Hybrid energy storage capacity optimization configuration system and method

Abstract

The invention discloses a hybrid energy storage capacity optimization configuration system and method, relates to the technical field of hybrid energy storage capacity configuration, and solves the problems that in the prior art, a single energy storage system is adopted for power grid frequency modulation, and high / low frequency fluctuation time periods and corresponding demand capacities in a peak period are not predicted in advance, so that the response speed is slow, and the power consumption is low. And the stability of the power grid is influenced. The method comprises the following steps: analyzing a peak period in one day according to historical power consumption data, and dividing the peak period into a high-frequency fluctuation period and a low-frequency fluctuation period according to power grid frequency change; analyzing the power consumption in the high / low frequency fluctuation period; according to the electricity consumption required in the high / low frequency fluctuation period, capacity configuration of the hybrid energy storage system is distributed; the capacity required by different frequency fluctuation periods is predicted in advance, the hybrid energy storage system is reasonably charged, the proper energy storage system is selected for frequency modulation according to the property of frequency fluctuation, and stable operation of a power grid is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of hybrid energy storage capacity configuration, and specifically relates to a hybrid energy storage capacity optimization configuration system and method. Background Art

[0002] The power grid often has peak and trough periods, resulting in an unbalanced power load. Therefore, peak load regulation is required to balance the peak and trough periods. Energy storage batteries have the characteristics of fast response speed and flexible adjustment methods, which can improve power peak regulation, short-term power supply, power quality and other aspects. In recent years, the technology of using large-scale energy storage batteries to participate in grid frequency regulation has developed rapidly. However, most of them currently use single lithium batteries, supercapacitors or batteries for grid frequency regulation, which cannot take into account both frequency regulation effects and economic indicators; and in recent years, the research ideas on energy storage optimization configuration are generally based on application and economic considerations, and the high / low frequency fluctuation periods during peak periods and the corresponding demand capacity are not predicted in advance, which can easily lead to slow response speed and affect the stability of the grid.

[0003] Therefore, the present invention provides a capacity optimization configuration system and method for hybrid energy storage. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a capacity optimization configuration system and method for hybrid energy storage, which is used to solve the technical problems that the prior art uses a single energy storage system for grid frequency regulation, and fails to predict in advance the high / low frequency fluctuation periods and the corresponding demand capacity during peak periods, which easily leads to slow response speed and affects the stability of the grid.

[0005] To achieve the above-mentioned object, a first aspect of the present invention provides a capacity optimization configuration system for hybrid energy storage, comprising a data acquisition module and an optimization configuration module;

[0006] Data collection module: used to collect some historical electricity consumption data; the historical electricity consumption data includes electricity consumption, electricity consumption time and grid frequency;

[0007] Optimization configuration module: used to analyze the peak period of electricity consumption in a day, divide the peak period into high-frequency fluctuation period and low-frequency fluctuation period according to the change of grid frequency; analyze the electricity consumption in high / low frequency fluctuation period; and,

[0008] The capacity configuration of the hybrid energy storage system is allocated according to the power consumption required during high / low frequency fluctuation periods.

[0009] Preferably, the analysis of the peak period of electricity consumption in a day includes:

[0010] The electricity consumption per unit time of several days is extracted from the historical electricity consumption data, and the electricity consumption per unit time at the same time of each day is averaged to obtain the average electricity consumption per unit time of each day. When the average electricity consumption per unit time of several consecutive days is greater than the preset threshold, the time period of the consecutive days is marked as the peak period.

[0011] The present invention uses historical electricity consumption data for analysis, analyzes the user's daily peak electricity consumption period, and after identifying the peak period, the power company can adjust the electricity consumption plan according to the actual situation, reasonably arrange the charging and discharging operations of the energy storage battery, maximize the use of renewable energy, and reduce the use of fossil fuels, thereby achieving the purpose of energy conservation and emission reduction. It is of great significance to balance the supply and demand of electricity in advance and better meet the electricity needs of users. For example, it is of great significance to ensure sufficient power supply during peak periods, avoid service interruptions caused by insufficient power, and avoid power shortages or surpluses.

[0012] Preferably, the step of dividing the peak period into a high-frequency fluctuation period and a low-frequency fluctuation period according to the change of the power grid frequency includes:

[0013] Extract the grid frequency change data of several different peak periods from the historical data, and establish the curves of the grid frequency change over time in different peak periods respectively; wherein, the grid frequency-time curve of the same peak period is in the same coordinate system, and the target grid operation frequency curve is used as the reference curve;

[0014] The fluctuations in the power grid frequency-time curve within the high frequency range are marked as high frequency fluctuations, and the corresponding fluctuation time period is marked as high frequency fluctuation period;

[0015] The fluctuations in the power grid frequency-time curve within the low frequency range are marked as low frequency fluctuations, and the corresponding fluctuation time period is marked as low frequency fluctuation period;

[0016] Determine whether the number of high-frequency fluctuations in the high / low-frequency fluctuation period is greater than the number of low-frequency fluctuations; if yes, the fluctuation period is a high-frequency fluctuation period; if not, the fluctuation period is a low-frequency fluctuation period.

[0017] Preferably, the high / low frequency fluctuation range is determined based on the variation range of the target power grid operating frequency.

[0018] The present invention plots the curve of the grid frequency changing with time in different peak periods in the same coordinate system, so that the changing trend of the grid frequency can be intuitively seen; and on the frequency-time curve, high-frequency fluctuations and low-frequency fluctuations can be identified. By comparing the number of high-frequency fluctuations and low-frequency fluctuations, the stability of the grid in a specific time period can be further understood, and targeted automated adjustment measures can be taken to meet electricity demand in a timely manner.

[0019] Preferably, the analyzing the power consumption during the high / low frequency fluctuation period includes:

[0020] The power consumption in each high / low frequency fluctuation period is extracted from the historical data, and the power consumption in the same high / low frequency fluctuation period is averaged to obtain the power consumption corresponding to each high / low frequency fluctuation period.

[0021] Preferably, the method of allocating the capacity configuration of the hybrid energy storage system according to the power consumption required during the high / low frequency fluctuation period includes:

[0022] Hybrid energy storage systems include batteries and supercapacitors;

[0023] The hybrid energy storage system is charged according to the power consumption during the high / low frequency fluctuation periods during the peak period;

[0024] Determine whether the remaining power of the supercapacitor is less than the power consumption during the high-frequency fluctuation period; if yes, charge it to the power consumption required during the high-frequency fluctuation period; if no, do not charge it;

[0025] Determine whether the remaining power of the battery is less than the power consumption during the low-frequency fluctuation period; if so, charge it to the power consumption required during the low-frequency fluctuation period; if not, do not charge it.

[0026] The present invention can effectively allocate energy storage resources and ensure the continuity and reliability of power supply by respectively judging whether the remaining power of supercapacitors and batteries meets the power demand of corresponding high-frequency and low-frequency fluctuation periods, and charging them as needed; by accurately charging the hybrid energy storage system according to power demand, the effective use of power resources can be ensured; and the supercapacitor is responsible for handling instantaneous high power demand, while the battery is responsible for continuous power supply task, which not only plays the fast response advantage of supercapacitors, but also makes full use of the high energy density of batteries. By adopting different energy storage systems for charging and discharging in different situations, the wear of energy storage equipment can be reduced.

[0027] Preferably, a configuration method of a capacity optimization configuration system for a hybrid energy storage comprises the following steps:

[0028] Step 1: Collect some historical electricity consumption data; the historical electricity consumption data includes electricity consumption, electricity consumption time and grid frequency;

[0029] Step 2: Analyze the peak period of electricity consumption in a day, divide the peak period into high-frequency fluctuation period and low-frequency fluctuation period according to the change of grid frequency; analyze the electricity consumption in high / low frequency fluctuation period;

[0030] Step 3: Allocate the capacity configuration of the hybrid energy storage system according to the power consumption required during high / low frequency fluctuation periods.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The present invention determines the peak period of a day based on historical electricity consumption data, analyzes the high-frequency fluctuation period and low-frequency fluctuation period during the peak period, and calculates the power consumption during the high / low frequency fluctuation period; charges the hybrid energy storage system appropriately according to the power consumption demand; ensures stable power service during the high / low frequency fluctuation period, which can significantly improve the user's power consumption experience, avoid power outages or voltage instability caused by insufficient power, and improve service quality;

[0033] 2. The present invention can effectively allocate energy storage resources by judging whether the remaining power of the supercapacitor / battery meets the power demand of the corresponding high-frequency and low-frequency fluctuation periods, and charging according to the power demand; and the supercapacitor is responsible for processing the instantaneous high power demand, while the battery is responsible for the continuous power supply task, which not only plays the fast response advantage of the supercapacitor, but also makes full use of the high energy density of the battery, avoiding the frequent charging and discharging of a single energy storage battery and accelerating the attenuation of its cycle life;

[0034] 3. The present invention analyzes the peak period of electricity consumption in a day based on historical electricity consumption data, and divides the peak period into high-frequency fluctuation period and low-frequency fluctuation period according to the change of power grid frequency; analyzes the electricity consumption in the high / low frequency fluctuation period; allocates the capacity configuration of the hybrid energy storage system according to the electricity consumption required in the high / low frequency fluctuation period; the present invention predicts the capacity required for different frequency fluctuations in advance, and deploys supercapacitors and batteries in a targeted manner to ensure rapid response and stable power supply in different frequency fluctuation periods, thereby improving the stability of the entire power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0037] Figure 2 It is a schematic diagram of the process flow of the high / low frequency fluctuation period analysis method of the present invention;

[0038] Figure 3 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0039] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] See also Figure 1 , the first aspect of the present invention provides a capacity optimization configuration system for hybrid energy storage, including a data acquisition module and an optimization configuration module;

[0041] The data collection module collects a number of historical electricity consumption data; the historical electricity consumption data includes electricity consumption, electricity consumption time and grid frequency;

[0042] The optimization configuration module analyzes the peak period of electricity consumption during the day;

[0043] Specifically, the electricity consumption per unit time for several days is extracted from the historical electricity consumption data, and the electricity consumption per unit time at the same time of each day is averaged to obtain the average electricity consumption per unit time of each day. When the average electricity consumption per unit time for several consecutive days is greater than a preset threshold, the time period of the consecutive days is marked as a peak period.

[0044] It should be noted that the unit time is set to every hour.

[0045] For example: extract the hourly electricity consumption for several days from the historical electricity consumption data. Assuming the average electricity consumption for a certain unit time of 17:00-19:00, the electricity consumption from 17:00 to 19:00 for several days is counted and averaged to get the average electricity consumption for the unit time of 17:00-19:00; the electricity consumption for other unit times is processed in this way to get the average electricity consumption for each unit time.

[0046] See also Figure 2 ,The optimization configuration module divides the peak period into high frequency fluctuation period and low frequency fluctuation period according to the ,change of the power grid;

[0047] Specifically, the grid frequency change data of several different peak periods are extracted from the historical data, and the curves of the grid frequency change over time of different peak periods are respectively established; wherein the grid frequency-time curve of the same peak period is in the same coordinate system, and the target grid operation frequency curve is used as the reference curve;

[0048] The fluctuations in the power grid frequency-time curve within the high frequency range are marked as high frequency fluctuations, and the corresponding fluctuation time period is marked as high frequency fluctuation period;

[0049] The fluctuations in the power grid frequency-time curve within the low frequency range are marked as low frequency fluctuations, and the corresponding fluctuation time period is marked as low frequency fluctuation period;

[0050] Determine whether the number of high-frequency fluctuations in the high / low-frequency fluctuation period is greater than the number of low-frequency fluctuations; if yes, the fluctuation period is a high-frequency fluctuation period; if not, the fluctuation period is a low-frequency fluctuation period.

[0051] It should be noted that the high / low frequency fluctuation range is a preset range within which the target power grid operating frequency varies up / down.

[0052] For example, frequency variation data of the power grid during peak periods can be obtained from historical records. These data usually include timestamps and corresponding frequency values.

[0053] Using the extracted data, several curves of the grid frequency variation over time in the same peak period are plotted in the same coordinate system, and each curve represents the variation trend of the grid frequency over time in the peak period.

[0054] A target grid operation frequency curve is set in the coordinate system as a reference curve. This curve can be a grid frequency curve under an ideal state or an average frequency curve.

[0055] For each curve, those parts whose frequencies fluctuate within the high-frequency range are identified and marked as high-frequency fluctuations, and the corresponding time periods are marked as high-frequency fluctuation periods.

[0056] Likewise, those parts whose frequencies fluctuate within the low-frequency range are identified and marked as low-frequency fluctuations, and the corresponding time periods are marked as low-frequency fluctuation periods.

[0057] In each fluctuation period, the number of high-frequency fluctuations and the number of low-frequency fluctuations are counted. If the number of high-frequency fluctuations is greater than the number of low-frequency fluctuations, the period is marked as a high-frequency fluctuation period. Otherwise, the period is marked as a low-frequency fluctuation period.

[0058] The optimization configuration module analyzes the power consumption during high / low frequency fluctuation periods;

[0059] Specifically, the power consumption in each high / low frequency fluctuation period is extracted from the historical data, and the power consumption in the same high / low frequency fluctuation period is averaged to obtain the power consumption corresponding to each high / low frequency fluctuation period.

[0060] The optimization configuration module allocates the capacity configuration of the hybrid energy storage system according to the power consumption required during high / low frequency fluctuation periods;

[0061] Specifically, the hybrid energy storage system includes batteries and supercapacitors;

[0062] The hybrid energy storage system is charged according to the power consumption during the high / low frequency fluctuation periods during the peak period;

[0063] Determine whether the remaining power of the supercapacitor is less than the power consumption during the high-frequency fluctuation period; if yes, charge it to the power consumption required during the high-frequency fluctuation period; if no, do not charge it;

[0064] Determine whether the remaining power of the battery is less than the power consumption during the low-frequency fluctuation period; if so, charge it to the power consumption required during the low-frequency fluctuation period; if not, do not charge it.

[0065] It should be noted that supercapacitors, due to their high power density and fast charging and discharging capabilities, are very suitable for high-frequency applications that require frequent and fast charging and discharging, such as vehicle regenerative braking systems, instantaneous peak power support in power systems, and power management in consumer electronic devices.

[0066] Due to their higher energy density, batteries are more suitable for low-frequency applications that require long-term stable power supply, such as providing continuous power for electric vehicles or serving as energy storage solutions for power grids.

[0067] See also Figure 3 The second aspect of the present invention proposes a configuration method for a capacity optimization configuration system of a hybrid energy storage, comprising the following steps:

[0068] Step 1: Collect some historical electricity consumption data; the historical electricity consumption data includes electricity consumption, electricity consumption time and grid frequency;

[0069] Step 2: Analyze the peak period of electricity consumption in a day, divide the peak period into high-frequency fluctuation period and low-frequency fluctuation period according to the change of grid frequency; analyze the electricity consumption in high / low frequency fluctuation period;

[0070] Step 3: Allocate the capacity configuration of the hybrid energy storage system according to the power consumption required during high / low frequency fluctuation periods.

[0071] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0072] The above embodiments are only used to illustrate the technical method 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 method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A capacity optimization configuration system for hybrid energy storage, It is characterized in that Including data acquisition module and optimization configuration module; Data collection module: used to collect some historical electricity consumption data; the historical electricity consumption data includes electricity consumption, electricity consumption time and grid frequency; Optimization configuration module: used to analyze the peak period of electricity consumption in a day, divide the peak period into high-frequency fluctuation period and low-frequency fluctuation period according to the change of grid frequency; analyze the electricity consumption in high / low frequency fluctuation period; and, The capacity configuration of the hybrid energy storage system is allocated according to the power consumption required during high / low frequency fluctuation periods.

2. A capacity optimization configuration system for hybrid energy storage according to claim 1, It is characterized in that The analysis of the peak period of electricity consumption during the day includes: The electricity consumption per unit time of several days is extracted from the historical electricity consumption data, and the electricity consumption per unit time at the same time of each day is averaged to obtain the average electricity consumption per unit time of each day. When the average electricity consumption per unit time of several consecutive days is greater than the preset threshold, the time period of the consecutive days is marked as the peak period.

3. A capacity optimization configuration system for hybrid energy storage according to claim 1, It is characterized in that The peak period is divided into a high-frequency fluctuation period and a low-frequency fluctuation period according to the change of the power grid frequency, including: Extract the grid frequency change data of several different peak periods from the historical data, and establish the curves of the grid frequency change over time in different peak periods respectively; wherein, the grid frequency-time curve of the same peak period is in the same coordinate system, and the target grid operation frequency curve is used as the reference curve; The fluctuations in the power grid frequency-time curve within the high frequency range are marked as high frequency fluctuations, and the corresponding fluctuation time period is marked as high frequency fluctuation period; The fluctuations in the power grid frequency-time curve within the low frequency range are marked as low frequency fluctuations, and the corresponding fluctuation time period is marked as low frequency fluctuation period; Determine whether the number of high-frequency fluctuations in the high / low-frequency fluctuation period is greater than the number of low-frequency fluctuations; if yes, the fluctuation period is a high-frequency fluctuation period; if not, the fluctuation period is a low-frequency fluctuation period.

4. A capacity optimization configuration system for hybrid energy storage according to claim 3, It is characterized in that The high / low frequency fluctuation range is determined based on the variation range of the target power grid operating frequency.

5. A capacity optimization configuration system for hybrid energy storage according to claim 3, It is characterized in that The analysis of power consumption during high / low frequency fluctuation periods includes: The power consumption in each high / low frequency fluctuation period is extracted from the historical data, and the power consumption in the same high / low frequency fluctuation period is averaged to obtain the power consumption corresponding to each high / low frequency fluctuation period.

6. A capacity optimization configuration system for hybrid energy storage according to claim 5, It is characterized in that The method of allocating the capacity configuration of the hybrid energy storage system according to the power consumption required during the high / low frequency fluctuation period includes: Hybrid energy storage systems include batteries and supercapacitors; The hybrid energy storage system is charged according to the power consumption during the high / low frequency fluctuation periods during the peak period; Determine whether the remaining power of the supercapacitor is less than the power consumption during the high-frequency fluctuation period; if yes, charge it to the power consumption required during the high-frequency fluctuation period; if no, do not charge it; Determine whether the remaining power of the battery is less than the power consumption during the low-frequency fluctuation period; if so, charge it to the power consumption required during the low-frequency fluctuation period; if not, do not charge it.

7. A capacity optimization configuration method for hybrid energy storage, based on the operation of a capacity optimization configuration system for hybrid energy storage according to any one of claims 1 to 6, It is characterized in that The following steps are involved: Step 1: Collect some historical electricity consumption data; the historical electricity consumption data includes electricity consumption, electricity consumption time and grid frequency; Step 2: Analyze the peak period of electricity consumption in a day, divide the peak period into high-frequency fluctuation period and low-frequency fluctuation period according to the change of grid frequency; analyze the electricity consumption in high / low frequency fluctuation period; Step 3: Allocate the capacity configuration of the hybrid energy storage system according to the power consumption required during high / low frequency fluctuation periods.

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