Full-life-cycle optimal configuration method, system and equipment for multi-time-scale hybrid shared energy storage station and medium

Abstract

The invention relates to a full-life-cycle optimal configuration method, system and equipment for a multi-time-scale hybrid shared energy storage station and a medium. The method comprises the following steps: according to fixed storage characteristics and regional energy demands of different time scales of hydrogen energy storage and electrochemical energy storage, carrying out optimal configuration on a multi-time-scale hybrid shared energy storage station; introducing mobile energy storage characteristics of the electric vehicle to establish a hybrid shared energy storage architecture; by analyzing and processing global decision data in the full life cycle of the hybrid shared energy storage station, a full life cycle self-optimization model of the hybrid shared energy storage station is constructed; and constructing collaborative optimization sub-models containing multiple subjects, configuring mutual transfer and iterative calculation of parameters among the sub-models, and finally outputting an optimal solution. According to the method, energy storage modes of different time scales are integrated, the full-life-cycle self-optimization model of the hybrid shared energy storage station is constructed, multi-agent collaborative optimization is achieved, an energy system can be better planned and managed, and efficient utilization of energy and sustainable development of the environment are achieved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage optimization technology, and in particular to a full life cycle optimization configuration method, system, equipment and medium for a multi-time scale hybrid shared energy storage station. Background Art

[0002] With the rapid development of renewable energy, the importance of energy storage technology has become increasingly prominent. However, the high cost of current energy storage systems is still a major obstacle to their widespread application. In order to solve this problem, the development of shared energy storage and mobile energy storage for electric vehicles has gradually become a new solution. Through shared energy storage, multiple users can use energy storage equipment together, thereby sharing costs and improving economic efficiency. In addition, as the proportion of renewable energy connected to the grid continues to increase, the contradiction between the imbalance between electricity supply and demand on a seasonal scale continues to intensify. It is an inevitable trend to develop long-term energy storage technology to improve the stability of the energy system.

[0003] However, in the existing technical system, shared energy storage mostly considers short-time scale electrochemical energy storage, and lacks consideration of the coordination between long-time scale energy storage and short-time scale energy storage. It is very important to consider the hybrid energy storage method of long-time scale and short-time scale at the same time in the context of a high proportion of renewable energy. In terms of configuration optimization of shared energy storage, only the shared energy storage station provides energy storage services, and no further exploration of the energy storage potential of mobile energy storage of electric vehicles and research on the coordination between the two have been conducted. They are only analyzed and processed as isolated objects. At the same time, when optimizing and evaluating the environmental performance of shared energy storage, current research is limited to the carbon emissions generated by the consumption of primary energy during the operation phase, ignoring the impact of emissions generated by the construction of shared energy storage stations throughout the life cycle from raw material acquisition, equipment production, operation and use. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a method, system, equipment and medium for optimizing the configuration of a multi-time-scale hybrid shared energy storage station throughout its life cycle, which solves the technical problems of the existing energy storage technology, such as high cost, lack of combination of long-term and short-term energy storage, failure to fully tap the energy storage potential of electric vehicles, incomplete environmental assessment and failure to consider collaboration among multiple subjects.

[0005] (II) Technical solution In order to achieve the above object, the main technical solutions adopted by the present invention include: In a first aspect, an embodiment of the present invention provides a method for optimizing the configuration of a multi-time-scale hybrid shared energy storage station throughout its life cycle, comprising: establishing a hybrid shared energy storage architecture including a hybrid shared energy storage station, a regional energy system, and mobile energy storage equipment for electric vehicles based on the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, as well as regional energy demand, and introducing the mobile energy storage characteristics of electric vehicles; constructing a full life cycle self-optimization model for the hybrid shared energy storage station by analyzing and processing at least one global decision-making data in the full life cycle of the hybrid shared energy storage station; in the hybrid shared energy storage architecture, based on the full life cycle self-optimization model of the hybrid shared energy storage station, constructing at least two joint optimization sub-models containing multiple subjects, and performing collaborative interactive solutions between the joint optimization sub-models to achieve mutual transmission and iterative calculation of parameters between the sub-models, until a preset termination condition is reached, and outputting an optimal solution including a charging / discharging strategy for the mobile energy storage of the electric vehicle and a capacity configuration parameter of the hybrid shared energy storage station.

[0006] In a second aspect, an embodiment of the present invention provides a system for optimizing the configuration of a hybrid shared energy storage station throughout its life cycle at multiple time scales, including: an architecture construction module for establishing a hybrid shared energy storage architecture including a hybrid shared energy storage station, a regional energy system, and mobile energy storage equipment for electric vehicles based on the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, as well as regional energy demand, and introducing the mobile energy storage characteristics of electric vehicles; a hybrid shared energy storage station self-optimization module for constructing a full life cycle self-optimization model for the hybrid shared energy storage station by analyzing and processing at least one global decision-making data in the full life cycle of the hybrid shared energy storage station; a multi-agent optimization module for constructing at least two joint optimization sub-models containing multiple agents in the hybrid shared energy storage architecture based on the full life cycle self-optimization model of the hybrid shared energy storage station, and performing collaborative interactive solutions between the joint optimization sub-models to realize mutual transmission and iterative calculation of parameters between the sub-models until a preset termination condition is reached, and outputting an optimal solution including a charging / discharging strategy for the mobile energy storage of the electric vehicle and a capacity configuration parameter of the hybrid shared energy storage station.

[0007] In a third aspect, an embodiment of the present invention provides a full life cycle optimization configuration device for a multi-time scale hybrid shared energy storage station, comprising: at least one database; and a memory communicatively connected to the at least one database; wherein the memory stores instructions executable by the at least one database, and the instructions are executed by the at least one database so that the at least one database can execute the full life cycle optimization configuration method for a multi-time scale hybrid shared energy storage station as described above.

[0008] In a fourth aspect, an embodiment of the present invention provides a computer-readable medium having computer executable instructions stored thereon, which, when executed by a processor, implements the full life cycle optimization configuration method for a multi-time-scale hybrid shared energy storage station as described above.

[0009] (III) Beneficial effects The beneficial effects of the present invention are: First, based on the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales and regional energy needs, and introducing the mobile energy storage characteristics of electric vehicles, a hybrid shared energy storage architecture was successfully established. In this architecture, energy storage needs at different time scales are integrated, and efficient energy storage services for multi-regional energy systems are realized. At the same time, the hydrogen refueling needs of hydrogen fuel vehicles are met, the mobile energy storage potential of electric vehicles is fully tapped, and the energy storage pressure of hybrid shared energy storage stations is effectively reduced, and their equipment configuration capacity is reduced.

[0010] Secondly, the present invention deeply analyzes the economic cost data of the hybrid shared energy storage station during its entire life cycle, and comprehensively considers the environmental impact of the raw material acquisition, production, operation, and recycling stages of the hybrid shared energy storage station, and constructs a full life cycle self-optimization model. This model aims to achieve a dynamic balance between multiple factors in the hybrid shared energy storage station at different life stages, thereby ensuring that the environmental impact of the hybrid shared energy storage station is minimized while taking into account the economic cost.

[0011] Finally, in order to further optimize the hybrid shared energy storage architecture, at least two multi-agent collaborative optimization sub-models were constructed for the hybrid shared energy storage station, regional energy system, and electric vehicle mobile energy storage equipment. By setting different optimization objectives and solving them, these sub-models can realize mutual transfer and iterative calculation between parameters. This collaborative optimization method not only improves the efficiency of the overall system, but also outputs the optimal solution including the charging / discharging strategy of the electric vehicle cluster and the capacity configuration parameters of the hybrid shared energy storage station after reaching the maximum number of iterations. In this way, the system not only achieves a balance in economic and environmental aspects, but also achieves higher efficiency and lower costs in actual operation.

[0012] Therefore, the present invention helps to better plan and manage the energy system by integrating energy storage methods of different time scales, constructing a full life cycle self-optimization model, and realizing multi-agent collaborative optimization, thereby achieving efficient energy utilization and sustainable environmental development. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram of a flow chart of a method provided by an embodiment of the present invention; Figure 2 A schematic diagram of a specific flow chart of step S1 of the method provided in an embodiment of the present invention; Figure 3 A schematic diagram of the architecture of a multi-time-scale hybrid shared energy storage system provided by an embodiment of the present invention; Figure 4 A schematic diagram of a specific flow chart of step S2 of the method provided in an embodiment of the present invention; Figure 5 A schematic diagram of a specific flow chart of step S3 of the method provided in an embodiment of the present invention; Figure 6 Schematic diagram of the collaborative optimization model of electric vehicle mobile energy storage, regional energy system and hybrid shared energy storage station provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0015] like Figure 1 As shown, a method for optimizing the configuration of a multi-time-scale hybrid shared energy storage station throughout its life cycle is proposed in an embodiment of the present invention, including: establishing a hybrid shared energy storage architecture including a hybrid shared energy storage station, a regional energy system, and mobile energy storage equipment for electric vehicles based on the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales and regional energy demand, and introducing the mobile energy storage characteristics of electric vehicles; constructing a hybrid shared energy storage station life cycle economic cost and environmental impact model by analyzing the economic cost data of the hybrid shared energy storage station during its life cycle, and combining environmental impact factors, so as to achieve a dynamic balance between environmental impact factors and economic costs of the hybrid shared energy storage station as a single subject at different life stages; in the hybrid shared energy storage architecture, constructing at least two multi-subject collaborative optimization sub-models for the hybrid shared energy storage station, the regional energy system, and the mobile energy storage equipment for electric vehicles, and realizing mutual transmission and iterative calculation of parameters between sub-models by setting different optimization goals and solving them, until the maximum number of iterations is reached, and finally outputting the optimal solution including the charging / discharging strategy of the electric vehicle cluster and the capacity configuration parameters of the hybrid shared energy storage station.

[0016] First, according to the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales and regional energy needs, and the mobile energy storage characteristics of electric vehicles are cleverly introduced, a hybrid shared energy storage architecture is successfully established. In this architecture, the energy storage needs of different time scales are integrated, and the efficient energy storage service of multi-regional energy systems is realized. At the same time, the hydrogenation needs of hydrogen fuel vehicles are met, the mobile energy storage potential of electric vehicles is fully tapped, and the energy storage pressure of hybrid shared energy storage stations is effectively reduced, and the equipment configuration capacity is reduced. Secondly, the present invention deeply analyzes the economic cost data of the hybrid shared energy storage station during the whole life cycle, and comprehensively considers the environmental impact of the raw material acquisition, production, operation, and recycling stages of the hybrid shared energy storage station, and constructs a full life cycle self-optimization model. This model aims to achieve a dynamic balance between multiple factors in the hybrid shared energy storage station at different life stages, so as to ensure that the environmental impact of the hybrid shared energy storage station is minimized while taking into account the economic cost. Finally, in order to further optimize the hybrid shared energy storage architecture, at least two multi-agent collaborative optimization sub-models are constructed for the hybrid shared energy storage station, the regional energy system, and the mobile energy storage equipment of electric vehicles. By setting different optimization goals and solving them, these sub-models can realize mutual transfer and iterative calculation between parameters. This collaborative optimization method not only improves the efficiency of the overall system, but also outputs the optimal solution including the charging / discharging strategy of the electric vehicle cluster and the capacity configuration parameters of the hybrid shared energy storage station after reaching the maximum number of iterations. In this way, the system not only achieves a balance in economic and environmental aspects, but also achieves higher efficiency and lower costs in actual operation. Therefore, the present invention helps to better plan and manage energy systems by integrating energy storage methods of different time scales, constructing a full life cycle self-optimization model, and realizing multi-agent collaborative optimization, thereby achieving efficient energy utilization and sustainable environmental development.

[0017] In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0018] Specifically, an embodiment of the present invention provides a method for optimizing the configuration of a multi-time scale hybrid shared energy storage station throughout its life cycle, which includes: S1. Based on the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, as well as regional energy demand, and introducing the mobile energy storage characteristics of electric vehicles, a hybrid shared energy storage architecture is established, including hybrid shared energy storage stations, regional energy systems and electric vehicle mobile energy storage equipment.

[0019] Furthermore, if Figure 2 As shown, step S1 includes: S11. According to the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, a hybrid shared energy storage station including a periodic energy storage unit, a seasonal energy storage unit and a hydrogen-to-electricity conversion unit is determined, and a mathematical model of the hybrid shared energy storage station is constructed.

[0020] Here, the periodic energy storage unit refers to an energy storage system that can store and release energy within a 24-hour time frame. This type of energy storage system mainly responds to fluctuations in energy demand in a short period of time (such as a few hours to a day). Electrochemical energy storage technologies, such as lithium-ion batteries, sodium-sulfur batteries, supercapacitors, etc., seasonal energy storage units are designed for energy storage needs on a longer time scale (such as weeks, months, or even across seasons). This type of energy storage unit needs to be able to store a large amount of energy and release it when needed to cope with seasonal imbalances in energy supply and demand.

[0021] S12. Based on the electricity demand, building cooling demand and heating demand of the buildings and electric vehicles in the target area, determine the regional energy system including power supply components that meet the electricity demand of buildings and electric vehicles, cooling components that meet the cooling load of buildings, heating components that meet the heating load of buildings and energy storage components, and construct a mathematical model of the regional energy system.

[0022] S13. Introduce electric vehicles as mobile energy storage devices in regional energy systems, determine the travel characteristics of electric vehicles in different regions, the travel energy consumption of a single electric vehicle, the minimum power calculation model to meet the travel needs of a single electric vehicle, the mobile energy storage constraint model of a single electric vehicle that meets the travel needs of electric vehicles, and the mobile energy storage charge / discharge power of electric vehicles in different regions, so as to establish a mathematical model of mobile energy storage equipment for electric vehicles.

[0023] S14. Construct a hybrid shared energy storage architecture by integrating and configuring the mathematical models of hybrid shared energy storage stations, regional energy systems, and electric vehicle mobile energy storage devices.

[0024] S15. In the hybrid shared energy storage architecture, an energy balance constraint model is configured to maintain the energy supply and demand balance between the hybrid shared energy storage station, the regional energy system and the electric vehicle mobile energy storage equipment.

[0025] In a specific embodiment, if Figure 3As shown in FIG. 1 , the architecture of a multi-time scale hybrid shared energy storage system includes a hybrid shared energy storage station, a regional energy system and a mobile energy storage device. The hybrid shared energy storage station provides energy storage services for the regional energy system. The regional energy system served by the hybrid shared energy storage station can be one or more. The hybrid shared energy storage station includes a time-based energy storage unit, a seasonal energy storage unit and a hydrogen-to-electricity conversion unit. The time-based energy storage unit includes a battery and a time-based hydrogen storage tank. The seasonal energy storage unit includes a seasonal hydrogen storage tank. The hydrogen-to-electricity conversion unit includes a proton exchange membrane fuel cell and a proton exchange membrane hydrogen electrolysis device. The regional energy system includes power supply components that meet the electricity demand of buildings and electric vehicles, and cooling and heating components that meet the cooling load and heating load of buildings. The power supply components include photovoltaic power generation units, wind power generation units, and gas turbine power generation units. The heating components include gas turbine waste heat recovery heat generation units, ground source heat pumps, and gas boilers. The cooling components include ground source heat pumps and absorption chillers. Mobile energy storage devices include electric vehicles as mobile energy storage devices.

[0026] The hybrid shared energy storage station combines the long-term storage characteristics of hydrogen energy storage with the short-term storage characteristics of electrochemical energy storage. It can meet the multi-time scale energy storage needs of multi-regional energy systems and provide energy storage services for multi-regional energy systems. At the same time, it can meet the hydrogen refueling needs of hydrogen fuel vehicles. In order to further tap the mobile energy storage potential of electric vehicles, mobile energy storage of electric vehicles is regarded as an auxiliary energy storage measure to reduce the energy storage pressure of the hybrid shared energy storage station and reduce the equipment configuration capacity of the hybrid shared energy storage station. Afterwards, a mathematical model of the hybrid shared energy storage station, regional energy system, and mobile energy storage equipment of electric vehicles is established.

[0027] In one embodiment, the mathematical model of the hybrid shared energy storage station device includes: The battery of the periodic energy storage unit meets the energy storage demand of the multi-regional energy system within 24 hours, and its mathematical model is: ; In the formula, for t Battery charge status at all times, for t- 1. Battery charge status at moment 1; represents the time step; is the battery charging efficiency, is the battery discharge efficiency; is the self-discharge rate of the battery; The power for charging the battery, is the battery discharge power; is the minimum charging power of the battery, is the maximum charging power of the battery; is the minimum discharge power of the battery, is the maximum discharge power of the battery; It is a 0-1 variable that controls the charging and discharging state of the battery. The battery is not allowed to be charged and discharged at the same time. Represents the rated capacity of the battery.

[0028] The periodic hydrogen storage unit meets the energy storage demand of the multi-regional energy system within 24 hours and the hydrogen filling load demand of hydrogen fuel vehicles. Its mathematical model is: ; In the formula, for t The hydrogen charge status of the hydrogen storage unit at all times and in all periods; The hydrogen charge state of the hydrogen storage unit at time t-1; is the self-dehydrogenation rate of the hydrogen storage unit over a period of time; is the hydrogen production efficiency of the proton exchange membrane electrolyzer; is the power generation efficiency of the fuel cell; is the electric power consumed by using the electrolyzer to charge the periodic hydrogen storage unit with hydrogen; The electric power consumed by the electrolyzer to charge the hydrogen storage unit periodically is The power generation of fuel cells using hydrogen energy in periodic hydrogen storage units; is the rated capacity of the short hydrogen storage unit; Hydrogen fuel cell vehicle charging load, is a 0-1 variable that characterizes the hydrogen charging and dehydrogenation state of the periodic hydrogen storage unit; so that the periodic hydrogen storage unit is only in the hydrogen charging or dehydrogenation state at each moment; The minimum value of the electric power consumed by using the electrolyzer to charge the periodic hydrogen storage unit with hydrogen; The maximum value of the electric power consumed by using the electrolyzer to charge the periodic hydrogen storage unit with hydrogen; It is the minimum value of the power generated by the proton exchange membrane fuel cell using the hydrogen energy stored in the periodic hydrogen storage unit; It is the maximum value of the power generated by a proton exchange membrane fuel cell using the hydrogen energy stored in the periodic hydrogen storage unit.

[0029] The seasonal hydrogen storage unit meets the energy storage needs of the multi-regional energy system on a seasonal long-term scale, transferring electricity from the season with surplus electricity to the season with scarce electricity. In each season, there is only one state of hydrogen filling or hydrogen discharge. The mathematical model is: ; In the formula, for t The hydrogen charge status of seasonal hydrogen storage units at all times; for t -1 The hydrogen charge status of seasonal hydrogen storage unit at time; is the self-dehydrogenation rate of the seasonal hydrogen storage unit; The electrical power consumed to charge the seasonal hydrogen storage unit with hydrogen using a proton exchange membrane electrolyser; Power generation using hydrogen energy from seasonal hydrogen storage units for fuel cells; is the rated capacity of the seasonal hydrogen storage unit; A 0-1 variable that characterizes the charging and decharging state of the seasonal hydrogen storage unit, so that the seasonal hydrogen storage unit is only in the charging or decharging state at each moment; The minimum value of the electrical power consumed to charge the seasonal hydrogen storage unit with hydrogen using an electrolyzer; The maximum value of the electrical power consumed to charge the seasonal hydrogen storage unit with hydrogen using an electrolyser; The minimum value of the power generated by a proton exchange membrane fuel cell using hydrogen energy stored in a seasonal hydrogen storage unit; The maximum power generated by a proton exchange membrane fuel cell using the hydrogen energy stored in a seasonal hydrogen storage unit.

[0030] The hydrogen-to-electricity conversion unit model includes the following two forms: The proton exchange membrane electrolyzer is a unit for converting electrical energy into hydrogen energy, and its mathematical model is: ; In the formula, Proton exchange membrane electrolyzer t Hydrogen production at the time; Proton exchange membrane electrolyzer t Power consumption at all times.

[0031] Proton exchange membrane fuel cell is a unit for converting hydrogen energy into electrical energy, and its mathematical model is: ; In the formula, Proton exchange membrane fuel cell t Power generation at all times, Proton exchange membrane fuel cell t Hydrogen consumption at each moment.

[0032] And, the mathematical model of the district energy system includes: Photovoltaic power generation unit model: ; In the formula, i Indicates the serial number of the regional energy system; For the i A photovoltaic power generation unit in a district energy system t The power generation at the moment; is the rated capacity of the photovoltaic power generation unit; For photovoltaic power generation units t The actual light intensity received at each moment; For photovoltaic power generation units t Actual surface temperature at the moment; For photovoltaic power generation units t The ambient temperature within a preset radius at all times, preferably, the preset radius is 2 meters.

[0033] Wind power unit model: ; In the formula, For the i Regional t Actual wind speed at the moment; For wind power generation units t The power generation at the moment; is the rated power of the wind power generation unit; is the cut-in wind speed of the wind power generation unit; is the rated wind speed of the wind power generation unit; is the cut-out wind speed of the wind turbine.

[0034] Gas turbine power generation unit model: ; In the formula, For the i Regional t The power generation capacity of the gas turbine power generation unit at the moment; For the i Regional t The waste heat power of the gas turbine power generation unit at the moment; is the power generation efficiency of the gas turbine; for t Natural gas input power of the gas turbine at time.

[0035] Gas turbine waste heat recovery heat generation unit model: ; In the formula, i Indicates the serial number of the regional energy system; For the i Waste heat recovery device for the area t Heat generation power at any time, is the waste heat recovery efficiency.

[0036] Gas boiler heat generation unit model: ; In the formula, For the i Gas boilers for the area t Heat generation power at all times; The heat generation efficiency of the gas boiler; For gas boilers t Natural gas power is consumed at all times.

[0037] Absorption Refrigeration Unit Model: ; In the formula, i Indicates the serial number of the regional energy system; For the i Absorption refrigeration units in the area t The cooling power at the time, For absorption refrigeration units t Heat consumption at all times; is the refrigeration coefficient of the absorption refrigeration unit.

[0038] Model of ground source heat pump cooling / heating unit: ; In the formula, For the i The ground source heat pump in the area t Power consumption at all times; Ground source heat pump t Refrigeration power at the time; Ground source heat pump t Heating power at the time; is the heating coefficient of the ground source heat pump, is the cooling coefficient of the ground source heat pump; It is a 0-1 variable, representing the cooling or heating state of the ground source heat pump, so that the ground source heat pump is only in the cooling or heating state at the same time.

[0039] And, the mathematical model of mobile energy storage equipment for electric vehicles includes: Electric vehicle travel distance model in different regions: ; In the formula, x represents the travel distance of electric vehicles; For the i The probability density distribution function of electric vehicle travel distance in a regional energy system; For the i The mathematical expectation of the travel distance of electric vehicles in a regional energy system, For the i The standard deviation of electric vehicle travel distances for each regional energy system.

[0040] Arrival time model of electric vehicles in different regions: ; In the formula, For thei The probability density function of electric vehicle arrival time in a regional energy system; and For the i The mathematical mean and standard deviation of the arrival time of electric vehicles in a regional energy system.

[0041] Electric vehicle departure time model in different regions: ; In the formula, t Represents time; For the i The probability density function of the departure time of electric vehicles in a regional energy system; and For the i The mathematical mean and standard deviation of the departure time of electric vehicles in a district energy system.

[0042] And, the mathematical model of mobile energy storage equipment for electric vehicles includes: The energy consumption model of a single electric vehicle in a region is: ; In the formula, For the i District Energy System m The energy consumption of electric vehicles For the i District Energy System m The distance travelled by an electric car; The energy consumption per kilometer of electric vehicles.

[0043] A calculation model for the minimum power required for a single electric vehicle to travel; ; In the formula, To meet the i District Energy System m The minimum amount of electricity required for an electric vehicle to travel; It is the rated capacity of the battery of electric vehicle.

[0044] Single electric vehicle mobile energy storage model to meet electric vehicle travel needs: ; In the formula, To meet the i District Energy System m The initial charge of an electric vehicle; For the i District Energy System m Electric vehicles in t Charging power at all times, For the i District Energy System m Electric vehicles in t Discharge power at all times; is a 0-1 variable, controlling i District Energy System m Electric vehicles in t The charging and discharging status at all times; is the discharge efficiency of electric vehicles, Charging efficiency for electric vehicles; It is the maximum value of electric vehicle storage capacity; For the i District Energy System m Charging time of electric vehicles, For the i District Energy System m Discharge time of electric vehicles; is the maximum charging power of the electric vehicle, The minimum charging power for electric vehicles; is the maximum discharge power of the electric vehicle, is the minimum discharge power of the electric vehicle.

[0045] The charging power of the regional electric vehicle mobile energy storage is the sum of the charging power of each electric vehicle in the region, and the discharging power of the regional electric vehicle mobile energy storage is the sum of the discharging power of each electric vehicle in the region, so the charging and discharging power model of the regional electric vehicle mobile energy storage is constructed as follows; ; In the formula, i Indicates the serial number of the regional energy system; Indicates i The serial number of the electric vehicle in the regional energy system, For the i Total number of electric vehicles in a district energy system; Mobile energy storage charging power for electric vehicles, Discharge power for mobile energy storage of electric vehicles.

[0046] The energy balance constraint model includes: Internal energy supply and demand balance constraint model of regional energy system: ; In the formula, , and Separate i The electrical, heating and cooling loads of the regional energy system; and For hybrid shared energy storage station i Discharging and charging power of a regional energy system; For the i The power purchased from the grid for a regional energy system.

[0047] Since the hybrid shared energy storage station meets the energy storage needs of multiple regional energy systems, the discharge power of the hybrid shared energy storage station is the sum of the discharge powers allocated to different regional energy systems by the hybrid shared energy storage station, and the charging power of the hybrid shared energy storage station is the sum of the charging powers allocated to different regional energy systems by the hybrid shared energy storage station. Therefore, the energy balance constraint model of the hybrid shared energy storage station and the multi-regional energy system is constructed as follows: ; In the formula, N is the number of district energy systems, is the discharge power of the hybrid shared energy storage station, Charging power for hybrid shared energy storage stations.

[0048] The charge of the hybrid shared energy storage station is stored in the hydrogen storage unit, battery and seasonal hydrogen storage unit for short-term energy storage during the day. Its charging power is equal to the sum of the charging power of the battery and the power consumption of the proton exchange membrane electrolyzer, and its discharge power is equal to the sum of the charging power of the battery and the power generation power of the proton exchange membrane fuel cell. Therefore, the internal energy balance constraint model of the hybrid shared energy storage station is: .

[0049] S2. By analyzing and processing at least one global decision-making data in the entire life cycle of the hybrid shared energy storage station, a full life cycle self-optimization model of the hybrid shared energy storage station is constructed.

[0050] Furthermore, if Figure 4 As shown, step S2 includes: S21. Collect the investment cost per unit capacity of the equipment, interest rate and equipment investment life.

[0051] S22. Calculate the investment recovery coefficient based on the collected interest rates and equipment investment years of the hybrid shared energy storage station over its entire life cycle, and combine the investment cost of the equipment rated capacity and the equipment unit capacity to obtain the total investment cost model of the hybrid shared energy storage station.

[0052] S23. Obtain an equipment operation and maintenance cost model based on the collected equipment operation and maintenance cost coefficients preset during the entire life cycle of the hybrid shared energy storage station.

[0053] S24. Based on the total investment cost model and the equipment operation and maintenance cost model, a full life cycle economic cost model of the hybrid shared energy storage station is constructed.

[0054] S25. Collect and process the economic cost data of the hybrid shared energy storage station over its entire life cycle, including the total investment cost and equipment operation and maintenance cost. Combined with the investment recovery coefficient, interest rate, equipment investment life, and the equipment capacity and unit capacity investment cost of the hybrid shared energy storage station, a total life cycle cost model of the hybrid shared energy storage station is constructed.

[0055] S26. Introduce pollutant emission equivalent factors, convert and process pollutant emission data, and obtain environmental impact values ​​that include global warming impact, acid rain impact and human respiratory impact.

[0056] In a specific embodiment, the present invention introduces the key concept of pollutant emission equivalent factors in order to accurately evaluate the various impacts of pollutant emissions on the environment. Table 1 is a pollutant emission equivalent factor table provided in an embodiment of the present invention, which lists in detail the equivalent factors corresponding to different pollutants. These factors take into account the relative contributions of pollutants to various environmental impacts such as global warming, acid rain formation, and human respiratory health.

[0057] Table 1 Pollutant emission equivalent factors provided by the embodiments of the present invention

[0058] S27. Determine the weights of the environmental impact factors based on historical data and expert experience, and construct an environmental impact model for the entire life cycle of the hybrid shared energy storage station based on the normalized environmental impact value and the weights assigned to each environmental impact factor. It should be understood that the purpose of establishing the economic cost and environmental impact model for the entire life cycle of the hybrid shared energy storage station is to comprehensively consider the environmental impact of the entire life cycle of the construction of the hybrid shared energy storage station, including the raw material acquisition, equipment production, equipment operation and equipment recycling stages, while taking into account the economic cost of the hybrid shared energy storage station and minimizing the environmental impact as much as possible.

[0059] The economic cost model of the hybrid shared energy storage station throughout its life cycle is: ; In the formula, is the total life cycle cost of the hybrid shared energy storage station, is the total investment cost of the hybrid shared energy storage station, The equipment operation and maintenance costs of the hybrid shared energy storage station. For the k Equipment investment recovery factor, is the interest rate, For the k Equipment service life, For hybrid shared energy storage station k Rated capacity of each device, For the kThe unit capacity investment cost of each device is is the corresponding equipment operation and maintenance cost coefficient, Hybrid shared energy storage station k The operating power of each device, T is the set time period, and K is the number of devices in the hybrid shared energy storage station; The environmental impact model of the hybrid shared energy storage station throughout its life cycle is: ; ; In the formula, is the environmental impact value of the hybrid shared energy storage station over its entire life cycle. , and They are the global warming impact value, acid rain impact value and human breathing impact value of the hybrid shared energy storage station over its entire life cycle. , and Carbon dioxide, sulfur dioxide and PM 2.5 The emission equivalent factor matrix is ​​a 1-row 6-column matrix. , and are the weight coefficients of global warming impact, acid rain impact and human respiration impact, , and are the normalized values ​​of global warming impact, acid rain impact and human respiration impact, respectively. For hybrid shared energy storage station k Device life cycle j The amount of emissions, For hybrid shared energy storage station k The sulfur dioxide emissions of each device during its entire life cycle, For hybrid shared energy storage station k The carbon dioxide emissions of a device over its entire life cycle, For hybrid shared energy storage station k The nitrogen oxide emissions of a device over its entire life cycle, For hybrid shared energy storage station k PM throughout the life cycle of a device 2.5 Emissions, For hybrid shared energy storage station k The carbon monoxide emissions of a device over its entire life cycle, For hybrid shared energy storage station k The methane emissions of a device over its entire life cycle, For hybrid shared energy storage station kThe emission of 6 types of emissions during the entire life cycle of each device is a 6-row 1-column matrix. , , and For hybrid shared energy storage station k The first generation of equipment in the four stages of raw material acquisition, production and manufacturing, operation and use, and retirement and recycling j The amount of emissions, For production u The first material produced by the process j The emission coefficient of the emission substance is To be the first u The first j The emission coefficient of the emission substance is The first j The pollutant emission coefficient, For the power generation efficiency of coal-fired power plants, is the transmission efficiency of the power grid, For the k The first device u The recycling rate of retired materials, For the k Device Required u The quality of the raw materials, U The number of raw materials required.

[0060] S3. In the hybrid shared energy storage architecture, based on the full life cycle self-optimization model of the hybrid shared energy storage station, at least two joint optimization sub-models containing multiple subjects are constructed, and collaborative interactive solutions are performed between the joint optimization sub-models to achieve mutual transfer and iterative calculation of parameters between sub-models until the preset end conditions are met, and the optimal solution including the charging / discharging strategy of the mobile energy storage of electric vehicles and the capacity configuration parameters of the hybrid shared energy storage station is output.

[0061] Furthermore, if Figure 5 As shown, step S3 includes: S31. In the hybrid shared energy storage architecture, based on the acquired hourly charging and discharging power of the hybrid shared energy storage station and the set charging / discharging power variables of the electric vehicle mobile energy storage device, a first sub-model of the joint optimization of the electric vehicle mobile energy storage and the hybrid shared energy storage station is constructed to coordinate the charging / discharging of the electric vehicle mobile energy storage and optimize the solution with the optimization goal of minimizing the operating power fluctuation of the hybrid shared energy storage station.

[0062] S32. In the hybrid shared energy storage architecture, according to the obtained building energy demand, renewable energy output and the hourly charging and discharging power of multi-regional electric vehicle mobile energy storage, the equipment capacity of the hybrid shared energy storage station, the power purchased by the regional energy system from the power grid and the natural gas power consumed are set as variables. The first optimization goal is to minimize the sum of the total cost of the hybrid shared energy storage station, the energy purchase cost of the regional energy system and the equipment operation and maintenance cost. The second optimization goal is to minimize the sum of the environmental impact value generated by the primary energy consumption of the regional energy system operation and the environmental impact value of the hybrid shared energy storage station over its entire life cycle. A second sub-model of the joint optimization of the regional energy system and the hybrid shared energy storage station is constructed and optimized and solved.

[0063] S33, promoting the mutual transfer of parameters and iterative calculation between the first joint optimization sub-model and the second joint optimization sub-model until a preset end condition is reached.

[0064] S34. After the iterative calculation is completed, the optimal solution including the charging / discharging strategy of the mobile energy storage of the electric vehicle and the capacity configuration parameters of the hybrid shared energy storage station is output.

[0065] In yet another embodiment, Figure 6 As shown, the optimization model of the present invention is composed of two sub-models; sub-model 1 is a collaborative optimization model of mobile energy storage of electric vehicles and hybrid shared energy storage stations, which coordinates the charging / discharging of mobile energy storage of electric vehicles, with the goal of reducing the charging / discharging power fluctuation of the hybrid shared energy storage station to the comprehensive energy system, and uses the Cplex solver to solve, obtain the optimized charging / discharging power of electric vehicles and pass it to sub-model 2; sub-model 2 is a collaborative optimization model of multi-regional energy system and hybrid shared energy storage station, which uses a multi-objective random paint optimization algorithm to reduce the environmental impact and economic cost of the system as two goals, optimizes the equipment capacity parameters of the hybrid shared energy storage station, and passes the charging / discharging power of the hybrid shared energy storage station to sub-program 1. Thus, sub-model 1 and sub-model 2 pass parameters to each other until the maximum number of iterations is reached, and the operation is terminated and the optimal solution is output, that is, the charging / discharging rate of the electric vehicle cluster and the capacity configuration parameters of the hybrid shared energy storage station.

[0066] Specifically, the multi-objective random paint optimization algorithm includes the following steps: (1) Set the initial parameters of the multi-objective random paint optimization algorithm (MOSPO), including population size, number of iterations, color coding method, etc. Determine the objective function and constraints of the optimization problem, that is, reduce the environmental impact and economic cost of the system; (2) Encode the possible solutions of the capacity parameters of the hybrid shared energy storage station equipment into "colors". Evaluate the fitness of each "color" according to the objective function, that is, its comprehensive performance on environmental impact and economic cost. Sort all "colors" according to fitness, and select the current optimal and suboptimal "colors"; (3) Based on the "colors" in the current population, use specific grouping strategies (such as clustering, random grouping, etc.) to create new color groups; (4) Within each group, generate new "colors" through complementary combinations, analogy combinations, ternary combinations, quaternary combinations, etc. These new "colors" represent new possible solutions for the capacity parameters of the hybrid shared energy storage station equipment; (5) Evaluate the fitness of the newly generated "colors" and calculate their corresponding environmental impact and economic costs. According to the evaluation results, update the optimal and suboptimal "colors" in the current population. If there is a solution in the newly generated "color" that is more adaptable than the current optimal "color", update the optimal "color". Keep a certain number of excellent "colors" as the basis for the next generation of population; (6) Check whether the maximum number of iterations or other preset termination conditions are met, and output the optimal solution. If so, stop the iteration; otherwise, return to step (2) to continue the optimization process.

[0067] Next, we introduce the multi-agent joint optimization sub-model: Sub-model 1, i.e. the joint optimization sub-model of electric vehicle mobile energy storage and hybrid shared energy storage station, has the following optimization objectives: ; ; In the formula, To jointly optimize the optimization objective of the first sub-model, the optimization content is to minimize the charging and discharging power fluctuations of the hybrid shared energy storage. Provides operating power for hybrid shared energy storage stations; Sub-model 2 includes two target models, as follows: The optimization objective 1 of the joint optimization submodel of the regional energy system and the hybrid shared energy storage station is; ; ; In the formula, To jointly optimize the first optimization objective of the second sub-model, minimize the system economic cost, , and They are the total cost of the hybrid shared energy storage station, the energy purchase cost of the regional energy system, and the equipment operation and maintenance cost. Time-of-use electricity price, is the natural gas price, is the gas turbine power generation unit operation and maintenance cost coefficient, is the operation and maintenance cost coefficient of the ground source heat pump cooling / heating unit, is the photovoltaic power generation unit operation and maintenance cost coefficient, is the wind power unit operation and maintenance cost coefficient, is the operation and maintenance cost coefficient of the absorption refrigeration unit, is the operation and maintenance cost coefficient of the gas boiler heat production unit.

[0068] The optimization objective 2 of the joint optimization sub-model of the regional energy system and the hybrid shared energy storage station is: ; ; In the formula, The second optimization objective of the second sub-model for the joint optimization of the regional energy system and the hybrid shared energy storage station is to minimize the environmental impact of the system; is the environmental impact value of the operation of the regional energy system, , and They are the impact values ​​of global warming, acid rain and human respiration caused by the operation of the regional energy system. The first phase of the regional energy system operation j The amount of emissions, The first j The emission coefficient of the emission substance is is the sulfur dioxide emission during the operation phase of the regional energy system, is the carbon dioxide emissions during the operation phase of the regional energy system, is the nitrogen oxide emissions during the operation phase of the regional energy system, For the operation phase of the regional energy system PM 2.5 Emissions, is the carbon monoxide emission during the operation phase of the regional energy system, is the methane emissions during the operation phase of the regional energy system, It is a 6-row and 1-column matrix consisting of the emissions of 6 types of emissions during the operation stage of the regional energy system.

[0069] The above two sub-models form a dynamic, closed-loop optimization process by passing key parameters to each other. This process will continue until the preset maximum number of iterations is reached. At that time, the operation will end and the global optimal solution will be output. This optimal solution covers the charging / discharging power strategy of the electric vehicle cluster and the capacity configuration parameters of the hybrid shared energy storage station.

[0070] In addition, an embodiment of the present invention provides a method for optimizing the configuration of a hybrid shared energy storage station for the entire life cycle at multiple time scales, including: an architecture construction module, which is used to establish a hybrid shared energy storage architecture including a hybrid shared energy storage station, a regional energy system, and an electric vehicle mobile energy storage device according to the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, as well as regional energy demand, and introduce the mobile energy storage characteristics of electric vehicles. A hybrid shared energy storage station self-optimization module, which is used to construct a full life cycle self-optimization model of a hybrid shared energy storage station by analyzing and processing at least one global decision-making data in the full life cycle of the hybrid shared energy storage station. A multi-agent optimization module, which is used to construct at least two joint optimization sub-models containing multiple agents in the hybrid shared energy storage architecture based on the full life cycle self-optimization model of the hybrid shared energy storage station, and to perform collaborative interactive solutions between the joint optimization sub-models to achieve mutual transmission and iterative calculation of parameters between the sub-models, until the preset end condition is reached, and the optimal solution including the charging / discharging strategy of the mobile energy storage of the electric vehicle and the capacity configuration parameters of the hybrid shared energy storage station is output.

[0071] Furthermore, an embodiment of the present invention provides a full life cycle optimization configuration device for a multi-time scale hybrid shared energy storage station, comprising: at least one database; and a memory communicatively connected to the at least one database; wherein the memory stores instructions executable by the at least one database, and the instructions are executed by the at least one database so that the at least one database can execute the full life cycle optimization configuration method for a multi-time scale hybrid shared energy storage station as described above.

[0072] At the same time, an embodiment of the present invention provides a computer-readable medium having computer executable instructions stored thereon, and when the executable instructions are executed by a processor, the above-mentioned multi-time-scale hybrid shared energy storage station full life cycle optimization configuration method is implemented.

[0073] In summary, the embodiments of the present invention provide a method, system, device and medium for optimizing the configuration of a multi-time-scale hybrid shared energy storage station throughout its life cycle. First, a hybrid shared energy storage system architecture is established, including a multi-regional energy system taking into account electric vehicles and a multi-time-scale hybrid shared energy storage station; the hybrid shared energy storage station includes a periodic energy storage unit, a seasonal energy storage unit and a hydrogen-to-electricity conversion unit; the periodic energy storage unit includes a battery and a short-term hydrogen storage tank during the day; the seasonal energy storage unit includes a seasonal hydrogen storage tank; the hydrogen-to-electricity conversion unit includes a proton exchange membrane fuel cell and a proton exchange membrane hydrogen electrolysis device; the regional energy system includes a power supply component that meets the electricity demand of buildings and electric vehicles, and a power supply component that meets the cooling load and heat load of the building. The cooling and heating components of the load; the power supply components include photovoltaic panels, wind turbines, gas turbines and electric vehicles as mobile energy storage; the heating components include gas turbine waste heat recovery devices and ground-source heat pumps; the cooling components include ground-source heat pumps and absorption chillers; the mobile energy storage equipment includes electric vehicles; secondly, establish a full life cycle economic cost and environmental impact model for the hybrid shared energy storage station; the full life cycle economic cost includes the investment cost and the equipment operation and maintenance cost throughout the life cycle; the full life cycle environmental impact includes the environmental impact of various emissions generated in the four stages of raw material acquisition, equipment production, equipment operation, and equipment recycling; the emissions considered include sulfur dioxide, carbon dioxide, nitrogen oxides, PM2.5 , carbon monoxide, and methane; environmental impacts include global warming, acid rain, and human respiration; secondly, a joint optimization model of electric vehicle mobile energy storage, regional energy system, and hybrid shared energy storage station is established and solved; the optimization model consists of two sub-models, sub-model 1 is a joint optimization model of hybrid shared energy storage station and electric vehicle mobile energy storage, and sub-model 2 is a joint optimization model of hybrid shared energy storage station and multi-regional energy system; the two sub-models transfer optimization parameters to each other until the maximum number of iterations is reached to obtain the optimal equipment capacity of the hybrid shared energy storage station; Therefore, the above-mentioned multi-time scale hybrid shared energy storage system architecture is established; combining the long-term storage characteristics of hydrogen energy storage and the short-term storage characteristics of electrochemical energy storage, a hybrid shared energy storage station model that meets the multi-time scale energy storage needs of multi-regional energy systems is established, providing energy storage services for multi-regional energy systems, and meeting the hydrogenation needs of hydrogen fuel vehicles; in order to further tap the potential of mobile energy storage for electric vehicles, mobile energy storage for electric vehicles is regarded as an auxiliary energy storage measure to reduce the energy storage pressure of hybrid shared energy storage stations and reduce the equipment configuration capacity of hybrid shared energy storage stations; mathematical models of hybrid shared energy storage stations, regional energy systems, and mobile energy storage equipment for electric vehicles are established. Secondly, the further improvement of the present invention is to establish a model of economic cost and environmental impact of the entire life cycle of a hybrid shared energy storage station, and use it as the optimization target of a hybrid shared energy storage station, comprehensively consider the environmental impact of the entire life cycle of building a hybrid shared energy storage station, including raw material acquisition, equipment production, equipment operation, and equipment recovery stages, while taking into account the economic cost of the hybrid shared energy storage station, minimizing the environmental impact as much as possible. Furthermore, a further improvement of the present invention is that the entire optimization model is divided into two sub-models for optimization. Sub-model 1 combines electric vehicle mobile energy storage and hybrid shared energy storage stations to smooth the charging / discharging power fluctuations of hybrid shared energy storage stations. Sub-model 2 combines multi-regional energy systems and hybrid shared energy storage stations to comprehensively consider the system's economic and environmental performance and optimize the equipment capacity of hybrid shared energy storage stations. The two sub-models transfer parameters and iterate with each other to obtain the optimal solution, thereby realizing the joint optimization of multiple subjects including electric vehicle mobile energy storage, multi-regional energy systems, and hybrid shared energy storage stations.

[0074] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments after knowing the basic creative concepts. Therefore, the technical solution should be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0075] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention should also include these modifications and variations.

Claims

1. A full life cycle optimization configuration method for a multi-time scale hybrid shared energy storage station, characterized in that: include: Based on the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, as well as regional energy demand, and introducing the mobile energy storage characteristics of electric vehicles, a hybrid shared energy storage architecture including hybrid shared energy storage stations, regional energy systems and electric vehicle mobile energy storage equipment is established; By analyzing and processing at least one type of global decision-making data in the entire life cycle of the hybrid shared energy storage station, a full life cycle self-optimization model of the hybrid shared energy storage station is constructed; In the hybrid shared energy storage architecture, based on the full life cycle self-optimization model of the hybrid shared energy storage station, at least two joint optimization sub-models containing multiple subjects are constructed, and collaborative interactive solutions are performed between the joint optimization sub-models to achieve mutual transfer and iterative calculation of parameters between sub-models until the preset end conditions are reached, and the optimal solution including the charging / discharging strategy of the mobile energy storage of electric vehicles and the capacity configuration parameters of the hybrid shared energy storage station is output.

2. The multi-time scale hybrid shared energy storage station full life cycle optimization configuration method according to claim 1, characterized in that: Based on the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, as well as regional energy demand, and the introduction of the mobile energy storage characteristics of electric vehicles, a hybrid shared energy storage architecture including hybrid shared energy storage stations, regional energy systems and electric vehicle mobile energy storage equipment is established, including: According to the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, a hybrid shared energy storage station including periodic energy storage units, seasonal energy storage units and hydrogen-to-electricity conversion units is determined, and a mathematical model of the hybrid shared energy storage station is constructed; According to the electricity demand, building cooling demand and heating demand of the buildings and electric vehicles in the target area, determine the regional energy system including power supply components that meet the electricity demand of the buildings and electric vehicles, cooling components that meet the cooling load of the buildings and heating components that meet the heating load of the buildings, and construct a mathematical model of the regional energy system; Introduce electric vehicles as mobile energy storage devices in regional energy systems, determine the travel characteristics of electric vehicles in different regions, the energy consumption of a single electric vehicle, the minimum power calculation model to meet the travel needs of a single electric vehicle, the single electric vehicle mobile energy storage constraint model to meet the travel needs of electric vehicles, and the mobile energy storage charge / discharge power of electric vehicles in different regions, so as to establish a mathematical model of mobile energy storage devices for electric vehicles; Construct a hybrid shared energy storage architecture by integrating and configuring mathematical models of hybrid shared energy storage stations, regional energy systems, and mobile energy storage devices for electric vehicles; In the hybrid shared energy storage architecture, an energy balance constraint model is configured to maintain the energy supply and demand balance between the hybrid shared energy storage station, the regional energy system, and the electric vehicle mobile energy storage equipment.

3. The multi-time scale hybrid shared energy storage station full life cycle optimization configuration method according to claim 1, characterized in that: By analyzing and processing at least one global decision-making data in the entire life cycle of the hybrid shared energy storage station, a full life cycle self-optimization model of the hybrid shared energy storage station is constructed, including: Collect the investment cost per unit capacity of the equipment, interest rate and equipment investment life; Based on the collected interest rates and equipment investment years of the hybrid shared energy storage station over its entire life cycle, the investment recovery coefficient is calculated, and the total investment cost model of the hybrid shared energy storage station is obtained by combining the equipment rated capacity and the investment cost per unit capacity of the equipment. According to the collected equipment operation and maintenance cost coefficients preset during the entire life cycle of the hybrid shared energy storage station, an equipment operation and maintenance cost model is obtained; Based on the total investment cost model and equipment operation and maintenance cost model, a full life cycle economic cost model of the hybrid shared energy storage station is constructed; Collect and process the economic cost data of the hybrid shared energy storage station over its entire life cycle, including the total investment cost and equipment operation and maintenance cost. Combine the investment recovery factor, interest rate, equipment investment life, and the equipment capacity and unit capacity investment cost of the hybrid shared energy storage station to build a life cycle total cost model for the hybrid shared energy storage station. Introduce pollutant emission equivalent factors, convert pollutant emission data, and obtain environmental impact values ​​that include global warming impact, acid rain impact and human respiratory impact; The weights of the environmental impact factors are determined based on historical data and expert experience, and a full life cycle environmental impact model of the hybrid shared energy storage station is constructed according to the normalized environmental impact value and the weights assigned to each environmental impact factor.

4. The multi-time scale hybrid shared energy storage station full life cycle optimization configuration method according to claim 3, characterized in that: The economic cost model of the hybrid shared energy storage station throughout its life cycle is: ; ； In the formula, is the total life cycle cost of the hybrid shared energy storage station, is the total investment cost of the hybrid shared energy storage station, The equipment operation and maintenance costs of the hybrid shared energy storage station include batteries, periodic hydrogen storage units, seasonal hydrogen storage units and hydrogen-electricity conversion units. is the interest rate, For the k Equipment service life, For hybrid shared energy storage station k Rated capacity of each device, For the k The unit capacity investment cost of each device is For hybrid shared energy storage station k Equipment operation and maintenance cost coefficient, t Represents the running time, Hybrid shared energy storage station k Devices t Running power at all times, T To set the time period, K It is the total number of hybrid shared energy storage station equipment.

5. The multi-time scale hybrid shared energy storage station full life cycle optimization configuration method according to claim 4, characterized in that: The environmental impact model of the hybrid shared energy storage station throughout its life cycle is: ； ； In the formula, is the environmental impact value of the hybrid shared energy storage station over its entire life cycle. , and They are the global warming impact value, acid rain impact value and human breathing impact value of the hybrid shared energy storage station over its entire life cycle. , and Carbon dioxide, sulfur dioxide and PM 2.5 The emission equivalent factor matrix is ​​a 1-row 6-column matrix. , and are the weight coefficients of global warming impact, acid rain impact and human respiration impact, , and are the normalized values ​​of global warming impact, acid rain impact and human respiration impact, respectively. For hybrid shared energy storage station k The emission of 6 types of emissions during the entire life cycle of each device is a 6-row 1-column matrix. For hybrid shared energy storage station k The sulfur dioxide emissions of each device during its entire life cycle, For hybrid shared energy storage station k The carbon dioxide emissions of a device over its entire life cycle, For hybrid shared energy storage station k The nitrogen oxide emissions of a device over its entire life cycle, For hybrid shared energy storage station k The entire life cycle of a device PM 2.5 Emissions, For hybrid shared energy storage station k The carbon monoxide emissions of a device over its entire life cycle, For hybrid shared energy storage station k The methane emissions of a device over its entire life cycle, For hybrid shared energy storage station k Device life cycle j The amount of emissions, , , and They are hybrid shared energy storage station k The first generation of equipment in the four stages of raw material acquisition, production and manufacturing, operation and use, and retirement and recycling j The amount of emissions.

6. The multi-time scale hybrid shared energy storage station full life cycle optimization configuration method according to claim 5, characterized in that: In the hybrid shared energy storage architecture, based on the full life cycle self-optimization model of the hybrid shared energy storage station, at least two joint optimization sub-models containing multiple agents are constructed, and collaborative interactive solutions are performed between the joint optimization sub-models to achieve mutual transfer and iterative calculation of parameters between the sub-models until the preset end conditions are reached. The output includes the charging / discharging strategy of the mobile energy storage of electric vehicles and the optimal solution of the capacity configuration parameters of the hybrid shared energy storage station, including: In the hybrid shared energy storage architecture, based on the acquired hourly charging and discharging power of the hybrid shared energy storage station and the set charging / discharging power variables of the electric vehicle mobile energy storage device, the first sub-model of the joint optimization of the electric vehicle mobile energy storage and the hybrid shared energy storage station is constructed, and the electric vehicle mobile energy storage charging / discharging is coordinated to minimize the operating power fluctuation of the hybrid shared energy storage station as the optimization goal for optimization solution; In the hybrid shared energy storage architecture, according to the obtained building energy demand, renewable energy output and the hourly charging and discharging power of multi-regional electric vehicle mobile energy storage, the equipment capacity of the hybrid shared energy storage station, the power purchased from the power grid by the regional energy system and the natural gas power consumed are set as variables. The first optimization goal is to minimize the sum of the total cost of the hybrid shared energy storage station, the energy purchase cost of the regional energy system and the equipment operation and maintenance cost. The second optimization goal is to minimize the sum of the environmental impact value generated by the primary energy consumption of the regional energy system operation and the environmental impact value of the hybrid shared energy storage station over its entire life cycle. The second sub-model of the joint optimization of the regional energy system and the hybrid shared energy storage station is constructed and optimized. Promoting the mutual transfer of parameters and iterative calculation between the first joint optimization sub-model and the second joint optimization sub-model until a preset end condition is reached; After the iterative calculation is completed, the output includes the optimal solution of the charging / discharging strategy of the electric vehicle mobile energy storage and the capacity configuration parameters of the hybrid shared energy storage station.

7. The multi-time scale hybrid shared energy storage station full life cycle optimization configuration method according to claim 6, characterized in that: The optimization objective of the first sub-model of joint optimization is: ； ； In the formula, i Indicates the serial number of the regional energy system, To jointly optimize the optimization objective of the first sub-model, the optimization content is to minimize the power fluctuation of the hybrid shared energy storage operation. N The number of district energy systems served by the hybrid shared energy storage station, To provide hybrid shared energy storage station operating power, For the i The electrical load of the regional energy system, For the i The power consumption of the ground source heat pump cooling / heating unit of a regional energy system, For the i Charging power of mobile energy storage for electric vehicles in a regional energy system, For the i Discharge power of mobile energy storage of electric vehicles in a regional energy system, For the i The power generated by the photovoltaic power generation unit of the regional energy system, For the i The power generated by wind power units in the regional energy system, For the i The power generated by the gas turbine power generation unit of the regional energy system, For the i A regional energy system consumes electrical power from the public grid; The first optimization objective of the joint optimization of the second sub-model is: ； ； In the formula, To jointly optimize the first optimization objective of the second sub-model, minimize the system economic cost, , and They are the total cost of the hybrid shared energy storage station, the energy purchase cost of the regional energy system, and the equipment operation and maintenance cost. Time-of-use electricity price, is the natural gas price, is the gas turbine power generation unit operation and maintenance cost coefficient, is the operation and maintenance cost coefficient of the ground source heat pump cooling / heating unit, is the photovoltaic power generation unit operation and maintenance cost coefficient, is the wind power unit operation and maintenance cost coefficient, is the operation and maintenance cost coefficient of the absorption refrigeration unit, is the operation and maintenance cost coefficient of the gas boiler heat generating unit, For the i The cooling capacity of the absorption cooling unit in the district energy system, For the i The heating power of the gas boiler heating unit of the district energy system, for t The natural gas input power of the gas turbine at time For gas boilers t Consume natural gas power at all times; The second optimization objective of the joint optimization second sub-model is: ； ； In the formula, is the second optimization objective of the second sub-model, and the optimization content is to minimize the environmental impact. is the environmental impact value of the operation of the regional energy system, , and They are the impact values ​​of global warming, acid rain and human respiration caused by the operation of the regional energy system. The first phase of the regional energy system operation j The amount of emissions, To consume natural gas to produce j The emission coefficient of the emission substance is To consume coal j The emission coefficient of the emission substance is is the sulfur dioxide emission during the operation phase of the regional energy system, is the carbon dioxide emissions during the operation phase of the regional energy system, is the nitrogen oxide emissions during the operation phase of the regional energy system, For the operation phase of the regional energy system PM 2.5 Emissions, is the carbon monoxide emission during the operation phase of the regional energy system, is the methane emissions during the operation phase of the regional energy system, It is a 6-row 1-column matrix consisting of the emissions of 6 types of emissions during the operation phase of the regional energy system. For the power generation efficiency of coal-fired power plants, Transmission efficiency for the public grid.

8. A multi-time scale hybrid shared energy storage station full life cycle optimization configuration system, characterized in that: include: An architecture building module is used to establish a hybrid shared energy storage architecture including a hybrid shared energy storage station, a regional energy system and an electric vehicle mobile energy storage device based on the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, as well as regional energy demand, and by introducing the mobile energy storage characteristics of electric vehicles; A hybrid shared energy storage station self-optimization module is used to construct a full life cycle self-optimization model of the hybrid shared energy storage station by analyzing and processing at least one global decision-making data in the full life cycle of the hybrid shared energy storage station; The multi-agent optimization module is used to construct at least two joint optimization sub-models containing multiple agents in the hybrid shared energy storage architecture based on the full life cycle self-optimization model of the hybrid shared energy storage station, and to perform collaborative interactive solutions between the joint optimization sub-models to achieve mutual transfer and iterative calculation of parameters between the sub-models until the preset end conditions are met, and output the optimal solution including the charging / discharging strategy of the mobile energy storage of electric vehicles and the capacity configuration parameters of the hybrid shared energy storage station.

9. A multi-time scale hybrid shared energy storage station full life cycle optimization configuration device, characterized in that: include: at least one database; and a memory in communication with at least one database; Among them, the memory stores instructions that can be executed by at least one database, and the instructions are executed by at least one database so that at least one database can execute the full life cycle optimization configuration method of a multi-time scale hybrid shared energy storage station as described in any one of claims 1-7.

10. A computer-readable medium having computer-executable instructions stored thereon, characterized in that: When the executable instructions are executed by the processor, the full life cycle optimization configuration method of a multi-time scale hybrid shared energy storage station as described in any one of claims 1 to 7 is implemented.

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