Peak-valley difference arbitrage energy storage control method considering peak and deep valley electricity prices

Abstract

The invention discloses a peak-valley difference arbitrage energy storage control method considering peak and deep valley electricity prices, which comprises load distribution prediction and charge and discharge starting time distribution based on the load distribution prediction, the load distribution prediction comprises charge prediction, discharge prediction and a prediction mode, the charge prediction comprises preconditions, opportunity and content, and the discharge prediction comprises preconditions, opportunity and content. The precondition is that the local electricity price has a deep valley; the charging prediction opportunity is prediction before the valley period starts; the charging prediction content comprises the amount of electricity capable of being charged in deep valleys and the amount of electricity capable of being charged in valley sections; the discharge prediction comprises opportunity and content, and the discharge prediction opportunity is predicted before the peak segment starts; the discharge prediction content comprises the discharge capacity of the peak and the peak section; the prediction method comprises the following steps: S1, finding an intra-day load change rule; s2, calculating a reference load base line of the day and comparing the reference load base line with the current load base line in each time period; s3, solving in a proper time period to obtain a predicated value of the chargeable and dischargeable electric quantity of each time period; the load prediction distributes charging and discharging starting time, and the charging logic and the discharging logic are included.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage control methods, and in particular to a peak-valley difference arbitrage energy storage control method taking into account peak and valley electricity prices. Background Art

[0002] At present, the industrial and commercial energy storage market is hot, and segmented electricity price policies are becoming more and more common in various places. Peak and deep valley electricity prices have appeared, and the duration of peak and deep valley electricity prices has become longer, and the potential benefits have increased. The existing scheme does not specifically consider peak and deep valley electricity prices, but only considers peak, flat and valley. Usually, the existing scheme does not specifically distinguish between peak and peak, and uniformly handles them according to peak time periods, and the valley and deep valley periods are similar. Simply put, it is charging during valley time and discharging during peak time. The profit of such operation is the difference between peak electricity price and valley electricity price. Considering that the capacity of energy storage system is usually designed to be small, and peak and deep valley usually do not appear at the front of peak and valley, it is easy to have insufficient utilization of peak and deep valley. Since 25% of the months of the year have peak electricity prices, and the existing automated operation strategy does not consider the utilization of peak and deep valley electricity prices separately, it may result in a loss of up to 5% of revenue.

[0003] Therefore, it is necessary to provide a peak-valley arbitrage energy storage control method that takes into account peak and valley electricity prices to solve the above technical problems. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract of the specification and the title of the invention of this application to avoid blurring the purpose of this section, the abstract of the specification and the title of the invention, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a peak-valley arbitrage energy storage control method considering peak and valley electricity prices, including load distribution prediction and allocation of charging and discharging start time based on load distribution prediction, the load distribution prediction including charging prediction, discharging prediction and prediction method, the charging prediction including prerequisite, timing and content, the prerequisite is that the local electricity price has a valley; the charging prediction timing is to make a prediction before the valley section starts; the charging prediction content includes how much electricity can be charged in the valley and how much electricity can be charged in the valley section; the discharging prediction includes timing and content, the discharging prediction timing includes making a prediction before the peak section starts; the discharging prediction content includes how much electricity can be discharged during the peak and how much electricity can be discharged during the peak section; the prediction method includes S1, finding the load change law within the day; S2, calculating the reference load baseline of the day and comparing it with the current load baseline in each time period; S3, solving in a suitable time period to obtain the predicted value of the chargeable and dischargeable amount in each time period; the load prediction allocation charging and discharging start time includes both charging logic and discharging logic.

[0006] As a preferred scheme of the peak-valley arbitrage energy storage control method considering peak and valley electricity prices described in the present invention, S1 includes continuously retaining the historical electricity consumption data of the past 14 days, and extracting the three historical data of the whole day of the previous day, the whole day of 7 days ago and the whole day of 14 days ago for prediction, and performing weighted averaging to obtain the reference load curve for the day, with weights of 0.4, 0.4 and 0.2 respectively.

[0007] As a preferred scheme of the peak-valley arbitrage energy storage control method considering peak and valley electricity prices described in the present invention, S2 counts the cumulative electricity consumption of the day before the prediction time and the cumulative electricity consumption of the reference load curve of the day, and divides the two cumulative electricity consumptions to obtain a calibration coefficient.

[0008] As a preferred solution of the peak-valley arbitrage energy storage control method considering peak and valley electricity prices described in the present invention, S3 counts the electricity consumption at the predicted time of the corresponding time period according to the electricity price distribution and multiplies it by the calibration coefficient obtained in the previous step.

[0009] As a preferred solution of the peak-valley arbitrage energy storage control method considering peak and valley electricity prices of the present invention, the charging logic is to operate in different ways to fully charge the valley electricity in different situations.

[0010] As a preferred solution of the peak-valley difference arbitrage energy storage control method considering peak and valley electricity prices of the present invention, the discharge logic is that the operation mode of all peak discharge is different under different circumstances.

[0011] Beneficial effects of the present invention: The present invention can adapt to the distribution of peaks and valleys. When there are peak and valley electricity prices, the appropriate charging and discharging time is determined according to the law of electricity load, the battery capacity is fully utilized, and deep valley charging is prioritized. However, even if it cannot be fully charged in the deep valley, it will be fully charged in other valley sections; when discharging, peak discharge is prioritized, but the battery will be fully discharged through calculation to avoid insufficient utilization of battery capacity; the energy storage system will charge (buy electricity) with low-priced electricity as much as possible, and will not be unable to be fully charged (waste system capacity) due to excessive greed. The energy storage system will try to discharge (sell electricity) during high electricity price periods, and will not be unable to sell completely due to excessive greed; the traditional solution does not specifically consider peaks and valleys, but combines peaks and peaks, and valleys and valleys. The present invention is applicable to a variety of working conditions such as one charge and one discharge, two charges and two discharges, and is applicable to various energy storage capacities. The traditional solution does not specifically consider peaks and valleys, but combines peaks and peaks, and valleys and valleys. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0013] in:

[0014] Figure 1 A schematic diagram of load distribution prediction of the peak-valley difference arbitrage energy storage control method considering peak and valley electricity prices according to the present invention;

[0015] Figure 2 The load data diagram of April 15th of the peak-valley difference arbitrage energy storage control method considering peak and valley electricity prices of the present invention;

[0016] Figure 3 The load data diagram of April 22 of the peak-valley difference arbitrage energy storage control method considering peak and valley electricity prices of the present invention;

[0017] Figure 4 The load data diagram of April 28th for the peak-valley difference arbitrage energy storage control method considering peak and valley electricity prices of the present invention;

[0018] Figure 5 This is a forecast data chart for April 29 of the peak-valley difference arbitrage energy storage control method taking into account peak and valley electricity prices as described in the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1

[0023] Reference Figure 1-5The first embodiment of the present invention is a peak-valley arbitrage energy storage control method considering peak and valley electricity prices, including load distribution prediction and allocation of charging and discharging start time based on load prediction, the load distribution prediction includes charging prediction, discharging prediction and prediction method, the charging prediction includes prerequisites, timing and content, the prerequisite is that the local electricity price has a valley; the charging prediction timing is to make a prediction before the valley section starts; the charging prediction content includes how much electricity can be charged in the valley and how much electricity can be charged in the valley section; the discharging prediction includes prerequisites, timing and content, the prerequisite is that the local electricity price has a valley, the discharge prediction timing includes making a prediction before the peak section starts; the discharge prediction content includes how much electricity can be discharged at the peak and how much electricity can be discharged in the peak section; the prediction method includes S1, finding the load change law within the day; S2, calculating the reference load baseline of the day and comparing it with the current load baseline in each time period; S3, solving in a suitable time period to obtain the predicted value of the chargeable and dischargeable amount in each time period; the load prediction allocates the charging and discharging start time, including charging logic and discharging logic. Specifically, the charging logic of allocating the start time of charging and discharging according to the load forecast includes whether all deep valleys can be fully charged. Whether all deep valleys can be fully charged depends on the following situations: (1) the existing power needs to be considered; (2) the deep valley charging amount is not greater than the predicted discharge amount of the peak section (including the spike); (3) it can only be fully charged in the deep valley; (4) it cannot be fully charged; if it cannot be fully charged, it depends on whether the valley following the deep valley can be fully charged. If it can be fully charged, it can be charged from the beginning of the deep valley to the end of the valley. If charging is started several times in advance, the earliest charging is at the valley price, then it cannot be fully charged. The discharge logic includes whether all peaks can be emptied. If you want to be able to empty, you can only discharge at the peak; if you cannot empty, it depends on whether the peak following the peak can be emptied; you can discharge from the beginning of the peak to the end of the peak, so that the peak following the peak can be emptied; if discharging is started several times in advance, the earliest discharge is at the peak price, then the peak following the peak cannot be emptied.

[0024] The S1 continuously retains the historical electricity consumption data of the past 14 days, and extracts the historical data of the whole day of the previous day, the whole day of 7 days ago and the whole day of 14 days ago for weighted average to obtain the reference load curve of the day, with weights of 0.4, 0.4 and 0.2 respectively. The S2 counts the cumulative electricity consumption of the day before the prediction time and the cumulative electricity consumption of the reference load curve of the day, and divides the two cumulative electricity consumptions to obtain the calibration coefficient; the S3 counts the electricity consumption at the prediction time of the corresponding period according to the electricity price distribution and multiplies it by the calibration coefficient obtained in the previous step.

[0025] For example, if you want to predict the electricity consumption data for each period on April 29:

[0026] You need to first collect the full-day load data for April 28, April 22, and April 15 as follows (the first 3 columns):

[0027] The unit of load is kW

[0028]

[0029]

[0030]

[0031] The last column is the predicted complete load data, and the first three columns are the known actual load data.

[0032] The real data of the first three columns after visualization are shown as follows:

[0033] The forecast value for April 29 is obtained by weighted average. Figure 5 As shown (the weighted average method refers to the above description):

[0034] Based on the above data, it can be seen that the weighted average method used in this application is only one of them, and other historical data combinations and weighted average coefficients can be selected based on effect feedback.

[0035] The following is a calculation of electricity consumption in each period based on Guangdong electricity prices:

[0036] First of all, we know that the charging period should be the valley price period from 0 to 8 o'clock. To determine how to charge, it is necessary to complete the prediction calculation of the chargeable amount in the valley period before 0 o'clock on April 29. In this example, the analysis and calculation are started at 23:45 on April 28. According to the local electricity price during the analysis stage, there is no deep valley electricity price in the local area. Therefore, charging during this period does not require special planning. It only needs to be charged in the entire valley period according to the available capacity limit of the transformer until it is fully charged.

[0037] According to the electricity price information, there are peak and peak periods between 10:00 and 12:00, and there are peak and peak periods between 14:00 and 19:00. In order to discharge electricity during the peak period with higher returns, it is necessary to predict the electricity consumption during the discharge period before the peak begins, that is, at 10:45 on April 29. We have completed the load trend forecast for the whole day on the 29th. Now we need to calibrate the load trend for the whole day based on the actual load situation recorded on the 29th.

[0038] At 10:45 on April 29, the actual load situation obtained is as follows:

[0039]

[0040]

[0041] A simple accumulation shows that until 10:45 on the 29th, the cumulative power consumption on that day was 3849.6 / 4=962.4kWh (each data only represents 15 minutes / 0.25 hours). The predicted power consumption for the corresponding period of the full-day load trend obtained earlier is 3916.32 / 4=979.08kWh. Based on the information available at that time, it can be seen that the predicted value is generally too large, so the overall predicted value for the 29th can be calibrated based on the current information. The calibration method is as follows:

[0042] 1. Cumulative value of measured data:

[0043] Assume that the measured data is P actual (t), where t represents the time interval (one point every 15 minutes), there are a total of n points from (t = 1) to (t = n), where (n = 44) represents the number of measured points from 0:00 to 10:45. The accumulated value of the measured data can be expressed as:

[0044]

[0045] 2. Cumulative value of forecast data:

[0046] Let the predicted data be P forecast (t), also one point every 15 minutes. For the forecast data from 0:00 to 10:45, the cumulative value is:

[0047]

[0048] 3. Calibration coefficient:

[0049] The calibration coefficient k is the ratio of the cumulative value of the measured data to the cumulative value of the predicted data:

[0050]

[0051] 4. Calibrated prediction data:

[0052] The coefficient k is used to calibrate the forecast data for the entire 24 hours. The calibrated forecast data P forecast,calibrated (t) is expressed as:

[0053] P forecast,calibrated (t) = k × P forecast (t), t=1,2,…,96 Thus, the whole formula can be summarized as:

[0054]

[0055] Wherein n=44 represents the number of points of the measured data (ie, the time period from 0:00 to 10:45).

[0056] Based on the above formula k = 0.983, the calibrated full-day forecast value for the 29th is as follows:

[0057]

[0058]

[0059]

[0060]

[0061] According to the above prediction values, combined with the known electricity price, the electricity consumption of the two peak periods, 11:00-12:00 and 15:00-17:00, is (the color-coded part in the table above, added up / 4) 275.332kWh and 427.5655kWh respectively. Since there is no valley section for charging between the two peak periods in Guangdong, the total electricity consumption of the two peak periods is 702.8975kWh.

[0062] Example 1: In this example, the installed capacity of the energy storage system is 500kWh and it is fully charged. The available power at this time is less than the demand during the peak period, so the system does not need to discharge during the non-peak period.

[0063] Example 2: In this example, the installed capacity of the energy storage system is 800kWh and it is fully charged. The available power at this time is greater than the demand during the peak period. Then, it is further determined whether the remaining capacity can be consumed during the peak period after the second peak. The power consumption between 17:00 and 19:00 on the 29th after calibration is statistically analyzed, and the power demand is 215.7kWh. It can be inferred that the remaining power of 800-702.8975≈97.1kWh after the system is discharged during the two peak periods can be used up during the peak period of 17:00 to 19:00 on the same day. The system's discharge period can be set to 11:00-12:00 and 15:00-19:00, until the power is discharged.

[0064] Example 3: In this example, the installed capacity of the energy storage system is 1000kWh and it is fully charged. According to the previous article, the above solution cannot fully absorb the power of the energy storage system. At this time, you can look forward to other available peak time periods to calculate whether their possible power consumption can absorb all the energy storage power until the absorption demand is met or all peak and peak time periods are in discharge mode. The rule for finding available peak time periods is from back to front. For the Guangzhou area, the power consumption during the period of 14:00-15:00 is checked first, and whether the total power consumption described in the previous article is sufficient to absorb it. If it is not enough, the power consumption during 10:00-11:00 is counted.

[0065] The charging logic of the deep valley is similar to the discharge logic of the peak. The deep valley is charged first. If it cannot be fully charged, the valley section after the deep valley is calculated to see if it can be fully charged. If it is still not enough, the valley section before the deep valley is calculated to see if it can be fully charged. This continues until all valley sections and deep valley sections are charged.

[0066] The logic of charging capacity statistics is slightly different from that of discharging, and is based on the accumulation of available charging capacity at each moment. The available charging capacity is calculated as follows:

[0067] Available charging capacity = transformer capacity - point-by-point predicted power value

[0068] The prior art does not consider the utilization of peak and valley electricity prices separately. Peak and valley electricity prices are higher than traditional peak and valley electricity prices. The peak and valley electricity prices of the present invention are lower, and have better price difference benefits. Peaks and valleys usually do not appear in the front of the peak and valley periods, but in the middle and back. Energy storage systems that operate according to conventional strategies cannot utilize these two price differences, or utilize them very insufficiently, depending on the capacity of the system. For conventionally configured energy storage systems, the full-power charging and discharging time usually does not exceed 2 hours. Peaks and valleys usually occur 1 to 2 hours after the start of the peaks and valleys, and last for 1 to 2 hours. If the energy storage capacity is increased so that the energy storage operated by the conventional strategy can also work during the peak and valley stages, a higher initial capital investment is required. In addition, the system rate of return will also be reduced.

[0069] In summary, the present application can adapt to the distribution of peaks and valleys. When there are peak and valley electricity prices, the appropriate charging and discharging times are determined according to the power load law, the battery capacity is fully utilized, and deep valley charging is prioritized. However, even if it cannot be fully charged in a deep valley, it will be fully charged in other valley sections. When discharging, peak discharge is prioritized, but calculations will be used to ensure that the battery is fully discharged (and discharged in other peak sections if necessary) to avoid insufficient utilization of the battery capacity.

[0070] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0071] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0072] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A peak-valley arbitrage energy storage control method for peak and valley electricity prices, characterized by: It includes load distribution prediction and allocation of charging and discharging start time based on load distribution prediction. The load distribution prediction includes charging prediction, discharging prediction and prediction method. The charging prediction includes prerequisites, timing and content. The prerequisites have deep valleys according to local electricity prices. The charging prediction timing is predicted before the valley section starts. The charging prediction content includes how much electricity can be charged in the deep valley and how much electricity can be charged in the valley section. The discharging prediction includes prerequisites, timing and content. The prerequisites have deep valleys according to local electricity prices. The discharge prediction timing is predicted before the peak section starts. The discharge prediction content includes how much electricity can be discharged at the peak and how much electricity can be discharged in the peak section. The prediction method includes S1, finding the load change law within the day; S2, calculating the reference load baseline of the day and comparing it with the current load baseline in each time period; S3, solving in a suitable time period to obtain the predicted value of the chargeable and dischargeable amount in each time period; the load prediction allocation charging and discharging start time includes charging logic and discharging logic.

2. The peak-valley arbitrage energy storage control method considering peak and valley electricity prices according to claim 1 is characterized in that : The S1 continuously retains the historical electricity consumption data of the past 14 days. When making predictions, the historical data of the whole day of the previous day, the whole day of 7 days ago and the whole day of 14 days ago are extracted and weighted averaged to obtain the reference load curve for the day, with weights of 0.4, 0.4 and 0.2 respectively.

3. The peak-valley arbitrage energy storage control method considering peak and valley electricity prices according to claim 1 is characterized in that : The S2 counts the cumulative power consumption of the day before the prediction time and the cumulative power consumption of the reference load curve of the day, and divides the two cumulative power consumptions to obtain a calibration coefficient.

4. The peak-valley arbitrage energy storage control method considering peak and valley electricity prices according to claim 1 is characterized in that : S3 counts the power consumption at the predicted time of the corresponding period according to the power price distribution and multiplies it by the calibration coefficient obtained in the previous step.

5. The peak-valley arbitrage energy storage control method considering peak and valley electricity prices according to claim 1 is characterized in that : The charging logic operates differently in different situations to fully charge the battery in the valley.

6. The peak-valley arbitrage energy storage control method considering peak and valley electricity prices according to claim 1 is characterized in that : The discharge logic operates differently in different situations to discharge all peaks.

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