Distributed energy storage configuration method and system based on regional power grid power distribution data

Abstract

The invention discloses a distributed energy storage configuration method based on regional power grid power distribution data, and belongs to the technical field of power system energy storage. Dividing the total load power consumption data into different seasonal intervals, and searching the maximum load daily data of the load power consumption data in different seasonal intervals; obtaining photovoltaic power generation access data of the regional power grid power transmission line in typical days of each season interval; determining a load value of the power transmission line according to the photovoltaic output coefficients and the load power consumption data of the typical days in the four seasons; predicting a load condition of a future power transmission line, and determining a new load value of the power transmission line; and calculating the energy storage capacity according to the new load value of the future power transmission line. By calculating the load value of the power transmission line and predicting the future load condition, the energy storage capacity is calculated according to the electric power and electric quantity balance, and the economical efficiency and practicability of energy storage configuration are ensured.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage technology, and in particular to a distributed energy storage configuration method and system based on regional power grid distribution data. Background Technology

[0002] Photovoltaic new energy power generation has become an important part of building a smart grid due to its advantages such as renewability and cleanliness. Furthermore, with the significant improvement in the performance of photovoltaic new energy power generation technology, it has provided strong support for ensuring the stability of energy supply in smart grids.

[0003] With the large-scale integration of photovoltaic (PV) renewable energy generation, smart grids need to manage the grid connection of PV power generation equipment. However, the randomness, volatility, and intermittency of PV power generation pose challenges to achieving stable grid connection. Furthermore, uncertainties in the regional power grid's load side, supply side, and PV power generation equipment lead to overloaded regional distribution lines and significant power imbalances.

[0004] Traditionally, addressing heavy overload issues in regional power distribution lines requires replacing the conductors with larger diameter ones. While this alleviates the problem to some extent, it is costly overall and time-consuming, heavily influenced by the installation environment. Furthermore, some research utilizes energy storage technology to improve grid flexibility and reliability; however, traditional energy storage configuration methods often overlook the specific conditions and load characteristics of the regional power grid, rendering traditional energy storage configurations ineffective. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by providing a distributed energy storage configuration method and system based on regional power grid distribution data.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: In a first aspect, the present invention provides a distributed energy storage configuration method based on regional power grid distribution data.

[0007] A method for configuring distributed energy storage based on regional power grid distribution data includes the following steps: S1: Collect total load power consumption data at the load end of the regional power grid transmission lines; S2: Divide the total load electricity consumption data into seasonal intervals, search for the maximum daily load electricity consumption data in each seasonal interval, and use the maximum daily load electricity consumption data in each season as the typical daily load electricity consumption data for the corresponding seasonal interval. S3: Obtain photovoltaic power generation access data for typical days of regional power grid transmission lines in each season, and determine the photovoltaic output coefficient for typical days in each season; S4: Determine the load value of the transmission line based on the photovoltaic output coefficient and load power consumption data of typical days in each seasonal interval. S5: Predict the future load condition of the transmission line based on the load rate information of the transmission line, and determine the new load value of the transmission line in the future; wherein, the load rate information includes the load value and the load rate; S6: Determine the maximum transmission capacity of the current regional power grid transmission lines, and calculate the energy storage capacity based on the new load values ​​of the future transmission lines.

[0008] Furthermore, in S4, based on the photovoltaic power output coefficient and load electricity consumption data of typical days in each seasonal interval, the load value of the transmission line is determined, specifically including the following steps: S4.1: Obtain the total power of the photovoltaic power generation system connected to the regional power grid transmission line, and calculate the photovoltaic power generation data based on the total power of the photovoltaic power generation system and the photovoltaic output coefficient of typical days in each season; where photovoltaic power generation data = total power of photovoltaic power generation system × output coefficient × time; S4.2: Subtract photovoltaic power generation data from the load power consumption data of typical days in each seasonal interval to obtain the load data of transmission lines; S4.3: Based on the load data of transmission lines, calculate the load rate of transmission lines per unit time on a typical day in each seasonal interval; S4.4: Compare the load rate of transmission lines per unit time on typical days in each seasonal interval, determine the maximum load rate of transmission lines, and determine the load value based on the maximum load rate; where load rate = load value / system capacity; Furthermore, the maximum load rate includes the maximum reverse load rate and the maximum forward load rate.

[0009] Furthermore, in S5, the future load condition of the transmission line is predicted based on the current load value, and the new load value of the transmission line is determined. This specifically includes the following steps: S5.1: Based on the determined load rate information of the transmission line, perform category confirmation; if the load rate information is the maximum forward load rate, then execute S5.2; if the load rate information is the maximum reverse load rate, then execute S5.3. S5.2: When the load rate information is the maximum positive load rate, determine the new load value of the transmission line based on the preset load growth rate; S5.3: When the load rate information is the maximum reverse load rate, determine the new load value of the transmission line based on the preset photovoltaic growth rate.

[0010] Furthermore, the preset load growth rate is 8.5%; the preset photovoltaic growth rate is 10%.

[0011] Furthermore, the formula for calculating energy storage capacity in S6 is: Energy storage capacity = New load value - Maximum transmission capacity × 0.8.

[0012] Furthermore, the S6 energy storage capacity calculation also includes: The total investment amount for configuring energy storage is calculated based on the unit cost of distributed energy storage. The direction of distributed energy storage configuration is determined based on whether the total investment amount exceeds the engineering cost of replacing the line. If the total investment amount is less than the engineering cost of replacing the line, then it is determined that distributed energy storage will be configured on the grid substation side. If the total investment amount is greater than or equal to the engineering cost of replacing the line, then it is determined that distributed energy storage will be configured on the user side.

[0013] Secondly, the present invention provides a distributed energy storage configuration system based on regional power grid distribution data.

[0014] A distributed energy storage configuration system based on regional power grid distribution data includes: The first acquisition module is used to collect total load power consumption data at the load end of the regional power grid transmission lines; The lookup module is used to divide the total load electricity consumption data into seasonal intervals, and to look up the maximum load day data in each seasonal interval as the typical daily load electricity consumption data for the corresponding season. The second data acquisition module is used to acquire photovoltaic power generation access data of regional power grid transmission lines on typical days in each seasonal period, and to determine the photovoltaic output coefficient. The first processing module is used to determine the load value of the transmission line based on the photovoltaic output coefficient and load power consumption data of typical days in each seasonal interval. The prediction module is used to predict the future load of the transmission line based on the determined load rate information of the transmission line, and to determine the new load value of the transmission line. The second processing module is used to determine the maximum transmission capacity of the current regional power grid transmission lines and calculate the energy storage capacity based on the new load values ​​of the future transmission lines.

[0015] In summary, compared with the prior art, the beneficial effects of the above technical solution are: This invention, by collecting regional power grid load data, can accurately understand the power grid's operational status and provide reliable data support for energy storage configuration. By seasonally segmenting total load data and searching for typical daily load data, it can more accurately grasp the load variation characteristics of the power grid in different seasons. By calculating the load values ​​of transmission lines and predicting future load conditions, the potential for heavy overload on future transmission lines can be identified. This allows for the calculation of energy storage capacity based on power balance, ensuring the economic efficiency and practicality of energy storage configuration. This enhances the flexibility and reliability of the regional power grid, contributing to the absorption of new energy sources and the safe and stable operation of the grid. It also provides a scientific method and basis for energy storage planning in regional power grids. Attached Figure Description

[0016] Figure 1 A flowchart of a distributed energy storage configuration method based on regional power grid distribution data; Figure 2 This is a load factor curve distribution diagram for transmission lines. Detailed Implementation

[0017] The principles and features of the present invention are described below with reference to all the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0018] This invention discloses a distributed energy storage configuration method and system based on regional power grid distribution data.

[0019] In a first aspect, the present invention provides a distributed energy storage configuration method based on regional power grid distribution data.

[0020] Reference Figure 1 A method for configuring distributed energy storage based on regional power grid distribution data includes the following steps: S1: Collect total load power consumption data at the load end of the regional power grid transmission lines; S2: Divide the total load electricity consumption data into seasonal intervals, search for the maximum load daily electricity consumption data in each seasonal interval, and use the maximum load daily electricity consumption data in each seasonal interval as the typical daily load electricity consumption data for the corresponding seasonal interval. S3: Obtain photovoltaic power generation access data for typical days of regional power grid transmission lines in each season, and determine the photovoltaic output coefficient for typical days in each season; S4: Determine the load value of the transmission line based on the photovoltaic output coefficient and load power consumption data of typical days in each seasonal interval. S5: Predict the future load of the transmission line based on the load rate information of the transmission line, and determine the new load value of the transmission line in the future; S6: Determine the maximum transmission capacity of the current regional power grid transmission lines, and calculate the energy storage capacity based on the new load values ​​of the future transmission lines.

[0021] It should be noted that, in this embodiment, the load value is an actual value measured in real time or over a specific time period. It represents the actual workload, power, electricity, current, data flow, etc., undertaken by the system, equipment, or component at a certain moment or within a certain period of time. The load value reflects the current actual operating status of the system.

[0022] Maximum load rate is a fixed, designed, or specified upper limit. It represents the maximum load ratio or absolute value that a system, device, or component can withstand under safe, reliable, and stable operating conditions. It is usually specified by the manufacturer (rated capacity, maximum load capacity) or determined according to system design, safety standards, and regulatory requirements. Maximum load rate defines the boundary of the system's capacity. Operating beyond this value is called "overload," which may lead to decreased efficiency, overheating, accelerated aging, failure, or even safety incidents.

[0023] Wherein, load rate = load value / system capacity; Load factor is the ratio of actual load to system capacity over a given period of time, usually expressed as a percentage. For example, if a power system has a load of 100 MW and a system capacity of 150 MW, then the load factor is 100 MW / 150 MW = 66.67%.

[0024] This invention provides a distributed energy storage configuration method based on regional power grid distribution data. By collecting load consumption data from the regional power grid, the operating status of the power grid can be accurately understood, providing reliable data support for energy storage configuration. Furthermore, by seasonally segmenting the total load consumption data and searching for typical daily load consumption data, the load variation characteristics of the power grid in different seasons can be more accurately grasped.

[0025] By calculating the load values ​​of transmission lines and predicting future load conditions, the potential for heavy overload on future transmission lines can be identified. This allows for the calculation of energy storage capacity based on power balance, ensuring the economic efficiency and practicality of energy storage configuration. This enhances the flexibility and reliability of the regional power grid, contributing to the absorption of new energy sources and the safe and stable operation of the grid. It also provides a scientific method and basis for energy storage planning in regional power grids.

[0026] The following is a detailed explanation of each of the above steps.

[0027] S1: Collect total load power consumption data at the load end of the regional power grid transmission line.

[0028] Specifically, based on the power distribution facilities and distribution transformers connected to the regional power grid transmission lines, the user groups at the load end of the transmission lines are determined, and the historical electricity consumption data of all user groups throughout the year are collected to obtain the total load electricity consumption data of the load end of the regional power grid transmission line.

[0029] S2: Divide the total load electricity consumption data into seasonal intervals, search for the maximum load daily data in each seasonal interval, and use the maximum load daily electricity consumption data in each seasonal interval as the typical daily load electricity consumption data for the corresponding seasonal interval.

[0030] Specifically, the total load electricity consumption data is divided into four seasonal intervals: spring, summer, autumn, and winter. The maximum load day data in each seasonal interval is then searched and filtered to serve as the load electricity consumption data for the typical day of the corresponding season.

[0031] Based on the load data of typical days in each season, it can be determined that the maximum load day data in summer is significantly higher than that in the other three seasons, and the load is the highest in summer due to the increase in air conditioning load at night.

[0032] S3: Obtain photovoltaic power generation access data for typical days of regional power grid transmission lines in each seasonal interval, and determine the photovoltaic output coefficient for typical days in each seasonal interval.

[0033] Specifically, the distributed power source data connected to the transmission line on typical days in different seasons are obtained through a sensor system, and the photovoltaic output coefficients for typical days in the four seasons are obtained through time-series simulation.

[0034] S4: Based on the photovoltaic power output coefficient and load electricity consumption data of typical days in each seasonal interval, determine the load value of the transmission line, specifically including the following steps: S4.1: Obtain the total power of the photovoltaic power generation system connected to the regional power grid transmission line, and calculate the photovoltaic power generation data by combining the total power of the photovoltaic power generation system with the photovoltaic output coefficient of typical days in each season. S4.2: Subtract photovoltaic power generation data from the load power consumption data of typical days in each seasonal interval to obtain the load data of transmission lines; S4.3: Calculate the load rate of transmission lines per unit time on a typical day in each seasonal interval based on the load data of transmission lines; S4.4: Compare the load rate of transmission lines per unit time on typical days in each seasonal interval, determine the maximum load rate of transmission lines, and determine the load value based on the maximum load rate. The maximum load rate includes the maximum reverse load rate and the maximum forward load rate.

[0035] Figure 2 The load factor distribution curve of a 10kV transmission line is shown below. Figure 2 As shown, the high daytime sunlight exposure and nighttime air conditioning power consumption in summer result in a higher transmission line load rate compared to typical days in the other three seasons.

[0036] The maximum reverse load rate occurred at 12:00 noon on a typical summer day, with a maximum reverse load rate of -57.78% and a load value of 4.453MW. The maximum forward load rate occurred at 20:00 on a typical summer day, with a maximum load rate of 57.57% and a load value of 4.4374MW. Load rate = load value / system capacity.

[0037] S5: Based on the load rate information of the transmission lines, predict the future load conditions of the transmission lines and determine the new load value of the transmission lines in the future. This includes the following steps: S5.1: Based on the determined load rate information of the transmission line, perform category confirmation; if the load rate information is the maximum forward load rate, then execute S5.2; if the load rate information is the maximum reverse load rate, then execute S5.3. S5.2: When the load rate information is the maximum positive load rate, determine the new load value of the transmission line based on the preset load growth rate; Specifically, , Indicates the new load value. Indicates the initial load value. This represents the preset load growth rate, where n represents the number of years. S5.3: When the load rate information is the maximum reverse load rate, determine the new load value of the transmission line based on the preset photovoltaic growth rate.

[0038] Specifically, , Indicates the new load value. Indicates the initial load value. This represents the preset growth rate of photovoltaic power, where n represents the number of years.

[0039] The preset load growth rate is 8.5%; the preset photovoltaic growth rate is 10%. Using a 5-year timeframe, the future new load value for the transmission lines is determined to be -7.19MW, with a load rate of -93.33%, indicating a reverse overload phenomenon on the transmission lines.

[0040] S6: Determine the maximum transmission capacity of the current regional power grid transmission lines, and calculate the energy storage capacity based on the new load values ​​of the future transmission lines.

[0041] Specifically, the maximum transmission capacity of the current transmission line is determined based on the conductor length and line length of the regional power grid transmission line; P max ≈(V 2 / XL)×k, where P maxThis represents the maximum transmission capacity; V represents the line rated voltage; XL represents the total line reactance; k represents the power factor, k < 1; XL = X × L, where X represents the reactance per unit length. X has a specific value for different kV lines. L represents the length, which is divided into conductor length and line length. Conductor length is the cable line, and line length is the insulated line length. The energy storage capacity is calculated based on the future load values ​​of the transmission lines. The calculation formula is as follows: Energy storage capacity = new load value - maximum transmission capacity × 0.8.

[0042] This embodiment takes a 10KV transmission line as an example. The total length of the 10KV transmission line is 7.991km, the cable length is 1.41km, the overhead line length is 6.581km, the overhead line is an insulated line, the conductor type is JKLGYJ-150, and its maximum transmission capacity is 7.7074MW.

[0043] When the maximum positive load rate increases with the growth rate of the load, the transmission line will be overloaded in the future. The new load value is 6.6723MW. By configuring energy storage, the transmission line will not be overloaded. The energy storage capacity is 0.5064MW.

[0044] By configuring energy storage batteries of appropriate capacity to store electrical energy, and amplifying it during peak load periods at night in summer, the power supply can be balanced according to the maximum load demand, which can effectively solve the problem of heavy load on transmission lines at night in summer.

[0045] In addition, after calculating the energy storage capacity in S6, it also includes: The total investment amount for configuring energy storage is calculated based on the unit cost of distributed energy storage. The configuration direction of distributed energy storage is determined according to whether the total investment amount exceeds the engineering cost of replacing the line. If the total investment amount is less than the engineering cost of replacing the line, it is determined that distributed energy storage is configured on the grid substation side. If the total investment amount is greater than or equal to the engineering cost of replacing the line, it is determined that distributed energy storage is configured on the user side.

[0046] Secondly, the present invention provides a distributed energy storage configuration system based on regional power grid distribution data.

[0047] A distributed energy storage configuration system based on regional power grid distribution data includes: The first acquisition module is used to collect total load power consumption data at the load end of the regional power grid transmission lines; The lookup module is used to divide the total load electricity consumption data into seasonal intervals, and to look up the maximum load day data in each seasonal interval as the typical daily load electricity consumption data for the corresponding season. The second data acquisition module is used to acquire photovoltaic power generation access data of regional power grid transmission lines on typical days in each seasonal period, and to determine the photovoltaic output coefficient. The first processing module is used to determine the load value of the transmission line based on the photovoltaic output coefficient and load power consumption data of typical days in each seasonal interval. The prediction module is used to predict the future load of the transmission line based on the determined load rate information of the transmission line, and to determine the new load value of the transmission line. The second processing module is used to determine the maximum transmission capacity of the current regional power grid transmission lines and calculate the energy storage capacity based on the new load values ​​of the future transmission lines.

[0048] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for configuring distributed energy storage based on regional power grid distribution data, characterized in that, Includes the following steps: S1: Collect total load power consumption data at the load end of the regional power grid transmission lines; S2: Divide the total load electricity consumption data into seasonal intervals, search for the maximum daily load electricity consumption data in each seasonal interval, and use the maximum daily load electricity consumption data in each season as the typical daily load electricity consumption data for the corresponding seasonal interval. S3: Obtain photovoltaic power generation access data for typical days of regional power grid transmission lines in each season, and determine the photovoltaic output coefficient for typical days in each season; S4: Determine the load value of the transmission line based on the photovoltaic output coefficient and load power consumption data of typical days in each seasonal interval. S5: Predict the future load condition of the transmission line based on the load rate information of the transmission line, and determine the new load value of the transmission line in the future; wherein, the load rate information includes the load value and the load rate; S6: Determine the maximum transmission capacity of the current regional power grid transmission lines, and calculate the energy storage capacity based on the new load values ​​of the future transmission lines.

2. The distributed energy storage configuration method based on regional power grid distribution data according to claim 1, characterized in that: In S4, the load value of the transmission line is determined based on the photovoltaic power output coefficient and load electricity consumption data of typical days in each seasonal interval. This includes the following steps: S4.1: Obtain the total power of the photovoltaic power generation system connected to the regional power grid transmission line, and calculate the photovoltaic power generation data based on the total power of the photovoltaic power generation system and the photovoltaic output coefficient of typical days in each season; where, photovoltaic power generation data = total power of photovoltaic power generation system × output coefficient × time; S4.2: Subtract photovoltaic power generation data from the load power consumption data of typical days in each seasonal interval to obtain the load data of transmission lines; S4.3: Based on the load data of transmission lines, calculate the load rate of transmission lines per unit time on a typical day in each seasonal interval; S4.4: Compare the load rate of transmission lines per unit time on typical days in each seasonal interval, determine the maximum load rate of transmission lines, and determine the load value based on the maximum load rate; where load rate = load value / system capacity.

3. The distributed energy storage configuration method based on regional power grid distribution data according to claim 2, characterized in that: The maximum load rate includes the maximum reverse load rate and the maximum forward load rate.

4. The distributed energy storage configuration method based on regional power grid distribution data according to claim 3, characterized in that: In S5, the future load condition of the transmission line is predicted based on the current load value, and the new load value of the transmission line is determined. This includes the following steps: S5.1: Based on the determined load rate information of the transmission line, perform category confirmation; if the load rate information is the maximum forward load rate, then execute S5.2; if the load rate information is the maximum reverse load rate, then execute S5.

3. S5.2: When the load rate information is the maximum positive load rate, determine the new load value of the transmission line based on the preset load growth rate; S5.3: When the load rate information is the maximum reverse load rate, determine the new load value of the transmission line based on the preset photovoltaic growth rate.

5. The distributed energy storage configuration method based on regional power grid distribution data according to claim 4, characterized in that: The preset load growth rate is 8.5%; the preset photovoltaic growth rate is 10%.

6. The distributed energy storage configuration method based on regional power grid distribution data according to claim 1, characterized in that: The formula for calculating energy storage capacity in S6 is: Energy storage capacity = New load value - Maximum transmission capacity × 0.

8.

7. The distributed energy storage configuration method based on regional power grid distribution data according to claim 1, characterized in that: The calculation of S6 energy storage capacity also includes: The total investment amount for configuring energy storage is calculated based on the unit cost of distributed energy storage. The direction of distributed energy storage configuration is determined based on whether the total investment amount exceeds the engineering cost of replacing the line. If the total investment amount is less than the engineering cost of replacing the line, then it is determined that distributed energy storage will be configured on the grid substation side. If the total investment amount is greater than or equal to the engineering cost of replacing the line, then it is determined that distributed energy storage will be configured on the user side.

8. A distributed energy storage configuration system based on regional power grid distribution data, characterized in that, include: The first acquisition module is used to collect total load power consumption data at the load end of the regional power grid transmission lines; The lookup module is used to divide the total load electricity consumption data into seasonal intervals, and to look up the maximum load day data in each seasonal interval as the typical daily load electricity consumption data for the corresponding season. The second data acquisition module is used to acquire photovoltaic power generation access data of regional power grid transmission lines on typical days in each seasonal period, and to determine the photovoltaic output coefficient. The first processing module is used to determine the load value of the transmission line based on the photovoltaic output coefficient and load power consumption data of typical days in each seasonal interval. The prediction module is used to predict the future load of the transmission line based on the determined load rate information of the transmission line, and to determine the new load value of the transmission line. The second processing module is used to determine the maximum transmission capacity of the current regional power grid transmission lines and calculate the energy storage capacity based on the new load values ​​of the future transmission lines.

Images