Energy storage equipment delivery measuring and calculating method and device, computer equipment and storage medium

Abstract

The invention discloses an energy storage equipment delivery measurement and calculation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining the load detail data of an ammeter, the basic configuration parameters of to-be-delivered energy storage equipment, and the charging and discharging mode information; based on the ammeter load detail data and the basic configuration parameters, determining a pre-estimated energy storage equipment putting quantity; calculating daily income based on the estimated energy storage equipment delivery quantity, the charging and discharging mode information and a peak, flat and valley electricity price rule; based on the annual attenuation coefficient, the daily income is accumulated and calculated according to years, the accumulated income of each year is obtained, and the annual attenuation coefficient reflects the attenuation condition of the income of different years. According to the embodiment of the invention, the energy storage delivery amount is determined through the electric meter data and the equipment parameters, the daily income is calculated by combining the charging and discharging mode and the time-of-use electricity price, the full-cycle income is presented by introducing the attenuation coefficient, and the method can help to quickly output a measurement report, eliminate customer worries, solve the traditional measurement problem, and promote the energy storage business expansion.

Description

Technical Field

[0001] This application relates to the field of energy storage equipment technology, and in particular to a method, apparatus, computer equipment and storage medium for calculating the deployment of energy storage equipment. Background Technology

[0002] Currently, the rapid development of the energy storage business is creating numerous market opportunities. However, potential customers often hesitate when deciding whether to equip themselves with energy storage equipment due to a lack of intuitive and reliable benefit analysis. Meanwhile, pre-sales personnel, as the primary point of contact with customers, urgently need efficient tools to support their initial communication. In the traditional model, energy storage calculations rely on manually compiling meter data and calculating the number of devices to be deployed and their returns. This process is not only cumbersome and time-consuming, but also prone to inaccuracies due to non-standard data processing and inconsistent calculation logic. Furthermore, existing tools often overlook the impact of energy storage equipment performance degradation on long-term returns, making it difficult to comprehensively present return expectations over long-term periods such as ten years. This prevents pre-sales personnel from quickly providing customers with professional assessment reports, impacting customer confidence in the returns of energy storage equipment and hindering business development efficiency, thus obstructing the conversion of potential business opportunities. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, apparatus, computer equipment, and storage medium for calculating the deployment of energy storage devices to address the above-mentioned technical problems, thereby solving at least one of the problems existing in the prior art.

[0004] Firstly, a method for calculating the deployment of energy storage devices is provided, including: Obtain detailed load data from electricity meters, basic configuration parameters of energy storage devices to be deployed, and charging / discharging mode information; Based on the detailed load data of the electricity meters and the basic configuration parameters, the estimated number of energy storage devices to be deployed is determined. Daily revenue is calculated based on the estimated number of energy storage devices to be deployed, the charging and discharging mode information, and the peak and off-peak electricity pricing rules. Based on the annual decay coefficient, the daily earnings are accumulated over the years to obtain the cumulative earnings for each year. The annual decay coefficient reflects the decay of earnings in different years.

[0005] In one possible implementation, determining the estimated number of energy storage devices to be deployed based on the meter load details and basic configuration parameters includes: Based on the meter load details and basic configuration parameters, the transformer load power capacity, annual charging period power, and energy storage device charging power are determined. The annual charging period power is the power value at 3 / 4 of the power list obtained after sorting the power values ​​from largest to smallest during the entire year when the energy storage device is in a charging state. Based on the transformer load power capacity, annual charging period power, and charging power, the estimated number of energy storage devices to be deployed is calculated.

[0006] In one possible implementation, the calculation of daily revenue based on the estimated number of energy storage devices deployed, the charging and discharging mode information, and peak-valley electricity pricing rules includes: Based on the charging and discharging mode, each charging period and each discharging period are determined; Based on the aforementioned peak and off-peak electricity pricing rules, the electricity price for each discharge period and the electricity price for each charging period are determined. Based on the estimated number of energy storage devices to be deployed, each discharge period, and the electricity price during each discharge period, the discharge revenue is determined. Based on the estimated number of energy storage devices to be deployed, each charging period, and the electricity price during each charging period, the charging cost is determined. The daily revenue is determined based on the difference between the discharge revenue and the charging cost.

[0007] In one possible implementation, the meter load details data includes peak-period electricity consumption, the basic configuration parameters include energy storage capacity and discharge power, and the step of determining the estimated number of energy storage devices to be deployed based on the meter load details data and the basic configuration parameters includes: Based on the peak electricity consumption, the energy storage capacity, and the discharge power, the estimated number of energy storage devices to be deployed is determined.

[0008] In one possible implementation, the charge / discharge mode information includes a one-charge-one-discharge mode, and the calculation of daily revenue based on the estimated number of energy storage devices deployed, the charge / discharge mode information, and peak / valley electricity pricing rules includes: Based on the peak-valley electricity price rules and the one-charge-one-discharge mode, the electricity price for a single peak period and the electricity price for a single valley period are determined. Based on the estimated number of energy storage devices to be deployed, energy storage capacity, discharge efficiency, and electricity price during a single peak period, the revenue per discharge is calculated. Based on the estimated number of energy storage devices to be deployed, energy storage capacity, charging power, and the electricity price during a single off-peak period, the cost of a single charging session is calculated. The daily revenue is obtained based on the difference between the revenue from a single discharge and the cost of a single charge.

[0009] In one possible implementation, the charge / discharge mode information includes a multi-charge / multi-discharge mode, and the calculation of daily revenue based on the estimated number of energy storage devices deployed, the charge / discharge mode information, and peak / valley electricity pricing rules includes: Based on the peak and off-peak electricity price rules and the multi-charge and multi-discharge mode, the electricity price for each peak period and the electricity price for each off-peak period are determined. Based on the estimated number of energy storage devices deployed, energy storage capacity, discharge efficiency, peak electricity price and off-peak electricity price, the discharge revenue and charging cost for each discharge are calculated respectively. Sum all the discharge gains to obtain the total discharge gain; Sum all charging costs to get the total charging cost; The daily revenue is obtained based on the difference between the total discharge revenue and the total charging cost. In one possible implementation, obtaining the meter load details includes: Based on the decision-making needs for energy storage equipment deployment, create corresponding calculation projects; Determine the regional electricity consumption rule template corresponding to the calculated item; Import the meter load details data into the regional electricity consumption rule template, and perform integrity verification and missing data filling processing on the meter load details data according to the built-in verification rules of the electricity consumption rule template; Based on the regional electricity consumption rule template, configure the basic configuration parameters and charging / discharging mode information of the energy storage device to be deployed.

[0010] Secondly, a device for calculating the deployment of energy storage equipment is provided, comprising: The information acquisition unit is used to acquire detailed data on electricity meter load, basic configuration parameters of energy storage devices to be deployed, and charging / discharging mode information. The unit for determining the estimated number of energy storage devices to be deployed is used to determine the estimated number of energy storage devices to be deployed based on the electricity meter load details data and basic configuration parameters. The daily revenue determination unit is used to calculate daily revenue based on the estimated number of energy storage devices deployed, the charging and discharging mode information, and the peak and off-peak electricity price rules. The annual return determination unit is used to calculate the cumulative return for each year by summing the daily returns based on the annual decay coefficient. The annual decay coefficient reflects the decay of returns in different years.

[0011] Thirdly, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that the processor executes the computer-readable instructions to implement the steps of the energy storage device deployment calculation method as described above.

[0012] Fourthly, a readable storage medium is provided, which stores computer-readable instructions that, when executed by a processor, implement the steps of the energy storage device deployment calculation method described above.

[0013] The aforementioned method, apparatus, computer equipment, and storage medium for calculating the deployment of energy storage devices include the following steps: acquiring detailed electricity meter load data, basic configuration parameters of the energy storage devices to be deployed, and charging / discharging mode information; determining the estimated number of energy storage devices to be deployed based on the detailed electricity meter load data and basic configuration parameters; calculating daily revenue based on the estimated number of energy storage devices to be deployed, the charging / discharging mode information, and peak / valley electricity pricing rules; and calculating the cumulative revenue for each year by summing the daily revenue according to the annual attenuation coefficient, where the annual attenuation coefficient reflects the attenuation of revenue in different years. In this embodiment, based on detailed electricity meter load data and basic configuration parameters of the energy storage devices, the number of energy storage devices to be deployed to meet customer electricity demand is determined. Daily revenue is dynamically calculated by combining the charging / discharging mode and peak / valley electricity pricing, and an annual attenuation coefficient is introduced to dynamically present the revenue attenuation curve throughout the entire life cycle of the equipment. This allows pre-sales personnel to generate professional assessment reports containing short-term and long-term benefits within minutes during the initial communication with customers, without having to manually process complex data. It presents the investment value to customers in a data-driven and visual way, eliminating their concerns about the uncertainty of returns. It solves the problems of low efficiency, insufficient accuracy, and insufficient period coverage of traditional manual calculations, significantly shortens the customer's decision-making cycle, and promotes the expansion of the energy storage business market. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating a method for calculating the deployment of energy storage devices in one embodiment of this application. Figure 1 ; Figure 2 This is a flowchart illustrating a method for calculating the deployment of energy storage devices in one embodiment of this application. Figure 2 ; Figure 3 This is a flowchart illustrating a method for calculating the deployment of energy storage devices in one embodiment of this application. Figure 3 ; Figure 4 This is a flowchart illustrating a method for calculating the deployment of energy storage devices in one embodiment of this application. Figure 4 ; Figure 5 This is a flowchart illustrating a method for calculating the deployment of energy storage devices in one embodiment of this application. Figure 5 ; Figure 6 This is a flowchart illustrating a method for calculating the deployment of energy storage devices in one embodiment of this application. Figure 6 ; Figure 7 This is a schematic diagram of the structure of an energy storage device deployment calculation device in one embodiment of this application; Figure 8 This is a schematic diagram of a computer device according to an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] In one embodiment, such as Figure 1 As shown, a method for calculating the deployment of energy storage devices is provided, including the following steps: In step S110, the detailed load data of the electricity meter, the basic configuration parameters of the energy storage device to be deployed, and the charging and discharging mode information are obtained; The detailed load data of the electricity meter may include: daily and time period power consumption values ​​(such as annual average power during peak periods, annual average power during off-peak periods, etc.), information such as discharge and charging volume during the corresponding time period, and time markers (such as the specific time intervals of daily peak / peak / off-peak / valley periods).

[0018] The basic configuration parameters of the energy storage equipment to be deployed may include parameters such as equipment parameters, transformer capacity, energy storage capacity, discharge power, charging power, depth of discharge, and depth of charge.

[0019] The charging and discharging mode information may include single-charge-single-discharge mode, multiple-charge-multiple-discharge mode, etc. It should be noted that a single-charge-single-discharge mode refers to the energy storage device completing one charging and one discharging cycle per day. For example, charging during off-peak hours at night and discharging during peak / peak hours during the day. A multiple-charge-multiple-discharge mode refers to completing multiple charging and discharging cycles per day, such as discharging multiple times during different time periods (peak, off-peak, off-peak, etc.) during the day, combined with multiple charging times during off-peak hours. For example, 10:00-11:00 is peak discharge, 11:00-12:00 is peak discharge, 14:00-15:00 is peak discharge, 15:00-17:00 is peak discharge, 12:00-14:00 is off-peak charging, and 0:00-8:00 is off-peak charging, etc.

[0020] It should be noted that the peak, mid-peak, off-peak, and valley periods mentioned above are electricity consumption periods categorized by the power system based on factors such as the level of electricity load and the tightness of power supply at different times. Peak periods are when electricity load is highest and power supply is relatively tight; electricity prices are usually higher during this time, making it suitable for energy storage devices to discharge and save on electricity costs. Mid-peak periods are when the load is even higher and the power supply pressure is greatest; electricity prices are often the highest among all periods, and energy storage devices can achieve more significant benefits from discharging during this phase. Off-peak periods are when electricity load and power supply pressure are at a moderate level; electricity prices are between those of peak and valley periods. In this example, it is used as a charging period, utilizing relatively lower electricity prices to replenish the energy storage devices. Valley periods are when electricity load is lowest and power supply is sufficient; electricity prices are lowest, making it the ideal time to charge energy storage devices and minimize charging costs.

[0021] Optionally, users can initiate the energy storage calculation process by using a pre-installed app, WeChat official account, or mini-program's energy storage calculation service as the entry point. They can then input their energy storage deployment decision requirements. Based on these requirements, a calculation project can be created, specifying the calculation object. Next, the project's region can be selected and matched with the corresponding peak / off-peak time period template (containing localized electricity pricing rules and time period divisions). Historical detailed data from the customer's electricity meter (including electricity load and consumption during each time period) can then be imported as a basis for analyzing electricity consumption characteristics. Simultaneously, the charging / discharging mode and basic configuration parameters of the energy storage equipment can be entered. Finally, based on regional rules and the customer's actual electricity consumption data, the system simulates the revenue of the energy storage equipment under different charging / discharging modes (such as peak discharge revenue and off-peak charging costs). A visualized revenue assessment report can also be generated, allowing customers to intuitively understand the investment value and assisting them in deciding whether to purchase energy storage equipment. This achieves accurate calculation of energy storage equipment deployment and efficiently connects pre-sales communication with customer decision-making needs.

[0022] In step S120, the estimated number of energy storage devices to be deployed is determined based on the electricity meter load details and basic configuration parameters. Optionally, based on the power load changes recorded in the electricity meter load details, and combined with basic configuration parameters (such as relevant performance parameters of the energy storage system and basic power network configuration requirements), the transformer load power capacity, annual charging period power, and charging power can be obtained. Then, based on the transformer load power capacity, annual charging period power, and charging power, the actual number of energy storage devices that need to be deployed can be calculated.

[0023] In step S130, daily revenue is calculated based on the estimated number of energy storage devices to be deployed, the charging and discharging mode information, and the peak and off-peak electricity price rules. The peak-valley electricity pricing rules may include peak-period electricity prices, off-peak electricity prices, valley-period electricity prices, and calculation rules.

[0024] Optionally, first calculate the charging and discharging amounts of a single energy storage device during the corresponding time period. Then, multiply the charging amount by the electricity price during the charging period to obtain the cost, and multiply the discharging amount by the electricity price during the discharging period and the electricity rate multiplier to obtain the revenue. The daily revenue per device is the total revenue of all charging and discharging cycles for that device in a single day minus the total cost. Finally, multiply the daily revenue per device by the number of devices deployed to obtain the total daily revenue. For example: During off-peak hours (electricity price 0.3 yuan / kWh), the charging amount is 223.96 kWh, so the cost = 223.96 × 0.3 = 67.19 yuan. During peak hours (electricity price 1.5 yuan / kWh), the discharging amount is 154.8 kWh, so the revenue = 154.8 × 1.5 = 232.2 yuan. The net revenue per charging and discharging cycle is 232.2 yuan - 67.19 yuan = 165.01 yuan.

[0025] The charging amount can be obtained by dividing the energy storage capacity of the energy storage device by the charging efficiency. The discharging amount can be obtained by multiplying the energy storage capacity of the energy storage device by the discharging efficiency (or: by multiplying the discharging power, discharging duration, and discharging efficiency).

[0026] In step S140, based on the annual decay coefficient, the daily revenue is accumulated by year to obtain the cumulative revenue for each year. The annual decay coefficient reflects the decay of revenue in different years.

[0027] The annual degradation coefficient is a parameter used to quantify the degree of revenue decline of energy storage equipment as its service life increases. It reflects the proportion of revenue decline in each year compared to the baseline revenue in the first year due to the gradual decrease in performance such as energy storage capacity and charging / discharging efficiency during use, thus demonstrating the year-on-year decrease in revenue throughout the equipment's lifespan. For example, taking 10 years as an example, the annual degradation coefficient could be [1, 0.94, 0.91, 0.89, 0.87, 0.85, 0.83, 0.81, 0.79, 0.77], where coefficient 1 corresponds to the first year (no degradation), and the coefficient decreases with increasing service life in subsequent years. This annual degradation coefficient can be adjusted according to actual circumstances.

[0028] Optionally, the daily revenue of the energy storage equipment in its first year of deployment is used as a benchmark. As the equipment's performance, such as storage capacity and charging / discharging efficiency, gradually declines with age, revenue decreases year by year. The annual degradation factor is a parameter used to quantify this degree of degradation. Then, the daily revenue in the first year is multiplied by the corresponding annual degradation factor for each year to obtain the daily revenue for each year. Next, the daily revenue for each year is multiplied by the number of days in the year to obtain the cumulative revenue for each year. Finally, the cumulative revenue for all years is summed to obtain the long-term revenue. This method presents the true picture of how revenue gradually declines over the entire lifecycle of the equipment, providing customers with an accurate quantitative basis for judging the investment return cycle and assessing the long-term investment value.

[0029] It should be noted that after obtaining the cumulative annual returns and long-term returns, the payback period can also be calculated based on the investment cost, and a visual calculation report can be generated for user reference.

[0030] This application provides a method for calculating the deployment of energy storage devices, including: acquiring detailed electricity meter load data, basic configuration parameters of the energy storage devices to be deployed, and charging / discharging mode information; determining the estimated number of energy storage devices to be deployed based on the detailed electricity meter load data and basic configuration parameters; calculating daily revenue based on the estimated number of energy storage devices to be deployed, the charging / discharging mode information, and peak / valley electricity pricing rules; and calculating the cumulative revenue for each year by summing the daily revenue according to the annual attenuation coefficient, wherein the annual attenuation coefficient reflects the attenuation of revenue in different years. In this application embodiment, based on detailed electricity meter load data and basic configuration parameters of the energy storage devices, the number of energy storage devices to be deployed to meet customer electricity demand is determined. Daily revenue is dynamically calculated by combining the charging / discharging mode and peak / valley electricity pricing, and an annual attenuation coefficient is introduced to dynamically present the revenue attenuation curve throughout the entire life cycle of the equipment. This allows pre-sales personnel to generate professional assessment reports containing short-term and long-term benefits within minutes during the initial communication with customers, without having to manually process complex data. It presents the investment value to customers in a data-driven and visual way, eliminating their concerns about the uncertainty of returns. It solves the problems of low efficiency, insufficient accuracy, and insufficient period coverage of traditional manual calculations, significantly shortens the customer's decision-making cycle, and promotes the expansion of the energy storage business market.

[0031] See Figure 2 In one embodiment of this application, determining the estimated number of energy storage devices to be deployed based on the electricity meter load details and basic configuration parameters includes: In step S210, based on the meter load details and basic configuration parameters, the transformer load power capacity, annual charging period power, and charging power of the energy storage device when it is charging are determined. The annual charging period power is the power value at 3 / 4 of the power list obtained after sorting the power from largest to smallest during the entire year when the energy storage device is charging. In step S220, the estimated number of energy storage devices to be deployed is calculated based on the transformer load power capacity, the annual charging period power, and the charging power.

[0032] Optionally, based on the power load changes recorded in the electricity meter load details, and combined with basic configuration parameters (such as relevant performance parameters of the energy storage system and basic power network configuration requirements), the transformer load power capacity, annual charging period power, and charging power can be determined. The transformer load power capacity is the maximum power that the transformer can stably carry under normal load operation. The annual charging period power needs to be selected from all power data collected within the preset target time period. After sorting these data from largest to smallest to form a power list, the power value corresponding to the 3 / 4 position of the list is taken; this 3 / 4 can be dynamically configured according to actual needs. Charging power refers to the power consumed by a single energy storage device during normal charging. After determining the above core parameters, the actual number of energy storage devices to be deployed is calculated based on the transformer load power capacity, annual charging period power, and charging power.

[0033] The actual number of energy storage devices that need to be deployed can be calculated using the following formula: The actual number of energy storage devices to be deployed = (power capacity under transformer load - power during the annual charging period) / charging power; If the calculated result is a number with a decimal part, it is rounded down, discarding the decimal part and retaining only the integer part as the final number of energy storage cabinets to be deployed. For example, if the calculated result is 5.8, it will be rounded down to 5. This rounding method ensures that the final number of energy storage cabinets configured is an integer that can actually be deployed, which meets the actual scenario requirements for equipment configuration (energy storage cabinets must be deployed as complete individual units, and it is not possible to deploy a fractional number of devices).

[0034] See Figure 3 In one embodiment of this application, the calculation of daily revenue based on the estimated number of energy storage devices deployed, the charging and discharging mode information, and peak-valley electricity pricing rules includes: In step S310, based on the charge-discharge mode, each charging period and each discharging period are determined; In step S320, based on the peak and off-peak electricity price rules, the electricity price for each discharge period and the electricity price for each charging period are determined; In step S330, the discharge revenue is determined based on the estimated number of energy storage devices to be deployed, each discharge period, and the electricity price during each discharge period. In step S340, the charging cost is determined based on the estimated number of energy storage devices to be deployed, each charging period, and the electricity price during each charging period. In step S350, the daily revenue is determined based on the difference between the discharge revenue and the charging cost.

[0035] The charging period can be either a low-end period or a normal period. The discharging period can be either a peak period or a high-peak period. The charging and discharging mode can include a one-charge-one-discharge mode or a multi-charge-multi-discharge mode.

[0036] Optionally, firstly, based on this charging and discharging mode, specific charging periods (e.g., off-peak 0-8 AM, off-peak 12-2 PM) and discharging periods (e.g., peak 10-11 AM, peak 11-12 PM) can be determined. According to peak-valley electricity pricing rules, the electricity price for each charging period (e.g., off-peak 0.3 yuan / kWh, off-peak 0.8 yuan / kWh) and the electricity price for each discharging period (e.g., peak 1.2 yuan / kWh, peak 1.5 yuan / kWh) are extracted. Then, combining the actual number of deployed energy storage devices, the duration of each discharging period, and the corresponding electricity price, the discharging revenue and charging cost are obtained. Finally, by subtracting the charging cost from the discharging revenue, the daily revenue can be obtained.

[0037] It should be noted that: In the one-charge-one-discharge mode, daily revenue = discharge revenue - charging cost; In the multi-charge and multi-discharge mode, the daily revenue = (revenue from the first discharge - cost of the first charge) + (revenue from the second discharge - cost of the second charge) + (revenue from the third discharge - cost of the third charge) + ... + (revenue from the nth discharge - cost of the nth charge). The net benefit of a single charge-discharge cycle is explained using the example of the first discharge benefit minus the first charge cost: First discharge revenue - first charge cost = [(peak discharge volume during the first discharge period × peak price)] + [(peak discharge volume during the first discharge period × peak price)] - number of storage units × single unit storage capacity (e.g., 215 / 261kWh) / charging efficiency × first charge electricity price; The discharge amount for each time period can be calculated from the data of that time period in the load table to determine the actual power consumption.

[0038] If there are periods of second discharge, second charge, third discharge, and third charge, the net benefit of each charge and discharge can be calculated using the formula above.

[0039] The daily earnings calculated above are summed according to the number of days in each month to obtain the monthly earnings. The monthly earnings are then summed to obtain the annual earnings. It's important to note that when calculating earnings, the battery's full charge capacity needs to be reduced each year; that is, the charging capacity during off-peak / normal periods decreases year by year. The decrease is 6% after the first year, 3% after the second year, and 2% each year thereafter, until 75% of the charging capacity remains at the end of the 10th year. This needs to be coupled with the degradation efficiency when calculating annual earnings: Annual earnings degradation: 6% in the first year, 6% + 3% in the second year, 6% + 3% + 2% in the third year, and so on.

[0040] Based on the calculated annual returns, the payback period can be further calculated. Starting from the annual returns of the first year, the returns are accumulated until the Nth year >= the investment cost. Then, (investment cost - (year 1 + year 2 ... + year N-1)) >= (year N return / 12) × M. The minimum value of (year N return / 12) × M is taken.

[0041] The investment return period is calculated as ((N-1)×12+M) / 12.

[0042] Investment cost = equipment cost + construction cost.

[0043] The following is a specific example: Assume that region A uses a charge-discharge cycle; import the load details for the entire year for asset number 08001SG00000002000024169, transformer capacity: 1880kW. Energy storage equipment configuration: Storage capacity 215kWh / unit, discharge efficiency 90%, charging efficiency 96%, depth of discharge 80%, charging power 100kW, electricity tariff multiplier 15000 yuan / MWh, electricity price configuration: peak period electricity price 1.7 yuan, off-peak period electricity price 1.2 yuan, normal period electricity price 0.9 yuan, and valley period electricity price 0.3 yuan. Charging and discharging mode configuration: peak period discharge from 10:00 to 11:00, peak period discharge from 11:00 to 13:00, and valley period charging from 0:00 to 8:00. Revenue unit: yuan.

[0044] The calculation process for the allocation of storage quantities is as follows: Assuming that according to the load table, the value at the 3 / 4 mark of the annual charging power (sorted from largest to smallest) is 800kW. Storage capacity = (1880kW - 800kW) / 100kW = 10 units Daily earnings calculation: Assuming the actual peak discharge during the first discharge period on a certain day (calculated by summing the power during that period from the load table) is 548 kWh, and the peak discharge during the first discharge period (calculated by summing the power during that period from the load table) is 1000 kWh, then the revenue for that day = (548 × 1.7 + 1000 × 1.2) - 10 × 215 / 0.96 × 0.3 = 1888.13.

[0045] Assuming the daily revenue is the same throughout the year (1000×1.7+1200×1.2)-10×215 / 0.96×0.3=2468.13 Monthly income: Month 1: 2468.13 × 31 = 76512.03; February: 2468.13 × 28 = 69107.64; March: 2468.13 × 31 = 76512.03; April: 2468.13 × 30 = 74043.9; May: 2468.13 × 31 = 76512.03; June: 2468.13 × 30 = 74043.9; July: 2468.13 × 31 = 76512.03; August: 2468.13 × 31 = 76512.03; September: 2468.13 × 30 = 74043.9; October: 2468.13 × 31 = 76512.03; November: 2468.13 × 30 = 74043.9; December: 2468.13 × 31 = 76512.03; Annual return calculation: Therefore, the annual income is the sum of the monthly figures above = 900867.45; Annual return calculated after depreciation: First year's return: 900867.4 × (1-6%) = 846815.40; Second year's return: 900867.45 × (1 - 6% - 3%) = 819789.38; Third-year return: 900867.45 × (1 - 6% - 3% - 2%) = 801772.03; Fourth-year return: 900867.45 × (1 - 6% - 3% - 2% - 2%) = 783754.68; Fifth-year return: 900867.45 × (1 - 6% - 3% - 2% - 2% - 2%) = 765737.33; Year 6 earnings: 900867.45 × (1 - 6% - 3% - 2% - 2% - 2% - 2%) = 747719.98; Seventh-year return: 900867.45 × (1 - 6% - 3% - 2% - 2% - 2% - 2%) = 729702.63; Eighth year return: 900867.45 × (1 - 6% - 3% - 2% - 2% - 2% - 2% - 2%) = 711685.29; Ninth year return: 900867.45 × (1 - 6% - 3% - 2% - 2% - 2% - 2% - 2% - 2%) = 693667.94; Tenth-year return: 900867.45 × (1 - 6% - 3% - 2% - 2% - 2% - 2% - 2% - 2% - 2%) = 675650.59.

[0046] Assuming the cost of each piece of equipment is 200,000, and the construction cost per unit is 50,000; Based on the calculated cost of 10 pieces of equipment, the cumulative profit over 10 years is 7,576,295.25 yuan, with a payback period of 3.1 years.

[0047] See Figure 4 In one embodiment of this application, the meter load details data includes peak-period electricity consumption, and the basic configuration parameters include energy storage capacity and discharge power. The step of determining the estimated number of energy storage devices to be deployed based on the meter load details data and the basic configuration parameters includes: In step S410, the estimated number of energy storage devices to be deployed is determined based on the peak electricity consumption, the energy storage capacity, and the discharge power.

[0048] Peak-period electricity consumption refers to the total electricity consumption of a user's power system during peak periods (such as peak electricity demand periods or periods with higher electricity prices). It can be obtained by extracting the corresponding peak-period electricity consumption data from the meter load details.

[0049] For example, the energy storage capacity of the energy storage device can be 215kWh or 261kWh, and the discharge efficiency can be 95%. Then, the estimated number of energy storage devices to be deployed = peak electricity consumption / 2 / (215 × 95%).

[0050] Where 2 can be the number of discharges.

[0051] See Figure 5 In one embodiment of this application, the charging and discharging mode information includes a one-charge-one-discharge mode. The calculation of daily revenue based on the estimated number of energy storage devices deployed, the charging and discharging mode information, and peak / valley electricity pricing rules includes: In step S510, based on the peak-valley electricity price rules and the one-charge-one-discharge mode, the single peak period electricity price and the single valley period electricity price are determined. In step S520, the revenue per discharge is calculated based on the estimated number of energy storage devices to be deployed, energy storage capacity, discharge efficiency, and electricity price during a single peak period. In step S530, the cost of a single charge is calculated based on the estimated number of energy storage devices to be deployed, the energy storage capacity, the charging power, and the electricity price during a single off-peak period. In step S540, the daily revenue is obtained based on the difference between the single discharge revenue and the single charge cost.

[0052] The "one charge, one discharge" mode refers to the energy storage device completing one charging and one discharging cycle per day. For example, charging during off-peak hours at night and discharging during peak / peak hours during the day.

[0053] Optionally, based on the actual number of energy storage devices deployed, combined with the storage capacity, discharge power, and peak-hour electricity price of each device, the revenue that can be obtained through discharge (i.e., discharge electricity price-related revenue) is calculated; then, based on the estimated number of energy storage devices deployed, combined with the storage capacity, charging power, and off-peak-hour electricity price of each device, the cost incurred during the charging process (i.e., charging electricity price-related expenditure) is calculated; finally, the daily revenue is the difference between the revenue from discharge and the cost of charging. That is: Daily revenue = Discharge revenue - Charging cost.

[0054] If the energy storage capacity of the energy storage device is 215kWh or 261kWh, and the discharge efficiency is 95%, then: Discharge revenue = [Estimated number of energy storage devices deployed × 215 (or 261) × Discharge efficiency 95%] × Peak electricity price; Charging cost = [Estimated number of energy storage devices deployed × 215 (or 261) / Charging efficiency 90%] × Peak season electricity price.

[0055] See Figure 6 In one embodiment of this application, the charging and discharging mode information includes a multi-charging and multi-discharging mode. The calculation of daily revenue based on the estimated number of energy storage devices deployed, the charging and discharging mode information, and peak / valley electricity pricing rules includes: In step S610, based on the peak and off-peak electricity price rules and the multi-charge and multi-discharge mode, the electricity price for each peak period and the electricity price for each off-peak period are determined. In step S620, based on the estimated number of energy storage devices deployed, energy storage capacity, discharge efficiency, peak electricity price and off-peak electricity price, the discharge revenue and charging cost for each discharge are calculated respectively. In step S630, all discharge gains are summed to obtain the total discharge gain; In step S640, all charging costs are summed to obtain the total charging cost; In step S650, the daily revenue is obtained based on the difference between the total discharge revenue and the total charging cost.

[0056] Among them, the multiple charge and discharge mode refers to completing multiple charging and discharging cycles within a day, such as discharging multiple times during different time periods of the day (peak, flat, valley, etc.) and charging multiple times during valley periods. For example, 10:00 to 11:00 is peak discharge, 11:00 to 12:00 is peak discharge, 14:00 to 15:00 is peak discharge, 15:00 to 17:00 is peak discharge, 12:00 to 14:00 is flat charging, and 0:00 to 8:00 is valley charging, etc.

[0057] Optionally, by estimating the number of energy storage devices deployed, the storage capacity and discharge power of a single device, and the electricity prices during different periods such as peak and off-peak hours, the revenue corresponding to each discharge operation and the cost corresponding to each charging operation are calculated separately. Then, the discharge revenue of all charge-discharge cycles within a day is added together, and the sum is the discharge revenue of the multi-charge-multi-discharge mode. At the same time, the charging cost of all charge-discharge cycles within a day is added together, and the sum is the charging cost of the multi-charge-multi-discharge mode. Subtracting the discharge revenue from the charging cost yields the daily revenue. This fully utilizes the differences in electricity prices at different times, increasing the profit margin through multiple low-charge and high-discharge cycles. For example, charging twice during off-peak hours and discharging twice during peak hours within a day requires calculating the net revenue of each charge-discharge cycle separately and then summing them up, allowing for more flexible adaptation to complex electricity price structures to maximize daily profitability.

[0058] For example, taking two charges and two discharges as an example, the daily earnings are as follows: Daily revenue = (revenue from the first discharge + revenue from the second discharge) - (cost of the first charge - cost of the second charge); Discharge revenue = [Estimated number of energy storage devices deployed × 215 (or 261) × Discharge efficiency 95%] × Peak electricity price; Charging cost = [Estimated number of energy storage devices deployed × 215 (or 261) / Charging efficiency 90%] × Peak season electricity price.

[0059] If the energy storage capacity of the energy storage device is 215kWh or 261kWh, and the discharge efficiency is 95%, then: Discharge revenue = estimated number of energy storage devices deployed × 215 (or 261) × discharge efficiency 95% × peak electricity price; Charging cost = [Estimated number of energy storage devices deployed × 215 (or 261) / Charging efficiency 90%] × Peak season electricity price.

[0060] After calculating the daily revenue, multiply the daily revenue by the number of effective operating days in a year (e.g., 365 or 330 days) to obtain the annual revenue, i.e.: Annual Revenue = Daily Revenue × Number of Days Therefore: 10-year return = sum of 10-year returns, where annual return = daily return × 330 days × annual decay factor; The annual decay coefficient can be -6% in the first year, -3% in the second year, and -2% every year thereafter.

[0061] For example, if the peak electricity price is 1.7 yuan, the off-peak electricity price is 1.2 yuan, the normal electricity price is 0.9 yuan, and the valley electricity price is 0.3 yuan, the input cost is 2,200,000 yuan, the peak discharge capacity is 4085 kWh, the selected charging and discharging scheme is one charge and one discharge, and the energy storage capacity is 215 kWh, then... The estimated number of energy storage devices to be deployed = 4085 / 2 / (215×95%) = 10 units. Taking 10 years as an example, the annual returns can be seen as follows: Daily profit = 10 × 215 × 95% × 1.2 - (10 × 215 / 90%) × 0.3 = 1734.34; First year's profit: 1734.34 × 330 × (1 - 6%) = 537992.27; Second year income: 1734.34 × 330 × (1 - 6% - 3%) = 520822.30; Third-year return: 1734.34 × 330 × (1 - 6% - 3% - 2%) = 509375.66; Fourth-year return: 1734.34 × 330 × (1 - 6% - 3% - 2% - 2%) = 497929.02; Fifth-year return: 1734.34 × 330 × (1 - 6% - 3% - 2% - 2% - 2%) = 486482.38; Year 6 profit: 1734.34 × 330 × (1 - 6% - 3% - 2% - 2% - 2%) = 475035.74; Seventh-year return: 1734.34 × 330 × (1 - 6% - 3% - 2% - 2% - 2% - 2%) = 463589.1; Eighth year return: 1734.34 × 330 × (1 - 6% - 3% - 2% - 2% - 2% - 2% - 2%) = 452142.46; Year 9 profit: 1734.34 × 330 × (1 - 6% - 3% - 2% - 2% - 2% - 2% - 2% - 2%) = 440695.82; Tenth-year return: 1734.34 × 330 × (1 - 6% - 3% - 2% - 2% - 2% - 2% - 2% - 2% - 2%) = 429249.18; The results show that the cumulative return over 10 years is 4,813,313.93, and the payback period is 4.3 years.

[0062] In this embodiment, based on detailed electricity meter load data and basic configuration parameters of energy storage devices, the number of energy storage devices to be deployed to meet customer electricity demand is determined. Daily revenue is dynamically calculated by combining charging / discharging modes and peak / off-peak electricity prices. An annual attenuation coefficient is introduced to dynamically present the revenue attenuation curve throughout the device's lifecycle. This allows pre-sales personnel to generate professional assessment reports containing short-term and long-term returns within minutes during the initial communication with customers, without manually processing complex data. The reports present investment value to customers in a data-driven and visual manner, eliminating concerns about revenue uncertainty. This solves the problems of low efficiency, insufficient accuracy, and inadequate period coverage in traditional manual calculations, significantly shortening the customer's decision-making cycle and promoting the expansion of the energy storage business market.

[0063] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0064] In one embodiment, an energy storage device deployment calculation device is provided, which corresponds one-to-one with the energy storage device deployment calculation method in the above embodiments. For example... Figure 7 As shown, the energy storage equipment deployment calculation device includes an information acquisition unit 10, an estimated energy storage equipment deployment quantity determination unit 20, a daily revenue determination unit 30, and an annual revenue determination unit 40. Detailed descriptions of each functional module are as follows: Information acquisition unit 10 is used to acquire detailed load data of electricity meters, basic configuration parameters of energy storage devices to be deployed, and charging and discharging mode information; The estimated number of energy storage devices to be deployed is determined by the estimated number of energy storage devices to be deployed based on the electricity meter load details and basic configuration parameters. The daily revenue determination unit 30 is used to calculate the daily revenue based on the estimated number of energy storage devices deployed, the charging and discharging mode information, and the peak and off-peak electricity price rules. The annual revenue determination unit 40 is used to calculate the cumulative revenue for each year by summing the daily revenue based on the annual decay coefficient. The annual decay coefficient reflects the decay of revenue in different years.

[0065] In one embodiment of this application, the unit 20 for estimating the number of energy storage devices to be deployed is further configured to: Based on the meter load details and basic configuration parameters, the transformer load power capacity, annual charging period power, and energy storage device charging power are determined. The annual charging period power is the power value at 3 / 4 of the power list obtained after sorting the power values ​​from largest to smallest during the entire year when the energy storage device is in a charging state. Based on the transformer load power capacity, annual charging period power, and charging power, the estimated number of energy storage devices to be deployed is calculated.

[0066] In one embodiment of this application, the daily revenue determination unit 30 is further configured to: Based on the charging and discharging mode, each charging period and each discharging period are determined; Based on the aforementioned peak and off-peak electricity pricing rules, the electricity price for each discharge period and the electricity price for each charging period are determined. Based on the estimated number of energy storage devices to be deployed, each discharge period, and the electricity price during each discharge period, the discharge revenue is determined. Based on the estimated number of energy storage devices to be deployed, each charging period, and the electricity price during each charging period, the charging cost is determined. The daily revenue is determined based on the difference between the discharge revenue and the charging cost.

[0067] In one embodiment of this application, the electricity meter load details data includes peak-period electricity consumption, the basic configuration parameters include energy storage capacity and discharge power, and the unit 20 for estimating the number of energy storage devices to be deployed is further used for: Based on the peak electricity consumption, the energy storage capacity, and the discharge power, the estimated number of energy storage devices to be deployed is determined.

[0068] In one embodiment of this application, the charge / discharge mode information includes a charge / discharge mode, and the daily revenue determination unit 30 is further used for: Based on the aforementioned peak and off-peak electricity pricing rules and the one-charge-one-discharge model, the peak period electricity price and the off-peak period electricity price are determined. Based on the estimated number of energy storage devices to be deployed, energy storage capacity, discharge efficiency, and peak electricity price, the discharge revenue is calculated. Based on the estimated number of energy storage devices to be deployed, energy storage capacity, charging power, and off-peak electricity price, the charging cost is calculated. The daily revenue is obtained based on the difference between the discharge revenue and the charging cost.

[0069] In one embodiment of this application, the charge / discharge mode information includes a multi-charge / multi-discharge mode, and the daily revenue determination unit 30 is further used for: Based on the aforementioned peak and off-peak electricity pricing rules and the multi-charge and multi-discharge model, the electricity price for each peak period and the electricity price for each off-peak period are determined. Based on the estimated number of energy storage devices deployed, energy storage capacity, discharge efficiency, peak electricity price and off-peak electricity price, the discharge revenue and charging cost for each discharge are calculated respectively. Sum all the discharge gains to obtain the total discharge gain; Sum all charging costs to get the total charging cost; The daily revenue is obtained based on the difference between the total discharge revenue and the total charging cost. In one embodiment of this application, the information acquisition unit 10 is further configured to: Based on the decision-making needs for energy storage equipment deployment, create corresponding calculation projects; Determine the regional electricity consumption rule template corresponding to the calculated item; Import the meter load details data into the regional electricity consumption rule template, and perform integrity verification and missing data filling processing on the meter load details data according to the built-in verification rules of the electricity consumption rule template; Based on the regional electricity consumption rule template, configure the basic configuration parameters and charging / discharging mode information of the energy storage device to be deployed.

[0070] In this embodiment, based on detailed electricity meter load data and basic configuration parameters of energy storage devices, the number of energy storage devices to be deployed to meet customer electricity demand is determined. Daily revenue is dynamically calculated by combining charging / discharging modes and peak / off-peak electricity prices. An annual attenuation coefficient is introduced to dynamically present the revenue attenuation curve throughout the device's lifecycle. This allows pre-sales personnel to generate professional assessment reports containing short-term and long-term returns within minutes during the initial communication with customers, without manually processing complex data. The reports present investment value to customers in a data-driven and visual manner, eliminating concerns about revenue uncertainty. This solves the problems of low efficiency, insufficient accuracy, and inadequate period coverage in traditional manual calculations, significantly shortening the customer's decision-making cycle and promoting the expansion of the energy storage business market.

[0071] Specific limitations regarding the energy storage equipment deployment calculation device can be found in the limitations of the energy storage equipment deployment calculation method described above, and will not be repeated here. Each module in the aforementioned energy storage equipment deployment calculation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0072] In one embodiment, a computer device is provided, which may be a terminal device, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a readable storage medium storing computer-readable instructions. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer-readable instructions implement a method for calculating the deployment of an energy storage device. The readable storage medium provided in this embodiment includes both non-volatile and volatile readable storage media.

[0073] In this application embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, it implements the steps of the energy storage device deployment calculation method described above.

[0074] In this embodiment of the application, a readable storage medium is provided, which stores computer-readable instructions. When the computer-readable instructions are executed by a processor, they implement the steps of the energy storage device deployment calculation method described above.

[0075] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When executed, these computer-readable instructions can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0076] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0077] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for calculating the deployment of energy storage equipment, characterized in that, The method includes: Obtain detailed load data from electricity meters, basic configuration parameters of energy storage devices to be deployed, and charging / discharging mode information; Based on the detailed load data of the electricity meters and the basic configuration parameters, the estimated number of energy storage devices to be deployed is determined. Daily revenue is calculated based on the estimated number of energy storage devices to be deployed, the charging and discharging mode information, and the peak and off-peak electricity pricing rules. Based on the annual decay coefficient, the daily earnings are accumulated over the years to obtain the cumulative earnings for each year. The annual decay coefficient reflects the decay of earnings in different years.

2. The energy storage equipment deployment calculation method as described in claim 1, characterized in that, The process of determining the estimated number of energy storage devices to be deployed based on the electricity meter load details and basic configuration parameters includes: Based on the meter load details and basic configuration parameters, the transformer load power capacity, annual charging period power, and energy storage device charging power are determined. The annual charging period power is the power value at 3 / 4 of the power list obtained after sorting the power values ​​from largest to smallest during the entire year when the energy storage device is in a charging state. Based on the transformer load power capacity, annual charging period power, and charging power, the estimated number of energy storage devices to be deployed is calculated.

3. The energy storage equipment deployment calculation method as described in claim 2, characterized in that, The calculation of daily revenue based on the estimated number of energy storage devices deployed, the charging and discharging mode information, and the peak-valley electricity pricing rules includes: Based on the charging and discharging mode, each charging period and each discharging period are determined; Based on the aforementioned peak and off-peak electricity pricing rules, the electricity price for each discharge period and the electricity price for each charging period are determined. Based on the estimated number of energy storage devices to be deployed, each discharge period, and the electricity price during each discharge period, the discharge revenue is determined. Based on the estimated number of energy storage devices to be deployed, each charging period, and the electricity price during each charging period, the charging cost is determined. The daily revenue is determined based on the difference between the discharge revenue and the charging cost.

4. The energy storage equipment deployment calculation method as described in claim 1, characterized in that, The meter load details include peak-period electricity consumption, and the basic configuration parameters include energy storage capacity and discharge power. Determining the estimated number of energy storage devices to be deployed based on the meter load details and basic configuration parameters includes: Based on the peak electricity consumption, the energy storage capacity, and the discharge power, the estimated number of energy storage devices to be deployed is determined.

5. The energy storage device deployment calculation method as described in claim 4, characterized in that, The charging and discharging mode information includes a one-charge-one-discharge mode. The calculation of daily revenue based on the estimated number of energy storage devices deployed, the charging and discharging mode information, and peak / valley electricity pricing rules includes: Based on the peak-valley electricity price rules and the one-charge-one-discharge mode, the electricity price for a single peak period and the electricity price for a single valley period are determined. Based on the estimated number of energy storage devices to be deployed, energy storage capacity, discharge efficiency, and electricity price during a single peak period, the revenue per discharge is calculated. Based on the estimated number of energy storage devices to be deployed, energy storage capacity, charging power, and the electricity price during a single off-peak period, the cost of a single charging session is calculated. The daily revenue is obtained based on the difference between the revenue from a single discharge and the cost of a single charge.

6. The energy storage device deployment calculation method as described in claim 4, characterized in that, The charging and discharging mode information includes a multi-charging and multi-discharging mode. The calculation of daily revenue based on the estimated number of energy storage devices deployed, the charging and discharging mode information, and peak / valley electricity pricing rules includes: Based on the peak and off-peak electricity price rules and the multi-charge and multi-discharge mode, the electricity price for each peak period and the electricity price for each off-peak period are determined. Based on the estimated number of energy storage devices deployed, energy storage capacity, discharge efficiency, peak electricity price and off-peak electricity price, the discharge revenue and charging cost for each discharge are calculated respectively. Sum all the discharge gains to obtain the total discharge gain; Sum all charging costs to get the total charging cost; The daily revenue is obtained based on the difference between the total discharge revenue and the total charging cost.

7. The method for calculating the deployment of energy storage equipment as described in any one of claims 1-6, characterized in that, The acquisition of detailed load data from electricity meters, basic configuration parameters of the energy storage devices to be deployed, and charging / discharging mode information includes: Based on the decision-making needs for energy storage equipment deployment, create corresponding calculation projects; Determine the regional electricity consumption rule template corresponding to the calculated item; Import the meter load details into the regional electricity consumption rule template, and perform integrity verification and missing data filling on the meter load details according to the built-in verification rules of the electricity consumption rule template; Based on the regional electricity consumption rule template, configure the basic configuration parameters and charging / discharging mode information of the energy storage device to be deployed.

8. A device for calculating the deployment of energy storage equipment, characterized in that, The device includes: The information acquisition unit is used to acquire detailed data on electricity meter load, basic configuration parameters of energy storage devices to be deployed, and charging / discharging mode information. The unit for determining the estimated number of energy storage devices to be deployed is used to determine the estimated number of energy storage devices to be deployed based on the electricity meter load details data and basic configuration parameters. The daily revenue determination unit is used to calculate daily revenue based on the estimated number of energy storage devices deployed, the charging and discharging mode information, and the peak and off-peak electricity price rules. The annual return determination unit is used to calculate the cumulative return for each year by summing the daily returns based on the annual decay coefficient. The annual decay coefficient reflects the decay of returns in different years.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that, When the processor executes the computer-readable instructions, it implements the steps of the energy storage device deployment calculation method as described in any one of claims 1-7.

10. A readable storage medium storing computer-readable instructions, characterized in that, When the computer-readable instructions are executed by the processor, they implement the steps of the energy storage device deployment calculation method as described in any one of claims 1 to 7.

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