time scale, the balance between load demand and supply capacity of the corresponding time scale can be obtained. The electric load balance equation is shown as follows. This constraint is an important balance condition in the comprehensive energy system of the industrial park and is related to the final configuration of energy storage; electric load balance constraint: ; ; In the formula, Providing power for wind power generation; Contribute to photovoltaic power generation; The power purchased; , , , , They are hydrogen fuel cells, gas turbines, diesel generators, electrolyzers, and electric refrigerators. t The electrical power at the time; for t The power of controllable resources of electric load in time period; and Respectively represent the discharge and charging power of electric vehicles; and Respectively represent the discharge and charge power of the electric storage; The power limit for purchasing electricity.

[0037] According to the historical heating demand and the operation strategy of the thermal system, the output of each device is determined, and finally the amount of electricity required to meet the thermal load is obtained and substituted into the electrical load balance equation; Thermal load balance constraints: ; In the formula, , , , They are gas turbines, gas boilers, hydrogen fuel cells, and absorption chillers. t Thermal power at the moment; express t Heat load power during the period; and Respectively represent the heat storage in t The heat release and charging efficiency at all times.

[0038] According to the cold bus equipment situation, consider all equipment output into the electric load balance equation; cold load balance constraints: ; In the formula, , are the cooling power of the electric refrigerator and absorption refrigerator at time t respectively; , They represent the charging and discharging power of water cooling storage at time t respectively; , They represent the charging and discharging power of ice storage at time t respectively; Indicates t Cooling load power for the time period.

[0039] According to the gas bus equipment situation, consider all equipment output into the electric load balance equation; gas power balance constraints: ; ; In the formula, for t Gas purchasing power at the time; for t The output power of the methane reactor at each moment; for t The required gas power of the gas boiler at any time, express t The required gas power of the gas turbine at the moment; for t Gas load at all times; and Respectively t Inflation and deflation power at all times; The power limit for gas purchase; hydrogen load balance constraint: ; In the formula, , , They are t The hydrogen power required for the electrolyzer, methane reactor, and hydrogen fuel cell at the time; and Respectively represent hydrogen storage in t The hydrogen discharge and charging efficiency at all times.

[0040] Furthermore, the transmission power constraint of the public network line is as follows: ; In the formula, , They are the upper limits of transmission power of power lines and gas lines at each moment; for t Gas purchasing power at the moment, To purchase electricity.

[0041] Furthermore, the transmission power constraint of the internal distribution network line is as follows: ; In the formula, , , , , They are the bus input power of electricity, gas, cold, heat and hydrogen at each moment respectively; , , , , They are the upper limits of bus transmission power for electricity, gas, cold, heat and hydrogen respectively.

[0042] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any technician familiar with the art can make many possible changes and modifications to the technical solution of the present invention by using the above-mentioned technical content without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. Therefore, any changes, modifications, equivalent changes and modifications made to the above embodiments based on the technology of the present invention without departing from the content of the technical solution of the present invention belong to the protection scope of the present technical solution.

Claims

1. A multi-energy coupling system optimization scheduling method considering grid-connected safety, characterized in that: The method includes: Construct a multi-energy coupling system architecture model; A distributed energy storage optimization operation model is constructed based on the multi-energy coupling system architecture model; its objective function is: ； In the formula, are different evaluation index values; for The weight value of N Indicates the number of dispatch targets; evaluation indicators include grid-connected variance volatility f 1. Power fluctuation range ratio f 2. Wind and solar power curtailment rate f 3. Indicators of insufficient power supply f 4 and safety evaluation indicators f 5; The constraints of the distributed energy storage optimization operation model include: Battery energy storage charging and discharging constraints, and state of charge constraints; Output constraints of energy conversion equipment; Operating constraints for electric vehicle charging stations; Constraints on renewable energy generation; Power balance constraints; Transmission power constraints of public network lines; Transmission power constraints of internal distribution network lines; The optimization algorithm is used to solve the distributed energy storage optimization operation model to obtain the optimal scheduling plan.

2. According to claim 1, a multi-energy coupling system optimization scheduling method considering grid safety is characterized by: Grid Variance Volatility f The calculation formula of 1 is as follows: ； In the formula, is the net load power of the system in the high penetration photovoltaic grid-connected scenario, T For data statistics duration, is the maximum value of net load; A moment in the data statistics duration; Power fluctuation range ratio f The calculation formula of 2 is as follows: ； In the formula, and They are Δ t The maximum and minimum values ​​of net load power within the time interval; M is the number of equally divided time intervals, Δ t i is the length of the unit time interval, A moment in the data statistics duration; Wind and solar power curtailment rate f The calculation formula for 3 is as follows: ； In the formula, The actual output of wind power at each moment; The actual output of photovoltaic power generation at each moment; is the maximum value of wind power generation at each moment; is the maximum value of photovoltaic power generation at each moment; T For data statistics duration, A moment in the data statistics duration; Power supply shortage indicator f The calculation formula for 4 is as follows: ； In the formula, The amount of power wasted at each moment; T The duration of data statistics; is the electric load after demand response; Safety evaluation indicators f The calculation formula for 5 is as follows: ； In the formula, Purchase electricity for the system at each moment; is the technical limit ratio; is the power grid limit power; T The duration of data statistics.

3. The method for optimizing and dispatching a multi-energy coupling system considering grid connection safety according to claim 1, characterized in that: The charge and discharge constraints are as follows: ； In the formula, , It is a collection of electricity storage, heat storage, gas storage, hydrogen storage, water storage and ice storage; For the i Energy storage devices t The capacity of the time slot; For the i Energy storage devices t-1 The capacity of the time slot; , Respectively i Energy storage devices t Charging and discharging power in each time period; , Respectively i The maximum power of a single charge and discharge of an energy storage device; is the ratio of energy storage power to capacity; Respectively i Energy storage devices t The state parameters of charging and discharging in the time period are binary variables. When When , the energy storage device is in the energy discharging state; , They represent the charging and discharging efficiency of the energy storage device respectively; The state of charge constraint is as follows: ； In the formula, For the The lower capacity limit of the energy storage device is For the The upper limit of the capacity of the energy storage device.

4. The method for optimizing and dispatching a multi-energy coupling system considering grid connection safety according to claim 1, characterized in that: The output constraint of the energy conversion equipment is as follows: ； In the formula, , For electrolytic cell, For the methane reactor, For hydrogen fuel cells, For gas boilers, For gas turbines, For electric refrigerator, It is an absorption refrigerator; express t The upper limit of the output power of each energy conversion device in the time period; is the input power of the i-th energy conversion device, is the output power of the i-th energy conversion device; is the conversion efficiency of the i-th energy conversion device.

5. The method for optimizing and dispatching a multi-energy coupling system considering grid connection safety according to claim 1, characterized in that: The operating constraints of electric vehicle charging stations are as follows: ； Where: The limit of the discharge capacity ratio; The charging capacity ratio limit; Charging station capacity for electric vehicles; , They are the charging and discharging power of the electric vehicle charging station at each moment.

6. The method for optimizing and dispatching a multi-energy coupling system considering grid connection safety according to claim 1, characterized in that: The constraints on renewable energy generation are as follows: ； In the formula, Respectively t The power output of wind power and photovoltaic power during the period; , They represent the upper limits of the predicted output power of wind power and photovoltaic power respectively.

7. The method for optimizing and dispatching a multi-energy coupling system considering grid connection safety according to claim 1, characterized in that: Power balancing constraints include: Electric load balancing constraints: ； ； In the formula, Produce power for wind power; Contribute to photovoltaic power generation; The power purchased; , , , , They are hydrogen fuel cells, gas turbines, diesel generators, electrolyzers, and electric refrigerators. t The electrical power at the time; for t The power of controllable resources of electric load in time period; and They represent the discharging and charging power of electric vehicles respectively; and Respectively represent the discharge and charge power of the electric storage; To limit the power of purchased electricity; Heat load balancing constraints: ； In the formula, , , , They are gas turbines, gas boilers, hydrogen fuel cells, and absorption chillers. t Thermal power at the moment; express t Heat load power during the period; and Respectively represent the heat storage in t Heat release and charging efficiency at each moment; Cooling load balancing constraints: ； In the formula, , are the cooling power of the electric refrigerator and absorption refrigerator at time t respectively; , They represent the charging and discharging power of water cooling storage at time t respectively; , They represent the charging and discharging power of ice storage at time t respectively; Indicates t Cooling load power of the time period; Gas power balance constraints: ； ； In the formula, for t Gas purchasing power at the time; for t The output power of the methane reactor at each moment; for t The required gas power of the gas boiler at any time, express t The required gas power of the gas turbine at the moment; for t Gas load at all times; and Respectively t Inflation and deflation power at the moment; To limit the power of gas purchase; Hydrogen load balance constraints: ； In the formula, , , They are t The hydrogen power required for the electrolyzer, methane reactor, and hydrogen fuel cell at the time; and Respectively represent hydrogen storage in t The hydrogen discharge and charging efficiency at all times.

8. The method for optimizing and dispatching a multi-energy coupling system considering grid connection safety according to claim 1, characterized in that: The transmission power constraint of the public network line is as follows: ； In the formula, , They are the upper limits of transmission power of power lines and gas lines at each moment; for t Gas purchasing power at the moment, To purchase electricity.

9. The method for optimizing and dispatching a multi-energy coupling system considering grid connection safety according to claim 1, characterized in that: The transmission power constraint of the internal distribution network line is as follows: ； In the formula, , , , , They are the bus input power of electricity, gas, cold, heat and hydrogen at each moment respectively; , , , , They are the upper limits of bus transmission power for electricity, gas, cold, heat and hydrogen respectively.

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