User side energy storage fine configuration method with demand cost saving

Abstract

The invention discloses a demand cost-saving user side energy storage fine configuration method, which relates to the field of energy storage configuration, and comprises the steps of selecting key evaluation indexes and classifying users based on electrical load characteristics of the users, a distributed power supply access condition and an electricity price structure; according to a user classification result, demand capacity and arbitrage capacity are calculated respectively, and differentiated energy storage capacity configuration is carried out for users; on the basis of the typical daily load curve of the user, the optimal energy storage configuration capacity and operation strategy are determined through an optimization algorithm with the aim of improving the overall economy of the system, and a final recommended configuration scheme is formed; and calculating the total cost according to the initial investment, the operation and maintenance cost, the equipment replacement cost and the charging cost, comprehensively considering the demand electric charge reduction income and the peak-valley arbitrage income, and carrying out full-life-cycle economic evaluation by taking the net present value and the internal return rate as core evaluation indexes. According to the method, the comprehensive economic benefits of the energy storage system are maximized through differential capacity division and a collaborative optimization strategy.

Description

Technical Field

[0001] This invention relates to the field of energy storage configuration, and in particular to a refined configuration method for user-side energy storage with demand-based cost savings. Background Technology

[0002] Against the backdrop of rapid economic growth and profound social transformation, the electricity consumption of industrial enterprises continues to climb, and high electricity costs have become a key challenge for cost control. Energy storage, as a flexible adjustment resource, is an indispensable part of the smart grid. However, traditional energy storage allocation methods lack a systematic assessment system for user energy storage potential and demand-based cost savings. They rely solely on single load data or electricity price information for energy storage allocation, leading to resource waste due to blind allocation by some low-potential users and insufficient allocation by high-potential users, failing to fully tap into demand-based cost savings. Furthermore, existing allocation methods focus only on the single benefit of "peak-valley arbitrage," neglecting the cost savings potential of demand-based electricity charges. Currently, the peak-valley difference in time-of-use electricity prices is only slightly higher than the cost per kilowatt-hour of energy storage, resulting in limited peak-valley arbitrage benefits and insufficient economic viability of energy storage. Some industrial enterprises have photovoltaic systems, resulting in photovoltaic power being fed back into the grid. However, existing energy storage allocation methods have not designed differentiated strategies for this scenario, leading to low application efficiency of energy storage among photovoltaic users.

[0003] Against this backdrop, "A Refined Configuration Method for User-Side Energy Storage with Demand-Based Cost Saving" firstly constructs a multi-dimensional potential tag library to accurately assess and classify user load characteristics and demand-based cost saving potential. Secondly, it proposes a differentiated energy storage capacity configuration strategy, dividing the total capacity into demand capacity and peak-valley arbitrage capacity, and establishing economic calculation models for each. Finally, based on typical daily load curves and full life-cycle cost-benefit analysis, it provides optimal energy storage capacity recommendations and operating schemes. This method is of great significance for improving the economic efficiency and scientific configuration of user-side energy storage. Technically, it innovatively integrates load characteristic analysis, demand management, and energy storage scheduling, achieving refined division and collaborative optimization of energy storage capacity. In terms of application, it can significantly reduce user electricity costs, shorten the investment payback period, and provide replicable and efficient energy storage configuration solutions for different types of industrial and commercial users, promoting the large-scale application of energy storage and the low-carbon transformation of the power grid. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a refined configuration method for user-side energy storage based on demand-based cost savings to address the problem of insufficient economic efficiency caused by energy storage configuration that does not fully consider the potential for cost savings based on user demand.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a method for refined configuration of user-side energy storage with demand-based cost savings, comprising: Based on users' electricity load characteristics, distributed power supply access status, and electricity price structure, key evaluation indicators are selected to classify users. Based on the user classification results, the demand capacity and arbitrage capacity are calculated separately, and differentiated energy storage capacity configurations are made for users. Based on the typical daily load curve of users, and with the goal of improving the overall economic efficiency of the system, the optimal energy storage configuration capacity and operation strategy are determined through optimization algorithms to form the final recommended configuration scheme; The total cost is calculated based on the initial investment, operation and maintenance costs, equipment replacement costs, and charging costs. The economic performance of the entire life cycle is evaluated by taking into account the benefits of demand-based electricity price reduction and peak-valley arbitrage, with net present value and internal rate of return as the core evaluation indicators.

[0007] As a preferred embodiment of the user-side energy storage refined configuration method for demand-based cost savings described in this invention, the key evaluation indicators refer to: daily peak-valley difference rate, correlation between load curve and time-of-use electricity price, percentage of days with monthly peak load, and percentage of duration of daily peak load.

[0008] As a preferred embodiment of the user-side energy storage fine configuration method for demand-based cost savings described in this invention, the user classification refers to dividing users into unsuitable storage users, general storage users, and high-quality storage users based on key indicators.

[0009] As a preferred embodiment of the user-side energy storage fine-grained configuration method for demand-based cost savings described in this invention, the specific steps of the row-differentiated energy storage capacity configuration are as follows: Based on the user classification results, the total energy storage capacity is divided into demand capacity and arbitrage capacity; Different photovoltaic-storage coordinated control strategies and capacity allocation methods are formulated based on whether users have installed photovoltaic systems.

[0010] As a preferred embodiment of the user-side energy storage fine-grained configuration method for demand-based cost savings described in this invention, the final recommended configuration scheme includes determining the optimal energy storage configuration capacity and operation strategy through an optimization algorithm based on the user's typical daily load curve, with the goal of improving the overall economy of the system, to form the final recommended configuration scheme.

[0011] As a preferred embodiment of the user-side energy storage fine configuration method for demand-based cost-saving described in this invention, the configuration process is terminated for users who are not suitable for energy storage configuration. For general storage users, a robust allocation strategy is adopted, with stricter economic thresholds set to calculate the required capacity, and the one-charge-one-discharge mode is prioritized to calculate the arbitrage capacity. For the aforementioned high-quality storage users, an aggressive allocation strategy is adopted, prioritizing the two-charge, two-release mode to maximize peak-valley arbitrage profits.

[0012] As a preferred embodiment of the user-side energy storage fine configuration method for demand-based cost savings described in this invention, the one-charge-one-discharge mode refers to the energy storage system being charged and discharged once a day, and the two-charge-two-discharge mode refers to the energy storage system being charged and discharged twice a day.

[0013] As a preferred embodiment of the user-side energy storage fine-grained configuration method for demand-based cost savings described in this invention, the total cost is calculated as follows: In the formula, The net present value of the total cost. For initial investment, For maintenance costs, For equipment replacement costs, For charging costs, The discount rate is... The usage time of the energy storage system, in years. This is an index of the usage time of the energy storage system.

[0014] As a preferred embodiment of the user-side energy storage fine-grained configuration method for demand-based cost savings described in this invention, the specific steps for determining the optimal energy storage configuration capacity are as follows: The grid power purchase capacity and energy storage charging and discharging capacity are used as optimization parameters to constrain the dynamic balance and energy storage system; Based on the monthly time-of-use electricity price, the power purchased by the grid, and the monthly demand, set an objective function to minimize the total cost; For each typical day, the objective function of minimizing total cost is optimized to obtain the daily optimal operation strategy. The corresponding minimum electricity cost and demand revenue are calculated, and the energy storage power purchase and sale curve, battery charging and discharging power curve, and demand cost saving revenue are output. The average monthly storage capacity for each year is used as the recommended capacity.

[0015] As a preferred embodiment of the user-side energy storage fine configuration method for demand-based cost savings described in this invention, the constraint on the power balance and energy storage system means that the energy storage constraint must meet the following conditions: the energy storage charging and discharging power must not exceed the maximum value of the energy storage charging and discharging power and must not be less than 0; similarly, the energy storage capacity must not exceed the maximum value and must not be less than 0.

[0016] The beneficial effects of this invention are as follows: By constructing a multi-dimensional user potential tag library and a refined evaluation system, this invention overcomes the limitation of the disconnect between load characteristic analysis and economic evaluation in traditional configuration methods, thereby improving the accuracy and adaptability of user-side energy storage configuration; based on differentiated capacity allocation and collaborative optimization strategies, it achieves efficient synergy between demand management functions and peak-valley arbitrage revenue, maximizing the comprehensive economic benefits of the energy storage system; relying on a full life-cycle cost-benefit analysis model, it constructs an energy storage configuration framework with dynamic response and continuous optimization capabilities, giving the system better investment feasibility and long-term operational economics, and significantly improving the practicality and scalability of the configuration scheme; ultimately, it forms a scientific and complete method for refined energy storage configuration and operation that considers multiple application scenarios, creating a high-efficiency solution that can be widely applied to various industrial and commercial users. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for refined configuration of user-side energy storage to save costs on demand.

[0019] Figure 2 A flowchart for calculating the cost of energy storage throughout its entire lifecycle. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Reference Figure 1 , Figure 2Table 1 illustrates an embodiment of the present invention, which provides a method for refined configuration of user-side energy storage with demand-based cost savings, comprising the following steps: This implementation case uses one year's load data from a new energy company in Anhui Province, with a sampling interval of one hour. The transformer capacity is 65,500 kVA and the photovoltaic installed capacity is 11,980 kW. The day with the highest monthly demand is selected as the typical daily load curve for each month. Based on the current energy storage configuration in Zhejiang Province, the energy storage specification is 3,870 kW / 7,687.68 kWh. The method of this invention is applied to allocate energy storage for demand-based cost savings. By collecting hourly load data and photovoltaic output data throughout the year, a refined configuration is implemented, and the energy storage specification is configured as 3,000 kW / 6,056.96 kWh. The key focus is on verifying the effectiveness of the potential tag library evaluation system and the differentiated capacity configuration model, as well as the economic efficiency and operational stability of the system under the photovoltaic-storage synergy strategy. A comprehensive evaluation is conducted on the investment payback period, internal rate of return, and demand charge reduction effect throughout the entire life cycle, demonstrating the significant advantages of this invention in improving user economic benefits and promoting the consumption of distributed energy.

[0024] S1. Based on the user's electricity load characteristics, distributed power supply access status, and electricity price structure, select key evaluation indicators to classify users.

[0025] To meet research needs, many load characteristic indicators have been set according to different standards in the study of power system load characteristics. The main indicators selected are the daily peak-valley difference rate, the correlation between the daily load curve and the time-of-use electricity price to describe the user's demand saving potential. The daily peak-valley difference rate refers to the ratio of a user's daily peak-valley difference to the highest load of that day. The daily peak-valley difference is the difference between the highest and lowest loads on a given day's load curve. A larger daily peak-valley difference rate indicates increased demand for grid flexibility and rapid response, providing greater arbitrage opportunities and development potential for energy storage. Therefore, a larger daily peak-valley difference rate indicates greater potential for energy storage allocation. The daily load curve is also correlated with time-of-use (TOU) electricity prices. A higher correlation between the daily load curve and TOU electricity price curve means that electricity prices are relatively lower during periods of low demand, allowing energy storage to charge and store inexpensive electricity. Conversely, during periods of high demand and high prices, energy storage can discharge. Utilizing larger price differences yields higher returns, thus indicating high potential for energy storage allocation. The calculation formula is as follows: In the formula, The correlation between daily load curve and time-of-use electricity price. This is the daily load curve. For time-of-use electricity pricing, for and covariance, and They represent and Standard deviation; When the demand is less than the capacity threshold, it is more cost-effective to calculate electricity costs based on demand. User-side energy storage can reduce demand and thus lower electricity costs. The percentage of days with monthly peak load and the percentage of cumulative daily peak load duration are used to assess a user's demand-based cost-saving potential. Fewer monthly peak days and shorter daily peak durations mean that the user's power consumption is at a low level most of the time, with peak demand only occurring occasionally, indicating significant demand-based cost-saving potential. By using energy storage devices, peak-hour electricity demand can be shifted to other times, reducing maximum demand. Since demand-based electricity costs are directly related to maximum demand, a reduction in maximum demand leads to a corresponding reduction in demand-based electricity costs, thereby achieving demand-based cost savings. By comparing the daily peak-valley difference rate, the correlation between the daily load curve and time-of-use pricing, the percentage of days with monthly peak load, and the percentage of cumulative time with daily peak load, users are categorized into unsuitable storage users, general storage users, and high-quality storage users. The evaluation of each indicator is shown in Table 1. Table 1 Indicator Evaluation Table

[0026] If F1 and F2 are both greater than 0.5, it means that the storage potential is high. If F3 and F4 are both less than 0.2, it means that the storage potential is high and the demand and cost-saving potential is also high. It is defined as a high-quality storage user. If either F3 or F4 is less than 0.2, it is defined as an average storage user. If both F1 and F2 are less than 0.5, it means that the storage potential is low. It is defined as an unsuitable storage user. It should be noted that the values ​​of 0.2 and 0.5 mentioned in the table are illustrative. S2. Based on the user classification results, calculate the demand capacity and arbitrage capacity respectively, and configure differentiated energy storage capacity for users.

[0027] Based on user classification results, this invention will adopt differentiated energy storage configuration strategies and economic evaluation thresholds to ensure that the configuration scheme is accurately matched with the user's potential. The specific logic is as follows: For users who are not suitable for energy storage, the configuration process should be terminated. These users have small load peak-valley differences, low load-electricity price correlation, and frequent peak loads. Configuring energy storage will not generate economic benefits through peak-valley arbitrage or demand management, and blind investment will lead to resource waste. For general energy storage users, a robust allocation strategy is adopted. Specifically, for demand capacity calculation, a stricter economic threshold is set. For example, the revenue from peak shaving must be significantly higher than the cost of electricity stored. Peak shaving is only performed if the revenue is more than 20% higher. This avoids allocating capacity in scenarios with low marginal benefits. For arbitrage capacity, a one-charge-one-discharge mode is preferred to reduce initial investment. When calculating, it is important to ensure that the investment payback period meets the preset short period, such as less than 5 years. If general energy storage users have photovoltaics, they are given priority to allocate from arbitrage capacity. However, it is necessary to ensure that the remaining capacity after allocation can still complete basic peak-valley arbitrage and guarantee basic returns. For high-quality storage and distribution users, an aggressive allocation strategy should be adopted. Specifically, the economic threshold can be appropriately relaxed to maximize the reduction of demand electricity costs. For example, even if the peak shaving revenue is only slightly higher than the cost per kilowatt-hour, such as more than 5%, it is recommended to allocate capacity to fully tap the cost-saving potential. The two-charge and two-discharge mode can be given priority to make full use of its characteristics of high load peak-valley difference and high load-electricity price correlation to maximize peak-valley arbitrage revenue. If high-quality storage and distribution users have photovoltaics, the economics of adding additional dedicated photovoltaic capacity can be evaluated on the basis of reusing arbitrage capacity, because the high return potential of high-quality users can more quickly dilute the new investment cost. The following are the calculations of the storage capacity, which is the sum of the demand capacity and the arbitrage capacity, for storage allocation with and without photovoltaic backfeed. (1) Storage and distribution without photovoltaic backfeed Three cases should be considered when calculating demand capacity: When demand occurs during peak hours, energy storage capacity can both profit from peak-valley arbitrage and capture demand revenue. To select the duration of peak demand for a given month, simply subtract the energy storage cost per kilowatt-hour from the demand revenue; the result should be greater than 0. In this case, the revenue should satisfy the following conditions: In the formula, Price per unit of demand. This refers to the duration of peak demand during the current month. The difference in demand that has been reduced; When demand occurs during normal periods, the revenue from the peak-to-off-peak price difference is reduced compared to the revenue from peak-to-off-peak arbitrage. In this case, the revenue should satisfy the following: In the formula, This refers to the duration of the month's demand during the normal period. Peak hour electricity price, This is the normal electricity price; When demand occurs during off-peak hours, the revenue from the electricity generated by the peak-valley price difference is reduced compared to the revenue from peak-valley arbitrage. In this case, the revenue should satisfy the following: In the formula, This refers to the duration of the month's demand during the off-peak period. Off-peak electricity pricing; The duration of the day is: From demand value to time By using energy storage to reduce peak demand, the original demand value Pdemand is reduced to a new demand value Pnew, resulting in the new demand value and the time exceeding the new demand value. At this point, the demand capacity is: In the formula, For demand capacity, The time exceeding the new demand value; Taking 2-hour energy storage as an example, new demand values ​​and demand capacity are selected, specifically based on... Recalculate the demand value for the next two hours and compare it with the original demand value. Select the larger value as the new demand value. Based on the difference between the maximum daily load and the new demand value, recalculate the demand capacity and output it. By calculating the peak discharge capacity and valley charge capacity under the one-charge-one-square mode respectively, the smaller value between the charge capacity and the discharge capacity is taken as the actual arbitrage capacity. In the two-charge-two-discharge mode, the chargeable amount during the valley period, the chargeable amount during the normal period, and the discharge amount during the two peak periods are calculated separately. In the same charge-one-side mode, the smaller value among the chargeable amount during the valley period, the chargeable amount during the normal period, and the discharge amount during the two peak periods is taken as the actual arbitrage capacity. (2) Storage allocation in cases of photovoltaic backfeeding When a user has photovoltaic backfeed, photovoltaic power is a zero-cost charging method, and there is income when discharging. Therefore, the daily capacity with the largest photovoltaic backfeed is selected as the photovoltaic backfeed storage capacity. At this time, a portion of the peak-valley arbitrage capacity can be allocated to the photovoltaic back-feeding storage capacity or additional energy storage can be added to participate in peak-valley arbitrage when photovoltaics are not back-feeding. A portion of the peak-valley arbitrage capacity can be allocated proportionally based on the amount of photovoltaic power that needs to be absorbed. Specifically, based on the user's annual photovoltaic power generation and consumption data, the maximum power of photovoltaic power fed back to the grid is recorded, and an energy storage system with a charging power equal to the maximum power fed back to the grid is directly configured. The allocated capacity is then deducted from the peak-valley arbitrage capacity according to the time required for the energy storage system to absorb the power, which is the product of the maximum power fed back to the grid by the photovoltaic power and the time required for absorption. Furthermore, considering the performance degradation of the energy storage system during actual operation, the battery capacity will decrease over time. The impact of temperature changes on energy storage efficiency (both high and low temperatures will reduce energy storage performance) should also be taken into account. At the same time, the limitation of grid voltage fluctuations on the charging and discharging power of energy storage should be considered. A safety margin for peak-hour electricity prices should be set, such as 1.2. That is, when the peak-hour electricity price increases by more than 20%, an additional capacity of energy storage can be added. When the photovoltaic power is not flowing back, it can participate in peak-valley arbitrage to profit. In addition, it is more cost-effective to allocate a portion of the capacity from peak-valley arbitrage for charging when the photovoltaic power is flowing back.

[0028] S3. Based on the typical daily load curve of users, with the goal of improving the overall economic efficiency of the system, the optimal energy storage configuration capacity and operation strategy are determined through optimization algorithms to form the final recommended configuration scheme.

[0029] There are 12 months in a year, and the load characteristics of each month are different. Energy storage is configured according to the day of demand in each month as the typical day of the month, resulting in a total of 12 energy storage capacities. The specific steps for constructing a model that minimizes the daily electricity cost for a typical day are as follows: The core data includes typical daily load data (24-hour load curve), monthly time-of-use electricity price (electricity price during peak, flat and valley periods), energy storage specifications (power, capacity, efficiency and other parameters), and monthly demand (total monthly electricity demand). The grid's power purchase capacity and the energy storage charging and discharging capacity are used as optimization parameters; The constraints include power balance constraints and energy storage constraints, the details of which are as follows: Power balance constraints must satisfy: In the formula, Daily load power, Power purchased for the power grid For energy storage discharge power, Power for energy storage charging; Energy storage constraints must meet the following: the energy storage charging and discharging power must not exceed the maximum energy storage charging and discharging power and must not be less than 0. Similarly, the energy storage capacity must not exceed the maximum value and must not be less than 0. Based on the monthly time-of-use electricity price, the grid's power purchase capacity, and the monthly demand, the objective function for minimizing total cost is defined as follows: In the formula, The objective function value is minimized to minimize the total cost. The electricity price is calculated on a monthly time-of-use basis. For the time of purchasing electricity from the power grid, Monthly demand; For each typical day, the objective function of minimizing total cost is optimized to obtain the optimal operating strategy for that day. The corresponding minimum electricity cost and demand revenue are calculated, and the energy storage power purchase and sale curve, battery charging and discharging power curve, and demand cost saving revenue are output. The average monthly storage capacity each year is used as the recommended capacity to balance seasonal fluctuations, reduce investment costs, and optimize the investment cycle.

[0030] S4. Calculate the total cost based on the initial investment, operation and maintenance costs, equipment replacement costs, and charging costs. Take into account the demand-based electricity cost reduction revenue and peak-valley arbitrage revenue. Conduct a full life-cycle economic assessment with net present value and internal rate of return as the core evaluation indicators.

[0031] Initial investment refers to the total investment during the construction phase of an energy storage system, including costs for the entire process of design, hardware, software, engineering, procurement, and construction. Based on the characteristics of energy storage technology, initial investment costs can be divided into capacity costs related to energy storage capacity and power costs related to power output. Capacity costs refer to the equipment and construction costs directly related to energy storage capacity in the energy storage system, which are obtained by calculating the unit energy storage capacity cost and the energy storage capacity product. Power costs refer to the equipment and construction costs related to power in the energy storage system, which are obtained by calculating the unit power cost and the installed capacity product. Operation and maintenance costs refer to the expenses incurred by an energy storage system during its annual operation and maintenance, including capacity maintenance costs, power maintenance costs, and labor operation costs. Equipment replacement cost refers to the expense incurred when energy storage system components need to be replaced at specified time intervals due to their limited lifespan. Charging cost refers to all expenses incurred by an energy storage system during its entire lifecycle when it is charged from the grid or renewable energy sources. Of the costs listed above, the initial investment is a one-time cost incurred during project construction, while the remaining costs are incurred annually. Using the energy storage commissioning date as the starting point for calculation, the net present value of the total cost over the entire lifecycle is calculated as follows: In the formula, The net present value of the total cost. For initial investment, For maintenance costs, For equipment replacement costs, For charging costs, The discount rate is... The usage time of the energy storage system, in years. Index of usage time for energy storage systems; By using an iterative method, the net present value of the total cost is set to zero, and the discount rate is calculated.

[0032] This embodiment also provides a computer device applicable to the demand-based cost-saving user-side energy storage fine-grained configuration method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the demand-based cost-saving user-side energy storage fine-grained configuration method proposed in the above embodiment.

[0033] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0034] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the user-side energy storage fine-grained configuration method for demand-based cost savings proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0035] In summary, this invention overcomes the limitations of traditional configuration methods that disconnect load characteristic analysis and economic evaluation by constructing a multi-dimensional user potential tag library and a refined evaluation system, significantly improving the accuracy and adaptability of user-side energy storage configuration; based on differentiated capacity allocation and collaborative optimization strategies, it achieves efficient synergy between demand management functions and peak-valley arbitrage revenue, maximizing the comprehensive economic benefits of the energy storage system; relying on a full life-cycle cost-benefit analysis model, it constructs an energy storage configuration framework with dynamic response and continuous optimization capabilities, giving the system better investment feasibility and long-term operational economics, greatly improving the practicality and scalability of the configuration scheme; ultimately, it forms a scientific and complete refined energy storage configuration and operation method that considers multiple application scenarios, creating a high-efficiency solution that can be widely applied to various industrial and commercial users.

[0036] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for refined configuration of user-side energy storage with demand-based cost savings, characterized in that: The application relates to a method for recommending energy storage capacity configuration for users. The method comprises the following steps: selecting key evaluation indexes according to the electricity load characteristics of users, the distributed power access conditions and the electricity price structure, and classifying the users; According to the classification results of the users, the demand capacity and the arbitrage capacity are calculated, and the energy storage capacity is configured differently for the users; Based on the typical daily load curve of the users, the optimal energy storage configuration capacity and operation strategy are determined through an optimization algorithm to form a final recommended configuration scheme, so that the overall economy of the system is improved; The total cost is calculated according to the initial investment, operation and maintenance cost, equipment replacement cost and charging cost, and the net present value and internal rate of return are taken as the core evaluation indexes for the whole life cycle economy evaluation by comprehensively considering the demand charge reduction income and the peak-valley arbitrage income.

2. The demand charge saving user side energy storage refinement configuration method of claim 1, wherein: The key evaluation indexes are the daily peak-valley difference rate, the correlation between the load curve and the time-of-use electricity price, the proportion of the number of days of monthly peak load and the proportion of the duration of daily peak load.

3. The method of claim 1, wherein the method further comprises: determining a time of day; and determining a time of year. The classification of the users is classified into users not suitable for energy storage, general energy storage users and high-quality energy storage users according to the key indexes.

4. The demand charge saving customer side energy storage refinement configuration method of claim 1, wherein: The specific steps of the differential energy storage capacity configuration are as follows: According to the classification results of the users, the total energy storage capacity is divided into demand capacity and arbitrage capacity; Different light-storage collaborative control strategies and capacity allocation methods are developed according to whether the user is configured with photovoltaic.

5. The method of claim 1, wherein the method further comprises: determining a time of day; and determining a time of year. The final recommended configuration scheme comprises the following steps: based on the typical daily load curve of the users, the optimal energy storage configuration capacity and operation strategy are determined through an optimization algorithm to form a final recommended configuration scheme, so that the overall economy of the system is improved.

6. The method of claim 3, wherein the method further comprises: determining a time of day; and determining a time of year. For the users not suitable for energy storage, the configuration process is terminated. For the general energy storage users, a robust configuration strategy is adopted, a more stringent economy judgment threshold is set to calculate the demand capacity, and a one-charge-one-discharge mode is preferentially adopted to calculate the arbitrage capacity. For the high-quality energy storage users, an aggressive configuration strategy is adopted, a two-charge-two-discharge mode is preferentially considered, and the peak-valley arbitrage income is maximized.

7. The demand charge saving customer side energy storage refinement configuration method of claim 6, wherein: The one-charge-one-discharge mode refers to that the energy storage system charges once and discharges once in a day, and the two-charge-two-discharge mode refers to that the energy storage system charges twice and discharges twice in a day.

8. The demand charge saving customer side energy storage refinement configuration method of claim 1, wherein: The total cost is calculated as follows: wherein, is the net present value of the total cost, is the initial investment, is the operation and maintenance cost, is the equipment replacement cost, is the charging cost, is the discount rate, is the energy storage system usage time, in years, is the energy storage system usage time index.

9. The demand charge saving customer side energy storage refinement configuration method of claim 1, wherein: The specific steps of determining the optimal energy storage configuration capacity are as follows: The grid power and the energy storage charging and discharging power are taken as optimization parameters, and the power balance and the energy storage system are constrained; According to the monthly time-of-use electricity price, the grid power and the monthly demand, a minimum total cost objective function is set; The minimum total cost objective function is optimized for each typical day to obtain a daily optimal operation strategy, calculate the corresponding minimum electricity cost and demand income, output the energy storage power curve, the battery charging and discharging power curve and the demand cost saving income, and output the energy storage power curve, the battery charging and discharging power curve and the demand cost saving income; The monthly average energy storage capacity of each year is taken as the recommended capacity.

10. The demand charge saving customer side energy storage refinement configuration method of claim 9, wherein: The constraints of the power balance and the energy storage system are that the energy storage constraints need to meet the conditions that the energy storage charging and discharging power cannot exceed the maximum energy storage charging and discharging power and cannot be less than 0, and similarly, the energy storage capacity cannot exceed the maximum value and cannot be less than 0.

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