Full-life-cycle Optimal Configuration Method, System, Equipment and Medium of Multi-time-scale Hybrid Shared Energy Storage Station

Abstract

The present invention relates to a full-life cycle optimal configuration method, system, device and medium for a multi-time scale hybrid shared energy storage station. The method includes: establishing a hybrid shared energy storage architecture by introducing the mobile energy storage characteristics of electric vehicles according to the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales and regional energy demands; constructing a full-life cycle self-optimization model of the hybrid shared energy storage station by analyzing and processing the global decision-making data in the full life cycle of the hybrid shared energy storage station; constructing a collaborative optimization sub-model including multiple subjects, configuring the mutual transfer and iterative calculation of parameters between sub-models, and finally outputting the optimal solution. By integrating energy storage methods at different time scales, constructing a full-life cycle self-optimization model of the hybrid shared energy storage station and realizing multi-subject collaborative optimization, the present invention can better plan and manage the energy system, and achieve the efficient utilization of energy and the sustainable development of the environment.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage optimization, and particularly to a full-life cycle optimal configuration method, system, device 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 remains a major obstacle to widespread application. To solve this problem, the development of shared energy storage and mobile energy storage of electric vehicles has gradually become a new solution. Through shared energy storage, multiple users can jointly utilize energy storage devices, thereby sharing costs and improving economy. In addition, with the continuous increase in the grid connection ratio of renewable energy, the contradiction between power supply and demand imbalance at the seasonal scale has intensified, and the development of long-duration energy storage technology to enhance the stability of the energy system is an inevitable trend.

[0003] However, in the existing technical system, most of the shared energy storage considers electrochemical energy storage at a short time scale, lacking the consideration of the interaction between long-duration energy storage and short-duration energy storage. It is very important to consider the hybrid energy storage method of long time scale and short time scale under the background of high proportion of renewable energy. In terms of the configuration optimization of shared energy storage, only the provision of energy storage services by the shared energy storage station is considered, and the energy storage potential of electric vehicle mobile energy storage has not been further explored, nor the mutual cooperation mode between the two has been studied, and they are only analyzed and processed as isolated objects. At the same time, when optimizing and evaluating the environmental performance of shared energy storage, the current research is limited to the carbon emissions generated by consuming primary energy during the operation stage, ignoring the impact of emissions generated throughout the life cycle of building a shared energy storage station, from raw material acquisition, equipment production to operation and use, on the environment. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] In view of the above-mentioned shortcomings and deficiencies of the existing technology, the present invention provides a full-life cycle optimal configuration method, system, device and medium for a multi-time scale hybrid shared energy storage station, which solves the technical problems existing in the existing energy storage technology, such as high cost, lack of combination of long-duration and short-duration energy storage, insufficient exploration of the energy storage potential of electric vehicles, incomplete environmental assessment and lack of consideration of cooperation among multiple entities.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the main technical solutions adopted by the present invention include:

[0008] In a first aspect, an embodiment of the present invention provides a full-life cycle optimal configuration method for a multi-time scale hybrid shared energy storage station, including: establishing 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, the regional energy demand, and introducing the mobile energy storage characteristics of electric vehicles; constructing a full-life cycle self-optimization model of the hybrid shared energy storage station by analyzing and processing at least one piece of 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 multi-agent joint optimization sub-models, and performing collaborative interactive solution between the respective joint optimization sub-models to realize the mutual transfer and iterative calculation of parameters between the sub-models until a preset end condition is reached, and outputting an optimal solution including the charge / discharge strategy of the electric vehicle mobile energy storage and the capacity configuration parameters of the hybrid shared energy storage station.

[0009] In a second aspect, an embodiment of the present invention provides a full-life cycle optimal configuration system for a multi-time scale hybrid shared energy storage station, 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 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, the 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 of the hybrid shared energy storage station by analyzing and processing at least one piece of global decision-making data in the full life cycle of the hybrid shared energy storage station; a multi-agent optimization module for, 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 multi-agent joint optimization sub-models, and performing collaborative interactive solution between the respective joint optimization sub-models to realize the mutual transfer and iterative calculation of parameters between the sub-models until a preset end condition is reached, and outputting an optimal solution including the charge / discharge strategy of the electric vehicle mobile energy storage and the capacity configuration parameters of the hybrid shared energy storage station.

[0010] In a third aspect, an embodiment of the present invention provides a full-life cycle optimal configuration device for a multi-time scale hybrid shared energy storage station, including: 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 optimal configuration method for the multi-time scale hybrid shared energy storage station as described above.

[0011] Fourthly, an embodiment of the present invention provides a computer-readable medium, on which computer-executable instructions are stored. 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.

[0012] (III) Beneficial effects

[0013] The beneficial effects of the present invention are as follows:

[0014] Firstly, according to the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales and regional energy demands, and introducing the mobile energy storage characteristics of electric vehicles, a hybrid shared energy storage architecture is successfully established. In this architecture, the energy storage demands at different time scales are integrated, efficient energy storage services for multi-regional energy systems are realized, the hydrogen refueling demands of hydrogen fuel vehicles are met at the same time, the mobile energy storage potential of electric vehicles is fully explored, the energy storage pressure of the hybrid shared energy storage station is effectively reduced, and its equipment configuration capacity is lowered.

[0015] Secondly, the present invention deeply analyzes the economic cost data in the full life cycle of the hybrid shared energy storage station, and comprehensively considers the environmental impacts in 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 the dynamic balance among multiple factors in different life stages of the hybrid shared energy storage station, so as to minimize the environmental impact of the hybrid shared energy storage station as much as possible while taking into account the economic cost.

[0016] 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 electric vehicle mobile energy storage device. By setting different optimization objectives and solving them, these sub-models can realize the mutual transfer and iterative calculation of parameters. This collaborative optimization method not only improves the efficiency of the overall system, but also can output the optimal solution including the charge / discharge strategies 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 terms of economy and environment, but also can achieve higher efficiency and lower cost in actual operation.

[0017] Therefore, the present invention helps to better plan and manage the energy system by integrating energy storage methods at different time scales, constructing a full-life-cycle self-optimization model, and realizing multi-agent collaborative optimization, so as to achieve the efficient utilization of energy and the sustainable development of the environment. Description of the drawings

[0018] Figure 1 It is a schematic flowchart of the method provided by the embodiment of the present invention;

[0019] Figure 2Schematic diagram of the specific process of step S1 of the method provided by the embodiment of the present invention;

[0020] Figure 3 Schematic diagram of the architecture of the multi-time-scale hybrid shared energy storage system provided by the embodiment of the present invention;

[0021] Figure 4 Schematic diagram of the specific process of step S2 of the method provided by the embodiment of the present invention;

[0022] Figure 5 Schematic diagram of the specific process of step S3 of the method provided by the embodiment of the present invention;

[0023] Figure 6 Schematic diagram of the collaborative optimization model of the electric vehicle mobile energy storage, regional energy system and hybrid shared energy storage station provided by the embodiment of the present invention. Detailed implementation manners

[0024] In order to better explain the present invention for easy understanding, the present invention will be described in detail below in conjunction with the accompanying drawings through specific implementation manners.

[0025] As Figure 1 shown, a full-life-cycle optimal configuration method for a multi-time-scale hybrid shared energy storage station proposed by the embodiment of the present invention includes: establishing 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 and the regional energy demand, and introducing the mobile energy storage characteristics of electric vehicles; constructing a full-life-cycle economic cost and environmental impact model of the hybrid shared energy storage station by analyzing the economic cost data in the full-life-cycle process of the hybrid shared energy storage station and combining environmental impact factors, so as to achieve the dynamic balance between the environmental impact factors and economic costs of the hybrid shared energy storage station as a single entity at different life stages; in the hybrid shared energy storage architecture, constructing at least two multi-agent collaborative optimization sub-models for the hybrid shared energy storage station, the regional energy system and the electric vehicle mobile energy storage device, and through setting different optimization objectives and solving, realizing the mutual transfer and iterative calculation of parameters between sub-models until the maximum number of iterations is reached, and finally outputting the optimal solution including the charge / discharge strategy of the electric vehicle cluster and the capacity configuration parameters of the hybrid shared energy storage station.

[0026] First, based on the fixed - storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, as well as regional energy demands, and by ingeniously introducing the mobile - energy - storage characteristics of electric vehicles, a hybrid shared energy - storage architecture is successfully established. In this architecture, the energy - storage demands at different time scales are integrated, realizing efficient energy - storage services for multi - regional energy systems. At the same time, it meets the hydrogen - refueling requirements of hydrogen - fuel vehicles, fully exploits the mobile - energy - storage potential of electric vehicles, effectively reduces the energy - storage pressure of the hybrid shared energy - storage station, and decreases its equipment configuration capacity. Secondly, the present invention deeply analyzes the economic - cost data in the whole - life cycle process of the hybrid shared energy - storage station, and comprehensively considers the environmental impacts in the raw - material acquisition, production, operation, and recycling stages of the hybrid shared energy - storage station, constructing a whole - life - cycle self - optimization model. This model aims to achieve the dynamic balance among multiple factors at different life stages of the hybrid shared energy - storage station, so as to minimize the environmental impact of the hybrid shared energy - storage station while taking economic costs into account. 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 electric - vehicle mobile - energy - storage equipment. By setting different optimization objectives and solving them, these sub - models can achieve the mutual transfer and iterative calculation of parameters. This collaborative - optimization method not only improves the efficiency of the overall system but also, after reaching the maximum number of iterations, outputs the optimal solution including the charge / discharge strategies of the electric - vehicle cluster and the capacity - configuration parameters of the hybrid shared energy - storage station. In this way, the system not only achieves a balance in terms of economy and environment but also realizes higher efficiency and lower costs in actual operation. Thus, through integrating energy - storage methods at different time scales, constructing a whole - life - cycle self - optimization model, and realizing multi - agent collaborative optimization, the present invention helps to better plan and manage the energy system, thereby achieving the efficient utilization of energy and the sustainable development of the environment.

[0027] To better understand the above - mentioned technical solution, the exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0028] 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:

[0029] S1. Based on the fixed - storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, as well as regional energy demands, and introducing the mobile - energy - storage characteristics of electric vehicles, establish a hybrid shared energy - storage architecture including a hybrid shared energy - storage station, a regional energy system, and electric - vehicle mobile - energy - storage equipment.

[0030] Further, as Figure 2 shown, step S1 includes:

[0031] S11. Determine a hybrid shared energy storage station including a time-periodic energy storage unit, a seasonal energy storage unit, and a hydrogen-electric conversion unit according to the fixed storage characteristics of hydrogen energy storage and electrochemical energy storage at different time scales, and construct a mathematical model of the hybrid shared energy storage station.

[0032] Here, the time-periodic energy storage unit refers to an energy storage system that can store and release energy within a 24-hour time range of a day. Such an energy storage system mainly addresses energy demand fluctuations within a short period (such as several hours to one day). Electrochemical energy storage technologies, such as lithium-ion batteries, sodium-sulfur batteries, and supercapacitors, while the seasonal energy storage unit is designed for energy storage requirements on a longer time scale (such as several weeks, months, or even across seasons). Such an energy storage unit needs to be able to store a large amount of energy and release it when needed to address seasonal energy supply-demand imbalances.

[0033] S12. Determine a regional energy system including a power supply component to meet the electricity demands of buildings and electric vehicles, a cooling supply component to meet the building cooling load, a heating supply component to meet the building heating load, and an energy storage component according to the electricity demands, building cooling demands, and heating demands of buildings and electric vehicles in the target area obtained, and construct a mathematical model of the regional energy system.

[0034] S13. Introduce electric vehicles as mobile energy storage devices in the regional energy system, determine the travel characteristics of electric vehicles in different regions, the travel energy consumption of a single electric vehicle, the calculation model for the minimum electricity required to meet the travel of a single electric vehicle, the mobile energy storage constraint model for a single electric vehicle to meet the travel demand of electric vehicles, and the charging / discharging power of mobile energy storage of electric vehicles in different regions to establish a mathematical model of the mobile energy storage device of electric vehicles.

[0035] S14. Construct a hybrid shared energy storage architecture by integrating and configuring the mathematical models of the hybrid shared energy storage station, the regional energy system, and the mobile energy storage device of electric vehicles.

[0036] S15. Configure an energy balance constraint model in the hybrid shared energy storage architecture to maintain the energy supply-demand balance among the hybrid shared energy storage station, the regional energy system, and the mobile energy storage device of electric vehicles.

[0037] In a specific embodiment, such as Figure 3As shown in the figure, the multi-time scale hybrid shared energy storage system architecture includes a hybrid shared energy storage station, a regional energy system, and mobile energy storage devices. 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-period energy storage unit, a seasonal energy storage unit, and a hydrogen-electric conversion unit. The time-period energy storage unit includes a storage battery and a time-period hydrogen storage tank. The seasonal energy storage unit includes a seasonal hydrogen storage tank. The hydrogen-electric conversion unit includes a proton exchange membrane fuel cell and a proton exchange membrane electrolytic hydrogen device. The regional energy system includes a power supply component that meets the electricity demand of buildings and electric vehicles, a cooling and heating component that meets the cooling and heating loads of buildings. The power supply component includes a photovoltaic power generation unit, a wind power generation unit, and a gas turbine power generation unit. The heating component includes a gas turbine waste heat recovery heat generation unit, a ground source heat pump, and a gas boiler. The cooling component includes a ground source heat pump and an absorption chiller. The mobile energy storage device includes an electric vehicle as a mobile energy storage device.

[0038] The hybrid shared energy storage station combines the long-term storage characteristics of hydrogen energy storage and the short-term storage characteristics of electrochemical energy storage, can meet the multi-time scale energy storage needs of multiple regional energy systems, provide energy storage services for multiple regional energy systems, and at the same time can meet the hydrogen refueling needs of hydrogen fuel vehicles. To further explore the potential of mobile energy storage of electric vehicles, the mobile energy storage of electric vehicles is regarded as an auxiliary energy storage measure to relieve the energy storage pressure of the hybrid shared energy storage station and reduce the equipment configuration capacity of the hybrid shared energy storage station. Then, a mathematical model of the hybrid shared energy storage station, the regional energy system, and the mobile energy storage device of the electric vehicle is established.

[0039] In an embodiment, the mathematical model of the hybrid shared energy storage station equipment includes:

[0040] The storage battery of the time-period energy storage unit meets the energy storage needs of the multi-regional energy system within 24 hours, and its mathematical model is:

[0041] ;

[0042] In the formula, is t the state of charge of the storage battery at time is t- the state of charge of the storage battery at time 1; represents the time step; is the charging efficiency of the storage battery, is the discharging efficiency of the storage battery; is the self-discharge rate of the storage battery; is the charging power of the storage battery, is the discharging power of the storage battery; is the minimum charging power of the storage battery, is the maximum charging power of the storage battery; is the minimum discharge power of the battery, is the maximum discharge power of the battery; is a 0-1 variable that controls the charging and discharging states of the battery and does not allow the battery to charge and discharge simultaneously; represents the rated capacity of the battery.

[0043] The time-periodic hydrogen storage unit meets the energy storage demand of the multi-region energy system within 24 hours and the hydrogen refueling load demand of hydrogen fuel vehicles. Its mathematical model is:

[0044] ;

[0045] In the formula, is t the hydrogen state of the time-periodic hydrogen storage unit at time is the hydrogen state of the time-periodic hydrogen storage unit at time t-1; is the self-hydrogen release rate of the time-periodic hydrogen storage unit; 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 for charging the time-periodic hydrogen storage unit using the electrolyzer; is the electric power consumed for charging the time-periodic hydrogen storage unit using the electrolyzer, is the power generation power of the fuel cell using the hydrogen energy in the time-periodic hydrogen storage unit; is the rated capacity of the short hydrogen storage unit; the hydrogen refueling load of the hydrogen fuel vehicle, is a 0-1 variable that characterizes the charging and discharging states of the time-periodic hydrogen storage unit; enables the time-periodic hydrogen storage unit to be in only the charging or discharging state at each moment; is the minimum value of the electric power consumed for charging the time-periodic hydrogen storage unit using the electrolyzer; is the maximum value of the electric power consumed for charging the time-periodic hydrogen storage unit using the electrolyzer; is the minimum value of the power generation power of the proton exchange membrane fuel cell using the hydrogen energy stored in the time-periodic hydrogen storage unit; is the maximum value of the power generation power of the proton exchange membrane fuel cell using the hydrogen energy stored in the time-periodic hydrogen storage unit.

[0046] The seasonal hydrogen storage unit meets the energy storage demand of the multi-region energy system on a seasonal long time scale, transferring the electric power in the season with surplus electricity to the season with scarce electricity. It has only one state of charging or discharging in each season. Its mathematical model is:

[0047] ;

[0048] In the formula, is t the hydrogen state of the seasonal hydrogen storage unit at time is t the hydrogen storage state of the seasonal hydrogen storage unit at time - 1; is the self - hydrogen - releasing rate of the seasonal hydrogen storage unit; is the electric power consumed for charging the seasonal hydrogen storage unit by using a proton - exchange membrane electrolyzer; is the power generation power of the fuel cell by using the hydrogen energy in the seasonal hydrogen storage unit; is the rated capacity of the seasonal hydrogen storage unit; is a 0 - 1 variable characterizing the hydrogen charging and discharging states of the seasonal hydrogen storage unit, such that the seasonal hydrogen storage unit is only in the hydrogen - charging or hydrogen - discharging state at each moment; is the minimum value of the electric power consumed for charging the seasonal hydrogen storage unit by using an electrolyzer; is the maximum value of the electric power consumed for charging the seasonal hydrogen storage unit by using an electrolyzer; is the minimum value of the power generation power of the proton - exchange membrane fuel cell by using the hydrogen energy stored in the seasonal hydrogen storage unit; is the maximum value of the power generation power of the proton - exchange membrane fuel cell by using the hydrogen energy stored in the seasonal hydrogen storage unit.

[0049] The hydrogen - electricity conversion unit model includes the following two forms:

[0050] The proton - exchange membrane electrolyzer is a conversion unit from electric energy to hydrogen energy, and its mathematical model is:

[0051] ;

[0052] In the formula, is the hydrogen production amount of the proton - exchange membrane electrolyzer t at time is the proton - exchange membrane electrolyzer t at time and the power consumption.

[0053] The proton - exchange membrane fuel cell is a conversion unit from hydrogen energy to electric energy, and its mathematical model is:

[0054] ;

[0055] In the formula, is the power generation power of the proton - exchange membrane fuel cell t at time is the proton - exchange membrane fuel cell t at time and the hydrogen consumption.

[0056] And, the mathematical model of the regional energy system includes:

[0057] Photovoltaic power generation unit model:

[0058] ;

[0059] In the formula, i represents the serial number of the district energy system; is the i th photovoltaic power generation unit of the district energy system at t time; is the rated capacity of the photovoltaic power generation unit; is the actual light intensity received by the photovoltaic power generation unit at t time; is the actual surface temperature of the photovoltaic power generation unit at t time; is the ambient temperature within the preset radius range of the photovoltaic power generation unit at t time. Preferably, the preset radius range is taken as 2 meters.

[0060] Wind power generation unit model:

[0061] ;

[0062] In the formula, is the actual wind speed at i th area at t time; is the power generation power of the wind power generation unit at t time; 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 power generation unit.

[0063] Gas turbine power generation unit model:

[0064] ;

[0065] In the formula, is the power generation power of the gas turbine power generation unit at i th area at t time; is the waste heat power of the gas turbine power generation unit at i th area at t time; is the power generation efficiency of the gas turbine; is t the natural gas input power of the gas turbine at

[0066] Gas turbine waste heat recovery heat production unit model:

[0067] ;

[0068] In the formula, iThe serial number representing the district energy system; is the i waste heat recovery device for the t heat production power at time and

[0069] is the heat production efficiency of the waste heat recovery device.

[0070] ;

[0071] In the formula, is the i gas boiler in the t heat production power at time is the heat production efficiency of the gas boiler; is the t natural gas consumption power of the gas boiler at time

[0072] Absorption refrigeration unit model:

[0073] ;

[0074] In the formula, i represents the serial number of the district energy system; is the i absorption refrigeration unit in the t refrigeration power at time is the t heat consumption power of the absorption refrigeration unit at time is the refrigeration coefficient of the absorption refrigeration unit.

[0075] Model of the ground source heat pump for cooling / heating production:

[0076] ;

[0077] In the formula, is the i ground source heat pump in the t power consumption at time is the t refrigeration power of the ground source heat pump at time is the t heating power of the ground source heat pump at time is the heating coefficient of the ground source heat pump, is the refrigeration coefficient of the ground source heat pump; 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.

[0078] In addition, the mathematical model of the electric vehicle mobile energy storage device includes:

[0079] Electric vehicle travel distance model for different regions:

[0080] ;

[0081] In the formula, x represents the travel distance of electric vehicles; is the probability density distribution function of the travel distance of electric vehicles in the i th regional energy system; is the mathematical expectation of the travel distance of electric vehicles in the i th regional energy system, is the standard deviation of the travel distance of electric vehicles in the i th regional energy system.

[0082] Electric vehicle arrival time model for different regions:

[0083] ;

[0084] In the formula, is the probability density function of the arrival time of electric vehicles in the i th regional energy system; and are the mathematical expectation and standard deviation of the arrival time of electric vehicles in the i th regional energy system.

[0085] Electric vehicle departure time model for different regions:

[0086] ;

[0087] In the formula, t represents time; is the probability density function of the departure time of electric vehicles in the i th regional energy system; and are the mathematical expectation and standard deviation of the departure time of electric vehicles in the i th regional energy system.

[0088] In addition, the mathematical model of the electric vehicle mobile energy storage device includes:

[0089] The travel energy consumption model of a single electric vehicle in the region is:

[0090] ;

[0091] In the formula, is the travel energy consumption of the i th electric vehicle in the m th regional energy system; is the i th regional energy system, them The travel distance of an electric vehicle; is the energy consumption per kilometer of the electric vehicle.

[0092] The minimum power calculation model to meet the travel of a single electric vehicle;

[0093] ;

[0094] In the formula, is the minimum power to meet the travel of the i th electric vehicle in the m th regional energy system; is the rated capacity of the electric vehicle battery.

[0095] The mobile energy storage model of a single electric vehicle to meet the travel demand of electric vehicles:

[0096] ;

[0097] In the formula, is the initial power of the i th electric vehicle in the m th regional energy system; is the charging power of the i th electric vehicle in the m th regional energy system at t time, is the discharging power of the i th electric vehicle in the m th regional energy system at t time; is a 0-1 variable to control the charging and discharging state of the i th electric vehicle in the m th regional energy system at t time; is the discharging efficiency of the electric vehicle, is the charging efficiency of the electric vehicle; is the maximum stored power of the electric vehicle; is the charging duration of the i th electric vehicle in the m th regional energy system, is the discharging duration of the i th electric vehicle in the m th regional energy system; is the maximum charging power of the electric vehicle, is the minimum charging power of the electric vehicle; is the maximum discharging power of the electric vehicle, is the minimum discharging power of the electric vehicle.

[0098] The charging power of the regional electric vehicle mobile energy storage is the sum of the charging powers 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 powers of each electric vehicle in the region. Thus, the charging and discharging power model of the regional electric vehicle mobile energy storage is constructed as follows;

[0099] ;

[0100] In the formula, i represents the serial number of the regional energy system; represents the serial number of the electric vehicle in the i th regional energy system, is the i th total number of electric vehicles in the regional energy system; is the charging power of the electric vehicle mobile energy storage, is the discharging power of the electric vehicle mobile energy storage.

[0101] The energy balance constraint model includes:

[0102] The internal energy supply and demand balance constraint model of the regional energy system:

[0103] ;

[0104] In the formula, , and are respectively the electric load, heat load and cold load of the i th regional energy system; and are the discharging and charging powers of the hybrid shared energy storage station to the i th regional energy system; is the power purchased from the power grid of the i th regional energy system.

[0105] Since the hybrid shared energy storage station meets the energy storage requirements of multiple regional energy systems, the discharging power of the hybrid shared energy storage station is the sum of the discharging 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. Thus, the energy balance constraint model of the hybrid shared energy storage station and multiple regional energy systems is constructed as follows:

[0106] ;

[0107] In the formula, N is the number of regional energy systems, is the discharging power of the hybrid shared energy storage station, is the charging power of the hybrid shared energy storage station.

[0108] The charging amount of the hybrid shared energy storage station is stored in the hydrogen storage unit, the battery, and the seasonal hydrogen storage unit of the short-term in-day energy storage. 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. Its discharging 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. Thus, the internal energy balance constraint model of the hybrid shared energy storage station is as follows:

[0109] 。

[0110] S2. By analyzing and processing at least one piece of global decision-making data in the whole life cycle of the hybrid shared energy storage station, a self-optimization model of the whole life cycle of the hybrid shared energy storage station is constructed.

[0111] Further, as Figure 4 shown, step S2 includes:

[0112] S21. Collect the investment cost per unit capacity of the equipment, the interest rate, and the equipment investment years.

[0113] S22. According to the collected interest rate and equipment investment years of the hybrid shared energy storage station in the whole life cycle, obtain the investment recovery coefficient. Combining the rated capacity of the equipment and the investment cost per unit capacity of the equipment, obtain the total investment cost model of the hybrid shared energy storage station.

[0114] S23. According to the preset equipment operation and maintenance cost coefficient of the hybrid shared energy storage station in the whole life cycle, obtain the equipment operation and maintenance cost model.

[0115] S24. Based on the total investment cost model and the equipment operation and maintenance cost model, construct the whole life cycle economic cost model of the hybrid shared energy storage station.

[0116] S25. Collect and process the economic cost data including the total investment cost and the equipment operation and maintenance cost of the hybrid shared energy storage station in the whole life cycle. Combining the investment recovery coefficient, the interest rate, the equipment investment years, and the equipment capacity and the investment cost per unit capacity of the hybrid shared energy storage station, construct the total life cycle cost model of the hybrid shared energy storage station.

[0117] S26. Introduce the pollutant emission equivalent factor, and perform conversion processing on the pollutant emission data to obtain the environmental impact values including the global warming impact, the acid rain impact, and the human respiration impact.

[0118] In a specific embodiment, in order to accurately evaluate the multi-faceted impact of pollutant emissions on the environment, the present invention introduces the key concept of the pollutant emission equivalent factor. Table 1 is the pollutant emission equivalent factor table provided by the embodiment of the present invention. This table details the equivalent factors corresponding to different pollutants, and these factors consider the relative contributions of pollutants to multi-faceted environmental impacts such as global warming, acid rain formation, and human respiration health.

[0119] Table 1 shows the pollutant emission equivalent factors provided by the embodiments of the present invention.

[0120]

[0121] S27. Determine the respective weights of 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 according to the normalized environmental impact values and the respective weights assigned to each environmental impact factor. It should be noted 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 building the hybrid shared energy storage station, including the raw material acquisition, equipment production, equipment operation, and equipment recycling stages, and to minimize the environmental impact as much as possible while taking into account the economic cost of the hybrid shared energy storage station.

[0122] The economic cost model for the entire life cycle of the hybrid shared energy storage station is:

[0123] ;

[0124] 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, is the equipment operation and maintenance cost of the hybrid shared energy storage station, is the k th equipment investment recovery coefficient, is the interest rate, is the k th equipment service life, is the k th equipment rated capacity of the hybrid shared energy storage station, is the k th equipment unit capacity investment cost, is the corresponding equipment operation and maintenance cost coefficient, The k th equipment operating power of the hybrid shared energy storage station, T is the set time period, and K is the number of hybrid shared energy storage station equipment;

[0125] The environmental impact model for the entire life cycle of the hybrid shared energy storage station is:

[0126] ;

[0127] ;

[0128] In the formula, is the environmental impact value for the entire life cycle of the hybrid shared energy storage station, , and They are respectively the global warming impact value, acid rain impact value, and human respiration impact value of the hybrid shared energy storage station throughout its life cycle. , and They are respectively carbon dioxide, sulfur dioxide, and PM 2.5 The emission equivalent factor matrices, all of which are 1-row and 6-column matrices. , and They are respectively the weight coefficients of the global warming impact value, acid rain impact value, and human respiration impact value. , and They are respectively the normalized values of the global warming impact value, acid rain impact value, and human respiration impact value. is the emission amount of the k th type of emission of the j th device in the entire life cycle of the hybrid shared energy storage station. is the sulfur dioxide emission amount of the k th device in the entire life cycle of the hybrid shared energy storage station. is the carbon dioxide emission amount of the k th device in the entire life cycle of the hybrid shared energy storage station. is the nitrogen oxide emission amount of the k th device in the entire life cycle of the hybrid shared energy storage station. is the PM k emission amount of the 2.5 th device in the entire life cycle of the hybrid shared energy storage station. is the carbon monoxide emission amount of the k th device in the entire life cycle of the hybrid shared energy storage station. is the methane emission amount of the k th device in the entire life cycle of the hybrid shared energy storage station. is a 6-row and 1-column matrix composed of the emission amounts of 6 types of emissions of the k th device in the entire life cycle of the hybrid shared energy storage station. , , and are the emission amounts of the k th device of the hybrid shared energy storage station in the j th type of emission generated in the four stages of raw material acquisition, production and manufacturing, operation and use, and retirement and recycling respectively. is the emission coefficient of the u th type of emission generated in the process of producing the j th type of material. is the emission coefficient of the u th type of emission generated when assembling the j th type of equipment. is the emission factor of the j th pollutant generated by coal consumption, is the power generation efficiency of a coal-fired power plant, is the power grid transmission efficiency, is the k th recycling rate of the u th material for the retirement of the th k th device, u is the mass of the U th raw material required for the

[0129] S3. In the hybrid shared energy storage architecture, based on the full-life cycle self-optimization model of the hybrid shared energy storage station, construct at least two joint optimization sub-models containing multiple agents, and perform collaborative interaction and solution among the joint optimization sub-models to achieve mutual parameter transfer and iterative calculation between the sub-models until the preset end condition is reached, and output the optimal solutions including the charging / discharging strategy of the electric vehicle mobile energy storage and the capacity configuration parameters of the hybrid shared energy storage station.

[0130] Furthermore, as Figure 5 shown, step S3 includes:

[0131] S31. In the hybrid shared energy storage architecture, according to the hourly charging and discharging power of the hybrid shared energy storage station obtained and the charging / discharging power variables of the electric vehicle mobile energy storage device set, construct the first joint optimization sub-model of the electric vehicle mobile energy storage and the hybrid shared energy storage station, and collaborate on the charging and discharging of the electric vehicle mobile energy storage, and perform optimization and solution with the goal of minimizing the power fluctuation of the hybrid shared energy storage station operation.

[0132] S32. In the hybrid shared energy storage architecture, according to the building energy demand, renewable energy output, and hourly charging and discharging power of the multi-region electric vehicle mobile energy storage obtained, set the device capacity of the hybrid shared energy storage station, the power purchase from the power grid by the regional energy system, and the natural gas consumption power as variables, with the goal of minimizing 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 as the first optimization goal, and the sum of the environmental impact value generated by the operation of the regional energy system consuming primary energy and the environmental impact value of the full-life cycle of the hybrid shared energy storage station as the second optimization goal, construct the second joint optimization sub-model of the regional energy system and the hybrid shared energy storage station and perform optimization and solution.

[0133] S33. Promote the mutual parameter transfer and iterative calculation between the first joint optimization sub-model and the second joint optimization sub-model until the preset end condition is reached.

[0134] S34. After the iterative calculation is completed, output the optimal solution including the charging / discharging strategy of the electric vehicle mobile energy storage and the capacity configuration parameters of the hybrid shared energy storage station.

[0135] In another embodiment, as Figure 6 shown, the optimization model of the present invention consists of two sub-models; Sub-model 1 is a collaborative optimization model of electric vehicle mobile energy storage and hybrid shared energy storage station, which collaborates on the charging / discharging of electric vehicle mobile energy storage, aims to reduce the charging / discharging power fluctuation of the hybrid shared energy storage station to the integrated energy system, uses the Cplex solver to solve, obtains the optimized charging / discharging power of the electric vehicle and transfers it to Sub-model 2; Sub-model 2 is a collaborative optimization model of multi-region energy system and hybrid shared energy storage station, which uses the multi-objective stochastic paint optimization algorithm to optimize the equipment capacity parameters of the hybrid shared energy storage station with the two objectives of reducing the environmental impact and economic cost of the system, and transfers the charging / discharging power of the hybrid shared energy storage station to Sub-program 1. Thus, Sub-model 1 and Sub-model 2 transfer parameters to each other until the maximum number of iterations is reached, end the operation and output the optimal solution, that is, the charging / discharging power of the electric vehicle cluster and the capacity configuration parameters of the hybrid shared energy storage station.

[0136] Specifically, the multi-objective stochastic paint optimization algorithm includes the following steps: (1) Set the initial parameters of the multi-objective stochastic paint optimization algorithm (MOSPO), including population size, number of iterations, color coding method, etc. Determine the objective function and constraint conditions of the optimization problem, that is, reduce the environmental impact and economic cost of the system; (2) Encode the possible solutions of the equipment capacity parameters of the hybrid shared energy storage station as "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 sub-optimal "colors"; (3) Based on the "colors" in the current population, use a specific grouping strategy (such as clustering, random grouping, etc.) to create new color groups; (4) Inside each group, generate new "colors" through complementary combination, analogical combination, ternary combination, quaternary combination, etc. These new "colors" represent new possible solutions of the equipment capacity parameters of the hybrid shared energy storage station; (5) Evaluate the fitness of the newly generated "colors", and calculate their corresponding environmental impact and economic cost. According to the evaluation results, update the optimal and sub-optimal "colors" in the current population. If there is a solution in the newly generated "colors" that is more suitable than the current optimal "color", update the optimal "color". Retain a certain number of excellent "colors" as the basis for the next generation of the population; (6) Check whether the maximum number of iterations or other preset termination conditions are met, and output the optimal solution. If met, stop the iteration; otherwise, return to step (2) to continue the optimization process.

[0137] Next, introduce the joint optimization sub-model of multiple agents:

[0138] Sub-model 1, that is, the joint optimization sub-model of the electric vehicle mobile energy storage and the hybrid shared energy storage station, has the following optimization objectives:

[0139] ;

[0140] ;

[0141] In the formula, is the optimization objective of the first sub-model of the joint optimization, and the optimization content is to minimize the charging and discharging power fluctuations of the hybrid shared energy storage. is the operating power of the hybrid shared energy storage station;

[0142] Sub-model 2 includes two objective models as follows:

[0143] The first optimization objective of the joint optimization sub-model of the regional energy system and the hybrid shared energy storage station is:

[0144] ;

[0145] ;

[0146] In the formula, is the first optimization objective of the second sub-model of the joint optimization, which is to minimize the system economic cost. , and 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 respectively. is the time-of-use electricity price. is the natural gas price. is the operation and maintenance cost coefficient of the gas turbine power generation unit. is the operation and maintenance cost coefficient of the ground source heat pump refrigeration / heating unit. is the operation and maintenance cost coefficient of the photovoltaic power generation unit. is the operation and maintenance cost coefficient of the wind power generation unit. 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.

[0147] The second optimization objective of the joint optimization sub-model of the regional energy system and the hybrid shared energy storage station is:

[0148] ;

[0149] ;

[0150] In the formula, is the second optimization objective of the second sub-model of the joint optimization of the regional energy system and the hybrid shared energy storage station, which is to minimize the system environmental impact. is the environmental impact value generated by the operation of the regional energy system, , and are respectively the global warming impact value, acid rain impact value and human respiration impact value generated by the operation of the regional energy system, is the emission amount of the j th emission during the operation stage of the regional energy system, is the emission coefficient of the j th emission generated by consuming natural gas, is the sulfur dioxide emission amount during the operation stage of the regional energy system, is the carbon dioxide emission amount during the operation stage of the regional energy system, is the nitrogen oxide emission amount during the operation stage of the regional energy system, is the PM 2.5 emission amount during the operation stage of the regional energy system, is the carbon monoxide emission amount during the operation stage of the regional energy system, is the methane emission amount during the operation stage of the regional energy system, is a 6×1 matrix composed of the emission amounts of 6 emissions during the operation stage of the regional energy system.

[0151] Through the mutual transfer of key parameters, the above two sub-models form a dynamic and closed-loop optimization process. 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.

[0152] In addition, the embodiment of the present invention provides a multi-time scale hybrid shared energy storage station full-life cycle optimization configuration method, including: an architecture construction module, configured 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 the regional energy demand, and introducing the mobile energy storage characteristics of electric vehicles. A hybrid shared energy storage station self-optimization module, configured 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. A multi-agent optimization module, configured to build at least two joint optimization sub-models including multiple agents in the hybrid shared energy storage architecture, and perform collaborative interaction and solution between the joint optimization sub-models to realize the mutual transfer and iterative calculation of parameters between the sub-models until the preset end condition is reached, and output the optimal solution including the charging / discharging strategy of the electric vehicle mobile energy storage and the capacity configuration parameters of the hybrid shared energy storage station.

[0153] Furthermore, an embodiment of the present invention provides an optimized configuration device for the whole life cycle of a multi-time-scale hybrid shared energy storage station, including: 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 to enable the at least one database to execute the above-mentioned method for optimizing the configuration of the whole life cycle of the multi-time-scale hybrid shared energy storage station.

[0154] Meanwhile, an embodiment of the present invention provides a computer-readable medium, on which computer-executable instructions are stored, and when the executable instructions are executed by a processor, the above-mentioned method for optimizing the configuration of the whole life cycle of the multi-time-scale hybrid shared energy storage station is realized.

[0155] In summary, embodiments of the present invention provide a method, a system, a device and a medium for optimizing the configuration of the whole life cycle of a multi-time-scale hybrid shared energy storage station. First, a hybrid shared energy storage system architecture including a multi-region energy system considering electric vehicles and a multi-time-scale hybrid shared energy storage station is established; the hybrid shared energy storage station includes a time-periodic energy storage unit, a seasonal energy storage unit and a hydrogen-electric conversion unit; the time-periodic energy storage unit includes a storage battery and a short-term hydrogen storage tank within a day; the seasonal energy storage unit includes a seasonal hydrogen storage tank; the hydrogen-electric conversion unit includes a proton exchange membrane fuel cell and a proton exchange membrane electrolytic hydrogen device; the regional energy system includes a power supply component for meeting the electricity demand of buildings and electric vehicles, and a cooling and heating component for meeting the cooling and heating loads of buildings; the power supply component includes a photovoltaic panel, a wind turbine, a gas turbine and an electric vehicle as a mobile energy storage; the heating component includes a gas turbine waste heat recovery device and a ground source heat pump; the cooling component includes a ground source heat pump and an absorption chiller; the mobile energy storage device includes an electric vehicle; secondly, a whole life cycle economic cost and environmental impact model of the hybrid shared energy storage station is established; the whole life cycle economic cost includes investment cost and equipment operation and maintenance cost during the whole life cycle; the whole life cycle environmental impact includes the environmental impacts of various emissions generated in four stages: raw material acquisition, equipment production, equipment operation and equipment recycling; the emissions considered include sulfur dioxide, carbon dioxide, nitrogen oxides, PM2.5 , carbon monoxide, methane; the environmental impacts include global warming impact, acid rain impact and human respiration impact; furthermore, a joint optimization model of the electric vehicle mobile energy storage, the regional energy system and the 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 the hybrid shared energy storage station and the electric vehicle mobile energy storage, and sub-model 2 is a joint optimization model of the hybrid shared energy storage station and the multi-region energy system; the two sub-models transfer optimization parameters to each other until the maximum number of iterations is reached, and the optimal equipment capacity of the hybrid shared energy storage station is obtained.

[0156] Thus, 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 requirements of the multi-region energy system is established to provide energy storage services for the multi-region energy system and simultaneously meet the hydrogen refueling requirements of hydrogen fuel vehicles; to further explore the potential of electric vehicle mobile energy storage, the electric vehicle mobile energy storage is regarded as an auxiliary energy storage measure to relieve the energy storage pressure of the hybrid shared energy storage station and reduce the equipment configuration capacity of the hybrid shared energy storage station; mathematical models of the hybrid shared energy storage station, the regional energy system, and the electric vehicle mobile energy storage equipment are established. Secondly, the further improvement of the present invention lies in: establishing a full-life-cycle economic cost and environmental impact model of the hybrid shared energy storage station and using it as the optimization goal of the hybrid shared energy storage station, comprehensively considering the environmental impact of the full life cycle of building the hybrid shared energy storage station, including the raw material acquisition, equipment production, equipment operation, and equipment recycling stages, and while taking into account the economic cost of the hybrid shared energy storage station, minimizing the environmental impact as much as possible. Furthermore, the further improvement of the present invention lies in: dividing the entire optimization model into two sub-models for optimization. Sub-model 1 combines the electric vehicle mobile energy storage and the hybrid shared energy storage station to suppress the charge / discharge power fluctuation of the hybrid shared energy storage station. Sub-model 2 combines the multi-region energy system and the hybrid shared energy storage station to optimize the equipment capacity of the hybrid shared energy storage station by comprehensively considering the system economic and environmental performance; the two sub-models transfer parameters to each other and iterate to obtain the optimal solution, realizing the multi-agent joint optimization of the electric vehicle mobile energy storage, the multi-region energy system, and the hybrid shared energy storage station.

[0157] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications after learning the basic creative concepts. Therefore, the technical solutions should be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0158] 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 fall within 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 global decision-making data including economic cost data and environmental impact data in the whole life cycle of the hybrid shared energy storage station, an economic cost model of the whole life cycle of the hybrid shared energy storage station is constructed based on the economic cost data, and a whole life cycle environmental impact model is constructed based on the normalized environmental impact data and weight distribution, so as to construct a full life cycle self-optimization model 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, at least two joint optimization sub-models containing multiple agents are constructed, including: according to the charging and discharging power of the hybrid shared energy storage station and the electric vehicle mobile energy storage device, with the minimization of the operating power fluctuation of the hybrid shared energy storage station as the optimization goal, a joint optimization first sub-model of the electric vehicle mobile energy storage and the hybrid shared energy storage station is constructed; According to the obtained building energy demand, renewable energy output and charging and discharging power of multi-regional electric vehicle mobile energy storage equipment, combined with 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, 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, and 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 for the joint optimization of the regional energy system and the hybrid shared energy storage station is constructed; The joint optimization sub-models are solved in a collaborative and interactive manner 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 including economic cost data and environmental impact data in the whole life cycle of the hybrid shared energy storage station, an economic cost model of the whole life cycle of the hybrid shared energy storage station is constructed based on the economic cost data, and a whole life cycle environmental impact model is constructed based on the normalized environmental impact data and weight distribution, so as to construct a full life cycle self-optimization model of the hybrid shared energy storage station, 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; 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 of the electric vehicle mobile energy storage device, the first joint optimization sub-model 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 equipment, 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. 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, No. 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, Gas boiler heat generating unit operation and maintenance cost coefficient, 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 Sulfur dioxide emissions during the operation phase of the district 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 the introduction of 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 economic cost model of the hybrid shared energy storage station based on the economic cost data by analyzing and processing at least one global decision-making data including economic cost data and environmental impact data in the full life cycle of the hybrid shared energy storage station, and to construct a full life cycle environmental impact model based on normalized environmental impact data and weight distribution, so as to construct a full life cycle self-optimization model of the hybrid shared energy storage station; A multi-agent optimization module is used to construct at least two joint optimization sub-models containing multiple agents in a hybrid shared energy storage architecture based on a full life cycle self-optimization model of a hybrid shared energy storage station, including: constructing a first joint optimization sub-model of mobile energy storage of electric vehicles and hybrid shared energy storage stations based on the charging and discharging power of the hybrid shared energy storage station and the mobile energy storage equipment of electric vehicles and taking minimizing the operating power fluctuation of the hybrid shared energy storage station as the optimization goal; According to the obtained building energy demand, renewable energy output and charging and discharging power of multi-regional electric vehicle mobile energy storage equipment, combined with 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, 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, and 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 for the joint optimization of the regional energy system and the hybrid shared energy storage station is constructed; The joint optimization sub-models are solved in a collaborative and interactive manner 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.

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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