Transformer area multi-scene mobile energy storage system oriented to space-time dynamic load and application method of transformer area multi-scene mobile energy storage system

Abstract

The invention provides a zone area multi-scene mobile energy storage system and an application method for space-time dynamic loads, and belongs to the technical field of mobile energy storage, and the method comprises the steps: 1, constructing a mobile energy storage scenarized technical index system; 2, executing a multi-scene dynamic deployment optimization algorithm; and step 3, standardizing the plug-and-play interface module. A scenarized technical index system is established, and mobile energy storage requirements in source network load multiple scenes are accurately matched; an efficient and economical dynamic allocation algorithm is provided, and optimal operation of mobile energy storage in multiple scenes is achieved; a standardized interface module is researched and developed, and plug-and-play and flexible networking of mobile energy storage are achieved.

Description

Technical Field

[0001] This invention belongs to the field of mobile energy storage technology, specifically relating to a mobile energy storage system and application method for multi-scenario distribution areas oriented towards dynamic loads in time and space. Background Technology

[0002] Driven by dual carbon targets, my country's renewable energy industry has achieved large-scale development. The state has clearly proposed to "explore and promote shared energy storage models, encourage new energy power plants to configure energy storage in the form of self-construction, leasing or purchase, give full play to the sharing role of energy storage in 'one station for multiple uses', and actively support various entities to carry out application demonstrations of innovative business models such as shared energy storage and cloud energy storage," providing policy guidance for the diversified application of energy storage technology.

[0003] Taking a local power grid as an example, as of the end of September 2023, its installed capacity of wind and solar power had reached 3.23 million kilowatts, accounting for 23% of the total power supply of the entire grid; according to the development plan of five batches of new energy projects in Hebei Province, the region will add another 11.2 million kilowatts of new energy installed capacity. However, large-scale grid connection of renewable energy has also brought a series of challenges:

[0004] Intermittent power output: The output of wind and solar power is significantly affected by natural conditions, exhibiting fluctuations with stronger output during the day and weaker output at night, and more output in summer and less output in winter. This makes it more difficult to balance the power grid's source and load, and easily leads to wind and solar power curtailment, resulting in energy waste and ineffective investment.

[0005] Distribution network operation pressure: As a key link between the power grid and users, distribution transformer areas face complex impacts from dynamic loads in time and space. For example, scenarios such as agricultural irrigation (peak summer irrigation season in July-August), aquaculture (seedling period in March-May, nighttime aeration in June-September), and renewable energy back-supply (heavy photovoltaic load during midday in summer) often lead to problems such as heavy overload of distribution transformers, low voltage, and unbalanced three-phase loads, which seriously affect the reliability of power supply.

[0006] Limitations of traditional energy storage: Existing fixed energy storage has the disadvantages of fixed location and poor flexibility, and cannot dynamically respond to load demands in multiple scenarios. At the same time, energy storage systems generally have problems such as non-standard electrical interfaces, insufficient standardization of communication protocols, and difficulty in parallel operation, resulting in cumbersome equipment access procedures, low adaptability, and difficulty in achieving "plug and play".

[0007] Against this backdrop, mobile energy storage, with its advantages of "dynamic transfer and flexible scheduling," has become a key technological direction for solving multi-scenario load problems in distribution transformer areas. However, current mobile energy storage technology still suffers from three major shortcomings: First, it lacks a scenario-based indicator system tailored to the spatiotemporal dynamic load characteristics of distribution transformer areas, making it impossible to accurately match the energy storage needs of different scenarios; second, it lacks dynamic allocation and optimization algorithms for multiple scenarios, making it difficult to achieve a balance between economy and power supply reliability; and third, it suffers from low interface standardization, resulting in low system access efficiency and poor interoperability. Therefore, developing a mobile energy storage system and application method adapted to multiple scenarios in distribution transformer areas has become an urgent technical problem to be solved. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention presents a mobile energy storage system and application method for multi-scenario distribution areas oriented towards dynamic spatiotemporal loads. It establishes a scenario-based technical indicator system to accurately match the mobile energy storage needs under multiple source-grid-load scenarios; proposes an efficient and economical dynamic allocation algorithm to achieve optimal operation of mobile energy storage in multiple scenarios; and develops standardized interface modules to enable plug-and-play and flexible networking of mobile energy storage.

[0009] The present invention employs the following technical solution.

[0010] A mobile energy storage system for transformer substations in various scenarios, designed for dynamic spatiotemporal loads, includes:

[0011] Mobile energy storage vehicle, energy management platform and standardized interface module:

[0012] Mobile energy storage vehicle: It adopts a towable trailer design, is equipped with 50kW and 100kWh energy storage units, integrates charging control module, discharging control module and main control panel, and supports multiple interface outputs;

[0013] Energy management platform: includes main charging / discharging program, data monitoring program, and scheduling optimization program;

[0014] Standardized interface module: Installed between the mobile energy storage vehicle and the JP cabinet in the distribution area, it includes quick connector wires, current and temperature detection sensors and communication modules, enabling plug-and-play functionality.

[0015] An application method for a mobile energy storage system in multiple scenarios for distribution transformer areas, oriented towards spatiotemporal dynamic loads, includes:

[0016] Step 1: Construct a scenario-based technical indicator system for mobile energy storage;

[0017] Step 2: Execute the multi-scenario dynamic allocation and optimization algorithm;

[0018] Step 3: Standardize plug-and-play interface modules.

[0019] Furthermore, step 1 specifically includes:

[0020] Based on the analytic hierarchy process and the entropy weight method, and taking into account the spatiotemporal dynamic load aggregation characteristics of the distribution area, mobile energy storage indicators are extracted from the source side, grid side and load side of the distribution area to establish a multi-scenario indicator boundary system.

[0021] Furthermore, in step 1, the energy storage scenario-based technical indicator system includes:

[0022] Source-side scenario indicators: including the renewable energy consumption rate R of the distribution area. 消纳 Charging efficiency η 充 Maximum charging power P 充，max ;

[0023] Grid-side scenario indicators: including the voltage regulation range U of the distribution transformer area. 调节范围 Power response time t 响应 Fault recovery time t 恢复 ;

[0024] Load-side scenario indicators: including the continuous discharge duration T of the distribution station area 放，持续 Maximum discharge power P 放，持续 Flexibility in capacity configuration K 弹性 .

[0025] Furthermore, in step 1, the energy storage scenario-based technical indicator system also includes:

[0026] The indicator weight calculation model includes: determining the subjective weight of indicator i using a modified analytic hierarchy process. By constructing a judgment matrix, the relative importance of each indicator is calculated.

[0027] Furthermore, in step 1, a modified analytic hierarchy process (AHP) is used to determine the subjective weights. The calculation formula is:

[0028]

[0029] Among them, a ij To determine the matrix elements, n represents the total number of indicators.

[0030] Furthermore, in step 1, the energy storage scenario-based technical indicator system also includes:

[0031] The objective weight of index i is determined using the entropy weight method. Calculate the objective weight of indicator i based on the dispersion of indicator data.

[0032] Furthermore, in step 1, the objective weight of index i The calculation formula is as follows:

[0033]

[0034] Among them, e i Let x be the entropy value of index i, m be the number of samples, and x be the entropy value of index i. ij p is the i-th index value of the j-th sample. ij Let be the normalized value of index i in the j-th sample.

[0035] Furthermore, in step 1, the energy storage scenario-based technical indicator system also includes:

[0036] Calculate the combined weight w of index i i That is, the subjective weight and objective weight of index i are combined using a linear weighting method.

[0037] Furthermore, in step 1, the combined weight w of index i i The calculation formula is as follows:

[0038]

[0039] Where α is the weighting coefficient.

[0040] Furthermore, step 2 specifically includes:

[0041] Based on the multi-faceted demands of energy sources, grids, and loads, a response model for mobile energy storage systems is established, namely, a dynamic allocation algorithm that takes into account both economic efficiency and service capacity is proposed.

[0042] Furthermore, in step 2, the dynamic allocation algorithm that takes into account both economy and service capacity includes:

[0043] Objective function: The objective function has two objectives: minimizing the total lifecycle cost of the mobile energy storage system and maximizing its service capacity. The formula for the objective function is as follows:

[0044]

[0045] Where min is the min operator, max is the max operator, and C 购置 C represents the purchase cost of energy storage devices for mobile energy storage systems. 运维 For the annual operation and maintenance cost of mobile energy storage systems, C 损耗 For the charging and discharging loss cost of mobile energy storage systems, C 收益 For the revenue of mobile energy storage systems; P s,t Let Δt be the energy storage output power of the s-th scenario at time t, Δt be the time step, T be the scheduling period, and S be the total number of scenarios;

[0046] Constraints:

[0047] Power constraint, i.e., P 充，min ≤P 充，t ≤P 充，max ,P 放，min≤P 放，t ≤P 放，max ;

[0048] Capacity constraint: SOC min ≤SOC t ≤SOC max ;

[0049] SOC t Let SOC be the state of charge of the mobile energy storage system at time t. min =20%, SOC max =90%;

[0050] Scenario timing constraints: Based on the time characteristics of different scenarios, the scheduling range of energy storage within a specific time period is limited.

[0051] Furthermore, in step 3, the standardized plug-and-play interface module includes:

[0052] Two standardized modules: physical interface and information interface.

[0053] Physical interface module: includes a DC 30kW fast charging interface and an AC 380V / 220V output interface, and adopts a waterproof and anti-misplugging design;

[0054] Information interface module: Based on the IEC61850 communication protocol, it integrates a current detection module, a temperature detection module, and a communication module to realize real-time uploading and remote control of energy storage operation status.

[0055] The beneficial effects of the present invention are as follows, compared with the prior art:

[0056] Improve the stability of distribution network operation: Through the dynamic allocation of mobile energy storage, problems such as heavy overload in distribution areas, low voltage, and backfeed of new energy are effectively solved. The occurrence rate of heavy overload in distribution transformers has decreased by 88%, and the voltage qualification rate has increased to over 99.5%, thus enhancing the resilience of the distribution network.

[0057] Improve energy efficiency: The renewable energy consumption rate will increase from 85% to 98%, which can reduce the amount of wind and solar power curtailed by about 120,000 kWh per year, equivalent to saving 43.2 tons of standard coal and reducing carbon dioxide emissions by 109.8 tons, thus helping to achieve dual carbon targets.

[0058] Ensuring reliable electricity supply for people's livelihoods: In critical scenarios such as agricultural irrigation and drainage and aquaculture, the reliability of emergency power supply reaches 100%, avoiding economic losses caused by power outages (such as losses exceeding 500,000 yuan for a single power outage during the fish seedling stage), and ensuring the safety of electricity supply for people's livelihoods and industries.

[0059] Reduced total lifecycle costs: Standardized interfaces reduce device access time from 4 hours to 15 minutes, and reduce operation and maintenance costs by 20%; Dynamic allocation algorithms can increase revenue by about 80,000 yuan per year through peak-valley arbitrage and ancillary services, and reduce the total lifecycle costs of energy storage by 18%.

[0060] It has promotional and application value: The scenario-based indicator system, dynamic allocation algorithm and standardized interface of this invention can be adapted to the needs of different transformer substations in different regions of the country (such as agricultural areas in the north, fishery areas in the south, and new energy-rich areas), providing technical support for the implementation of the shared energy storage business model. Attached Figure Description

[0061] Figure 1 This is an overall structural diagram of the mobile energy storage system for multiple scenarios in the distribution area, which is oriented towards spatiotemporal dynamic loads in this invention.

[0062] Figure 2 This is a flowchart of the application method of the mobile energy storage system for multiple scenarios in the distribution area, which is oriented towards dynamic load in time and space in this invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0064] like Figure 1 As shown, the present invention provides a mobile energy storage system for multiple scenarios in a distribution area oriented towards dynamic spatiotemporal loads, comprising:

[0065] The system consists of three parts: a mobile energy storage vehicle, an energy management platform, and standardized interface modules.

[0066] Mobile energy storage vehicle: It adopts a towable trailer design and is equipped with 50kW and 100kWh energy storage units (which can be expanded to 100kW and 200kWh through modular splicing). It integrates a charging control module, a discharging control module and a main control panel, and supports multiple interface outputs.

[0067] Energy management platform: includes main charging and discharging program, data monitoring program, and scheduling optimization program, which can realize energy storage vehicle positioning, real-time charging and discharging monitoring, and dynamic allocation in multiple scenarios;

[0068] Standardized interface module: Installed between the mobile energy storage vehicle and the JP cabinet in the distribution area, it includes quick connector wires, current and temperature detection sensors and communication modules, enabling plug-and-play functionality.

[0069] like Figure 2As shown, the application method of a mobile energy storage system for multi-scenario distribution areas oriented to dynamic spatiotemporal loads according to the present invention includes:

[0070] Step 1: Construct a scenario-based technical indicator system for mobile energy storage;

[0071] In a preferred but non-limiting embodiment of the present invention, step 1 specifically includes:

[0072] Based on the Analytic Hierarchy Process (AHP) and the entropy weight method, and taking into account the spatiotemporal dynamic load aggregation characteristics of the distribution area, mobile energy storage technical indicators are extracted from the source side, grid side and load side of the distribution area to establish a multi-scenario indicator boundary system.

[0073] In a preferred but non-limiting embodiment of the present invention, in step 1, the energy storage scenario-based technical indicator system includes:

[0074] Source-side scenario indicators: including the renewable energy consumption rate R of the distribution area. 消纳 Charging efficiency η 充 Maximum charging power P 充，max It is primarily designed for scenarios involving heavy-duty photovoltaic power transmission.

[0075] Grid-side scenario indicators: including the voltage regulation range U of the distribution transformer area. 调节范围 Power response time t 响应 Fault recovery time t 恢复 It is primarily adapted for scenarios such as low voltage management and disaster relief;

[0076] Load-side scenario indicators: including the continuous discharge duration T of the distribution station area 放，持续 Maximum discharge power P 放，持续 Flexibility in capacity configuration K 弹性 It is primarily designed for applications such as agricultural irrigation and drainage, and aquaculture.

[0077] Capacity configuration flexibility K 弹性 Defined as the ratio of the maximum scalable capacity to the minimum basic capacity of a mobile energy storage system.

[0078] In a preferred but non-limiting embodiment of the present invention, in step 1, the energy storage scenario-based technical indicator system further includes:

[0079] The indicator weight calculation model includes: determining the subjective weight of indicator i using a modified analytic hierarchy process. By constructing a judgment matrix, the relative importance of each indicator is calculated.

[0080] Indicator i represents the renewable energy absorption rate R of the distribution area. 消纳 Charging efficiency η 充 Maximum charging power P 充，max Voltage adjustment range U 调节范围Power response time t 响应 Fault recovery time t 恢复 Duration of continuous discharge T 放，持续 Maximum discharge power P 放，持续 Or capacity configuration flexibility K 弹性 .

[0081] In a preferred but non-limiting embodiment of the present invention, in step 1, a modified analytic hierarchy process is used to determine the subjective weights. The calculation formula is:

[0082]

[0083] Among them, a ij To determine the importance ratio of indicator i to indicator j in the matrix, n is the total number of indicators.

[0084] a ij This represents the ratio of the importance of technical indicator i to technical indicator j at a specific target level (such as energy storage demand in a source-side scenario), a ij The values ​​must follow the AHP's universal 1-9 scaling method (internationally accepted scaling rules). The meaning of the scale and examples of scenario adaptation are shown in Table 1 below:

[0085] Table 1

[0086]

[0087]

[0088] In a preferred but non-limiting embodiment of the present invention, in step 1, the energy storage scenario-based technical indicator system further includes:

[0089] The objective weight of index i is determined using the entropy weight method. Calculate the objective weight of indicator i based on the dispersion of indicator data.

[0090] In a preferred but non-limiting embodiment of the present invention, in step 1, the objective weight of index i... The calculation formula is as follows:

[0091]

[0092] Among them, e i Let x be the entropy value of index i, m be the number of samples, and x be the entropy value of index i. ij p is the i-th index value of the j-th sample. ij Let be the normalized value of index i in the j-th sample.

[0093] In a preferred but non-limiting embodiment of the present invention, in step 1, the energy storage scenario-based technical indicator system further includes:

[0094] Calculate the combined weight w of index i i That is, the subjective weight and objective weight of index i are combined using a linear weighting method.

[0095] In a preferred but non-limiting embodiment of the present invention, in step 1, the combined weight w of index i i The calculation formula is as follows:

[0096]

[0097] Where α is the weighting coefficient (α∈[0,1]), α is adjusted according to the needs of the scenario, such as taking α=0.6 for the new energy consumption scenario, focusing on subjective needs; and taking α=0.4 for the fault rescue scenario, focusing on objective data.

[0098] Step 2: Execute the multi-scenario dynamic allocation and optimization algorithm;

[0099] In a preferred but non-limiting embodiment of the present invention, step 2 specifically includes:

[0100] Based on the multi-faceted demands of energy sources, grids, and loads, a response model for mobile energy storage systems is established, namely, a dynamic allocation algorithm that takes into account both economic efficiency and service capacity is proposed.

[0101] In a preferred but non-limiting embodiment of the present invention, the dynamic allocation algorithm taking into account both economy and service capacity in step 2 includes:

[0102] Objective function: Minimize the total lifecycle cost of the mobile energy storage system (C 总最小 ) and maximizing service capacity (S 服务最大 The objective function is a bi-objective function, and its formula is as follows:

[0103]

[0104] Where min is the min operator, max is the max operator, and C 购置 C represents the purchase cost of energy storage devices for mobile energy storage systems. 运维 For the annual operation and maintenance cost of mobile energy storage systems, C 损耗 For the charging and discharging loss cost of mobile energy storage systems, C 收益 For revenue from peak-valley arbitrage and ancillary services of mobile energy storage systems; P s,t Let Δt be the energy storage output power of the s-th scenario at time t, Δt be the time step, T be the scheduling period, and S be the total number of scenarios;

[0105] Constraints:

[0106] Power constraint, i.e., P充，min ≤P 充，t ≤P 充，max ,P 放，min ≤P 放，t ≤P 放，max ;

[0107] Capacity constraint: SOC min ≤SOC t ≤SOC max ;

[0108] SOC t Let SOC be the state of charge of the mobile energy storage system at time t. min =20%, SOC max =90%;

[0109] Scenarios and time constraints: Based on the time characteristics of different scenarios (such as agricultural drainage in July-August and fishery in March-May), the scheduling range of energy storage is limited within a specific time period.

[0110] Step 3: Standardize plug-and-play interface modules.

[0111] In a preferred but non-limiting embodiment of the present invention, in step 3, the standardized plug-and-play interface module includes:

[0112] Develop standardized plug-and-play interface modules for modular mobile energy storage systems, comprising two standardized modules: physical interface and information interface.

[0113] Physical interface module: Includes DC 30kW fast charging interface and AC 380V / 220V output interface, with waterproof and anti-misplugging design, compatible with equipment such as transformer substation JP cabinet, agricultural drainage pump, and fishery aerator;

[0114] Information interface module: Based on the IEC61850 communication protocol, it integrates a current detection module (current detection range: 0-100A), a temperature detection module (temperature detection range: -20℃~85℃) and a communication module (supports 4G / 5G / LoRa) to realize real-time uploading and remote control of energy storage operation status (voltage, current, SOC, temperature).

[0115] The core innovation of the application method of the mobile energy storage system for multi-scenario distribution areas oriented to spatiotemporal dynamic loads of the present invention is as follows:

[0116] Innovative scenario-based indicator system: For the first time, the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (UWT) are combined to establish a three-dimensional mobile energy storage technology indicator system that integrates source, grid, and load. This achieves precise matching of scenarios, indicators, and equipment, solving the problem of one-size-fits-all adaptation of traditional technologies.

[0117] Innovation in dynamic allocation algorithm: A dynamic allocation algorithm with dual objectives is proposed, which takes into account both economy and service capacity. It can pre-arrange the operation trajectory of energy storage vehicles according to the time sequence of the scenario (such as summer irrigation and seedling period), thereby improving the response efficiency of multiple scenarios.

[0118] Interface standardization innovation: Develop modular "plug-and-play" interface modules to unify physical and information interface standards, realize modular splicing and rapid access of energy storage systems, simplify equipment debugging process, and improve interoperability.

[0119] The beneficial effects of the present invention are as follows, compared with the prior art:

[0120] Improve the stability of distribution network operation: Through the dynamic allocation of mobile energy storage, problems such as heavy overload in distribution areas, low voltage, and backfeed of new energy are effectively solved. The occurrence rate of heavy overload in distribution transformers has decreased by 88%, and the voltage qualification rate has increased to over 99.5%, thus enhancing the resilience of the distribution network.

[0121] Improve energy efficiency: The renewable energy consumption rate will increase from 85% to 98%, which can reduce the amount of wind and solar power curtailed by about 120,000 kWh per year, equivalent to saving 43.2 tons of standard coal and reducing carbon dioxide emissions by 109.8 tons, thus helping to achieve dual carbon targets.

[0122] Ensuring reliable electricity supply for people's livelihoods: In critical scenarios such as agricultural irrigation and drainage and aquaculture, the reliability of emergency power supply reaches 100%, avoiding economic losses caused by power outages (such as losses exceeding 500,000 yuan for a single power outage during the fish seedling stage), and ensuring the safety of electricity supply for people's livelihoods and industries.

[0123] Reduced total lifecycle costs: Standardized interfaces reduce device access time from 4 hours to 15 minutes, and reduce operation and maintenance costs by 20%; Dynamic allocation algorithms can increase revenue by about 80,000 yuan per year through peak-valley arbitrage and ancillary services, and reduce the total lifecycle costs of energy storage by 18%.

[0124] It has promotional and application value: The scenario-based indicator system, dynamic allocation algorithm and standardized interface of this invention can be adapted to the needs of different transformer substations in different regions of the country (such as agricultural areas in the north, fishery areas in the south, and new energy-rich areas), providing technical support for the implementation of the shared energy storage business model.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention without departing from the spirit and scope of the present invention. Any modifications or equivalent substitutions should be covered within the scope of protection of the claims of the present invention.

Claims

1. A mobile energy storage system for multiple scenarios in a distribution area, oriented towards dynamic spatiotemporal loads, characterized in that, include: Mobile energy storage vehicle, energy management platform and standardized interface module: Mobile energy storage vehicle: It adopts a towable trailer design, is equipped with 50kW and 100kWh energy storage units, integrates charging control module, discharging control module and main control panel, and supports multiple interface outputs; Energy management platform: includes main charging / discharging program, data monitoring program, and scheduling optimization program; Standardized interface module: Installed between the mobile energy storage vehicle and the JP cabinet in the distribution area, it includes quick connector wires, current and temperature detection sensors and communication modules, enabling plug-and-play functionality.

2. An application method for a mobile energy storage system in multiple scenarios for distribution transformer areas oriented towards spatiotemporal dynamic loads, characterized in that, include: Step 1: Construct a scenario-based technical indicator system for mobile energy storage; Step 2: Execute the multi-scenario dynamic allocation and optimization algorithm; Step 3: Standardize plug-and-play interface modules.

3. The application method of the mobile energy storage system for multi-scenario distribution areas oriented towards spatiotemporal dynamic loads as described in claim 2, characterized in that, Step 1 specifically includes: Based on the analytic hierarchy process and the entropy weight method, and taking into account the spatiotemporal dynamic load aggregation characteristics of the distribution area, mobile energy storage indicators are extracted from the source side, grid side and load side of the distribution area to establish a multi-scenario indicator boundary system. In step 1, the energy storage scenario-based technical indicator system includes: Source-side scenario indicators: including the renewable energy consumption rate R of the distribution area. 消纳 Charging efficiency η 充 Maximum charging power P 充，max ; Grid-side scenario indicators: including the voltage regulation range U of the distribution transformer area. 调节范围 Power response time t 响应 Fault recovery time t 恢复 ; Load-side scenario indicators: including the continuous discharge duration T of the distribution station area. 放，持续 Maximum discharge power P 放，持续 Flexibility in capacity configuration K 弹性 .

4. The application method of the mobile energy storage system for multi-scenario distribution areas oriented towards spatiotemporal dynamic loads as described in claim 3, characterized in that, In step 1, the energy storage scenario-based technical indicator system also includes: The indicator weight calculation model includes: determining the subjective weight of indicator i using a modified analytic hierarchy process. By constructing a judgment matrix, the relative importance of each indicator is calculated; In step 1, the subjective weights are determined using the improved analytic hierarchy process. The calculation formula is: Among them, a ij To determine the matrix elements, n represents the total number of indicators.

5. The application method of the mobile energy storage system for multi-scenario distribution areas oriented towards spatiotemporal dynamic loads as described in claim 4, characterized in that, In step 1, the energy storage scenario-based technical indicator system also includes: The objective weight of index i is determined using the entropy weight method. Calculate the objective weight of indicator i based on the dispersion of indicator data. In step 1, the objective weight of index i The calculation formula is as follows: Among them, e i Let x be the entropy value of index i, m be the number of samples, and x be the entropy value of index i. ij p is the i-th index value of the j-th sample. ij Let be the normalized value of index i in the j-th sample.

6. The application method of the mobile energy storage system for multi-scenario distribution areas oriented towards spatiotemporal dynamic loads as described in claim 5, characterized in that, In step 1, the energy storage scenario-based technical indicator system also includes: Calculate the combined weight w of index i i That is, the subjective weight and objective weight of index i are combined using a linear weighting method.

7. The application method of the mobile energy storage system for multi-scenario distribution areas oriented towards spatiotemporal dynamic loads as described in claim 6, characterized in that, In step 1, the combined weight w of index i i The calculation formula is as follows: Where α is the weighting coefficient.

8. The application method of the mobile energy storage system for multi-scenario distribution areas oriented towards spatiotemporal dynamic loads as described in claim 7, characterized in that, Step 2 specifically includes: Based on the multi-faceted demands of energy sources, grids, and loads, a response model for mobile energy storage systems is established, namely, a dynamic allocation algorithm that takes into account both economic efficiency and service capacity is proposed.

9. The application method of the mobile energy storage system for multi-scenario distribution areas oriented towards spatiotemporal dynamic loads as described in claim 8, characterized in that, In step 2, the dynamic allocation algorithm that takes into account both economy and service capacity includes: Objective function: The objective function has two objectives: minimizing the total lifecycle cost of the mobile energy storage system and maximizing its service capacity. The formula for the objective function is as follows: Where min is the min operator, max is the max operator, and C 购置 C represents the purchase cost of energy storage devices for mobile energy storage systems. 运维 For the annual operation and maintenance cost of mobile energy storage systems, C 损耗 For the charging and discharging loss cost of mobile energy storage systems, C 收益 For the revenue of mobile energy storage systems; P s,t Let Δt be the energy storage output power of the s-th scenario at time t, where Δt is the time step, T is the scheduling period, and S is the total number of scenarios. Constraints: Power constraint, i.e., P 充，min ≤P 充，t ≤P 充，max ,P 放，min ≤P 放，t ≤P 放，max ; Capacity constraint: SOC min ≤SOC t ≤SOC max ; SOC t Let SOC be the state of charge of the mobile energy storage system at time t. min =20%, SOC max =90%; Scenario timing constraints: Based on the time characteristics of different scenarios, the scheduling range of energy storage within a specific time period is limited.

10. The application method of the mobile energy storage system for multi-scenario distribution areas oriented towards spatiotemporal dynamic loads as described in claim 9, characterized in that, In step 3, the standardized plug-and-play interface module includes: Two standardized modules: physical interface and information interface. Physical interface module: includes a DC 30kW fast charging interface and an AC 380V / 220V output interface, and adopts a waterproof and anti-misplugging design; Information interface module: Based on the IEC61850 communication protocol, it integrates a current detection module, a temperature detection module, and a communication module to realize real-time uploading and remote control of energy storage operation status.

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