Distributed energy storage application scene research method suitable for power distribution network

Abstract

The invention discloses a distributed energy storage application scene research method suitable for a power distribution network, and belongs to the technical field of power distribution network planning, the distributed energy storage application scene research method suitable for the power distribution network comprises the following steps: S1, investigating the initial information of a current line and the information after energy storage, S2, cleaning data, and removing abnormal values and missing values; normalizing the data to ensure the comparability among different indexes; s3, analyzing the data by using a principal component analysis method; and S4, according to a result of the principal component analysis method, an application scene after energy storage is added is obtained, and the application scene comprises power supply reliability improvement, power supply capability improvement and power supply quality improvement. According to the invention, services such as power grid congestion relief, equipment capacity expansion demand delay and reactive power support are provided, and the flexibility and reliability of the power grid are effectively improved; according to the scheme, the economic burden of power grid upgrading is reduced, and the adaptability of the power grid to new energy is improved.

Description

Technical Field

[0001] The present invention relates to the field of distribution network planning, and in particular to a method for studying distributed energy storage application scenarios applicable to distribution networks. Background Art

[0002] Currently, the grid is evolving from a traditional grid dominated by one-way, step-by-step transmission to an energy internet that includes AC / DC hybrid grids, microgrids, local DC grids, and load-adjustable energy. Its operational characteristics are shifting from a real-time balancing mode where sources follow loads and a large-grid integrated control mode to a non-complete real-time balancing mode where sources, grids, loads, and storage interact in a coordinated manner, and a large-grid and microgrid coordinated control mode.

[0003] The above-mentioned existing technical solutions have the following defects: for the distribution network in a specific area, the main challenges currently faced include high investment costs, complex implementation processes and low operational benefits. Summary of the Invention

[0004] In order to make up for the above shortcomings, the present invention discloses a research method for distributed energy storage application scenarios suitable for distribution networks, aiming to solve the problems of complex implementation process and low operation efficiency.

[0005] The embodiment of the present invention discloses a method for studying distributed energy storage application scenarios applicable to a distribution network, comprising the following steps:

[0006] S1: Investigate the initial information of the existing lines and after adding energy storage. This information includes power load, line capacity, rated load, reactive power, power supply reliability, substation overload, whether the dual substation passes the 'N-1' test, seasonal high-voltage line overload, PV reverse transmission, voltage limit violations, and low voltage.

[0007] S2: Clean the data to remove outliers and missing values; normalize the data to ensure comparability between different indicators;

[0008] S3: Data were analyzed using principal component analysis;

[0009] S4: Based on the results of principal component analysis, application scenarios after adding energy storage are obtained. These scenarios include improved power supply reliability, improved power supply capacity, and improved power supply quality.

[0010] S5: Analyze the current status of the specific scenario obtained, use the conventional solution and the energy storage solution to calculate the specific scenario respectively, and compare to obtain the optimal result.

[0011] In a preferred embodiment of the present invention, S2 also includes data collection and integration: collecting data from different sources, including time series data and geographic location information; integrating these data into a unified data framework to ensure consistency in data format and time series.

[0012] In a preferred embodiment of the present invention, S2 also includes missing value processing: checking missing values ​​in the data set, and for time series data, using the data of the previous day or the next day to fill in the missing values; for non-time series data, deciding whether to delete or fill in the missing values ​​depends on the specific situation.

[0013] In a preferred embodiment of the present invention, S2 also includes missing value processing: checking missing values ​​in the data set, and for time series data, using a time series prediction model to estimate missing values; for non-time series data, deciding whether to delete or fill missing values ​​depends on the specific situation.

[0014] In a preferred embodiment of the present invention, S2 also includes outlier detection and processing: using statistical methods to identify outliers in the data; deleting or replacing these outliers, and selecting corresponding statistics as replacement values.

[0015] In a preferred embodiment of the present invention, in S2, a data consistency check is also included: ensuring that the data is logically consistent; including whether the load level is consistent with historical data and expected patterns.

[0016] In a preferred embodiment of the present invention, in S3, the data is analyzed in terms of data dimensionality reduction and feature extraction, extracting the most important information from multidimensional data, simplifying the data structure, and retaining the main variations in the data; the normalized data is input into the principal component analysis method to obtain the main data results.

[0017] In a preferred embodiment of the present invention, in the principal component analysis method, the original data is standardized using the formula to ensure that each variable has the same dimension in the analysis, z = (x-μ) / σ

[0018] Where: x is the original data, μ is the mean, and σ is the standard deviation.

[0019] In a preferred embodiment of the present invention, the correlation coefficient matrix between the variables is calculated using the following formula:

[0020] For two variables Xi and Xj in the data set, the correlation coefficient rij between them is defined as:

[0021]

[0022] Where: Cov(Xi,Xj) is the covariance between Xi and Xj.

[0023] In a preferred embodiment of the present invention, the correlation coefficient matrix is ​​converted into eigenvalues ​​and eigenvectors through orthogonal transformation; these eigenvalues ​​represent the variance explained by each principal component, and the eigenvectors represent the direction of the principal component; then the contribution rate of each principal component is calculated based on the eigenvalues, and the cumulative contribution rate is determined; finally, based on the contribution rate and the cumulative contribution rate, the appropriate number of principal components is selected for extraction; the cumulative contribution rate is between 80% and 90%.

[0024] Beneficial Effects: The present invention discloses a method for studying distributed energy storage application scenarios applicable to distribution networks. Using principal component analysis, the energy storage application scenarios screened out mainly include improved power supply reliability, improved power supply capacity, and improved power supply quality after adding energy storage. Principal component analysis extracts the most important information from multidimensional data, simplifies the data structure, and retains the main variations in the data. Furthermore, the energy storage system can also provide services such as grid congestion relief, delayed equipment expansion requirements, and reactive power support, effectively improving the flexibility and reliability of the grid. This solution not only reduces the economic burden of grid upgrades, but also improves the grid's adaptability to new energy sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 A flow chart of a method for studying distributed energy storage application scenarios applicable to distribution networks provided by an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of a substation provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In the present invention, unless otherwise expressly specified or limited, terms such as "mounted," "connected," "connect," and "fixed" should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; they may refer to direct connection or indirect connection through an intermediate medium; they may refer to internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0029] In the present invention, unless otherwise expressly specified or limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features being in contact not directly but through another feature between them. Furthermore, a first feature being "above," "above," and "above" a second feature may include the first feature being directly above or obliquely above the second feature, or may simply mean that the first feature is higher in level than the second feature. A first feature being "below," "below," and "below" a second feature may include the first feature being directly below or obliquely below the second feature, or may simply mean that the first feature is lower in level than the second feature.

[0030] The technical solutions in the embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention.

[0031] See also Figure 1-Figure 2 , the present invention discloses a research method for distributed energy storage application scenarios applicable to distribution networks;

[0032] like Figure 1 As shown, the following steps are included:

[0033] S1. Investigate the initial and post-storage load of existing lines, including line capacity, rated load, reactive power, power supply reliability, substation overload, whether dual substations pass 'N-1', seasonal high-voltage line overload, PV reverse transmission, voltage over-limit, and low voltage.

[0034] S2. Perform data cleaning on the acquired regional data to ensure the accuracy and usability of the data.

[0035] ① Data collection and integration: Data needs to be collected from various sources. This data may include time series data, geographic location information, etc. This data must be integrated into a unified data framework to ensure consistency in data format and time series.

[0036] ② Missing Value Handling: Check for missing values ​​in the dataset. For time series data, you can use data from the previous or next day to fill in missing values, or use a time series forecasting model to estimate missing values. For non-time series data, you may need to decide whether to delete or fill missing values ​​based on the specific situation.

[0037] ③ Outlier detection and processing: Use statistical methods to identify outliers in the data. These outliers can be deleted or replaced. The replacement value can be the average value of the adjacent time points or other statistics.

[0038] ④ Data consistency check: Ensure that the data is logically consistent. For example, whether the load level is consistent with historical data and expected patterns.

[0039] ⑤ Normalize data: ensure comparability between different indicators.

[0040] S3. The principal component analysis method for analyzing data is mainly used in data dimensionality reduction and feature extraction, extracting the most important information from multidimensional data, simplifying the data structure, and retaining the main variations in the data; inputting normalized data into the principal component analysis method to obtain the main data results.

[0041] S4. Explain the principal component analysis method: Based on the results of the principal component analysis method, the application scenarios after adding energy storage are obtained. The application scenarios include improved power supply reliability, improved power supply capacity, and improved power supply quality.

[0042] S5. Analyze the current status of the specific scenario obtained, use the conventional solution and the energy storage solution to calculate the specific scenario respectively, and compare to obtain the optimal result.

[0043] The principal component analysis process is as follows:

[0044] Step 1: First, the original data is standardized to ensure that each variable has the same dimension in the analysis, thereby eliminating the impact of dimensional differences on the analysis results.

[0045] z=(x-μ) / σ

[0046] Where: x is the original data, μ is the mean, and σ is the standard deviation.

[0047] Step 2: Calculate the correlation coefficient matrix between variables. This step helps to understand the correlation between variables and provides a basis for principal component analysis. The calculation formula of the correlation coefficient matrix is ​​as follows:

[0048] For two variables Xi and Xj in the data set, the correlation coefficient rij between them is defined as:

[0049]

[0050] Where: Cov(Xi,Xj) is the covariance between Xi and Xj, calculated as:

[0051]

[0052] where Xik and Xjk are the k-th observation values ​​of variables Xi and Xj respectively, Xii and Xjj are the means of Xi and Xj respectively, and n is the number of observations.

[0053] Step 3: Through a series of orthogonal transformations, the correlation coefficient matrix is ​​converted into eigenvalues ​​and eigenvectors. These eigenvalues ​​represent the amount of variance explained by each principal component, while the eigenvectors represent the direction of the principal component.

[0054] Step 4: Calculate the contribution rate of each principal component based on the eigenvalue and determine the cumulative contribution rate. The contribution rate indicates the proportion of each principal component in the total variance explained, and the cumulative contribution rate helps determine the number of principal components to be extracted.

[0055] Step 5: Based on the contribution rate and cumulative contribution rate, select the appropriate number of principal components for extraction. Usually, the principal components with cumulative contribution rate reaching a certain threshold, such as 85%, are selected.

[0056] Step 6: Finally, the principal component scores are calculated based on the extracted principal components, and the high-dimensional grid energy storage data is converted into a low-dimensional space representation.

[0057] The present invention provides a regional division method based on cluster analysis. The main challenges currently faced include high investment costs, complex implementation processes, and inefficient operating benefits, which are usually related to grid expansion and upgrades. In order to meet these challenges, energy storage technology can be used as an innovative solution. By investigating the initial power load of the existing line and after adding energy storage, line capacity, rated load, reactive power, power supply reliability, substation overload, whether the double substation passes 'N-1', seasonal high-voltage line overload, photovoltaic reverse transmission, voltage over-limit and low voltage and other information; cleaning data, removing outliers and missing values; normalizing data to ensure comparability between different indicators. The data was analyzed by principal component analysis, and it was found that the application scenarios after adding energy storage mainly include improved power supply reliability, improved power supply capacity, and improved power supply quality.

[0058] Using principal component analysis, we screened out energy storage application scenarios, finding that the main scenarios for adding energy storage include improved power supply reliability, improved power supply capacity, and improved power supply quality. Principal component analysis extracts the most important information from multidimensional data, simplifying the data structure while retaining the main variations in the data.

[0059] Energy storage systems can also alleviate grid congestion, defer equipment expansion requirements, and provide reactive power support, effectively improving grid flexibility and reliability. This technological approach not only reduces the economic burden of grid upgrades but also enhances the grid's adaptability to renewable energy.

[0060] Specific examples:

[0061] 1. Investigate the initial power load of existing lines and after adding energy storage, line capacity, rated load, reactive power, power supply reliability, substation overload, whether dual substations pass the 'N-1' rule, seasonal high-voltage line overload, PV reverse transmission, voltage limit and low voltage, etc.; clean the data to remove outliers and missing values; and normalize the data to ensure comparability between different indicators.

[0062] 2. Current power supply situation

[0063] The 35kVX substation is located in a remote area. It consists of two main transformers with a capacity of 2×10MVA. The maximum load of the substation is 17.34MW, the average load is 11.54MW, and the load rate is 86.7%. For specific lines, see Figure 2 .

[0064] Table 1 Substation situation

[0065]

[0066] 3. Problems

[0067] (1) 35kVX heavy load

[0068] The 35kVX transformer has a capacity of 20MVA. In 2024, the maximum load was 17.34MW, the average load was 11.54MW, and the load factor was 86.7%. The heavy load is due to the dispersed distribution of users in remote areas and the lack of substations. As a result, the 35kVX transformer is overloaded with users and there are no other nearby substations to transfer the load.

[0069] 4. Solution Construction

[0070] Based on the voltage over-limit problem of 35kVX transformer in remote areas, the conventional solution is to increase the capacity of the substation main transformer; the energy storage solution is to configure energy storage equipment to connect to the substation to supply power for the load that exceeds the overload standard of the substation, ensuring the safe and reliable operation of the power grid.

[0071] (1) Conventional plan

[0072] 35kVX main transformer capacity expansion project in a certain city

[0073] 1) Substation capacity selection:

[0074] According to the survey, there will be no users or loads in the power supply area that will affect the substation load level in the future. Therefore, it is only necessary to increase the 35kVX transformer capacity from 2×10MVA to 10+20MVA to ensure the stable operation of the substation.

[0075] 2) Construction scheme design:

[0076] 3) Construction scale and investment

[0077] Table 2 Traditional investment

[0078]

[0079] 4) Implementation difficulties and risks

[0080] Generally, users in remote areas do not have dual power supplies, and power outage measures need to be taken when increasing the capacity of the main transformer in the construction plan, causing inconvenience to users.

[0081] (2) Energy storage solutions

[0082] 1) Energy storage technology route selection

[0083] Drawing on the energy storage technology route's adaptability to scenarios and the energy storage's own technical characteristics combined with domestic and international practice, this project selected lithium iron phosphate batteries and container layout.

[0084] 2) Main operating parameters and construction costs shall be designed in accordance with standards.

[0085] 3) Construction plan design

[0086] ①Energy storage power design

[0087] There are generally two methods for selecting energy storage power: one is calculated based on the maximum load, and the other is calculated based on the average load. Considering the voltage over-limit problem in this analysis, with safety and supply as the primary condition, the maximum load is selected as the energy storage power.

[0088] In 2023, the maximum load of the 35kVX transformer will be 17.34MW. Based on the calculation that 80% of the substation load is considered heavy load, 1.34MW of the substation power needs to be transferred. Based on the calculation that heavy load needs to be transferred under heavy load conditions, the 35kVX transformer needs to be equipped with energy storage power of 1500kW.

[0089] ②Energy storage capacity calculation

[0090] According to substation load information retrieved from the distribution network automation system, the maximum load of the 35kVX transformer in 2023 was 17.34MW, with an average load of 11.54MW. When the substation load reached 16MW, the load factor reached 80%, resulting in 1.34MW of load outside the 80% load factor. Research indicates that the substation is heavily loaded for two hours. Therefore, 3000kWh was selected as the final energy storage capacity requirement.

[0091] ③Layout point selection

[0092] Design open space near the substation.

[0093] ④Operation control strategy

[0094] This plan supports on-grid and off-grid operation strategies. In the later stage, it will consider profits from off-peak photovoltaic consumption, peak-valley arbitrage, and power auxiliary services. It is recommended to build it according to independent energy storage standards.

[0095] 4) Energy storage specifications and investment

[0096] This plan deploys a 1500kW / 3000kWh energy storage system in an open space near the 35kVX transformer. This storage system is housed in a 40-foot container, occupying approximately 30m². Based on the current average cost of electrochemical energy storage (lithium iron phosphate) at 1 yuan per Wh, the initial investment in the storage system is 3 million yuan.

[0097] Table 3 Energy storage solution investment

[0098]

[0099] 5) Implementation difficulties and risks

[0100] The key considerations for energy storage site selection are safety distance, and the key considerations for construction are safety protection to avoid secondary damage to the power grid.

[0101] 5. Technical and economic simulation analysis of the scheme

[0102] (1) Using the "Time-Sequence Active Distribution Network Intelligent Decision-Making Platform" independently developed by Tianjin University to complete the traditional solution and energy storage solution:

[0103] 1) Time series power flow analysis to verify whether the technical indicators of the solution meet the requirements of the guidelines;

[0104] 2) Reliability analysis: calculate reliability indicators, verify the reliability improvement of the two solutions, and provide indicators for evaluation;

[0105] (2) The calculation parameters and boundaries are in accordance with the main boundary conditions in Section 4.1.2.

[0106] Simulation results: After connecting to energy storage, the voltage of each node meets the requirements under different connection methods. The regularity shows that the power supply voltage and reliability are higher after connecting to energy storage than before connecting to energy storage. The simulation results are as follows:

[0107] Table 4 Simulation results analysis

[0108]

[0109] 6. Technical and economic comparison: A qualitative and quantitative comparison of the technical and economic aspects of traditional methods and energy storage construction solutions.

[0110] (1) Qualitative evaluation

[0111] The qualitative evaluation is based on two aspects: implementation difficulty and voltage level. From the qualitative evaluation results, the energy storage solution is superior to the traditional solution.

[0112] Table 5 Qualitative evaluation comparison table

[0113]

[0114] (2) Quantitative evaluation

[0115] Table 6 Economic and technical comparison table

[0116]

[0117] (3) Full life cycle analysis

[0118] Boundary conditions: 1. Electrical equipment is considered old after 30 years; 2. Energy storage batteries are considered to have decayed and need to be replaced every 15 years; 3. Changes in bank lending rates are not considered;

[0119] Assuming this energy storage solution costs 6 million yuan, with an investment of approximately 3 million yuan every 15 years, and a unit price of less than 1 yuan per Wh, the energy storage solution offers a more economical advantage over conventional solutions over their full lifecycle costs. The economic indicators are compared below.

[0120] Table 7 Energy storage economic calculation

[0121]

[0122] 7. Conclusion: Comparison between this energy storage configuration scheme and conventional schemes

[0123] (1) In terms of technology, if the 35kVX variable load is heavy, both the conventional solution and the energy storage solution can solve the problem and achieve the same power supply reliability.

[0124] (2) In terms of management, the conventional solution requires power outages during the construction process, and there is no power source nearby to serve as a backup power source. In addition, the conventional solution has a long project approval cycle and design plan adjustments during the project approval process. The overall construction cycle of the conventional solution is long, and it is impossible to quickly achieve safe and reliable power supply from the power grid.

[0125] (3) In terms of economy, considering the entire life cycle of the conventional solution and the energy storage solution, the conventional solution is compared with the energy storage solution during the same period, and the energy storage solution has better economic efficiency.

[0126] Therefore, this energy storage solution is superior to conventional solutions in terms of management methods, and its economic efficiency and last investment are better than conventional solutions.

[0127] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention may be subject to various modifications and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention. It should be noted that similar reference numerals and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined or explained in subsequent figures.

Claims

1. A research method for distributed energy storage application scenarios applicable to distribution networks, characterized in that: The following steps are involved: S1: Investigate the initial information of the existing lines and after adding energy storage. This information includes power load, line capacity, rated load, reactive power, power supply reliability, substation overload, whether the dual substation passes the 'N-1' test, seasonal high-voltage line overload, PV reverse transmission, voltage limit violations, and low voltage. S2: Clean the data to remove outliers and missing values; normalize the data to ensure comparability between different indicators; S3: Data were analyzed using principal component analysis; S4: Based on the results of principal component analysis, application scenarios after adding energy storage are obtained. These scenarios include improved power supply reliability, improved power supply capacity, and improved power supply quality. S5: Analyze the current status of the specific scenario obtained, use the conventional solution and the energy storage solution to calculate the specific scenario respectively, and compare to obtain the optimal result.

2. A method for studying distributed energy storage application scenarios applicable to distribution networks according to claim 1, characterized in that: In S2, data collection and integration are also included: collecting data from different sources, including time series data and geographic location information; Integrate this data into a unified data framework to ensure consistency in data format and time series.

3. A method for studying distributed energy storage application scenarios applicable to distribution networks according to claim 2, characterized in that: S2 also includes missing value processing: checking missing values ​​in the dataset, filling in missing values ​​with data from the previous or next day for time series data, and deciding whether to delete or fill in missing values ​​for non-time series data based on the specific situation.

4. A method for studying distributed energy storage application scenarios applicable to distribution networks according to claim 2, characterized in that: S2 also includes missing value processing: checking missing values ​​in the dataset, using a time series prediction model to estimate missing values ​​for time series data, and deciding whether to delete or fill missing values ​​for non-time series data based on the specific situation.

5. A method for studying distributed energy storage application scenarios applicable to distribution networks according to claims 3 and 4, characterized in that: S2 also includes outlier detection and processing: using statistical methods to identify outliers in the data; for these outliers, delete or replace them, and select corresponding statistics for the replacement values.

6. A method for studying distributed energy storage application scenarios applicable to distribution networks according to claim 5, characterized in that: In S2, data consistency checks are also included: ensuring that the data is logically consistent; including whether the load level is consistent with historical data and expected patterns.

7. The method for studying distributed energy storage application scenarios applicable to distribution networks according to claim 1, characterized in that: In S3, data analysis is reflected in data dimensionality reduction and feature extraction, extracting the most important information from multidimensional data, simplifying the data structure, and retaining the main variations in the data; the normalized data is input into the principal component analysis method to obtain the main data results.

8. The method for studying distributed energy storage application scenarios applicable to distribution networks according to claim 1, characterized in that: In principal component analysis, the formula is used to standardize the raw data to ensure that each variable has the same dimension in the analysis, z = (x-μ) / σ Where: x is the original data, μ is the mean, and σ is the standard deviation.

9. A method for studying distributed energy storage application scenarios applicable to distribution networks according to claim 8, characterized in that: The correlation coefficient matrix between the variables is calculated using the following formula: For two variables Xi and Xj in the data set, the correlation coefficient rij between them is defined as: Where: Cov(Xi,Xj) is the covariance between Xi and Xj.

10. A method for studying distributed energy storage application scenarios applicable to distribution networks according to claim 9, characterized in that: Through orthogonal transformation, the correlation coefficient matrix is ​​converted into eigenvalues ​​and eigenvectors; these eigenvalues ​​represent the variance explained by each principal component, and the eigenvectors represent the direction of the principal component; then the contribution rate of each principal component is calculated based on the eigenvalues, and the cumulative contribution rate is determined; finally, based on the contribution rate and cumulative contribution rate, the appropriate number of principal components is selected for extraction; the cumulative contribution rate is between 80% and 90%.

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