Configuration method and system for network configuration type energy storage promotion park distribution network

Abstract

The invention discloses a configuration method and system for a distribution network of a network construction type energy storage promotion park, and relates to the technical field of energy storage optimization, and the method comprises the steps: collecting the basic and operation data of a distribution network, recognizing weak nodes, such as large voltage fluctuation and poor new energy consumption, for a medium-low voltage distribution network of a park containing distributed photovoltaic and charging piles, and carrying out the optimization of the energy storage. A multi-factor coupling model is adopted to determine the capacity and the access position of network construction type energy storage, a module containing voltage / frequency control and new energy consumption control is established, a cloud platform-local controller cooperation mechanism is constructed, accurate matching of network construction type energy storage and a park distribution network is realized, and the network construction type energy storage and the park distribution network are optimized. The problems of unstable voltage of the park distribution network, high new energy power abandoning rate and low power supply reliability are solved, and the operation stability and economical efficiency of the park distribution network are improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization technology, specifically to a configuration method and system for grid-type energy storage to enhance the distribution network of a park. Background Technology

[0002] With the advancement of the "dual carbon" target, my country's industrial parks are accelerating their energy structure transformation. As of 2024, more than 60% of industrial and commercial parks nationwide had been connected to distributed photovoltaic (such as rooftop photovoltaic) systems, and the penetration rate of electric vehicle charging piles exceeded 40% (data source: "China Industrial Park Energy Development Report 2024"). As a key link in energy consumption and supply, the industrial park distribution network is gradually upgrading from a "single power supply" model to a "source-grid-load-storage coordinated" model. However, it also faces challenges such as complex load types (coexistence of precision equipment, charging piles, and ordinary loads), large fluctuations in renewable energy output, and high requirements for operational stability. Energy storage technology is one of the core means to solve the problems of power distribution networks in industrial parks. Among them, grid-based energy storage is regarded as a key supporting technology for the next generation of industrial park power distribution networks because it has the ability to independently construct voltage and frequency. At present, the application of grid-based energy storage in industrial park scenarios is in its initial stage. The industry mainly focuses on the performance optimization of the energy storage unit itself (such as battery life and charge and discharge efficiency), and research on its systematic configuration with industrial park power distribution networks is still relatively fragmented.

[0003] The shortcomings and deficiencies of existing technologies are as follows: 1. Lack of precision in configuration, with both resource waste and insufficient capacity: Existing technologies (such as fixed-capacity grid-connected energy storage and experience-based grid-connected energy storage configuration) have not established a coupled calculation model of "load-new energy-energy storage". They determine the energy storage capacity based on a single factor (such as photovoltaic installed capacity or experience value), and the selection of access location does not take into account the power flow characteristics of the park's distribution network.

[0004] 2. Limited control strategies fail to adapt to the complex load characteristics of the industrial park: Existing technologies (such as traditional reactive power compensation devices and simple charge / discharge sequence control) only design control strategies for single objectives (such as voltage regulation and basic charge / discharge), without taking into account the coordinated needs of multiple loads in the park. For example, traditional SVG can only regulate reactive power and cannot cope with fluctuations in the active power load of charging piles, resulting in voltage deviations exceeding ±7% during peak charging periods; the charge / discharge sequence of grid-connected energy storage does not combine the photovoltaic output and the electricity consumption patterns of charging piles. During peak photovoltaic output periods in the daytime, energy storage charges while charging piles still draw power from the grid, resulting in photovoltaic power curtailment. During peak charging periods at night, energy storage discharges insufficiently, requiring large-scale power purchases from the grid. 3. Lack of collaborative mechanism and insufficient operational stability and reliability: Existing technologies (such as independent energy storage control and single-level regulation) have not built a "cloud-edge-device" collaborative operation architecture, and rely only on local control or simple remote commands, which cannot respond to dynamic changes in the park's power distribution network in real time. 4. Cost-benefit imbalance and poor economic efficiency: Due to inaccurate configuration and low control efficiency, the existing technology results in an energy storage utilization rate of less than 60%, and cannot effectively reduce the electricity cost of the park. Summary of the Invention

[0005] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, one objective of this invention is to propose a configuration method and system for grid-based energy storage to enhance the distribution network of a park. Targeting low-voltage distribution networks in parks containing distributed photovoltaic systems and charging piles, this method collects basic and operational data of the distribution network to identify weak points such as large voltage fluctuations and poor renewable energy absorption. A multi-factor coupling model is used to determine the capacity and access location of the grid-based energy storage. Modules including voltage / frequency control and renewable energy absorption control are built, and a "cloud platform-local controller" collaborative mechanism is constructed to achieve precise matching between the grid-based energy storage and the park's distribution network. This solves the problems of unstable voltage, high renewable energy curtailment rate, and low power supply reliability in the park's distribution network, thereby improving the operational stability and economy of the park's distribution network.

[0006] To address the aforementioned problems, this invention provides a configuration method for grid-based energy storage to enhance the distribution network in a park, comprising the following steps: S1. Multi-dimensional data acquisition and preprocessing: Collect basic static data, dynamic operation data and environmental data of the park's power distribution network, filter outliers and fill in missing values, classify and store the data, and convert electrical parameters into per-unit values. S2. Hierarchical weak node identification: Construct an evaluation index system that includes voltage stability, renewable energy absorption capacity, load adaptability and power supply reliability. Use a weighted summation method to calculate the comprehensive score of each node and identify nodes with scores below a set threshold as weak nodes. S3. Precise configuration of grid-type energy storage: Based on the weak nodes identified in step S2, the energy storage capacity is determined by a multi-factor coupled capacity calculation model. This model comprehensively considers load fluctuations, photovoltaic curtailment, voltage regulation requirements and temperature effects. Through power flow simulation, the node that can achieve the optimal reduction rate of line loss, voltage improvement rate and new energy consumption improvement rate is selected from the candidate access points as the final access point. S4. Multi-objective collaborative control module construction: Configure a hardware system including a central control unit, detection unit, and power regulation unit, and implement software control strategies including voltage / frequency droop control, timing optimization based on photovoltaic prediction and charging pile reservation, and differentiated protection. S5. Construct a cloud-edge collaborative operation mechanism: Establish a three-layer architecture of "cloud platform - edge control layer - terminal device layer". Under normal operating conditions, the cloud platform generates instructions and the edge layer optimizes their execution. Under abnormal operating conditions of communication interruption, the edge layer switches to local control mode to prioritize power supply to critical loads.

[0007] Preferably, in step S2, the weight allocation of the evaluation index system is as follows: voltage stability weight 0.35, new energy absorption capacity weight 0.25, load adaptability weight 0.2, and power supply reliability weight 0.2; the threshold for determining the weak node is a comprehensive score of 85 points.

[0008] Preferably, in step S3, the multi-factor coupling capacity calculation model is as follows:

[0009] in: C: Rated capacity of the energy storage system (kWh); k1: Power balance coefficient, with a value ranging from 1.0 to 1.2; P_fluctuation: Maximum daily load fluctuation at the node (kW), calculated as the difference between the maximum and minimum load values ​​over the past 30 days; P_curtailment: Average daily curtailment of photovoltaic power in kW, taken as the average value over the past 30 days; k2: Voltage regulation coefficient, with a value range of 0.7-0.9; ΔU_max: Maximum deviation of node voltage in kV; S_node: Node rated apparent power (MVA); k_t: Temperature coefficient, taken as 1.0 when the ambient temperature is between 5-35℃, and as 1.2 when it is outside this range.

[0010] Preferably, in step S3, the optimal criteria for selecting power flow simulation points include: line loss reduction rate ≥12%, voltage improvement rate ≥30%, and new energy consumption improvement rate ≥50%.

[0011] Preferably, in step S4, the voltage / frequency droop control strategy includes: Active power-frequency control: base frequency 50Hz, adjustment coefficient 0.03Hz / kW, energy storage discharges when the frequency is below 49.8Hz, and energy storage charges when the frequency is above 50.2Hz; Reactive power-voltage control: The reference voltage is the node rated voltage, with an adjustment coefficient of 0.02kV / kVar. When the voltage is lower than 0.97 times the reference voltage, the energy storage generates reactive power, and when the voltage is higher than 1.03 times the reference voltage, the energy storage absorbs reactive power.

[0012] Preferably, in step S4, the timing optimization strategy includes: using an LSTM model to predict photovoltaic output, reading charging pile reservation data to statistically analyze demand during different time periods, controlling the energy storage to charge at a rate of 0.8C and prioritizing power supply to the charging piles during peak photovoltaic output periods, controlling the energy storage to discharge at a rate of 1.0C during peak charging load periods to supplement power deficits, and controlling the energy storage to charge at a rate of 0.5C during off-peak nighttime load periods to reduce electricity costs.

[0013] Preferably, in step S4, the differentiated protection strategy includes: for precision loads, when the voltage deviation exceeds ±3% or the current fluctuation exceeds ±10%, adjusting the energy storage output within 100ms; for the energy storage itself, setting the SOC operating range to 10% to 90%, and setting a temperature protection threshold.

[0014] A system for configuring grid-type energy storage to enhance the power distribution network in a park includes: The data acquisition and preprocessing module is used to acquire and process multi-dimensional data from the park's power distribution network; The weak node identification module is used to calculate node scores and identify weak nodes based on the evaluation index system; The energy storage configuration optimization module is used to perform multi-factor coupled capacity calculation and power flow simulation to determine energy storage capacity and access point; A multi-objective collaborative control module is used to implement droop control, timing optimization, and differentiated protection strategies; The cloud-edge collaborative operation module is used to build a three-tier architecture and manage the flow of operating instructions under normal and abnormal conditions.

[0015] The advantages of the grid-type energy storage configuration method and system for improving the industrial park distribution network of the present invention compared with the prior art are as follows: Superior performance: By using multi-factor coupled capacity calculation (adapting to load / curtailment), voltage scenario-based design (meeting the needs of precise loads), and dynamic temperature compensation, the traditional solutions address the issues of blind capacity calculation and poor adaptability, achieving stable voltage (precision equipment failure reduced to 0), improved renewable energy absorption rate (curtailment rate ≤8%), and capacity compliance under extreme temperatures, resulting in significantly improved operational stability. Lower cost: Precise capacity configuration reduces energy storage investment by 19%, eliminates the need for additional reactive power compensation devices, reduces annual maintenance costs by 40%, extends battery life to 5 years, and reduces total cost by 25% compared to traditional solutions; More convenient to use: Automatic temperature compensation eliminates the need for manual adjustment, and cloud-edge collaboration enables all-weather adaptive operation without frequent intervention, making operation more worry-free. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the implementation of the present invention. Detailed Implementation

[0018] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0019] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0020] The present invention will now be described in further detail with reference to the accompanying drawings.

[0021] Combination Figure 1 The present invention provides a configuration method and system for grid-type energy storage to enhance the distribution network of a park, comprising: (1) Multi-dimensional data collection and preprocessing of industrial park power distribution network 1) Data Collection Targets and Device Deployment Basic static data (distribution network topology, line parameters, load / photovoltaic / charging pile distribution) is collected through GIS and equipment ledger terminals, deployed in the control center and equipment side, and updated when equipment changes; Dynamic operating data (node ​​electrical parameters, photovoltaic output, charging pile status, and precise load conditions) are collected through smart meters, photovoltaic terminals, etc., and deployed on the power supply side of each node and equipment. Data is collected every minute under normal circumstances, and reduced to every 30 seconds during peak periods. Environmental and operating condition data (temperature, power consumption time series, voltage sensitivity threshold) are collected through temperature and humidity sensors, which are deployed in energy storage units and control centers, and collected every 5 minutes, and every 1 minute when the temperature exceeds the limit.

[0022] 2) Data preprocessing Outliers (voltage fluctuations exceeding ±20%) were filtered using the 3σ criterion, and missing data of ≤5 minutes were filled using linear interpolation. Store data on the cloud platform according to the categories of "basic-real-time-historical" (real-time data is stored for 1 year, and historical data is stored long-term). Convert electrical parameters to per-unit values ​​(reference 10kV / 10MVA) using the formula: "per-unit value = actual value / reference value".

[0023] 3) Function of the data acquisition system Comprehensive data acquisition of the status of "source-grid-load-storage" provides accurate data for subsequent steps and avoids configuration deviations.

[0024] (2) Hierarchical identification of weak nodes in the industrial park power distribution network 1) Evaluation indicator system Four primary indicators are set: voltage stability (weight 0.35, secondary indicator voltage qualification rate, precision node threshold ±3%, others ±5%), renewable energy absorption capacity (weight 0.25, secondary indicator photovoltaic curtailment rate), load adaptability (weight 0.2, secondary indicator charging pile load fluctuation adaptability rate, peak defined as power ≥80% of rated value), and power supply reliability (weight 0.2, secondary indicator annual power outage duration at nodes). The data are from smart meters, photovoltaic terminals, charging pile modules, and GIS fault records, respectively.

[0025] 2) Weak point identification The extreme value method is used to convert the indicators into a score of 0-100 (positive indicators: (actual - minimum) / (maximum - minimum)×100; negative indicators: (maximum - actual) / (maximum - minimum)×100); the weighted sum is used to obtain the comprehensive score (formula: 0.35×voltage score + 0.25×waste power score + 0.2×adaptation score + 0.2×(1-outage duration / 525600)×100); the score <85 is a weak node, and energy storage is prioritized according to the score.

[0026] 3) Role of the recognition model Accurately identify key nodes to avoid blindly allocating energy storage and improve resource efficiency.

[0027] (3) Precise configuration of grid-based energy storage 1) Multi-factor coupling capacity calculation A. Capacity Model (Core Improvement)

[0028] in: C: Rated capacity of the energy storage system (kWh); k1: Power balance coefficient, with a value ranging from 1.0 to 1.2; P_fluctuation: Maximum daily load fluctuation at the node (kW), calculated as the difference between the maximum and minimum load values ​​over the past 30 days; P_curtailment: Average daily curtailment of photovoltaic power in kW, taken as the average value over the past 30 days; k2: Voltage regulation coefficient, with a value range of 0.7-0.9; ΔU_max: Maximum deviation of node voltage in kV; S_node: Node rated apparent power (MVA); k_t: Temperature coefficient, taken as 1.0 when the ambient temperature is between 5-35℃, and as 1.2 when it is outside this range.

[0029] B. Capacity Verification Calculate the initial capacity (e.g., 2840kWh for node #12); select a typical day for simulation: peak charging period (17:00-19:00) discharge voltage regulation (≤±3%), peak photovoltaic period (12:00-14:00) charging consumption (curtailment rate ≤8%); if not met, adjust k_1 and k_2 and recalculate.

[0030] 2) Selection of locations for trend simulation Select 3-5 candidate points (near load center, with installation space, and voltage matching); build a simulation model using PSCAD / EMTDC (input line / load parameters, output / charging curves) to simulate peak and fault scenarios; calculate indicators (line loss reduction rate ≥12%, voltage improvement rate ≥30%, absorption improvement rate ≥50%), and select the best overall result (e.g., the low-voltage side of the distribution transformer at node 12, with a loss reduction of 15% and a voltage improvement of 35%).

[0031] (4) Construction of multi-objective collaborative control module 1) Hardware Components It includes five main modules: a central control unit (PLC, for receiving data and executing algorithms); a voltage / frequency detection unit (0.2-level voltage sensor, 0.01Hz frequency sensor, for data feedback); a power regulation unit (1.2MW bidirectional PCS + reactor + filter, for adjusting charging and discharging power); a communication unit (4G / Ethernet, for connecting to the cloud platform and equipment); and a protection unit (overvoltage / overcurrent / temperature protector, for over-threshold disconnection).

[0032] 2) Software Strategy A. Voltage-frequency droop control Active power - frequency: base 50Hz, coefficient 0.03Hz / kW; discharge when f < 49.8Hz (P=(50-f) / 0.03), charge when f > 50.2Hz (P=(f-50) / 0.03).

[0033] Reactive power-voltage: base 10kV (or 0.4kV), coefficient 0.02kV / kVar; discharge when U < 0.97Ue (Q = (10-U) / 0.02), charge when U > 1.03Ue (Q = (U-10) / 0.02).

[0034] B. New Energy - Charging Pile Collaboration Photovoltaic forecast: LSTM model (data from the past year + weather forecast, 15-minute forecast for the next day, error ±10%). Charging plan: Read charging station reservation data and calculate demand during different time periods; Timing optimization: 0.8C charging + power supply to charging piles during peak photovoltaic hours (12-14 o'clock), 1.0C discharge to fill the gap during peak charging hours (17-19 o'clock), and 0.5C charging to reduce costs during off-peak hours at night (23-6 o'clock).

[0035] C. Differential Protection Precision load: If the voltage deviation exceeds ±3% or the current fluctuation exceeds ±10%, energy storage will be adjusted within 100ms. Energy storage itself: discharge stops when SOC < 10%, charging stops when SOC > 90%, fan starts at 45℃, and circuit is disconnected at 50℃.

[0036] (5) Construction of cloud-edge collaborative operation mechanism 1) Three-tier architecture Cloud platform layer: Big data platform + visualization system + algorithm software, storing data, generating instructions, making predictions, Ethernet / 5G communication; Edge control layer: Energy storage local controller + distribution network area controller, receiving commands, local control, and handling faults, with 4G / Ethernet communication; Terminal device layer: smart meters, inverters, etc., collect data, execute instructions, and connect to the edge layer via wired / wireless connections.

[0037] 2) Collaborative Process Normal operating conditions: Terminal collects data → Edge preprocessing → Cloud platform generates instructions → Edge optimizes and controls the terminal; Abnormal operating conditions (such as communication interruption): The edge layer automatically switches to local control, prioritizing power supply to precision loads and charging piles, and synchronizes data to the cloud platform after communication is restored.

[0038] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A configuration method for grid-based energy storage to enhance the distribution network of a park, characterized in that, Includes the following steps: S1. Multi-dimensional data acquisition and preprocessing: Collect basic static data, dynamic operation data and environmental data of the park's power distribution network, filter outliers and fill in missing values, classify and store the data, and convert electrical parameters into per-unit values. S2. Hierarchical weak node identification: Construct an evaluation index system that includes voltage stability, renewable energy absorption capacity, load adaptability and power supply reliability. Use a weighted summation method to calculate the comprehensive score of each node and identify nodes with scores below a set threshold as weak nodes. S3. Precise configuration of grid-type energy storage: Based on the weak nodes identified in step S2, the energy storage capacity is determined by a multi-factor coupled capacity calculation model. This model comprehensively considers load fluctuations, photovoltaic curtailment, voltage regulation requirements and temperature effects. Through power flow simulation, the node that can achieve the optimal reduction rate of line loss, voltage improvement rate and new energy consumption improvement rate is selected from the candidate access points as the final access point. S4. Multi-objective collaborative control module construction: Configure a hardware system including a central control unit, detection unit, and power regulation unit, and implement software control strategies including voltage / frequency droop control, timing optimization based on photovoltaic prediction and charging pile reservation, and differentiated protection. S5. Construct a cloud-edge collaborative operation mechanism: Establish a three-layer architecture of "cloud platform - edge control layer - terminal device layer". Under normal working conditions, the cloud platform generates instructions and the edge layer optimizes their execution. Under abnormal working conditions of communication interruption, the edge layer switches to local control mode to prioritize power supply to critical loads.

2. The configuration method for upgrading the industrial park distribution network using grid-type energy storage according to claim 1, characterized in that: In step S2, the weight allocation of the evaluation index system is as follows: voltage stability weight 0.35, new energy absorption capacity weight 0.25, load adaptability weight 0.2, and power supply reliability weight 0.2; the threshold for determining the weak node is a comprehensive score of 85 points.

3. The configuration method for upgrading the industrial park distribution network using grid-type energy storage according to claim 1, characterized in that: In step S3, the multi-factor coupling capacity calculation model is as follows: ； in: C: Rated capacity of the energy storage system (kWh); k1: Power balance coefficient, with a value ranging from 1.0 to 1.2; P_fluctuation: Maximum daily load fluctuation at the node (kW), calculated as the difference between the maximum and minimum load values ​​over the past 30 days; P_curtailment: Average daily curtailment of photovoltaic power in kW, taken as the average value over the past 30 days; k2: Voltage regulation coefficient, with a value range of 0.7-0.9; ΔU_max: Maximum deviation of node voltage in kV; S_node: Node rated apparent power (MVA); k_t: Temperature coefficient, taken as 1.0 when the ambient temperature is between 5-35℃, and as 1.2 when it is outside this range.

4. The configuration method for upgrading the industrial park distribution network using grid-type energy storage according to claim 1, characterized in that: In step S3, the optimal criteria for selecting power flow simulation points include: line loss reduction rate ≥12%, voltage improvement rate ≥30%, and new energy consumption improvement rate ≥50%.

5. The configuration method for upgrading the industrial park distribution network using grid-type energy storage according to claim 1, characterized in that: In step S4, the voltage / frequency droop control strategy includes: Active power-frequency control: base frequency 50Hz, adjustment coefficient 0.03Hz / kW, energy storage discharges when the frequency is below 49.8Hz, and energy storage charges when the frequency is above 50.2Hz; Reactive power-voltage control: The reference voltage is the node rated voltage, with an adjustment coefficient of 0.02kV / kVar. When the voltage is lower than 0.97 times the reference voltage, the energy storage generates reactive power, and when the voltage is higher than 1.03 times the reference voltage, the energy storage absorbs reactive power.

6. The configuration method for upgrading the industrial park distribution network using grid-type energy storage according to claim 1, characterized in that: In step S4, the timing optimization strategy includes: using an LSTM model to predict photovoltaic output, reading charging pile reservation data to statistically analyze demand during different time periods, controlling energy storage to charge at a rate of 0.8C and prioritizing power supply to charging piles during peak photovoltaic output periods, controlling energy storage to discharge at a rate of 1.0C during peak charging load periods to supplement power deficits, and controlling energy storage to charge at a rate of 0.5C during off-peak nighttime load periods to reduce electricity costs.

7. The configuration method for upgrading the industrial park distribution network using grid-type energy storage according to claim 1, characterized in that: In step S4, the differentiated protection strategy includes: for precision loads, when the voltage deviation exceeds ±3% or the current fluctuation exceeds ±10%, the energy storage output is adjusted within 100ms; for the energy storage itself, the SOC operating range is set to 10% to 90%, and a temperature protection threshold is set.

8. A system for implementing the method according to any one of claims 1-7, characterized in that, include: The data acquisition and preprocessing module is used to acquire and process multi-dimensional data from the park's power distribution network; The weak node identification module is used to calculate node scores and identify weak nodes based on the evaluation index system; The energy storage configuration optimization module is used to perform multi-factor coupled capacity calculation and power flow simulation to determine energy storage capacity and access point; A multi-objective collaborative control module is used to implement droop control, timing optimization, and differentiated protection strategies; The cloud-edge collaborative operation module is used to build a three-tier architecture and manage the flow of operating instructions under normal and abnormal conditions.

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