Power distribution network grading protection optimization method for high-proportion new energy and stored energy access

Abstract

The invention discloses a grading protection optimization method for a high-proportion new energy and stored energy access power distribution network, and the method comprises the steps: 1, building a transient equivalent model of new energy and stored energy, and restoring the fault response characteristics of the transient equivalent model; step 2, based on the accurate model, analyzing fault features and evolution paths, mastering propagation rules of faults under a new power grid structure, and identifying risk points and protection difficulties; step 3, integrating load, new energy and fault data to construct an index system, and converting a protection problem into a multi-objective optimization problem; step 4, performing global optimization by applying an intelligent algorithm, and automatically searching an optimal protection configuration scheme considering quickness, selectivity and reliability in massive possibilities; 5, simulation verification and dynamic adjustment are carried out, and it is ensured that the optimization scheme is effective and robust in practical application; the method has the advantages of being high in new energy access scene adaptability, high in hierarchical protection intelligence level, high in resource allocation and operation efficiency and good in overall safety and stability of the power distribution network.

Description

Technical Field

[0001] This invention belongs to the field of distribution network hierarchical protection optimization technology, specifically relating to a distribution network hierarchical protection optimization method with a high proportion of new energy and energy storage access. Background Technology

[0002] In recent years, investment in the construction of new energy power plants has been continuously increasing. With widespread attention to clean energy power generation and continuous technological breakthroughs in new energy sources such as wind power and photovoltaics, the construction of an integrated active distribution network encompassing power generation, grid, load, and storage has become a trend. Distributed power sources connecting to the grid from different ports can effectively reduce energy losses caused by long-distance transmission. Large-scale battery energy storage power stations will effectively solve problems such as system power, voltage, and frequency fluctuations caused by new energy sources, thus contributing to the absorption of clean energy.

[0003] The grid connection of distributed renewable energy sources alters the power flow distribution patterns and the nature of grid-supplied loads in the distribution network, leading to changes in distribution network fault characteristics and placing higher demands on the selection and configuration of hierarchical protection systems. However, traditional hierarchical protection optimization methods for distribution networks still have several problems: 1. The grid connection of distributed renewable energy sources causes the power flow in the distribution network to change from unidirectional to multidirectional, and the nature of grid-supplied loads to change from passive to active, resulting in significant changes in the magnitude, direction, and distribution patterns of fault currents. Traditional protection selection schemes based on fixed power flow are no longer applicable; 2. The transient response of renewable energy sources (such as the current-limiting characteristics of inverter interfaces) differs from that of traditional synchronous machines, leading to more complex fault characteristics (such as short-circuit current and harmonic content). Furthermore, the fault evolution path is affected by the distribution and operating status of renewable energy sources, increasing the difficulty of protection configuration; 3. Existing hierarchical protection optimization methods still have several problems. The selection of protection points (such as overcurrent protection and distance protection) depends on simple fault scenarios. However, after the integration of new energy sources, the fault scenarios are diversified (such as symmetrical / asymmetrical faults and islanded operation). The selection of protection points needs to consider more factors (such as load importance and new energy penetration rate). Otherwise, it is easy to cause protection to malfunction or fail to operate. 4. Traditional protection schemes lack adaptive capabilities and cannot dynamically respond to changes in distribution network topology and operating status, resulting in poor adaptability to new energy integration scenarios and decreased protection performance. To solve the above problems, it is necessary to develop a distribution network hierarchical protection optimization method with a high proportion of new energy and energy storage integration. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a distribution network hierarchical protection optimization method with a high proportion of new energy and energy storage access, which has strong adaptability to new energy access scenarios, high level of intelligent hierarchical protection, high resource allocation and operation efficiency, and good overall security and stability of the distribution network. It is applicable to the intelligent construction of new active distribution network relay protection systems.

[0005] The objective of this invention is achieved as follows: a hierarchical protection optimization method for distribution networks with a high proportion of new energy sources and energy storage, comprising the following steps: Step 1, Precise Modeling: This step is used to establish transient equivalent models for various types of distributed new energy sources and energy storage. By establishing transient equivalent models for new energy sources and energy storage, their fault response characteristics, which differ from those of traditional power sources, are restored, laying an accurate model foundation for subsequent analysis. Step 2, Feature Analysis: This step is used to analyze the fault characteristics and evolution paths of new energy sources connected to the distribution network. Based on an accurate model, it analyzes the fault characteristics and evolution paths, understands the propagation law of faults under the new power grid structure, and identifies risk points and protection difficulties. Step 3, Data-driven: This step involves collecting distribution network operation data and constructing an optimization indicator system. By integrating load, new energy, and fault data, the indicator system is built, transforming the protection problem into a multi-objective optimization problem. This allows the optimization process to quantify and measure the comprehensive impact of protection schemes on grid security, power supply reliability, and economy. Step 4, Intelligent Decision-Making: This step involves applying intelligent algorithms to optimize the selection of protection points at different levels and to perform global optimization. It automatically finds the optimal protection configuration scheme that balances speed, selectivity, and reliability from a vast number of possibilities. Step 5, Closed-loop verification: This step is used to verify and dynamically adjust the protection scheme. Through simulation verification and dynamic adjustment, a "design-verification-update" closed loop is formed to ensure that the optimized scheme is effective and robust in practical applications and to give the protection system the adaptive ability to continuously evolve.

[0006] Furthermore, step 1 specifically includes: (1) Collect equipment parameters of distributed new energy and energy storage systems, including inverter control strategies, capacity, and impedance characteristics; (2) Based on these parameters, a transient equivalent model is established, including a simplified circuit model of the voltage source inverter VSC and a charging and discharging dynamic model of the energy storage system, in order to simulate the output characteristics of new energy during faults, including current limiting and low voltage ride-through capability. (3) Verify the accuracy of the model through simulation or experiment to ensure that it can reflect the transient behavior under real faults.

[0007] Furthermore, step 2 specifically includes: (1) Based on the equivalent model in step 1, simulate the occurrence process of different fault scenarios in the distribution network, and record the fault current, voltage waveform and timing characteristics; (2) Analyze the failure evolution path, consider the impact of new energy distribution location, penetration rate and network topology on failure propagation, and identify key failure points and high-risk areas; (3) Extract fault characteristic parameters, including short-circuit current amplitude, phase jump, and harmonic distortion, for subsequent protection setting calculation.

[0008] Furthermore, the fault scenarios include three-phase short-circuit faults and single-phase ground faults.

[0009] Furthermore, step 3 specifically involves: (1) Collect real-time or historical data of the distribution network, including load distribution, load importance, new energy distribution and fault statistics; (2) Construct an optimized index system, including protection sensitivity, speed, reliability and economic indicators, and assign weights to reflect the importance of the load and the priority of operation.

[0010] Furthermore, the reliability indicators include false start rate and failure to start rate, and the economic indicators include protection equipment cost and power outage loss.

[0011] Furthermore, step 4 specifically involves: (1) Select a suitable intelligent algorithm, take the index system in step 3 as the objective function, and optimize the selection location and setting of the hierarchical protection. The hierarchical protection includes overcurrent protection and differential protection. (2) Input parameters include: fault characteristic data from step 2, load and new energy data from step 3, and network topology constraints, including node voltage and branch capacity; (3) The algorithm outputs the optimal protection point selection scheme, including the installation location of the protection device, the action setting value and the coordination logic. The action setting value includes the current setting value and the time delay. The coordination logic includes the main and backup protection timing. (4) Through iterative optimization, ensure that the solution can achieve rapid and selective fault removal in different fault scenarios.

[0012] Furthermore, the intelligent algorithm employs genetic algorithms, particle swarm optimization, or machine learning methods.

[0013] Furthermore, step 5 specifically includes: (1) Use digital simulation or hardware-in-the-loop testing to verify the performance of the optimized protection scheme under various operating scenarios and check for any protection blind spots or conflicts. (2) Establish an online monitoring system to collect the operating status of the distribution network in real time, and dynamically adjust the protection settings and selection points in combination with intelligent algorithms to cope with topology changes or new energy fluctuations; (3) Update the protection scheme regularly and re-execute steps 1-4 based on the new data to ensure the long-term adaptability of the protection scheme.

[0014] Furthermore, the intelligent algorithm employs adaptive logic or online learning.

[0015] Due to the adoption of the above technical solution, the beneficial effects of the present invention are: (1) This invention accurately captures the fault characteristics of new energy sources through transient equivalent models and fault feature analysis, making the protection point selection more in line with actual operating conditions, reducing false tripping and failure to trip, improving the accuracy and reliability of fault clearing, and enhancing the adaptability of the protection scheme to new energy access scenarios. (2) This invention achieves dynamic optimization and adaptive adjustment of protection selection points through the application of intelligent algorithms, enabling the protection system to respond to the fluctuations of distribution network topology and new energy sources, enhancing self-healing ability and operational flexibility, and improving the level of intelligence of hierarchical protection. (3) By considering load distribution and importance, this invention ensures priority protection of critical loads and reduces power outage losses; at the same time, by optimizing the selection of protection points, it reduces the redundancy of protection equipment, improves economy, and optimizes resource allocation and operating efficiency. (4) Through verification and dynamic adjustment, the protection scheme of this invention can effectively cope with the complex fault scenarios brought about by the high proportion of new energy and energy storage access, suppress fault propagation, support the safe and stable operation of the distribution network, promote the consumption of new energy, and enhance the overall safety and stability of the distribution network. In summary, this invention has the advantages of strong adaptability to new energy access scenarios, high level of intelligent hierarchical protection, high efficiency in resource allocation and operation, and good overall security and stability of the distribution network. It can accurately analyze the fault behavior of new energy through digital means and utilize the global optimization capability of intelligent algorithms to transform the protection system from a static and passive configuration to a dynamic and adaptive intelligent system. Detailed Implementation

[0016] The technical solution of the present invention will be further described in detail below through embodiments.

[0017] This invention provides a hierarchical protection optimization method for distribution networks with a high proportion of new energy sources and energy storage, comprising the following steps: Step 1, Precise Modeling: This step is used to establish transient equivalent models for various types of distributed new energy sources and energy storage. By establishing these transient equivalent models, the fault response characteristics that differ from traditional power sources are theoretically restored, laying an accurate model foundation for subsequent analysis.

[0018] (1) Collect equipment parameters (such as inverter control strategy, capacity, and impedance characteristics) of distributed new energy (such as photovoltaic and wind power) and energy storage systems.

[0019] (2) Based on these parameters, a transient equivalent model is established, including a simplified circuit model of the voltage source inverter (VSC) and a dynamic charging and discharging model of the energy storage system, in order to simulate the output characteristics of new energy during faults (such as current limiting and low voltage ride-through capability).

[0020] (3) Verify the accuracy of the model through simulation or experiment to ensure that it can reflect the transient behavior under real faults.

[0021] Step 2, Feature Analysis: This step is used to analyze the fault characteristics and evolution paths of new energy sources connected to the distribution network. Based on an accurate model, the fault characteristics and evolution paths are analyzed to understand the propagation law of faults under the new power grid structure in principle, and to identify risk points and protection difficulties.

[0022] (1) Based on the equivalent model in step 1, simulate the occurrence process of different fault scenarios (such as three-phase short-circuit fault and single-phase ground fault) in the distribution network, and record the fault current, voltage waveform and timing characteristics.

[0023] (2) Analyze the failure evolution path, consider the impact of new energy distribution location, penetration rate and network topology (such as radial or ring) on ​​failure propagation, and identify key failure points and high-risk areas.

[0024] (3) Extract fault characteristic parameters, such as short-circuit current amplitude, phase jump, and harmonic distortion, for subsequent protection setting calculation.

[0025] Step 3, Data-driven: This step involves collecting distribution network operation data and constructing an optimization index system. By integrating load, new energy, and fault data, the index system is built, transforming the protection problem into a multi-objective optimization problem. This allows the optimization process to quantify and measure the comprehensive impact of protection schemes on grid security, power supply reliability, and economy.

[0026] (1) Collect real-time or historical data of the distribution network, including load distribution (such as peak load, load curve), load importance (such as key users, sensitive load level), new energy distribution (such as installation location, capacity) and fault statistics (such as fault type, fault frequency).

[0027] (2) Construct an optimized index system, including protection sensitivity, speed, reliability index (such as false trip rate, failure to trip rate) and economic index (such as protection equipment cost, power outage loss), and assign weights to reflect the importance of the load and the operation priority.

[0028] Step 4, Intelligent Decision-Making: This step involves applying intelligent algorithms to optimize the selection of protection points at different levels and to perform global optimization. It transcends the limitations of human experience and automatically finds the optimal protection configuration scheme that balances speed, selectivity, and reliability from a vast number of possibilities.

[0029] (1) Select a suitable intelligent algorithm, such as genetic algorithm, particle swarm optimization or machine learning method, and optimize the selection location and setting of hierarchical protection (such as overcurrent protection and differential protection) with the index system in step 3 as the objective function. (2) Input parameters include: fault characteristic data from step 2, load and new energy data from step 3, and network topology constraints (such as node voltage and branch capacity).

[0030] (3) The algorithm outputs the optimal protection point selection scheme, including the installation location of the protection device, the action setting (such as the current setting value and time delay) and the coordination logic (such as the main and backup protection sequence).

[0031] (4) Through iterative optimization, ensure that the solution can achieve rapid and selective fault removal in different fault scenarios (such as new energy switching and islanded operation).

[0032] Step 5, Closed-loop verification: This step is used to verify and dynamically adjust the protection scheme. Through simulation verification and dynamic adjustment, a "design-verification-update" closed loop is formed to ensure that the optimized scheme is effective and robust in practical applications and to give the protection system the adaptive ability to continuously evolve.

[0033] (1) Use digital simulation (such as EMTP, PSCAD) or hardware-in-the-loop testing to verify the performance of the optimized protection scheme under various operating scenarios (such as high new energy penetration, energy storage charging and discharging) and check whether there are protection blind spots or conflicts.

[0034] (2) Establish an online monitoring system to collect the operating status of the distribution network (such as power flow and fault information) in real time, and dynamically adjust the protection settings and selection points in conjunction with intelligent algorithms (such as adaptive logic or online learning) to cope with topology changes or new energy fluctuations.

[0035] (3) Update the protection scheme regularly and re-execute steps 1-4 based on the new data to ensure the long-term adaptability of the protection scheme.

[0036] In summary, based on the analysis of transient equivalent models of various types of distributed renewable energy and the fault characteristics and evolution paths of their access to the distribution network, this invention further combines load distribution, load importance, fault scenarios, renewable energy distribution and other conditions, and incorporates the application of intelligent algorithms to optimize the existing distribution network hierarchical protection point selection scheme. This helps to improve the adaptability of the protection scheme to renewable energy access scenarios, as well as the intelligence level of the new active distribution network hierarchical protection, and enhance the security and reliability of the distribution network under the background of high proportion of renewable energy access.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A hierarchical protection optimization method for distribution networks with a high proportion of new energy sources and energy storage, characterized in that, Includes the following steps: Step 1, Precise Modeling: This step is used to establish transient equivalent models for various types of distributed new energy sources and energy storage. By establishing transient equivalent models for new energy sources and energy storage, their fault response characteristics, which differ from those of traditional power sources, are restored, laying an accurate model foundation for subsequent analysis. Step 2, Feature Analysis: This step is used to analyze the fault characteristics and evolution paths of new energy sources connected to the distribution network. Based on an accurate model, it analyzes the fault characteristics and evolution paths, understands the propagation law of faults under the new power grid structure, and identifies risk points and protection difficulties. Step 3, Data-driven: This step involves collecting distribution network operation data and constructing an optimization indicator system. By integrating load, new energy, and fault data, the indicator system is built, transforming the protection problem into a multi-objective optimization problem. This allows the optimization process to quantify and measure the comprehensive impact of protection schemes on grid security, power supply reliability, and economy. Step 4, Intelligent Decision-Making: This step involves applying intelligent algorithms to optimize the selection of protection points at different levels and to perform global optimization. It automatically finds the optimal protection configuration scheme that balances speed, selectivity, and reliability from a vast number of possibilities. Step 5, Closed-loop verification: This step is used to verify and dynamically adjust the protection scheme. Through simulation verification and dynamic adjustment, a "design-verification-update" closed loop is formed to ensure that the optimized scheme is effective and robust in practical applications and to give the protection system the adaptive ability to continuously evolve.

2. The distribution network hierarchical protection optimization method for high-proportion new energy and energy storage access as described in claim 1, characterized in that, Step 1 specifically involves: (1) Collect equipment parameters of distributed new energy and energy storage systems, including inverter control strategies, capacity, and impedance characteristics; (2) Based on these parameters, a transient equivalent model is established, including a simplified circuit model of the voltage source inverter VSC and a charging and discharging dynamic model of the energy storage system, in order to simulate the output characteristics of new energy during faults, including current limiting and low voltage ride-through capability. (3) Verify the accuracy of the model through simulation or experiment to ensure that it can reflect the transient behavior under real faults.

3. The distribution network hierarchical protection optimization method for high-proportion new energy and energy storage access according to claim 1, characterized in that, Step 2 specifically involves: (1) Based on the equivalent model in step 1, simulate the occurrence process of different fault scenarios in the distribution network, and record the fault current, voltage waveform and timing characteristics; (2) Analyze the failure evolution path, consider the impact of new energy distribution location, penetration rate and network topology on failure propagation, and identify key failure points and high-risk areas; (3) Extract fault characteristic parameters, including short-circuit current amplitude, phase jump, and harmonic distortion, for subsequent protection setting calculation.

4. The distribution network hierarchical protection optimization method for high-proportion new energy and energy storage access according to claim 3, characterized in that: The fault scenarios include three-phase short-circuit faults and single-phase ground faults.

5. The distribution network hierarchical protection optimization method for high-proportion new energy and energy storage access according to claim 1, characterized in that, Step 3 specifically involves: (1) Collect real-time or historical data of the distribution network, including load distribution, load importance, new energy distribution and fault statistics; (2) Construct an optimized index system, including protection sensitivity, speed, reliability and economic indicators, and assign weights to reflect the importance of the load and the priority of operation.

6. The distribution network hierarchical protection optimization method for high-proportion new energy and energy storage access according to claim 5, characterized in that: The reliability indicators include the false start rate and the failure to start rate, and the economic indicators include the cost of protection equipment and the loss due to power outage.

7. The distribution network hierarchical protection optimization method for high-proportion new energy and energy storage access according to claim 1, characterized in that, Step 4 specifically involves: (1) Select a suitable intelligent algorithm, take the index system in step 3 as the objective function, and optimize the selection location and setting of the hierarchical protection. The hierarchical protection includes overcurrent protection and differential protection. (2) Input parameters include: fault characteristic data from step 2, load and new energy data from step 3, and network topology constraints, including node voltage and branch capacity; (3) The algorithm outputs the optimal protection point selection scheme, including the installation location of the protection device, the action setting value and the coordination logic. The action setting value includes the current setting value and the time delay. The coordination logic includes the main and backup protection timing. (4) Through iterative optimization, ensure that the solution can achieve rapid and selective fault removal in different fault scenarios.

8. The distribution network hierarchical protection optimization method for high-proportion new energy and energy storage access according to claim 7, characterized in that: The intelligent algorithm employs genetic algorithms, particle swarm optimization, or machine learning methods.

9. The method for optimizing the hierarchical protection of distribution networks with a high proportion of new energy and energy storage access according to claim 1, characterized in that, Step 5 specifically involves: (1) Use digital simulation or hardware-in-the-loop testing to verify the performance of the optimized protection scheme under various operating scenarios and check for any protection blind spots or conflicts. (2) Establish an online monitoring system to collect the operating status of the distribution network in real time, and dynamically adjust the protection settings and selection points in combination with intelligent algorithms to cope with topology changes or new energy fluctuations; (3) Update the protection scheme regularly and re-execute steps 1-4 based on the new data to ensure the long-term adaptability of the protection scheme.

10. The distribution network hierarchical protection optimization method for high-proportion new energy and energy storage access according to claim 9, characterized in that: The intelligent algorithm employs adaptive logic or online learning.