User energy storage capacity optimal configuration method

Abstract

The invention discloses a user energy storage capacity optimal configuration method, which overcomes the problems that the power utilization cost cannot be effectively reduced by too small energy storage capacity and the investment cost is too high possibly due to too large energy storage capacity in the prior art, and comprises the following steps: calculating a user typical energy utilization curve based on historical energy utilization data; calculating the electricity consumption of the user in the peak period and the measurement step length of the distribution storage capacity of the user; dynamically measuring and calculating user energy storage daily earnings under different distribution and storage capacities based on the distribution and storage capacity measurement and calculation step length; and calculating the energy storage configuration capacity of the user as the optimal configuration capacity by taking the optimal user income as an objective function. The optimal configuration capacity and the net income of the energy storage system are optimally calculated, and the optimal income can be achieved for user capacity configuration; the charging and discharging strategy can be adjusted at any time according to the power grid load condition, the power grid is helped to deal with emergencies and faults, and the toughness and anti-interference capability of the power grid are improved.

Description

Technical Field

[0001] This invention relates to the field of user energy storage technology, and in particular to a method for optimizing the configuration of user energy storage capacity. Background Technology

[0002] In recent years, the proportion of renewable energy generation, represented by wind and solar power, in the power system has increased rapidly, and the installed capacity of distributed renewable energy has grown year by year. On the user side, connecting distributed renewable energy can reduce electricity costs. However, due to the volatility and randomness of renewable energy, its integration poses challenges to the stable operation of the system and the quality of power supply. Therefore, energy storage is often configured on the user side to improve electricity costs, smooth the load curve, promote stable system operation, and meet electricity demand. With the rapid development of renewable energy, especially the increasing popularity of wind and solar power, energy storage has become a necessary means to solve the problems of volatility and intermittency of renewable energy.

[0003] Currently, the cost of energy storage remains relatively high, resulting in less than ideal cost-effectiveness despite improved system operating costs after energy storage deployment. Determining the optimal economic benefits for capacity allocation during the initial planning stages of energy storage projects remains a significant challenge for many researchers. Against this backdrop, the capacity configuration and cost analysis of user-side energy storage systems are crucial for the development of the energy storage industry. Understanding user needs and benefits within a constantly evolving market environment is of paramount importance for optimizing energy storage capacity and conducting cost analysis. Summary of the Invention

[0004] The purpose of this invention is to address the problem in existing technologies where insufficient energy storage capacity fails to effectively reduce electricity costs, while excessive energy storage capacity may lead to excessively high costs. This invention provides a method for optimizing the configuration of user energy storage capacity. It optimizes the calculation of the optimal configuration capacity of the energy storage system by using historical user electricity consumption data, considering user electricity consumption characteristics, and configuring energy storage to maximize user benefits, thereby determining the optimal energy storage configuration capacity. Furthermore, it sets a step size for calculating user energy storage capacity and establishes a dynamic step size cyclic calculation method to reduce the calculation cycle and improve calculation efficiency.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for optimizing user energy storage capacity configuration includes the following steps: S1: Calculate the typical energy consumption curve for users based on historical energy consumption data; S2: Calculate the user's peak electricity consumption and the step size for calculating the user's storage capacity; S3: Based on the calculated step size of the energy storage capacity, dynamically calculate the daily energy storage revenue of users under different energy storage capacities; S4: Using the optimal user benefit as the objective function, calculate the user's energy storage configuration capacity as the optimal configuration capacity.

[0006] The method of this invention, based on historical user electricity consumption data, comprehensively considers the operating characteristics of energy storage batteries and the user's electricity price, using optimal user benefit as the objective function to determine the optimal energy storage capacity. Simultaneously, during the calculation of the optimal energy storage capacity, a dynamic calculation step-size cyclical calculation method is established. Based on the user's historical energy consumption, different capacity calculation step sizes are used to dynamically calculate energy storage indicators, reducing the calculation cycle. The charging and discharging strategy can be adjusted in real time according to the grid load conditions, helping the grid cope with emergencies and faults, and improving the grid's resilience and anti-interference capability.

[0007] Preferably, S2 includes: calculating user electricity consumption during peak hours and user electricity consumption during off-peak hours respectively, and extracting user electricity consumption during off-peak hours; and obtaining the user storage capacity calculation step size by multiplying the proportional coefficient by the user electricity consumption during off-peak hours.

[0008] Preferably, S3 includes: starting from a storage capacity of 0, and using the user storage capacity calculation step size as the unit, successively increasing from n times the user storage capacity calculation step size, to calculate the user's daily energy storage revenue during the energy storage life cycle under different storage capacities, where n is a positive integer.

[0009] Preferably, S4 includes: calculating the energy storage index under different energy storage capacities based on the calculation step size of the energy storage capacity; establishing an objective function and setting constraints based on the energy storage index; and selecting the energy storage capacity that satisfies the objective function under the constraints as the optimal configuration capacity.

[0010] Preferably, based on the user electricity consumption during peak hours and the user electricity consumption during off-peak hours, an energy consumption index for off-peak hours is extracted. If the calculation step size of n times the user's storage capacity exceeds the energy consumption index for off-peak hours, then the dynamic calculation is stopped.

[0011] Preferably, the typical energy load value at time i is extracted. The typical energy load value at time i is the average of the sum of the instantaneous load values ​​at time i on historical day T, where T = 1, 2, ..., N; and N is the number of historical days. Based on the typical energy load value at time i, the user electricity consumption during peak hours and the user electricity consumption during peak periods are calculated respectively. Based on the user electricity consumption during peak hours and the user electricity consumption during peak periods, the user electricity consumption during peak periods is obtained.

[0012] Preferably, if there is no corresponding user energy storage configuration capacity when the objective function is satisfied, the user will not perform energy storage; according to the objective function value corresponding to the user's optimal configuration capacity, the user's energy storage is divided into priority levels, and the user's energy storage is configured according to the priority.

[0013] As a preferred method, the dynamic calculation uses off-peak hours as the charging period and other peak hours as the discharging period, and calculates the energy storage using a daily operation strategy of two charging and two discharging cycles, with full charging and discharging.

[0014] As a preferred option, if the user's daily energy storage revenue is less than the revenue threshold, the user will not configure energy storage capacity; if the user's daily energy storage revenue is greater than or equal to the revenue threshold, the optimal energy storage capacity configuration for the user will be calculated.

[0015] Preferably, S1 includes: collecting load data with an hour as the period, identifying outliers in historical load data at the same time using a box plot, calculating the load interval at each hour in sequence, and obtaining a typical daily energy consumption curve at point m.

[0016] Therefore, the present invention has the following beneficial effects: 1. In the process of calculating the optimal energy storage capacity, set the calculation step size for user-allocated storage capacity and establish a dynamic calculation step size cyclic calculation method. Based on the user's historical energy consumption, dynamically calculate the energy storage index with different calculation step sizes to reduce the calculation cycle and improve calculation efficiency.

[0017] 2. Construct an energy storage calculation and analysis method for industrial and commercial users under optimal returns, with the objective function of maximizing user returns, so that the maximum returns can be obtained when configuring user energy storage capacity.

[0018] 3. Optimal configuration of user energy storage capacity can improve the flexibility and resilience of the power grid. The energy storage system can adjust the charging and discharging strategy at any time according to the power grid load, provide flexible power support, help the power grid cope with emergencies and faults, and improve the power grid's resilience and anti-interference ability. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the overall steps of the user energy storage capacity optimization configuration method in Example 1.

[0020] Figure 2 This is a schematic diagram showing the priority level distribution of user energy storage capacity configuration in the region in Example 2. Detailed Implementation

[0021] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1: This embodiment provides a method for optimizing the configuration of user energy storage capacity, such as... Figure 1As shown, the operation process is as follows: Step 1, calculate the typical energy consumption curve of the user based on historical energy consumption data; Step 2, calculate the user's peak electricity consumption and the calculation step size of the user's energy storage capacity; Step 3, dynamically calculate the user's daily energy storage revenue under different energy storage capacities based on the calculated step size of the energy storage capacity; Step 4, calculate the user's energy storage configuration capacity with the objective function of maximizing user revenue, and use this as the optimal configuration capacity.

[0022] The user energy storage capacity optimization configuration method provided in this embodiment determines the optimal energy storage configuration capacity by taking into account the user's historical electricity consumption data, considering the user's electricity consumption characteristics, and selecting the characteristics of the user's best economic benefits.

[0023] The following examples and specific application scenarios further illustrate the technical solution and effects of the present invention. The following examples are explanations of the present invention, but the present invention is not limited to the following examples.

[0024] Step 1: Based on historical energy consumption data, calculate the typical energy consumption curve of users and depict the typical energy consumption profile of users.

[0025] Specifically, it includes: Using h as the period, user load data is collected based on the electricity information system. Anomalies are identified using box plots for historical load data at the same time. The load interval at each h time is calculated sequentially to obtain the typical daily energy consumption curve at point m, where m = 24 * 60 / t. The typical energy load value at time i is calculated based on the average value of the instantaneous load at time i in history.

[0026] In this embodiment, y is 15 minutes, m = 96, then: Based on the 15-minute frequency load data collected by the electricity information system, after identifying outliers using box plots for historical load data at the same time, the average load for each 15-minute period is calculated sequentially to obtain 96 typical daily energy consumption curves.

[0027] Calculate the typical energy load value at time i: In the formula: Let P be the typical energy load value at time i. (i,t) Let N be the instantaneous load value at time i on day T in history, and N be the number of historical days.

[0028] Step 2: Calculate the user's peak electricity consumption and the step size for calculating the user's storage capacity.

[0029] include: (1) Calculate the electricity consumption of users during peak and peak periods, and extract the electricity consumption index during peak periods.

[0030] In accordance with the time-of-use electricity pricing policy for industrial and commercial users, and dividing the market into peak and off-peak periods, the electricity consumption of users during peak and peak hours is calculated separately, and the energy consumption index for peak hours is extracted. The calculation method is as follows: 0.8*Q 尖高峰 =Q 尖峰 +Q 高峰 In the formula, Q 尖峰 Q represents electricity consumption during peak hours. 高峰 This indicates the electricity consumption during peak and high-peak periods. t1 corresponds to the start time of the peak period, t2 corresponds to the end time of the peak period, t3 corresponds to the start time of the high-peak period, and t4 corresponds to the end time of the high-peak period.

[0031] (2) Calculate the step size for measuring the user's allocated storage capacity.

[0032] In this embodiment, the step size for calculating the user's storage capacity is the product of a proportional coefficient and the electricity consumption during peak hours. This can be expressed by the formula: in, The step size for calculating storage capacity for users is K, which is a proportional coefficient. K is preferably set to 2%, but it can be adjusted according to the actual situation in practical applications.

[0033] Step 3: Based on the calculated step size of the allocated storage capacity, dynamically calculate the daily energy storage revenue of users under different allocated storage capacities.

[0034] Starting with a storage capacity of 0, and using the user storage capacity calculation step size calculated in the second step as the unit, the calculation step size is increased successively by n times the user storage capacity calculation step size. The net income of energy storage over its life cycle is calculated for different storage capacities. If the calculated capacity exceeds q times the peak electricity consumption, the calculation is stopped, where n is a positive integer.

[0035] The calculation process assumes that the available energy storage capacity starts at midnight each day as 0 kWh. Charging periods are defined as the off-peak hours during which industrial and commercial users are subject to time-of-use pricing, while other peak hours are defined as discharging periods. The energy storage calculation is performed daily using a two-charge, two-discharge, full-charge, and full-discharge operating strategy. The overall daily revenue is as follows: This embodiment uses Taking capacity measurement as an example, this section introduces the process of calculating the net revenue over the lifecycle of energy storage. The configuration parameters for energy storage measurement are shown in the table below: In this embodiment, depending on the different investment methods for energy storage, the energy storage capacity of mechanical energy users can be calculated from different perspectives.

[0036] The specific calculation process is as follows: (1) Calculate the daily revenue model of energy storage under different investment methods.

[0037] Daily returns on energy storage investments are calculated differently depending on the investment perspective: (1.1) When energy storage is invested in by the user, the daily revenue calculation model for energy storage users is as follows: The daily revenue FF0 for energy storage users is the difference between the daily peak-valley arbitrage revenue S, the increase in electricity costs D after changing from a single-rate electricity price to a two-rate time-of-use electricity price, and the demand management revenue X. This can be expressed as: FF0 = SDX.

[0038] (1.2) When energy storage is invested in by third-party users, a certain proportion of the revenue cost of the third-party users needs to be allocated to the energy storage users. Therefore, the daily revenue model of the third-party investment users is as follows: The daily return FF1 for third-party investors is the product of the daily return FF0 for energy storage users and the proportion (1-K) not allocated to energy storage users. This can be expressed as: FF1 = (SDX) * (1-K).

[0039] (2) Calculate the energy storage peak-valley arbitrage profit S.

[0040] Specifically, it includes: (2.1) Calculate the charging amount per t period during the charging period.

[0041] The actual charging power is calculated as follows: P i Cha =min(P max ,P min ,P e ), Where: P i Cha The actual charging power is given by T, the charging / discharging duration is given by η, and the depth of charge / discharge is given by Q. e Where β is the rated capacity of the user's transformer, P is the charging and discharging efficiency, and β is the rated capacity of the transformer. max To meet the maximum charge / discharge power limit for fully charging the energy storage within the charge / discharge duration, P min P is the minimum full charge power limit during the current charging period. e To meet the limit of the open capacity of transformers under electrical load.

[0042] The formula for calculating charging capacity is as follows: in, Q represents the charging capacity during time period t. i-1 Q represents the available energy stored at the previous moment. 可用t represents the actual usable capacity of the energy storage, and t represents the charging duration.

[0043] (2.2) Calculate the discharge capacity for each period of the discharge period.

[0044] The actual discharge power of the energy storage is:

[0045] Among them, P i dis P represents the actual discharge power of the stored energy. max To meet the maximum charge / discharge power limit for energy storage during the charge / discharge duration, The typical daily energy consumption curve represents the electrical load at that moment, and η represents the depth of charge and discharge of the energy storage.

[0046] The formula for calculating the discharge capacity is as follows: Among them, among them, Let Q be the discharge charge during the discharge period. i-1 The available energy stored at the previous moment.

[0047] (2.3) Calculate the daily charging and discharging cost and revenue of energy storage (i.e., the peak-valley arbitrage revenue of energy storage) S.

[0048] Where β is the charge / discharge efficiency, f i This refers to the electricity price for the corresponding period after the allocation and storage.

[0049] (3) Calculate the incremental cost of electricity, D.

[0050] Among them, f i The electricity price is for the corresponding time period. To implement single-rate time-of-use electricity pricing.

[0051] (4) Calculate the average daily revenue X for demand management.

[0052] Among them, P m ′ ax To meet the maximum demand of the month preceding the allocation of reserves, P m " ax This represents the maximum monthly demand after the allocation of reserves. The electricity price is applied to the demand portion of users under the two-part tariff system.

[0053] Step 4: Using the goal of maximizing user benefits, calculate the user's energy storage configuration capacity as the optimal configuration capacity.

[0054] Energy storage indicators are dynamically calculated over the lifecycle of energy storage using different capacity calculation step sizes. These indicators include initial investment cost, annual return, present value, net present value of investment, and payback period. Based on these indicators, the optimal configuration capacity is selected when the net present value of investment is maximized, provided that the payback period and net present value of investment meet the thresholds.

[0055] Specifically, it includes: Calculate energy storage indicators throughout the entire lifecycle of the energy storage project.

[0056] The calculation method is as follows: (1.1) Calculate the initial investment cost FB.

[0057] FB = Q * f0, Where Q represents the energy storage capacity and f0 represents the unit cost of the energy storage equipment.

[0058] (1.2) Calculate the annual return FAy in year y.

[0059] Where FF represents the daily energy storage revenue calculated in step three, and T... on γ represents the number of operating days per year, and γ represents the annual battery degradation rate.

[0060] (1.3) Net discounted value of investments (NPV).

[0061] Where λ is the investment discount rate, preferably 0.042.

[0062] (1.4) Internal Rate of Return (IRR).

[0063] (1.5) Investment recovery period T back .

[0064] Where I represents the number of years in which the cumulative annual return exceeds the investment cost. If the cumulative annual return fails to exceed the investment cost throughout the entire lifecycle, the investment payback period is empty.

[0065] Calculation of optimal storage capacity for economic benefits.

[0066] (2.1) User investment methods.

[0067] In this embodiment, the constraints are: the investment payback period is less than 5.5 years, and the net present value (NPV) is greater than 100,000. Therefore, the storage capacity corresponding to the largest NPV under the conditions of an investment payback period of less than 5.5 years and a net present value (NPV) greater than 100,000 is taken as the optimal allocation capacity.

[0068] (2.2) Third-party investment method.

[0069] The constraints are: the third-party internal rate of return (IRR) is greater than 10%, and the net present value (NPV) of the third-party investment is greater than 100,000. Therefore, the optimal allocation capacity is selected based on the maximum net present value of the investment under the conditions that the third-party IRR is greater than 10% and the net present value (NPV) of the third-party investment is greater than 100,000.

[0070] The user energy storage capacity optimization configuration method provided in this embodiment has the following beneficial effects: (1) In the process of calculating the optimal energy storage capacity, a dynamic calculation step size cyclic calculation method is established. Based on the user's historical energy consumption, the energy storage index is dynamically calculated with different capacity calculation step sizes to reduce the calculation cycle.

[0071] (2) Construct an energy storage calculation and analysis method for industrial and commercial users under the optimal benefit. The benefits can be calculated from different perspectives according to different investment methods, and the optimal economic energy storage configuration capacity for different investment methods can be selected.

[0072] Example 2: This embodiment provides a method for optimizing the configuration of user energy storage capacity. Based on Embodiment 1, it incorporates a specific application scenario and performs actual calculations.

[0073] Under the two-part electricity pricing policy, users' monthly electricity bills should include two parts: a basic electricity fee and a power consumption fee. The power consumption fee can be calculated based on time-of-use pricing, while the basic electricity fee is calculated based on the user's maximum demand (kW). In regions with significant peak-valley electricity price differences, energy storage projects have become highly sought after by developers due to their economic benefits, achieved through a strategy of low-level storage and high-level release. However, determining the optimal capacity allocation for energy storage projects during the initial planning stages presents a significant challenge for researchers. The accuracy of these calculations directly impacts investors' economic returns, thus influencing the success or failure of the investment.

[0074] This embodiment addresses the problem of calculating the optimal economic energy storage capacity configuration for users by providing a method for dynamic calculation and investment potential rating of user energy storage configuration based on optimal economics. It utilizes historical user electricity consumption data, considers user electricity consumption characteristics, and selects energy storage configurations based on the user's optimal economic benefits to determine the optimal energy storage capacity. Simultaneously, it constructs a standard for classifying energy storage investment potential levels using net present value and internal rate of return (IRR) as indicators, calculating the energy storage investment potential of industrial and commercial users, and providing theoretical guidance for energy storage investment by industrial and commercial users.

[0075] Specifically: Step 1: Calculate the typical energy consumption curve for users based on historical energy consumption data.

[0076] Based on the 15-minute frequency load data collected by the electricity information system, after identifying outliers using box plots for historical load data at the same time, the average load for each 15-minute period is calculated sequentially to obtain 96 typical daily energy consumption curves.

[0077] Step 2: Calculate the user's peak electricity consumption and the step size for calculating the user's storage capacity.

[0078] In accordance with the time-of-use electricity pricing policy for industrial and commercial users, and dividing the market into peak and off-peak periods, the electricity consumption of users during peak and peak periods is calculated separately, and the energy consumption index for peak period electricity consumption is extracted. The calculation step size for distribution and storage capacity is then calculated based on the energy consumption index for peak period electricity consumption.

[0079] Step 3: Based on the calculated step size of the allocated storage capacity, dynamically calculate the daily energy storage revenue of users under different allocated storage capacities.

[0080] Starting with a storage capacity of 0, the calculation step size is based on the storage capacity calculated in the second step. as a unit, with By incrementally increasing the capacity, the net revenue over the lifecycle of the energy storage system is calculated for different storage capacity allocation steps. When the calculated capacity (storage capacity calculation step size) exceeds 0.8*Q... 尖高峰 That is, stop the calculation.

[0081] The calculation process assumes that the available energy storage capacity starts at midnight every day as 0 kWh. The charging period is the off-peak period when industrial and commercial users implement time-of-use electricity pricing, and the other peak periods are the discharging periods. The energy storage calculation is carried out daily with a two-charge and two-discharge, full-charge and full-discharge operation strategy.

[0082] If the calculated daily energy storage revenue is greater than the revenue threshold, it indicates that the user needs energy storage, and the optimal energy storage capacity for the user is calculated; otherwise, it indicates that the user does not need energy storage, and no energy storage capacity is configured.

[0083] Step 4: Using the goal of maximizing user benefits, calculate the user's energy storage configuration capacity as the optimal configuration capacity.

[0084] Calculate the economic indicators of energy storage throughout the entire project lifecycle, and adjust them according to different investment methods. Based on the calculated energy storage indicators under the given capacity, and considering the user's optimal economic benefits, the optimal capacity is selected. If no suitable capacity is available, energy storage is not recommended for the user.

[0085] In this embodiment, the energy storage investment potential level is also evaluated based on the calculated energy storage indicators and the classification criteria for energy storage investment potential levels.

[0086] Specifically, in this embodiment, by calculating the energy storage indicators under the user's optimal storage capacity, the net investment value (NPV) and internal annualized rate of return (IRR) of the investor are selected as two consideration indicators to divide the user's energy storage investment potential into 1-5 levels. The division range of each level is shown in the table below.

[0087] According to the table above, in this embodiment, users with a net present value (NPV) of less than 500,000 and an internal rate of return (IRR) of less than 12.6% are classified as Level 1; users with an NPV of less than 500,000 and an IRR between 12.4% and 16.8%, users with an NPV between 500,000 and 1,000,000 and an IRR between 14.7%, or users with an NPV between 500,000 and 1,000,000 and an IRR less than 12.6% are classified as Level 2; Users with a net present value (NPV) of less than 500,000 and an internal rate of return (IRR) between 16.8% and 18.9%, or an NPV between 500,000 and 1,000,000 and an IRR between 14.7% and 16.8%, or an NPV between 1,000,000 and 2,500,000 and an IRR between 12.6% and 14.7%, or an NPV between 2,500,000 and 5,000,000 and an IRR less than 12.6%, are classified into the third tier. Users with an investment value less than 500,000 and an internal rate of return (IRR) greater than 18.9%, or an investment net present value (NPV) between 500,000 and 1,000,000 and an IRR between 16.8% and 18.9%, or an investment NPV between 1,000,000 and 2,500,000 and an IRR between 14.7% and 16.8%, or an investment NPV between 2,500,000 and 5,000,000 and an IRR between 12.6% and 14.7%, or an investment NPV greater than 5,000,000 and an IRR greater than 18.9% ... Users with a return of less than 12.6% are classified as Level 4; users with an investment net present value of 500,000 to 1,000,000 and an internal annualized return of more than 18.9%, or an investment net present value of 1,000,000 to 2,500,000 and an internal annualized return of more than 16.8%, or an investment net present value of 2,500,000 to 5,000,000 and an internal annualized return of more than 14.7%, or an investment net present value of more than 5,000,000 and an internal annualized return of more than 12.6% are classified as Level 5.

[0088] This embodiment also calculates and verifies the user energy storage capacity optimization configuration method: The calculation targets are large industrial and general commercial customers with an operating capacity of 2000kVA or more and a voltage level of 10kV or more in the main urban area of ​​a certain region (including three power supply areas A, B, and C). The total number of users is 956, including 316 industrial users and 640 commercial users.

[0089] The calculation results for energy storage investment from the perspective of user self-investment are as follows: The calculation results for energy storage investment from the perspective of third-party investment are as follows: Based on the economic indicators used in calculating the optimal user capacity, and considering two metrics—Net Present Value (NPV) and Internal Rate of Return (IRR)—the investment potential of recommended users is categorized into priority levels for energy storage configuration. Higher priority indicates higher returns, and these users should be prioritized for energy storage configuration. The results are as follows: Figure 2 As shown. Figure 2 (a) indicates the priority distribution of energy storage configuration for users in this region from the perspective of user investment, with 1 star representing the lowest priority and 5 stars representing the highest priority. Similarly, Figure 2 (b) indicates the priority distribution of user energy storage configuration in the region from the perspective of third-party investment, with 1 star indicating the lowest priority and 5 stars indicating the highest priority.

[0090] pass Figure 2 From the perspectives of investment potential level and user distribution, the overall investment potential of energy storage is better from the perspective of user investment than from the perspective of third-party investment.

[0091] The user energy storage capacity optimization configuration method provided in this embodiment, based on the user's historical electricity consumption data, comprehensively considers the operating characteristics of energy storage batteries and the user's electricity price, establishes a user-side energy storage optimization configuration model, optimizes the net income of the user's entire life cycle, and measures the energy storage investment potential from different perspectives of user investment and third-party investment, which can achieve the following benefits: (1) In terms of economic benefits, based on users' historical electricity consumption data and the electricity price, by calculating the optimal energy storage configuration capacity, users can obtain the maximum economic benefits when investing in energy storage systems. This avoids the problem that too small an energy storage capacity cannot effectively reduce electricity costs, while too large an energy storage capacity may lead to excessively high investment costs, thus providing users with a reference for investment decisions. At the same time, optimizing the calculation of the optimal configuration capacity and net income of the energy storage system from different investment perspectives can attract third-party investors to participate in energy storage projects, improve the rate of return on investment, and promote the development of the energy storage industry.

[0092] (2) In terms of social benefits, the widespread application of energy storage systems can effectively solve the problems of intermittency and uncontrollability of new energy sources. Through the calculation of the optimal capacity of energy storage, the utilization efficiency and predictability of new energy sources can be improved, promoting the large-scale development and application of renewable energy, driving the transformation of energy structure, and achieving sustainable development goals.

[0093] (3) In terms of grid management benefits, optimal energy storage configuration can improve the flexibility and resilience of the grid. The energy storage system can adjust the charging and discharging strategy at any time according to the grid load, providing flexible power support, helping the grid to cope with emergencies and faults, and improving the grid's resilience and anti-interference ability. On the other hand, the calculation of optimal energy storage configuration capacity can serve as a reference for grid planning, helping grid planners to better regulate and balance grid load, and improve the stability and reliability of the grid.

[0094] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Other variations and modifications are possible without departing from the technical solutions described in the claims.

Claims

1. A method for optimizing the configuration of user energy storage capacity, characterized in that, include: S1: Calculate the typical energy consumption curve for users based on historical energy consumption data; S2: Calculate the user's peak electricity consumption and the step size for calculating the user's storage capacity; S3: Based on the calculated step size of the energy storage capacity, dynamically calculate the daily energy storage revenue of users under different energy storage capacities; S4: Using the optimal user benefit as the objective function, calculate the user's energy storage configuration capacity as the optimal configuration capacity.

2. The user energy storage capacity optimization configuration method according to claim 1, characterized in that, The S2 includes: calculating the user electricity consumption during peak hours and the user electricity consumption during off-peak hours respectively, and extracting the user electricity consumption during off-peak hours; and using the product of the proportional coefficient and the user electricity consumption during off-peak hours to obtain the user storage capacity calculation step size.

3. The method for optimizing user energy storage capacity configuration according to claim 1, characterized in that, S3 includes: starting from a storage capacity of 0, and using the user storage capacity calculation step size as the unit, increasing the calculation step size from n times the user storage capacity, and calculating the user's daily energy storage revenue during the energy storage life cycle under different storage capacities, where n is a positive integer.

4. The method for optimizing user energy storage capacity configuration according to claim 1, characterized in that, S4 includes: calculating the energy storage index under different energy storage capacities based on the calculated step size of the energy storage capacity; establishing an objective function and setting constraints based on the energy storage index; and selecting the energy storage capacity that satisfies the objective function under the constraints as the optimal configuration capacity.

5. The method for optimizing user energy storage capacity configuration according to claim 3, characterized in that, Based on the user electricity consumption during peak hours and the user electricity consumption during off-peak hours, extract the energy consumption index for off-peak hours. If the calculation step size of n times the user's storage capacity exceeds the energy consumption index for off-peak hours, then stop the dynamic calculation.

6. The method for optimizing user energy storage capacity configuration according to claim 2, characterized in that, Extract the typical energy load value at time i, where the typical energy load value at time i is the average of the sum of the instantaneous load values ​​at time i on historical day T, where T = 1, 2, ..., N; N represents the number of historical days; based on the typical energy load value at time i, calculate the user electricity consumption during peak hours and the user electricity consumption during off-peak hours respectively; Based on the user electricity consumption during peak hours and the user electricity consumption during off-peak hours, the user electricity consumption during off-peak hours is obtained.

7. The method for optimizing user energy storage capacity configuration according to claim 4, characterized in that, If there is no corresponding user energy storage configuration capacity when the objective function is satisfied, the user will not perform energy storage; according to the objective function value corresponding to the user's optimal configuration capacity, the user's energy storage is divided into priority levels, and the user's energy storage is configured according to the priority.

8. A method for optimizing user energy storage capacity configuration according to any one of claims 1 to 7, characterized in that, The dynamic calculation uses off-peak hours as the charging period and other peak hours as the discharging period, and calculates the energy storage using a two-charge and two-discharge operation strategy every day.

9. A method for optimizing the configuration of user energy storage capacity according to claim 1, 2, 3, or 4, characterized in that, If a user's daily energy storage revenue is less than a preset threshold, the user will not configure energy storage capacity. If the user's daily energy storage revenue is greater than or equal to the preset threshold, the optimal energy storage capacity for the user will be calculated.

10. A method for optimizing user energy storage capacity configuration according to any one of claims 1 to 7, characterized in that, S1 includes: collecting load data with an hour as the period, identifying outliers in historical load data at the same time using a box plot, calculating the load interval at each hour in sequence, and obtaining a typical daily energy consumption curve at point m.

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