A user-side energy storage power station capacity planning method

Abstract

The application provides a user-side energy storage power station capacity planning method, comprising the following steps: obtaining historical power consumption data of users in the power supply range of the energy storage power station; obtaining real-time peak-valley electricity prices in the power supply range of the energy storage power station, including accurate prices of peak, valley and flat periods and period information of each price; obtaining rated capacity information of a transformer to which the energy storage power station is expected to be connected; configuring a working mode of the energy storage power station; calculating and analyzing a charging and discharging scheme of the energy storage power station and outputting a result. The application provides a user-side energy storage power station capacity planning method based on peak-valley difference arbitrage revenue maximization. The method firstly performs quantitative analysis according to historical power consumption data of users to calculate a peak-valley difference of the user side. According to the peak-valley difference and a cost and benefit model of the energy storage device, peak-valley difference arbitrage revenues of different energy storage capacities are calculated. According to the principle of peak-valley difference arbitrage revenue maximization, the highest energy storage capacity with the highest investment benefit of the energy storage power station is evaluated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage, in particular to a user-side energy storage power station capacity planning method. BACKGROUND

[0002] User-side energy storage refers to deploying energy storage devices at the user side to meet the user-side power demand and improve the flexibility and reliability of the user-side power system. User-side energy storage power stations can be used for peak load shifting, distributed power source access, emergency backup, and other scenarios. In the domestic power grid and market environment, peak load shifting is the most common. Peak load shifting corresponds to user-side scenarios, also known as peak-valley difference arbitrage, which refers to taking advantage of the price difference between different time periods to charge energy storage during the low price period and discharge energy storage during the peak price period, thereby reducing the overall power cost. At present, the peak-valley price difference in China is widening, providing favorable conditions for peak load shifting applications. The use of this technology can effectively reduce the user's power cost and promote the development of the energy storage industry.

[0003] User-side energy storage power station capacity planning refers to calculating the capacity of the energy storage power station that meets the user-side demand based on user-side power load, power market price, and energy storage device technical parameters. Existing user-side energy storage power station capacity planning methods mainly include the following:

[0004] Capacity planning based on user power load: This method calculates the capacity demand of the user-side energy storage power station through statistical analysis of the user-side power load. Capacity planning based on power market price: This method calculates the capacity demand of the user-side energy storage power station through prediction of the power market price. Capacity planning based on energy storage device technical parameters: This method calculates the capacity demand of the user-side energy storage power station through analysis of the performance parameters of the energy storage device.

[0005] These methods have certain limitations. Capacity planning based on user power load cannot fully consider the impact of the power market price, resulting in insufficient accuracy of capacity planning. Capacity planning based on the power market price cannot fully consider the characteristics of the user-side power load, resulting in low economic efficiency of capacity planning. Capacity planning based on energy storage device technical parameters cannot fully consider the user-side demand, resulting in insufficient flexibility of capacity planning.

[0006] The present application proposes a user-side energy storage power station capacity planning method based on peak-valley difference arbitrage revenue maximization. SUMMARY

[0007] The application aims to provide a user-side energy storage power station capacity planning method, solve the influence of peak-valley difference arbitrage income, improve the accuracy of capacity planning, combine the characteristics of user-side power consumption load, improve the economy of capacity planning, have strong flexibility, and can be adjusted according to user-side demand.

[0008] To achieve the above purpose, the application provides the following technical scheme: a user-side energy storage power station capacity planning method, characterized by comprising the following steps:

[0009] S1, obtaining historical power consumption data of users in the power supply range of the energy storage power station; obtaining real-time peak-valley electricity prices in the power supply range of the energy storage power station, including accurate prices of peak, valley and flat periods and time period information of each price; obtaining rated capacity information of the expected access transformer of the energy storage power station;

[0010] S2, configuring the working mode of the energy storage power station, the working mode being configured as one charging and one discharging mode or two charging and two discharging modes, and determining the capacity utilization degree of the system, the utilization degree being the matching degree of the energy storage system capacity and the historical power consumption data;

[0011] S3, calculating and analyzing the charging and discharging scheme of the energy storage power station and outputting the result;

[0012] S31, first, pre-processing the input historical power consumption data to obtain hourly load power consumption data in the whole year, and defining the data sequence as P Load1 , P Load2 , …, P Load8760 ;

[0013] S32, according to the input peak-valley period, extracting the power consumption data of the peak segment of each day, and according to the mode setting, selecting one charging and one discharging or two charging and two discharging, obtaining the total of the hourly power consumption of the peak segment of each day in 365 days, that is, E peak1 , E peak2 , …, E peak365 , wherein P load (t) is the power consumption of a certain hour in the peak electricity price period, T peakStart is the start time of the peak electricity price period, and T peakStop is the end time of the peak electricity price period;

[0014] S33, sorting the data in S32 from large to small, and then selecting a certain order in the sorted data list as the maximum reasonable power consumption of the system peak segment according to the set capacity utilization degree, that is, the recommended energy storage capacity of the system, and then

[0015]

[0016] Consider the efficiency of the energy storage system, judge whether the system can charge the required power in the valley section;

[0017] Configure the energy storage power station as one charge and one discharge mode, the maximum reasonable power consumption of the system divided by the system efficiency as the expected charging capacity:

[0018]

[0019] Where E dischar is the maximum reasonable power consumption of the system, η sys is the system efficiency, E* char is the expected charging capacity of the system;

[0020] For the input of historical electricity information, check whether the chargeable capacity of the valley period is greater than the expected charging capacity every day, the calculation method is: subtract the load power consumption of the valley period from the rated capacity of the transformer, and then calculate the total chargeable capacity according to the valley price period:

[0021]

[0022] Where P trans is the rated capacity of the upper transformer of the system, P load (t) is the power consumption of a certain hour in the valley price period, T VallyStart is the time when the valley price period starts, T VallyStop is the time when the valley price period ends, E char is the chargeable capacity of the valley price period.

[0023] If the available chargeable capacity is less than the expected charging capacity, use the available chargeable capacity as the expected charging capacity of the system, otherwise the expected charging capacity value remains unchanged and the reasonable energy storage capacity of the system is obtained by inverse operation, that is, the recommended battery capacity of the energy storage system is:

[0024]

[0025] Where E char is the chargeable capacity of the valley price period, E * char is the expected charging capacity of the valley price period, E ba tt is the recommended battery capacity of the system;

[0026] Configure the energy storage power station as two charge and two discharge mode, first judge whether the input price has two valley periods, if so, the system charges in two valley periods, supplies the subsequent two peak periods and discharges, if the local price has only one valley period, the two charge and two discharge mode cannot be activated;

[0027] After the two charge and two discharge mode is activated, the recommended battery capacity of the energy storage system is:

[0028]

[0029] wherein E d1 , E d2 are the maximum reasonable power consumptions of the first peak period and the second peak period respectively, η sys is the system efficiency, E char1 , E char2 are the chargeable powers of the first and second valley periods.

[0030] S4, the power of the minimum PCS is calculated, the shortest time interval of the peak and valley periods throughout the day is determined, the power of the time interval with the shortest time length is analyzed, the maximum power in the time interval of each day throughout the year is determined first, and then a numerical sequence is obtained by sorting the maximum power data throughout the year from large to small, i.e.

[0031] When the charging period is analyzed, the maximum power in the time interval of each day throughout the year is determined first, and then a numerical sequence is obtained by sorting the 365 difference values from large to small after the available capacity of the transformer is subtracted from the maximum value;

[0032] i.e.

[0033] Then, the corresponding data value in the sequence is selected according to the capacity utilization degree as the PCS power;

[0034] S5, the daily average cycle number of the battery is calculated, the system chargeable power is calculated based on the historical power consumption data and the battery capacity and PCS power data calculated in the foregoing steps, the method is that the battery is charged in the valley period, the smaller value between the difference between the transformer capacity and the current power and the PCS power is accumulated by time, until the battery is fully charged or the valley period is ended, the total electric energy of the time is recorded as the charging capacity, when there are multiple valley periods in a day, the charging capacities of the multiple valley periods need to be stacked, the charging capacity of each day is accumulated by day for 365 days, and the accumulated sum is divided by 365 and then divided by the battery capacity, which is the daily average cycle number, i.e. E char is the total charging electric energy throughout the day, E batt is the battery capacity calculated in the foregoing steps, N avg is the average daily cycle number of the battery throughout the year;

[0035] Preferably, the following steps are further included:

[0036] S6, the internal rate of return of the energy storage system is calculated,

[0037] To obtain the project's cash flow, the operating life of the energy storage equipment is determined based on the previously input battery life and the calculated average daily cycle count. The annual cash return of the project can then be easily calculated using the average daily cycle count and peak-valley price difference. After summing the returns, the internal rate of return (IRR) of the energy storage project can be obtained using the formula:

[0038] CF i =E batt *N avg *Peak-valley price difference*365;

[0039]

[0040] Ebatt is the recommended battery capacity, Navg is the battery's daily cycle count, and Cfi is the annual charge-discharge gain. This assumes the annual charge-discharge amount is the same. avg The average number of battery cycles per day throughout the year; the peak-valley price difference is derived from previously input electricity price information and the difference between peak and valley electricity prices; battery life is derived from the system's default parameters; n is the number of years the system can operate; and the project's NPV is the project's cash discount rate, which is considered to be 0 when calculating the internal rate of return.

[0041] Preferably, the data acquired in S1 is historical electricity consumption data for at least one year, with at least one sampling point per hour. The historical electricity consumption data includes timestamps and power information. The timestamps are used to specify the exact sampling time and should at least include month, day, and hour information. The power information is used to characterize the electricity consumption of the entire system during the corresponding period.

[0042] Preferably, when configuring the working mode of the energy storage power station in S2, the default is a one-charge-one-discharge mode. In the one-charge-one-discharge mode, the energy storage system charges during off-peak electricity price at night and discharges during peak electricity price during the day. In the two-charge-two-discharge mode, the energy storage system charges during off-peak electricity price or flat electricity price during the day and discharges during peak electricity price at night.

[0043] Preferably, a sensitivity analysis is performed on the rate of return in S6. Based on the energy storage capacity and recommended PCS power given in the above steps, a reasonable step size and trial value are selected, and the trial values ​​are arranged and combined. For the combined trial values, S3 and S4 of the previous step are repeatedly executed to obtain the total revenue and rate of return of the system under this configuration. After completing all the combinations that need to be tried, the curves of total revenue and rate of return are plotted so as to finally determine the reasonable capacity of the system.

[0044] Beneficial effects: The technical solution of this application has the following technical effects:

[0045] 1. The application provides a user-side energy storage power station capacity planning method based on peak-valley difference arbitrage revenue maximization. The method first quantitatively analyzes the user's historical power consumption data to calculate the user's peak-valley difference. According to the peak-valley difference and the cost-benefit model of the energy storage device, the peak-valley difference arbitrage revenue of different energy storage capacity is calculated. According to the principle of maximizing the peak-valley difference arbitrage revenue, the energy storage capacity with the highest investment return of the energy storage power station is evaluated.

[0046] 2. The application fully considers various operating modes of the user-side energy storage system, such as one charging and one discharging mode, two charging and two discharging mode, overload prevention mode, battery cycle life, etc., to avoid large differences between expected yield and actual yield. The scheme is based on statistical principles and can obtain reasonable configuration parameters and expected yield of the system based on a small amount of calculation. The calculation amount is reduced, and the efficiency of the energy storage system scheme design is greatly improved.

[0047] It should be understood that all combinations of the aforementioned concepts and additional concepts described in greater detail below can be seen as part of the subject matter of the present disclosure, as long as such concepts do not contradict each other.

[0048] The foregoing and other aspects, embodiments and features of the present teachings can be better understood from the following description of the present teachings taken in conjunction with the accompanying drawings. Additional aspects, embodiments and features of the present teachings will be apparent from the following description {e.g., examples of structures and devices, manufacturing processes, etc.), taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0049] The accompanying drawings are not intended to be drawn to scale. In the drawings, each same or like component will be denoted by the same reference signs throughout the several views. For the purpose of clarity, not every component can be marked in every drawing. Embodiments of various aspects of the present teachings will now be described, by way of example and with reference to the drawings, in which:

[0050] Figure 1 This is a graph of the power consumption during the peak electricity price each day within a year (sorted by date) for the present application.

[0051] Figure 2 This is a graph of the power consumption during the peak electricity price each day within a year (sorted by size) for the present application.

[0052] Figure 3 This is a graph of the power consumption during the peak electricity price each day within a year (sorted by size) for the present application. DETAILED DESCRIPTION

[0053] For a more complete understanding of the technical content of the present application, specific embodiments are described below with reference to the accompanying drawings. In the present disclosure, aspects of the present application are described with reference to the accompanying drawings, which show many illustrative embodiments. The embodiments of the present disclosure are not necessarily defined in all aspects of the present application. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any of a number of ways, as the concepts and embodiments disclosed herein are not limited to any implementation. In addition, some aspects of the present application can be used alone, or in any appropriate combination with other aspects of the present application.

[0054] The technical problem to be solved by the present embodiment is to quickly analyze and calculate the capacity and power data suitable for the user side energy storage power station with as little input data as possible, while giving the expected revenue of the power station under this configuration. The overall steps are as follows:

[0055] S1, input basic information, including the following: obtain the historical power consumption of the place where the energy storage power station is located, obtain the existing peak-valley electricity price policy of the place where the energy storage power station is located, and obtain the rated capacity information of the transformer to which the energy storage power station is expected to be connected;

[0056] The historical power consumption of the place where the energy storage power station is located is obtained, and the data needs to have one year of historical power consumption data, which can come from the owner's own system record or the power consumption data exported by the power grid company. The data is at least one sampling point per hour, and higher sampling density data such as 15 minutes per sampling point can also be used. The data should have a timestamp to clearly indicate the specific sampling time, and the timestamp should at least include month, day, and hour information. The power information unit should be kilowatt (kWh) to represent the power consumption of the entire system during the corresponding period.

[0057] The existing peak-valley electricity price policy of the place where the energy storage power station is located is obtained, including the accurate prices of peak, valley and flat periods and the period information of each price; the peak-valley electricity price information should include how many specific electricity prices in a day; each electricity price is executed for how many hours; whether there is a peak electricity price, and the peak electricity price is executed in which months.

[0058] The rated capacity information of the transformer to which the energy storage power station is expected to be connected is obtained, where the transformer refers to the transformer capacity corresponding to the electricity metering point of the place where the energy storage power station is located, and the capacity of the subordinate transformer directly connected by the energy storage system (if any).

[0059] S2, configure the working mode of the energy storage power station, specifically as follows, select one charging and discharging mode or two charging and discharging mode; the default is one charging and discharging mode, that is, the energy storage system charges during the night valley electricity price period and discharges during the day peak electricity price period. If the two charging and discharging mode is allowed, the energy storage system will charge during the day valley electricity price period or even flat electricity price period, and discharge during the evening peak electricity price period. One charging and discharging mode is suitable for more areas or scenarios, and two charging and discharging mode is suitable for areas with valley electricity price (at least flat electricity price) during the day, especially at noon. Of course, at present, the energy storage market is relatively active, and most of the areas with good investment returns that can do two charging and discharging are also areas with the above-mentioned electricity price mode. If there is such an electricity price mode in the local area, two charging and discharging mode will generally be adopted, so as to improve the income of the energy storage system and shorten the project investment recovery period.

[0060] Determine the capacity utilization of the system, which is used to represent the matching degree of the capacity of the energy storage system and the historical electricity consumption data. Due to economic reasons, the energy storage system should be fully utilized as much as possible, otherwise it will waste investment. For example, Figures 2-3 , the embodiment adopts D90 and D80 to calculate, that is, the capacity of the energy storage system meets the use demand in 90% of the days of the year, or the capacity of the energy storage system meets the use demand in 80% of the days. When it is considered that the historical electricity consumption rule and the future electricity consumption rule are very close, D90 mode can be selected, otherwise D80 mode can be selected. Wherein 90 and 80 represent that the future electricity consumption rule is only 90% or 80% consistent with the historical data. The inconsistency here means that the electricity consumption may be lower than the historical electricity consumption. The historical electricity consumption data will be analyzed first in the software, and then a safety margin will be left through back-off. Determine other information including system charging and discharging efficiency, unit construction cost, battery available SOC, cycle life and other information.

[0061] S3, calculate and analyze the recommended scheme of the energy storage power station and output the result, calculate the recommended capacity of the energy storage system, first preprocess the input historical electricity consumption data to obtain the hourly load electricity consumption data in the whole year, here the data sequence is defined as P Load1 , P Load2 , …, P Load8760 ;

[0062] According to the input peak, flat and valley period, the electricity consumption data of the peak period of each day is extracted. Here, according to the mode setting, whether one charging and discharging or two charging and discharging is selected, the result is the total of the hourly electricity consumption of the peak period of each day in 365 days, that is, E peak1 , E peak2 , …, E peak365 . Wherein (the calculation method is the same below), P load (t) is the electricity consumption of a certain hour in the peak electricity price period, T peakStartT is the start time of the peak electricity price period. peakStop The peak electricity price period ends at the specified time, or the sum of the electricity consumption during the first and second peak periods of the day, i.e., E. peakA1 E peakA2 , ..., E peakA365 and E peakB1 E peakB2 , ..., E peakB365 .

[0063] Sort the above data from largest to smallest. Then, based on the capacity utilization of the set stage, select the 328th position (corresponding to D90 utilization) or the 292nd position (corresponding to D80 utilization) of the sorted data list as the maximum reasonable power consumption during the system peak period, which is the recommended energy storage capacity of the system. Therefore:

[0064]

[0065] After considering the efficiency of the energy storage system, check whether the system can charge the required amount of electricity during off-peak hours. Here, it's necessary to consider whether the mode configuration selects one charge-one discharge or two charge-two discharge, as well as the total capacity of the transformer. If one charge-one discharge is selected, all charging will be completed during the off-peak hours at night. Specifically, the expected charging amount is calculated by dividing the system's maximum reasonable peak-hour power consumption by the system efficiency.

[0066] Right now:

[0067] Where E dischar To determine the maximum reasonable power consumption of the system, η sys For system efficiency, E* char The expected charge amount for the system.

[0068] The system checks the input historical electricity consumption information daily to see if the available charging capacity during off-peak hours is greater than the expected charging capacity. The calculation method is as follows: subtract the load electricity consumption during off-peak hours from the available capacity of the transformer, and then calculate the total available charging capacity based on the off-peak price period each day.

[0069] Right now

[0070] Where P trans P represents the rated capacity of the upstream transformer in the system. load (t) represents the electricity consumption during a certain hour of off-peak electricity pricing period, T VallyStart T is the start time of the off-peak electricity pricing period. VallyStop The end time of off-peak electricity pricing period, E char This refers to the amount of electricity that can be charged during off-peak electricity pricing periods.

[0071] If the available charging capacity is less than the expected charging capacity, the available charging capacity is used as the expected charging capacity of the system, otherwise the expected charging capacity value remains unchanged and the reasonable energy storage capacity of the system is reversely obtained by inverse operation, that is, the recommended battery capacity of the energy storage system is:

[0072]

[0073] wherein E char is the chargeable capacity in the valley price period, E * char is the expected charging capacity in the valley price period, E ba tt is the recommended battery capacity of the system.

[0074] If the user activates the two charging and two discharging mode, firstly, it should be judged whether the input electricity price has two valley periods, if yes, the system charges in the two valley periods respectively, and discharges to supply the subsequent two peak periods. If the local electricity price has only one valley period, a dialog box is popped up to prompt the user that the two charging and two discharging mode cannot be enabled.

[0075] After the two charging and two discharging mode is activated, E d In the two charging and two discharging mode, E d1 and E d2 represent the electricity consumption in the first peak price period and the second peak price period respectively. Correspondingly, the charging capacity before the arrival of the first peak period and the second peak period is represented by E char1 and E char2 . The calculation method of E char1 and E char2 is consistent with the calculation method of E char in the foregoing. In order to ensure the full use of the energy storage battery, the selection of E d and E char should be the smaller one of the two, that is, the recommended battery capacity of the energy storage system is:

[0076]

[0077] wherein E d1 and E d2 are the maximum reasonable electricity consumption in the first peak period and the second peak period respectively, η sys is the system efficiency, E char1 and E char2 are the chargeable capacities in the first and second valley price periods.

[0078] S4, calculate the recommended power of PCS, the energy storage system usually uses the battery with the maximum rate of 1C, so the PCS power is usually not greater than the battery capacity data. Considering that the smaller the PCS power, the lower the system cost, it is necessary to determine the minimum PCS power that does not affect the normal charging and discharging of the system. Determine which of the peak and valley periods throughout the day is the shortest. According to the price policy in different regions, most places have shorter peak periods, but some places with two valley periods may have a second valley period that is very narrow. Therefore, the power bottleneck of the system is most obvious in the second valley period. The judgment method here is to compare the lengths of the continuous peak and valley periods directly according to the price data. Note that in the two charging and two discharging areas, the lengths of the four periods should be compared separately. Record the shortest period for further analysis. If there are two periods with the same length, both of them need to be analyzed in the next step.

[0079] Perform power analysis on the period with the shortest length. If there are multiple shortest periods, the power of these periods needs to be analyzed. The analysis needs to distinguish between charging and discharging periods. When analyzing the discharging period, find the power distribution of the corresponding period in 365 days of the year according to the input load data. The specific method is to first determine the maximum power in this period of each day in 365 days of the year, and then sort the 365 maximum power data from large to small to get a numerical sequence. That is

[0080] When analyzing the charging period, the method is to first determine the maximum power in this period of each day in 365 days of the year, and then subtract this maximum value from the available capacity of the transformer, and then sort the 365 difference values from large to small to get a numerical sequence.

[0081] That is

[0082] Then select the 36th data or the 54th data in the sequence according to whether the D90 or D85 degree is selected in the previous stage. When there are multiple periods with the same length, each period needs to be processed in the above manner and the maximum value of the several values is finally found as the recommended power of the PCS.

[0083] S5, calculate the daily cycle number of the battery, after determining the capacity and PCS power of the energy storage system, the historical electricity consumption data and related transformer capacity data can be used to accurately simulate how much electricity the system charges and discharges in 365 days of the year. This set of data can be used to calculate the daily cycle number of the battery. The calculation method is:

[0084] The smaller value of the PCS power or the system chargeable power is accumulated by hour when the battery is charged during the valley period, until the battery is fully charged or the valley period ends. The total electrical energy of this period is recorded as the charging capacity. When there are multiple valley periods in a day, the charging capacities of multiple valley periods need to be added. The charging capacity of each day is accumulated day by day for 365 days, and the accumulated sum is divided by 365 and then divided by the battery capacity to obtain the daily average cycle number.

[0085] That is:

[0086] E char is the total electrical energy of the whole day charging, E batt is the battery capacity calculated in the previous step, N avg is the average daily cycle number of the battery per year.

[0087] S6, calculate the internal rate of return of the energy storage system, the calculation of the internal rate of return needs to know the cash flow of the project, according to the battery life and the average daily cycle number input in the previous stage, it is not difficult to obtain the operation period of the project. Then the annual cash income of the project is easily obtained from the average daily cycle number and the peak-valley price difference. After accumulating the income, the internal rate of return of the energy storage project can be obtained by using the internal rate of return formula. The formula needed in this process is: CF i = E batt * N avg * peak-valley price difference * 365;

[0088]

[0089] Ebatt is the recommended battery capacity, Navg is the average daily cycle number of the battery, Cfi is the annual charging and discharging income, which is considered to be the same every year, N avg is the average daily cycle number of the battery per year, the peak-valley price difference comes from the electricity price information input in the previous stage and the difference between the peak price and the valley price, the battery life comes from the system default parameter, n is the number of years the system can operate, the NPV of the project is the cash discount rate of the project, and when calculating the internal rate of return, it is considered as 0.

[0090] Sensitivity analysis of the rate of return, since the rate of return is not the only measure of project quality, the project mainly considers the changes in the total amount of income under different rates of return. The principle is: under the premise of unchanged power consumption, installing more or larger energy storage systems will lead to lower utilization and thus lower rates of return, but larger systems mean more electricity savings, so the total income will increase with the capacity of the system. Therefore, this analysis helps to further determine the reasonable capacity of the system from a business perspective. The implementation method here is: on the basis of the recommended energy storage capacity and recommended PCS power given in the previous steps, select a reasonable step size and try value. And arrange the combination of the trial values; for the combined trial values, repeat steps 3 and 4 of the previous step to get the total income and rate of return of the system under this configuration; after completing all the combinations that need to be tried, draw the total income and rate of return curve to determine the reasonable capacity of the system.

[0091] Implementation case

[0092] For example, there is the following power consumption history data, limited by the length of this article, only a part of it is taken out, the complete data is the power data of every 15 minutes in a year.

[0093]

[0094]

[0095]

[0096] In addition, the user's electricity price data is as follows:

[0097] Period Electricity price 0-1 0.4153 1-2 0.4153 2-3 0.4153 3-4 0.4153 4-5 0.4153 5-6 0.4153 6-7 0.4153 7-8 0.4153 8-9 1.0187 9-10 1.3714 10-11 1.3714 11-12 0.4153 12-13 0.4153 13-14 1.0187 14-15 1.0187 15-16 1.3714 16-17 1.3714 17-18 1.0187 18-19 1.0187 19-20 1.0187 20-21 1.0187 21-22 1.0187 22-23 0.4153 23-24 0.4153

[0098] The transformer capacity of this project is 630kVA, the user selects two charging and two discharging modes, the system capacity utilization is selected as D80, the unit construction cost is considered as 1.5 yuan per watt hour, and the cycle life is considered as 6000 times.

[0099] Note that the actual calculation needs to consider the reactive power factor of the transformer, so the maximum available power is 504kW by considering 630kVA*0.8.

[0100] Through the calculation method proposed in this patent, the recommended battery capacity is 169kWh, the PCS power is 94.5kW, the battery daily cycle number is 1.67 times, and the internal rate of return IRR is 15.45%.

[0101] Since the market battery cannot be fully charged and slowly discharged for product consistency, the battery capacity should not be less than 187.8 kWh according to the available discharge depth (DOD) of 90% of the battery. The PCS power needs to be adapted upwards according to the common model power on the market, that is, the PCS power cannot be less than the recommended value calculated during actual installation, so a PCS of 100 kW can be selected in this case.

[0102] Although the present application has been disclosed with reference to preferred embodiments, it is not intended to limit the application. Those skilled in the art who have the ordinary knowledge in the art can make various modifications and improvements without departing from the spirit and scope of the application. Therefore, the scope of protection of the present application shall be subject to the scope defined by the claims.

Claims

1. A capacity planning method for user-side energy storage power stations, characterized in that: Includes the following steps: S1: Obtain historical electricity consumption data of users within the power supply range of the energy storage power station; obtain real-time peak and valley electricity prices within the power supply range of the energy storage power station, including accurate prices for peak, valley, and normal periods, as well as time period information for each price; obtain the rated capacity information of the transformer to be connected to the energy storage power station. S2, Configure the working mode of the energy storage power station, which can be configured as a one-charge-one-discharge mode or a two-charge-two-discharge mode, and determine the capacity utilization level adopted by the system. The utilization level is the degree of matching between the energy storage system capacity and historical electricity consumption data. S3 calculates and analyzes the charging and discharging scheme of the energy storage power station and outputs the results; S31, firstly, the input historical electricity consumption data is preprocessed to obtain hourly load electricity consumption data for the whole year. Here, the data sequence is defined as P. Load1 P Load2 , ..., P Load8760 ; S32 extracts daily peak-hour electricity consumption data based on the input peak, off-peak, and valley periods. The mode selected here is either one charge / one discharge or two charge / two discharge cycles. The result is the sum of hourly peak-hour electricity consumption for 365 days, i.e., E. peak1 E peak2 , ..., E peak365 ,in ;P load (t) represents the electricity consumption during a certain hour of the peak electricity price period, T peakStart T is the start time of the peak electricity price period. peakStop This refers to the end time of the peak electricity price period; S33, sort the data in S32 from largest to smallest, and then select a certain position in the sorted data list as the maximum reasonable power consumption during the system peak period, i.e., the recommended energy storage capacity of the system, based on the capacity utilization of the setting stage. Then: ； After considering the efficiency of the energy storage system, determine whether the system can be charged with the required amount of electricity during off-peak hours; If the energy storage power station is configured in a charge-discharge mode, the expected charging amount is obtained by dividing the maximum reasonable peak power consumption of the system by the system efficiency. ； Where E dischar To determine the maximum reasonable power consumption of the system, η sys For system efficiency, E* char The expected charge amount for the system; The system checks the input historical electricity consumption information daily to see if the available charging capacity during off-peak hours exceeds the expected charging capacity. The calculation method is as follows: subtract the load consumption during off-peak hours from the transformer's rated capacity, and then calculate the total available charging capacity based on the daily off-peak price. ； Where P trans P represents the rated capacity of the upstream transformer of the system. load (t) represents the electricity consumption during a certain hour of off-peak electricity pricing period, T VallyStart T is the start time of the off-peak electricity pricing period. VallyStop The end time of off-peak electricity pricing period, E char The expected charge amount for the system; If the available charging capacity is less than the expected charging capacity, then the available charging capacity is used as the expected charging capacity of the system; otherwise, the expected charging capacity remains unchanged, and the reasonable energy storage capacity of the system is obtained by inverse calculation. That is, the recommended battery capacity for the energy storage system is: ； Where E char E represents the amount of electricity that can be charged during off-peak electricity pricing periods. * char E represents the expected charging volume during off-peak electricity pricing periods. batt Recommend battery capacity for the system; To configure an energy storage power station in a two-charge-two-discharge mode, it is necessary to first determine whether there are two off-peak periods in the input electricity price. If so, the system will charge during the two off-peak periods to supply the subsequent two peak periods for discharging. If the local electricity price has only one off-peak period, the two-charge-two-discharge mode cannot be enabled. After activating the two-charge-two-discharge mode, the recommended battery capacity for the energy storage system is: ； Where E d1 E d2 η represents the maximum reasonable electricity consumption for the first and second peak periods, respectively. sys For system efficiency, E char1 E char2 The amount of chargeable energy during the first and second valley price periods; S4, calculate the power of the minimum PCS, determine all peak and valley time intervals throughout the day, perform power analysis on the shortest time period, first determine the maximum power of that time period for each day of the year, and then sort these maximum power data from largest to smallest to obtain a numerical sequence, i.e. ; When analyzing charging periods, first determine the maximum power for each day of the year during that period. Then, subtract the maximum value from the available transformer capacity and sort the 365 differences from largest to smallest to obtain a numerical sequence. Right now ; Then, based on the capacity utilization, the corresponding data value in the sequence is selected as the PCS power; S5, calculate the average daily battery cycle count. Based on existing historical power consumption data and the battery capacity and PCS power data calculated in step S33, calculate the daily system charging power. The method is to charge the battery during off-peak hours, accumulating the smaller value between the transformer capacity minus the current power and the PCS power hourly until the battery is fully charged or the off-peak period ends. Record the total energy input during this period as the charging amount. When there are multiple off-peak periods in a day, the charging amounts from multiple off-peak periods need to be added together. Accumulate the daily charging amount over 365 days. Divide the sum by 365 and then by the battery capacity to obtain the average daily cycle count. Therefore: E char It is the total charging capacity for the whole day, E batt N represents the battery capacity calculated in step S33 above. avg This represents the average number of battery cycles per day throughout the year.

2. The user-side energy storage power station capacity planning method according to claim 1, characterized in that: It also includes the following steps: S6, Calculate the internal rate of return of the energy storage system. To obtain the project's cash flow, the operating life of the energy storage equipment is determined based on the previously input battery life and the calculated average daily cycle count. The annual cash return of the project can then be easily calculated using the average daily cycle count and peak-valley price difference. After summing the returns, the internal rate of return (IRR) of the energy storage project can be obtained using the formula: ； ； ； N avg For battery daily cycle count, CF i The annual charge / discharge revenue is represented by IRR, which is the internal rate of return. Here, it is assumed that the annual charge / discharge volume is the same. The peak-valley price difference is derived from the previously input electricity price information and the difference between the peak and valley electricity prices. Battery life is derived from the system's default parameters. Y This is the number of years the system can operate. The NPV of the project is the project's cash discount rate, and this value is considered to be 0 when calculating the internal rate of return.

3. The user-side energy storage power station capacity planning method according to claim 1, characterized in that: The data acquired by S1 is at least one year of historical electricity consumption data. The historical electricity consumption data consists of at least one sampling point per hour. The historical electricity consumption data includes timestamps and power information. The timestamps are used to specify the exact sampling time and should at least include month, day, and hour information. The power information is used to characterize the electricity consumption of the entire system during the corresponding period.

4. The user-side energy storage power station capacity planning method according to claim 1, characterized in that: When configuring the working mode of the energy storage power station in S2, the default is one-charge-one-discharge mode. In the one-charge-one-discharge mode, the energy storage system charges during the off-peak electricity price period at night and discharges during the peak electricity price period during the day. In the two-charge-two-discharge mode, the energy storage system charges during the off-peak electricity price period or the flat electricity price period during the day and discharges during the peak electricity price period at night.

5. The user-side energy storage power station capacity planning method according to claim 2, characterized in that: A sensitivity analysis is performed on the rate of return in S6. Based on the energy storage capacity and recommended PCS power given in step S33 above, a reasonable step size and trial value are selected, and the trial values ​​are arranged and combined. For the combined trial values, S3 and S4 of the previous step are repeatedly executed to obtain the total revenue and rate of return of the system under the corresponding configuration. After completing all the combinations that need to be tried, the curves of total revenue and rate of return are plotted in order to finally determine the reasonable capacity of the system.

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