A method and system for energy storage use

Abstract

The embodiment of the application discloses a kind of energy storage power utilization method and system, the method comprises: according to the historical power utilization data of target enterprise, the off-season estimated peak time power utilization ratio of target enterprise, the daily average peak time power utilization of target enterprise and the target enterprise storage capacity are determined;According to the off-season estimated peak time power utilization ratio of target enterprise and the daily average peak time power utilization of target enterprise, the target enterprise reserved off-season peak time power utilization is determined;According to the target enterprise reserved off-season peak time power utilization and the target enterprise storage capacity, the target enterprise today flat section power utilization is determined;Based on the target enterprise today flat section power utilization, power utilization is carried out in today flat section time period. By reasonably allocating power utilization time, more comprehensive energy storage cost reduction than traditional peak clipping is realized.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to an energy storage and power consumption method and system. Background Technology

[0002] Traditional pure energy storage solutions reduce costs through peak shaving and valley filling. This means that the stored energy is fully charged during off-peak hours and then used during peak hours, effectively charging off-peak rates. However, daily electricity prices include not only off-peak and peak times but also flat rates. Traditional methods only utilize the largest price difference between peak and off-peak periods, neglecting flat rates. Ideally, the best solution is to fully charge during off-peak hours and use it all during peak hours. However, in reality, businesses experience seasonal fluctuations in electricity consumption. A reasonable deployment of energy storage systems is not based on the lowest electricity consumption on a single day but rather on a comprehensive consideration of the entire operational period. Therefore, a reasonable energy storage configuration must be lower than, but higher than, off-peak demand. While it's possible to ensure daily charging and use during the off-peak season, there might be surplus energy. However, the daily amortized cost of the energy storage remains unchanged, significantly reducing cost-saving efficiency.

[0003] Better allocation of energy charging and discharging time can not only improve the utilization rate of energy storage devices and further reduce costs, but also make battery procurement plans more flexible. Summary of the Invention

[0004] Therefore, embodiments of this application provide an energy storage power consumption method and system that achieves more comprehensive energy storage cost reduction than traditional peak shaving and valley filling by rationally allocating power consumption time.

[0005] To achieve the above objectives, the embodiments of this application provide the following technical solutions: According to a first aspect of the embodiments of this application, a method for storing and utilizing electricity is provided, the method comprising: Based on the target company’s historical electricity consumption data, determine the target company’s estimated off-season peak-hour electricity consumption ratio, the target company’s average daily peak-hour electricity consumption, and the target company’s electricity storage capacity. The target company's reserved off-season peak electricity consumption is determined based on the target company's estimated off-season peak electricity consumption ratio and the target company's average daily peak electricity consumption. The target company's average electricity consumption for today is determined based on the target company's reserved off-season peak electricity consumption and the target company's electricity storage capacity. Based on the target enterprise's average electricity consumption today, electricity consumption is carried out during the average electricity consumption period today.

[0006] Optionally, the reserved off-season peak-hour electricity consumption for the target enterprise is determined based on the ratio of the target enterprise's estimated off-season peak-hour electricity consumption and the target enterprise's average daily peak-hour electricity consumption, including: The target company's reserved off-season peak electricity consumption ratio is obtained by multiplying the target company's average daily peak electricity consumption by the off-season peak electricity consumption ratio.

[0007] Optionally, the off-season estimated peak-hour electricity consumption ratio of the target enterprise is determined based on the estimated peak-hour electricity consumption ratio of the entire industry.

[0008] Optionally, the estimated peak-hour electricity consumption ratio for the entire industry is determined according to the following formula:

[0009]

[0010] Where m represents the number of companies in the entire industry with historical statistical data. This is the change in electricity consumption over time period t, based on the average annual electricity consumption data of company i. This represents the electricity consumption of company i under condition t.

[0011] Optionally, determining the target enterprise's average electricity consumption for the day based on the target enterprise's reserved off-season peak-hour electricity consumption and the target enterprise's electricity storage includes: The target company's electricity storage capacity is subtracted from its reserved off-season peak-hour electricity consumption to obtain the target company's average electricity consumption for today.

[0012] Optionally, the average daily peak electricity consumption of the target enterprise is determined as follows: The average daily peak electricity consumption within the specified dates is determined as the target company's average daily peak electricity consumption; or The maximum peak-hour electricity consumption within the specified date period is determined as the target company's average daily peak-hour electricity consumption; or The electricity consumption with a probability distribution within a set confidence interval during peak hours on a set date is defined as the target company's average daily peak electricity consumption; or The average daily peak electricity consumption of the target enterprise is determined based on the estimated value of the linear fitting line of electricity consumption within the set date; or Based on historical data, algorithmic models are used to predict the average daily peak electricity consumption of target enterprises.

[0013] Optionally, the historical electricity consumption data of the target enterprise is statistically analyzed in the following manner: If the target company's historical electricity consumption data includes hourly historical electricity consumption data, then the hourly historical electricity consumption data will be used directly; if the target company's historical electricity consumption data only includes daily data and peak and off-peak hours cannot be distinguished, then the target company's historical electricity consumption data will be determined based on the industry statistical average ratio of peak electricity consumption or total electricity consumption.

[0014] According to a second aspect of the embodiments of this application, an energy storage power system is provided, the system comprising: The data statistics module is used to determine the target company's off-season estimated peak-hour electricity consumption ratio, the target company's average daily peak-hour electricity consumption, and the target company's electricity storage capacity based on the target company's historical electricity consumption data. The module for reserving off-season peak electricity consumption is used to determine the reserved off-season peak electricity consumption of the target enterprise based on the target enterprise's estimated off-season peak electricity consumption ratio and the target enterprise's average daily peak electricity consumption. The electricity consumption module is used to determine the target company's average electricity consumption for today based on the target company's reserved off-season peak-hour electricity consumption and the target company's stored electricity. The power consumption module is used to consume electricity during the normal electricity consumption period of the target enterprise today.

[0015] According to a third aspect of the present application, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0016] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, on which computer-readable instructions are stored, which can be executed by a processor to implement the method described in the first aspect above.

[0017] In summary, the embodiments of this application provide an energy storage electricity consumption method and system. This method determines the target enterprise's estimated off-season peak-hour electricity consumption ratio, average daily peak-hour electricity consumption, and energy storage capacity based on the target enterprise's historical electricity consumption data. It then determines the target enterprise's reserved off-season peak-hour electricity consumption based on the estimated off-season peak-hour electricity consumption ratio and average daily peak-hour electricity consumption. Finally, it determines the target enterprise's current day's average-hour electricity consumption based on the reserved off-season peak-hour electricity consumption and the target enterprise's energy storage capacity. Electricity consumption is then carried out during the current day's average-hour period based on the target enterprise's current average-hour electricity consumption. By rationally allocating electricity consumption time, this method achieves more comprehensive energy storage cost reduction than traditional peak shaving and valley filling. Attached Figure Description

[0018] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0019] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0020] Figure 1 This application provides a schematic diagram of an energy storage and power consumption method according to an embodiment of the present application. Figure 2 A block diagram of an energy storage power system provided in this application embodiment; Figure 3 This illustration shows a structural schematic diagram of an electronic device provided in an embodiment of this application; Figure 4 A schematic diagram of a computer-readable storage medium provided in an embodiment of this application is shown. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another.

[0023] The common three-tier electricity pricing system can be divided into the following three categories: Scenario 1: There is only a single peak-hour electricity price period; since low-cost charging is not possible from the start of the peak hour until the electricity is depleted, there is no room for further cost reduction. This is not within the scope of the discussion of the embodiments in this application.

[0024] Scenario 2: There are multiple peak-hour electricity pricing periods, but there are off-peak electricity pricing periods in between the two peaks; for example, starting from October 15, 2021, Zhejiang implemented time-of-use electricity pricing for large industrial users above 220 kV. Assuming sufficient charging speed, the battery is fully charged during off-peak hours, and the battery is used during peak hours, which is the optimal charging and discharging scheme. In this case, the multi-peak scenario is equivalent to splicing together a series of single-peak scenarios. Similar to Scenario 1, there is no further room for cost reduction, and it is not within the scope of discussion in the embodiments of this application.

[0025] Scenario 3: There are multiple peak-hour electricity price periods, and there are no off-peak electricity price periods between peaks, but there are flat-peak electricity price periods. For example, in March 2022, the time-of-use electricity price for large industrial users above 220 kV in Jiangsu Province was as follows: off-peak electricity price was 0.2453 yuan / kWh, flat-peak electricity price was 0.5862 yuan / kWh, and peak-hour electricity price was 1.0080 yuan / kWh; this situation falls within the scope of discussion of the embodiments in this application. In daily charging, there are off-peak hours during the day. If we consider recharging the battery during these off-peak hours, then the two peak hours can be considered as two independent power consumption cycles, which is not discussed in the embodiments of this application. For ease of understanding of the functions provided by the embodiments of this application, daytime charging is not considered.

[0026] The method provided in this application focuses on how to change the charging and discharging decisions of the energy storage device after it has been deployed, so as to achieve further cost reduction. Therefore, the calculation process focuses on the additional cost reduction effect and no longer considers the deployment cost of the energy storage device.

[0027] This application embodiment considers the situation where there are both flat periods and peak periods between the end of the previous valley period and the beginning of the next valley period, and the energy storage device is fully charged at the beginning.

[0028] Figure 1 This application illustrates an energy storage and electricity consumption method according to an embodiment of the present application, the method comprising: Step 101: Determine the target company's estimated off-season peak-hour electricity consumption ratio, average daily peak-hour electricity consumption, and electricity storage capacity based on the target company's historical electricity consumption data; Step 102: Determine the target company's reserved off-season peak electricity consumption based on the target company's estimated off-season peak electricity consumption ratio and the target company's average daily peak electricity consumption; Step 103: Determine the target company's average electricity consumption for today based on the target company's reserved off-season peak electricity consumption and the target company's electricity storage; Step 104: Based on the target enterprise's average electricity consumption today, conduct electricity consumption within the average time period today.

[0029] In one possible implementation, step 102 involves determining the target enterprise's reserved off-season peak-hour electricity consumption based on the target enterprise's estimated off-season peak-hour electricity consumption ratio and the target enterprise's average daily peak-hour electricity consumption, including: The target company's reserved off-season peak electricity consumption ratio is obtained by multiplying the target company's average daily peak electricity consumption by the off-season peak electricity consumption ratio.

[0030] In one possible implementation, the target enterprise's estimated off-season peak-hour electricity consumption ratio is determined based on the industry-wide estimated peak-hour electricity consumption ratio.

[0031] In one possible implementation, the estimated peak-hour electricity consumption ratio for the entire industry is determined according to the following formula:

[0032]

[0033] Where m represents the number of companies in the entire industry with historical statistical data. This is the change in electricity consumption over time period t, based on the average annual electricity consumption data of company i. This represents the electricity consumption of company i under condition t.

[0034] In one possible implementation, step 103, determining the target enterprise's electricity consumption during the current off-peak season based on the target enterprise's reserved off-peak peak electricity consumption and the target enterprise's stored electricity, includes: The target company's electricity storage capacity is subtracted from its reserved off-season peak-hour electricity consumption to obtain the target company's average electricity consumption for today.

[0035] In one possible implementation, the target enterprise's average daily peak electricity consumption is determined as follows: The average daily peak electricity consumption within a set date period is determined as the target company's average daily peak electricity consumption; or the maximum peak electricity consumption within a set date period is determined as the target company's average daily peak electricity consumption; or the electricity consumption within a set date period whose peak electricity consumption probability distribution falls within a set confidence interval is determined as the target company's average daily peak electricity consumption; or the estimated value of the linear fitting line of electricity consumption within a set date period is determined as the target company's average daily peak electricity consumption; or the target company's average daily peak electricity consumption is predicted based on historical data using an algorithm model.

[0036] In one possible implementation, the target enterprise's historical electricity consumption data is statistically analyzed in the following manner: If the target company's historical electricity consumption data includes hourly historical electricity consumption data, then the hourly historical electricity consumption data will be used directly; if the target company's historical electricity consumption data only includes daily data and peak and off-peak hours cannot be distinguished, then the target company's historical electricity consumption data will be determined based on the industry statistical average ratio of peak electricity consumption or total electricity consumption.

[0037] This application embodiment considers the situation where there are both flat periods and peak periods between the end of the previous valley period and the beginning of the next valley period, and the energy storage device is fully charged at the beginning.

[0038] The energy storage and electricity utilization method provided in the embodiments of this application will be described in further detail below. The solutions in the embodiments of this application mainly discuss the case of two peaks, which is the most common in daily life. For cases with more than two peaks, they can be discussed sequentially according to two adjacent peaks, and so on.

[0039] This application's embodiments are described under the background of practical application, specifically as follows: 1. Off-peak electricity price is approximately 0.3 yuan / kWh, flat-peak electricity price is approximately 0.8 yuan / kWh, and peak-peak electricity price is approximately 1.1 yuan / kWh. 2. Charge-discharge efficiency is approximately 100%. 3. Battery leakage rate is approximately 0%, meaning that unused electricity can be used the next day without increasing costs. Therefore, a full charge is considered the initial state each day, and the situation of starting with a partially charged battery is not discussed. 4. Assume that company A uses 200 kWh per hour during the off-peak season and 400 kWh per hour during peak season. 5. Assume that the company's average daily peak electricity consumption throughout the year is Q(A, average daily peak consumption throughout the year) = 1400 kWh (greater than 1400 kWh during the off-peak season), and the energy storage device deployed accordingly has a storage capacity E. A =1400 (degrees) (Note: E) A =Q(A, annual average peak time) is not constant, so it cannot be mixed in the formula.

[0040] Therefore, considering the above background, the original electricity cost for peak shaving and valley filling without using batteries is: C 原始成本 =C 平段 +C 高峰 =0.8×200×(3+7)+1.1×400×2=2480 (yuan) Traditional peak shaving and valley filling methods are based on peak and valley reduction techniques. The unit cost reduction result for the day is calculated as follows: C 传统 =C 平段 +C 峰时削峰填谷 =0.8×200×(3+7)+0.3×400×2=1840 (yuan); Cost reduction rate: D 传统 =(2480-1840) / 2480=25.8%.

[0041] It can be seen that because the demand for energy storage is greater than the peak electricity consumption during the off-season, even if electricity is used continuously during peak hours, it is still impossible to use up all the electricity.

[0042] First, the following parameters are defined in this application embodiment: This represents the statistical electricity consumption of company i under condition t.

[0043] ; This means that the comparison is based on the company's own average annual electricity consumption data, comparing changes in electricity consumption over different time periods. This is equivalent to normalizing the electricity consumption data for each company, making it easier to compare the fluctuations in electricity consumption during peak and off-peak seasons.

[0044] ; or ; m represents the number of enterprises sampled statistically within the industry; the average value represents the general fluctuation rule of the entire industry. m represents the scope of statistical enterprises, t represents the scope of statistical time. It is assumed that at the first peak, the electricity consumption Q (a certain enterprise, peak 1) has been consumed, and 0 < Q (a certain enterprise, peak 1) < E (a certain enterprise).

[0045] Ei represents the energy storage capacity purchased by enterprise i.

[0046] Q (a certain enterprise, valley electricity used in today's peak 1) = min (Q (a certain enterprise, today's peak 1), E (a certain enterprise)).

[0047] The energy storage and electricity consumption method provided in the embodiments of the present application includes two methods, which are specifically as follows: The first energy storage and electricity consumption method provided in the embodiments of the present application is as follows: 1. Based on industry statistical information, count the estimated peak electricity consumption ratio of the whole industry R (whole industry, off-peak peak time) = x%, where x% > 100%; considering the differences between individual enterprises, in order to avoid excessive power replenishment, resulting in waste and additional costs, set the estimated off-peak peak electricity consumption ratio of the target enterprise R (target enterprise, off-peak peak time), which is an estimated peak electricity consumption ratio between 100% and R (whole industry, off-peak peak time), for example, R (target enterprise, off-peak peak time) = (x% + 100%) / 2. Count the annual average daily peak electricity consumption of the target enterprise Q (target enterprise, annual average daily peak time); count the energy storage capacity of the target enterprise E (target enterprise); 2. Calculate the off-peak peak electricity consumption that the target enterprise needs to reserve: Q (target enterprise, off-peak peak time) = R (target enterprise, off-peak peak time) × Q (target enterprise, annual average daily peak time); 3. Calculate the available electricity in off-peak periods Q (target enterprise, off-peak electricity consumption) = E (target enterprise) - Q (target enterprise, off-peak peak time); 4. Find any off-peak time in advance (in the foregoing case 3, it is any off-peak time between two peaks) for electricity consumption, so as to achieve cost reduction.

[0048] With the above method, in practical application, taking enterprise A mentioned above as an example, assuming it is known that R (whole industry, off-peak peak time) = 3 / 7 = 42.8%; then the reserved peak electricity proportion of enterprise A R (enterprise A, off-peak peak time) = (R (whole industry, off-peak peak time) + 100%) / 2 = 5 / 7 = 71.43%, the corresponding reserved electricity consumption is 1000 kWh, which exceeds the actual peak electricity consumption of 800 kWh, and can guarantee peak electricity consumption. Therefore, the available off-peak electricity is 1400 - R (enterprise A, off-peak peak time)*1400 = (1 - 5 / 7)*1400 = 400 (kWh). Finally, use 400 kWh at any selected off-peak time.

[0049] The solution in this application embodiment is applied to how to change the charging and discharging decisions of energy storage devices after they have been deployed, in order to achieve further cost reduction. Therefore, in the calculation process, the focus is on the additional cost reduction effect, and the deployment cost of the energy storage devices is no longer considered.

[0050] The cost of using method one of the embodiments of this application is: C 方案一 =C 剩余平段 +C 平段削峰填谷 +C 峰时削峰填平 =0.8×(200×(3+7)-400)+0.3×400+0.3×800=1640 (yuan); Cost reduction rate: D 方案一 =(2480-1640) / 2480=33.87%>D 传统 It is evident that as long as the power supply for peak shaving and valley filling is sufficient, the remaining power stored in the energy storage devices will reduce costs as long as it is used during off-peak periods.

[0051] In one possible implementation, this application embodiment also provides a second method for energy storage and electricity consumption, as follows: 1. Obtain the target company's historical daily peak electricity consumption. Methods for obtaining this data include, but are not limited to: a) If there is historical electricity consumption data accurate to the hour or even the minute, then the peak electricity consumption can be directly statistically analyzed.

[0052] b) If only daily data is available and it is impossible to distinguish between peak and off-peak periods, then the daily historical data can be approximately converted into peak historical data based on the industry statistical average ratio of "peak electricity consumption / total electricity consumption".

[0053] 2. Based on recent historical peak-hour electricity consumption, select a statistically predicted value as the estimated value Q (target company, today's peak-hour) for the reserved peak-hour electricity consumption. Specific prediction methods include, but are not limited to: a) Take the average peak-hour electricity consumption over the past few days as today's peak-hour electricity consumption; b) Take the highest peak electricity consumption in the past few days as today's peak electricity consumption; c) Take the position of the 95% confidence interval of the probability distribution of peak electricity consumption in recent days as today's peak electricity consumption; d) Plot a linear curve of electricity consumption over the past few days, and take today's estimated value as today's peak electricity consumption; e) Predict today's peak electricity consumption using algorithmic models based on historical data.

[0054] 3. Then E (target company) - Q (target company, today's peak time) is the amount of electricity that the company can use in advance during the off-peak period. By using it at any off-peak period, cost reduction can be achieved.

[0055] Applying Method 2 to practical calculations, taking Company A as an example, assuming we obtain the peak electricity consumption of Company A for the past 3 working days, which are 790 kWh, 820 kWh, and 790 kWh respectively; taking the average of 800 kWh as the predicted peak electricity consumption for today; then the energy storage capacity to be used in advance during the normal period can be estimated as 1400-800=600 kWh; in any normal period, 600 kWh of electricity can be used.

[0056] After implementing Option 2, the cost calculation is as follows: C 方案二 =C 其他平段 +C 最后时段削峰填谷 +C 峰时削峰填平 +C 最后平段剩余 =0.8×(200×(3+7)-600)+0.3×600+0+0.3×800=1540 (yuan); Cost reduction rate: D 方案二 =(2480-1540) / 2480=37.9%>D 方案一 .

[0057] Since all costs are reduced during peak hours, and all remaining electricity is used for cost reduction during off-peak hours, the current calculation results represent the maximum cost reduction rate that can be achieved under ideal conditions by utilizing electricity during off-peak hours.

[0058] In one possible implementation, the target company's historical daily peak-hour electricity consumption is obtained. Methods for obtaining this data include, but are not limited to: if historical electricity consumption data is available down to the hour or even minute, then the peak-hour electricity consumption can be directly statistically analyzed. If only daily data is available and peak and off-peak periods cannot be distinguished, then the daily historical data can be approximated as peak-hour historical data based on the industry statistical average ratio "peak-hour electricity consumption / total electricity consumption".

[0059] In one possible implementation, based on recent historical peak-hour electricity consumption, a statistically predicted value is selected as the estimated value Q (target enterprise, today's peak hour) for the reserved peak-hour electricity consumption. Specific prediction methods include, but are not limited to: a) Take the average peak-hour electricity consumption over the past few days as today's peak-hour electricity consumption; b) Take the highest peak electricity consumption in the past few days as today's peak electricity consumption; c) Take the position of the 95% confidence interval of the probability distribution of peak electricity consumption in recent days as today's peak electricity consumption; d) Plot a linear curve of electricity consumption over the past few days, and take today's estimated value as today's peak electricity consumption; e) Predict today's peak electricity consumption using algorithmic models based on historical data.

[0060] This application embodiment also provides an energy storage and power consumption method, the method comprising: Step 1: Determine the target company's estimated off-season peak-hour electricity consumption ratio, average daily peak-hour electricity consumption, and electricity storage capacity based on the target company's historical electricity consumption data; Step 2: Multiply the estimated off-season peak-hour electricity consumption ratio of the target enterprise by the average daily peak-hour electricity consumption of the target enterprise to obtain the reserved off-season peak-hour electricity consumption of the target enterprise; Step 3: Subtract the target company's stored electricity from its reserved off-season peak-hour electricity consumption to obtain the target company's average electricity consumption for today; Step 4: Based on the target enterprise's average electricity consumption today, conduct electricity consumption within the average time period today.

[0061] In one possible implementation, the target enterprise's estimated off-season peak-hour electricity consumption ratio is determined based on the industry-wide estimated peak-hour electricity consumption ratio.

[0062] In one possible implementation, the estimated peak-hour electricity consumption ratio for the entire industry is determined according to the following formula:

[0063]

[0064] Where m represents the number of companies in the entire industry with historical statistical data. This is the change in electricity consumption over time period t, based on the average annual electricity consumption data of company i. This represents the electricity consumption of company i under condition t.

[0065] In one possible implementation, the target enterprise's average daily peak electricity consumption is determined as follows: The average daily peak electricity consumption within the specified dates is determined as the target company's average daily peak electricity consumption; or The maximum peak-hour electricity consumption within the specified date period is determined as the target company's average daily peak-hour electricity consumption; or The electricity consumption with a probability distribution within a set confidence interval during peak hours on a set date is defined as the target company's average daily peak electricity consumption; or The average daily peak electricity consumption of the target enterprise is determined based on the estimated value of the linear fitting line of electricity consumption within the set date; or Based on historical data, algorithmic models are used to predict the average daily peak electricity consumption of target enterprises.

[0066] In one possible implementation, the target enterprise's historical electricity consumption data is statistically analyzed in the following manner: If the target company's historical electricity consumption data includes hourly historical electricity consumption data, then the hourly historical electricity consumption data will be used directly; if the target company's historical electricity consumption data only includes daily data and peak and off-peak hours cannot be distinguished, then the target company's historical electricity consumption data will be determined based on the industry statistical average ratio of peak electricity consumption or total electricity consumption.

[0067] In summary, this application provides an energy storage electricity consumption method. It determines the target enterprise's estimated off-season peak-hour electricity consumption ratio, average daily peak-hour electricity consumption, and energy storage capacity based on the target enterprise's historical electricity consumption data. It then determines the target enterprise's reserved off-season peak-hour electricity consumption based on the estimated off-season peak-hour electricity consumption ratio and average daily peak-hour electricity consumption. Finally, it determines the target enterprise's current day's average-hour electricity consumption based on the reserved off-season peak-hour electricity consumption and the target enterprise's energy storage capacity. Electricity consumption is then carried out during the current day's average-hour period based on the target enterprise's current average-hour electricity consumption. By rationally allocating electricity consumption time, it achieves more comprehensive energy storage cost reduction than traditional peak shaving and valley filling methods.

[0068] Based on the same technical concept, embodiments of this application also provide an energy storage power system, such as... Figure 2 As shown, the system includes: Data statistics module 201 is used to determine the off-season estimated peak-hour electricity consumption ratio, the average daily peak-hour electricity consumption, and the electricity storage capacity of the target enterprise based on the target enterprise's historical electricity consumption data. The off-season peak electricity consumption module 202 is used to determine the off-season peak electricity consumption of the target enterprise based on the target enterprise's estimated off-season peak electricity consumption ratio and the target enterprise's average daily peak electricity consumption. Electricity consumption module 203 is used to determine the target enterprise's average electricity consumption for today based on the target enterprise's reserved off-season peak electricity consumption and the target enterprise's stored electricity. The power consumption module 204 is used to consume electricity during the normal time period of the day based on the target enterprise's normal electricity consumption today.

[0069] In one possible implementation, the reserved off-season peak-hour power consumption module 202 is specifically used for: The target company's reserved off-season peak electricity consumption ratio is obtained by multiplying the target company's average daily peak electricity consumption by the off-season peak electricity consumption ratio.

[0070] In one possible implementation, the target enterprise's estimated off-season peak-hour electricity consumption ratio is determined based on the industry-wide estimated peak-hour electricity consumption ratio.

[0071] In one possible implementation, the estimated peak-hour electricity consumption ratio for the entire industry is determined according to the following formula:

[0072]

[0073] Where m represents the number of companies in the entire industry with historical statistical data. This is the change in electricity consumption over time period t, based on the average annual electricity consumption data of company i. This represents the electricity consumption of company i under condition t.

[0074] In one possible implementation, the power consumption module 203 is specifically used for: The target company's electricity storage capacity is subtracted from its reserved off-season peak-hour electricity consumption to obtain the target company's average electricity consumption for today.

[0075] In one possible implementation, the target enterprise's average daily peak electricity consumption is determined as follows: The average daily peak electricity consumption within the specified dates is determined as the target company's average daily peak electricity consumption; or The maximum peak-hour electricity consumption within the specified date period is determined as the target company's average daily peak-hour electricity consumption; or The electricity consumption with a probability distribution within a set confidence interval during peak hours on a set date is defined as the target company's average daily peak electricity consumption; or The average daily peak electricity consumption of the target enterprise is determined based on the estimated value of the linear fitting line of electricity consumption within the set date; or Based on historical data, algorithmic models are used to predict the average daily peak electricity consumption of target enterprises.

[0076] In one possible implementation, the target enterprise's historical electricity consumption data is statistically analyzed in the following manner: If the target company's historical electricity consumption data includes hourly historical electricity consumption data, then the hourly historical electricity consumption data will be used directly; if the target company's historical electricity consumption data only includes daily data and peak and off-peak hours cannot be distinguished, then the target company's historical electricity consumption data will be determined based on the industry statistical average ratio of peak electricity consumption or total electricity consumption.

[0077] This application also provides an electronic device corresponding to the method provided in the foregoing embodiments. Please refer to... Figure 3 The diagram illustrates an electronic device provided by some embodiments of this application. The electronic device 20 may include: a processor 200, a memory 201, a bus 202, and a communication interface 203, wherein the processor 200, the communication interface 203, and the memory 201 are connected via the bus 202; the memory 201 stores a computer program that can run on the processor 200, and when the processor 200 runs the computer program, it executes the method provided by any of the foregoing embodiments of this application.

[0078] The memory 201 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one physical port 203 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0079] Bus 202 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used to store programs. After receiving an execution instruction, the processor 200 executes the program. The method disclosed in any of the foregoing embodiments of this application can be applied to the processor 200, or implemented by the processor 200.

[0080] The processor 200 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 200 or by instructions in software form. The processor 200 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 201. The processor 200 reads the information in memory 201 and, in conjunction with its hardware, completes the steps of the above method.

[0081] The electronic devices and methods provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0082] This application also provides a computer-readable storage medium corresponding to the method provided in the foregoing embodiments. Please refer to... Figure 4The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored, which, when run by a processor, executes the methods provided in any of the foregoing embodiments.

[0083] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0084] The computer-readable storage medium provided in the above embodiments of this application and the method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0085] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. Various general-purpose devices can also be used in conjunction with the teachings herein. The required structure for constructing such devices is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0086] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0087] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0088] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0089] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0090] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the virtual machine creation apparatus according to embodiments of this application. This application can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0091] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0092] 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 a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0093] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for storing and utilizing electricity, characterized in that, The method includes: Based on the target company’s historical electricity consumption data, determine the target company’s off-season peak-hour electricity consumption ratio, the target company’s average daily peak-hour electricity consumption, and the target company’s electricity storage capacity. The target company's reserved off-season peak electricity consumption is determined based on the target company's estimated off-season peak electricity consumption ratio and the target company's average daily peak electricity consumption. The target company's average electricity consumption for today is determined based on the target company's reserved off-season peak electricity consumption and the target company's electricity storage capacity. Based on the target enterprise's average electricity consumption today, electricity consumption is carried out during the average electricity consumption period today; The estimated peak-hour electricity consumption ratio for the target enterprise during the off-season is determined based on the estimated peak-hour electricity consumption ratio for the entire industry, which is determined according to the following formula: Where m represents the number of companies in the entire industry with historical statistical data. This is the change in electricity consumption over time period t, based on the average annual electricity consumption data of company i. This represents the electricity consumption of company i under condition t.

2. The method as described in claim 1, characterized in that, The target company's reserved off-season peak-hour electricity consumption is determined based on the ratio of its estimated off-season peak-hour electricity consumption and its average daily peak-hour electricity consumption, including: The target company's reserved off-season peak electricity consumption ratio is obtained by multiplying the target company's average daily peak electricity consumption by the off-season peak electricity consumption ratio.

3. The method as described in claim 1, characterized in that, The step of determining the target enterprise's average electricity consumption for the day based on the target enterprise's reserved off-season peak-hour electricity consumption and the target enterprise's electricity storage includes: The target company's electricity storage capacity is subtracted from its reserved off-season peak-hour electricity consumption to obtain the target company's average electricity consumption for today.

4. The method as described in claim 1, characterized in that, The average daily peak electricity consumption of the target enterprise is determined as follows: The average daily peak electricity consumption within the specified dates is determined as the target company's average daily peak electricity consumption; or The maximum peak-hour electricity consumption within the specified date period is determined as the target company's average daily peak-hour electricity consumption; or The electricity consumption with a probability distribution within a set confidence interval during peak hours on a set date is defined as the target company's average daily peak electricity consumption; or The average daily peak electricity consumption of the target enterprise is determined based on the estimated value of the linear fitting line of electricity consumption within the set date; or Based on historical data, algorithmic models are used to predict the average daily peak electricity consumption of target enterprises.

5. The method as described in claim 1, characterized in that, The historical electricity consumption data of the target enterprise is statistically analyzed in the following manner: If the target company's historical electricity consumption data includes hourly historical electricity consumption data, then the hourly historical electricity consumption data will be used directly; if the target company's historical electricity consumption data only includes daily data and peak and off-peak hours cannot be distinguished, then the target company's historical electricity consumption data will be determined based on the industry statistical average ratio of peak electricity consumption or total electricity consumption.

6. An energy storage power system, characterized in that, The system includes: The data statistics module is used to determine the target company's off-season estimated peak-hour electricity consumption ratio, the target company's average daily peak-hour electricity consumption, and the target company's electricity storage capacity based on the target company's historical electricity consumption data. The module for reserving off-season peak electricity consumption is used to determine the reserved off-season peak electricity consumption of the target enterprise based on the target enterprise's estimated off-season peak electricity consumption ratio and the target enterprise's average daily peak electricity consumption. The electricity consumption module is used to determine the target company's average electricity consumption for today based on the target company's reserved off-season peak-hour electricity consumption and the target company's stored electricity. The power consumption module is used to consume electricity during the average electricity consumption period of the target enterprise today. The estimated peak-hour electricity consumption ratio for the target enterprise during the off-season is determined based on the estimated peak-hour electricity consumption ratio for the entire industry, which is determined according to the following formula: Where m represents the number of companies in the entire industry with historical statistical data. This is the change in electricity consumption over time period t, based on the average annual electricity consumption data of company i. This represents the electricity consumption of company i under condition t.

7. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method as claimed in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, It stores computer-readable instructions that can be executed by a processor to implement the method as described in any one of claims 1-5.

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