Intelligent power grid optimization control method based on dynamic network topology reconstruction

Abstract

The invention relates to an intelligent power grid optimization control method based on dynamic network topology reconstruction. The process comprises the following steps: establishing a mixed integer linear programming optimization model; screening the optimal deployment position of the loop closing device; actual power grid data are collected in real time, the net load fluctuation range of each power grid node is calculated, the flexible supply and demand state of a power grid line is evaluated, and if the flexible demand of the power grid line exceeds the supply or power flow out-of-limit, the optimal power regulation quantity and the optimal power grid network topology structure of a loop closing device are calculated; deploying the loop closing device according to the optimal deployment position and the optimal power regulation quantity of the loop closing device, changing a power grid connection structure according to the optimal power grid network topology structure, and optimally controlling an actual power grid; and calculating to obtain the power grid performance after the deployment of the loop closing device, and if any power grid performance is lower than a preset threshold value, carrying out the above process again. Compared with the prior art, the power of the network topology and the loop closing device can be quickly adjusted according to the actual power grid condition.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grid optimization control, and in particular to a smart grid optimization control method based on dynamic network topology reconstruction. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, the power system is facing unprecedented challenges of change. The large-scale grid connection of distributed energy, represented by wind and solar energy, has brought clean energy while also bringing significant complexity and uncertainty to the operation of the power system. The static radial topology commonly used in traditional distribution networks has advantages such as simple design and clear protection logic. However, in the new scenario of high proportion of renewable energy access and dynamic load changes, its structure is basically fixed once it is built. This static operation mode has exposed many problems in actual application:

[0003] 1. Faced with significant temporal and spatial fluctuations in power demand, such as differences in peak loads between morning and evening and seasonal variations, static topologies lack dynamic adjustment capabilities, resulting in some lines being chronically overloaded or underloaded, impacting power supply efficiency and shortening equipment lifespan.

[0004] 2. When absorbing intermittent and unpredictable renewable energy, static topologies are unable to cope with issues such as local voltage limits and line power flow reversals caused by output fluctuations.

[0005] 3. Under the time-of-use electricity price mechanism in the power market, the power supply costs in different time periods and regions vary significantly, and the static topology cannot dynamically adjust the power supply path to utilize low-cost power sources, resulting in poor system operation economy.

[0006] To improve the flexibility and economy of distribution networks, network reconfiguration (NR) technology has emerged. This technology changes the grid topology by adjusting the states of section switches and tie switches, achieving goals such as reducing network losses, balancing loads, and improving voltage quality.

[0007] Chinese patent application CN118713053A discloses a method for selecting the access location of a loop closure device. The method can form a final access solution for the loop closure device that integrates multiple indicators in multiple scenarios, ensuring that the access location of the loop closure device can be fully evaluated and optimized in all possible failure scenarios. However, the optimization results of this patent are based on static scenario simulation and do not design a continuous dynamic adjustment mechanism. If the actual operation deviates from the preset scenario, the solution may fail.

[0008] It can be seen that the existing network reconstruction technology still has obvious shortcomings. Most methods are based on one-time optimization of static load levels, using typical daily load curves as input, and fixed operation after calculating the optimal topology. They are unable to respond to real-time load fluctuations and changes in renewable energy output; the few dynamic reconstruction studies that consider the time dimension mostly adopt open-loop control strategies and rely on predicted data to formulate reconstruction plans, which may fail or even deteriorate system performance when the prediction error is large; in addition, existing studies often focus on technical goals and ignore the economic optimization of electricity costs, failing to fully tap the cost optimization potential in the electricity market environment.

[0009] In summary, there is still a need for a dynamic reconstruction method with real-time response capabilities to incorporate electricity costs into the optimization objectives and achieve coordinated optimization of technical performance and economic efficiency. Summary of the Invention

[0010] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a smart grid optimization control method based on dynamic network topology reconstruction.

[0011] The purpose of the present invention can be achieved by the following technical solutions:

[0012] A smart grid optimization control method based on dynamic network topology reconstruction, the method process comprising:

[0013] Obtain actual power grid data and establish a mixed integer linear programming optimization model with the goal of minimizing total electricity cost;

[0014] Based on actual grid data, the optimal deployment location of the loop closing device is selected based on the active power flow sensitivity coefficient method and the injection transfer distribution factor;

[0015] Real-time data collection of actual grid node loads, renewable energy output, line flow, and voltage deviation is used to calculate the net load fluctuation range of each grid node and assess the supply and demand of grid line flexibility. If the demand for grid line flexibility exceeds the supply or the flow exceeds the limit, a mixed integer linear programming optimization model is used to dynamically optimize and calculate the optimal power regulation of the loop closing device and the optimal grid network topology.

[0016] Deploy the loop closing device based on its optimal deployment location and optimal power regulation, change the grid connection structure based on the optimal grid network topology, and optimize the control of the actual grid;

[0017] The grid performance after the loop closing device is deployed is calculated and obtained. If any grid performance is lower than the preset threshold, the above process is repeated.

[0018] Furthermore, the objective function of the mixed integer linear programming optimization model is:

[0019]

[0020] Among them, T is the time set, I is the substation set, c it represents the electricity cost of substation i at time t, P it is the output power of substation i at time t.

[0021] Furthermore, the constraints of the mixed integer linear programming optimization model include power balance constraints, line thermal capacity constraints, voltage phase angle relationship constraints and topology radial constraints; wherein,

[0022] The power balance constraint is:

[0023]

[0024] Among them, K(n-) and K(n+) represent the set of non-flexible lines with bus n as the starting point and end point, respectively. kt is the power flow of line k at time t, d nt is the load demand of bus n at time t;

[0025] The circuit heat capacity constraint is:

[0026]

[0027] Among them, J lt is a binary variable, indicating the switch state of the flexible circuit l at time t, Rating k and Rating l are the thermal capacity limits of lines k and l, respectively;

[0028] The voltage phase angle relationship constraints include the constraints on the relationship between the non-flexible line power flow and the bus phase angle and the constraints on the relationship between the flexible line power flow and the bus phase angle.

[0029] The constraint on the relationship between the power flow of the non-flexible line and the bus phase angle is:

[0030]

[0031] Among them, x k is the reactance of line k, θ mt is the voltage phase angle of busbar m at time t, θ nt is the voltage phase angle of bus n at time t.

[0032] The constraint on the relationship between the flexible line power flow and the bus phase angle is:

[0033]

[0034] Where M is a constant used to relax the constraints of unclosed circuits;

[0035] The topological radial constraint is:

[0036]

[0037] Among them, N n is the total number of buses, N s is the total number of substations.

[0038] Furthermore, when the actual data of the monitored power grid line does not meet any constraint, it is determined that the power grid current is out of limit.

[0039] Furthermore, the grid performance after the deployment of the ring closing device includes:

[0040] Grid loss reduction rate: the percentage change in total grid loss before and after the loop closing device is connected;

[0041] Congestion relief rate: the ratio of the number of line flow exceeding the limit or the reduction of the flow amplitude;

[0042] Voltage stability index: node voltage deviation range.

[0043] Furthermore, the process of screening the optimal deployment position of the ring closing device includes:

[0044] The active power flow sensitivity coefficient method is used to calculate the sensitivity of each line to the power change of the ring closing device;

[0045] Construct an injection transfer distribution factor matrix to quantify the transmission capacity of each node's flexibility resources to the line flow;

[0046] According to the sensitivity of each line to the power change of the ring closing device and the transmission capacity of each node flexibility resource to the line flow, the comprehensive weight of each line is calculated, and the optimal line is obtained as the optimal deployment position of the ring closing device through weight sorting.

[0047] Furthermore, the calculation expression of the active power flow sensitivity coefficient method is:

[0048]

[0049] Among them, A p is the sensitivity coefficient, P ij is the power of the ring closing device on line j at node i.

[0050] Furthermore, the process of constructing the injection transfer distribution factor matrix includes: calculating the transmission coefficient of the node power change in each line to the line power flow, and constructing the transfer distribution factor matrix using the transmission coefficient, wherein,

[0051] The calculation expression of the transfer coefficient is:

[0052]

[0053] Where ΔP i is the net load fluctuation of the i-th node, ΔP ij is the line power flow from node i to line j, ISDF is the transfer coefficient;

[0054]

[0055] Among them, P load，i is the real-time load of node i, P DER，i Real-time output of renewable energy for node i, is the net load of node i at time t;

[0056] The transmission capacity of each node's flexibility resource for line flow is the absolute value of the transfer distribution factor matrix. Furthermore, the calculation expression of the comprehensive weight is:

[0057] W l =α·|A p |+β·|ISDF|

[0058] Among them, α and β are preset weight coefficients, A p is the sensitivity coefficient, and ISDF is the transfer coefficient.

[0059] Furthermore, the calculation expression for the net load fluctuation range of each grid node is:

[0060] F i up / down =ΔP i ±w i

[0061] Among them, w i is the forecast error used to quantify flexibility demand, ΔP i is the net load fluctuation of the i-th node;

[0062]

[0063] Among them, P load，i is the real-time load of node i, is the measured real-time output of renewable energy at node i, is the net load of node i at time t, ΔP i is the net load fluctuation of the i-th node, ΔP ij is the line power flow from node i to line j, P DER，i is the real-time output of renewable energy at node i predicted based on historical data;

[0064] The calculation expression of the grid line flexibility demand is:

[0065]

[0066] in, is the flexibility transmission coefficient of node i to line i, F i up is the flexibility requirement of node i;

[0067] The calculation expression for the grid line flexibility supply is:

[0068]

[0069] Among them, A p is the power flow sensitivity coefficient of line k, ΔP opt，k is the power adjustment of line k.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] 1. This invention achieves coordinated optimization of the safety, economy, and flexibility of smart grids by establishing a closed-loop control framework that coordinates the control of mixed-integer linear programming optimization models and loop-closing devices. By combining real-time data acquisition, mixed-integer linear programming optimization models, and dynamic adjustment of power electronics, this invention overcomes the limitations of traditional static topologies in responding to renewable energy fluctuations, temporal and spatial variations in loads, and dynamic changes in electricity costs. Through real-time data acquisition and closed-loop control, network topology and loop-closing device power can be rapidly adjusted based on actual grid conditions, significantly reducing network losses, balancing line loads, and ensuring voltage stability, thereby improving overall grid reliability and operational efficiency. Based on the active power flow sensitivity coefficient method and the injection transfer distribution factor, this invention optimizes the deployment location of loop-closing devices and improves the utilization efficiency of flexible resources.

[0072] 2. The mixed integer linear programming optimization model established in this invention is centered on minimizing total power cost, integrating power balance, line thermal capacity, voltage stability, and radial structure constraints, and dynamically optimizing the flexible line switch state and loop closing device adjustment amount, which can significantly reduce operating costs while ensuring safety.

[0073] 3. While optimizing the deployment locations of loop closures, this invention quantifies the flexibility coupling relationship between nodes and lines, selecting key lines with high sensitivity to power flow regulation and strong transmission capacity for loop closure deployment. This maximizes the regulation potential of the loop closures, reduces redundant investment costs, and improves the utilization efficiency of flexibility resources.

[0074] 3. The present invention collects node load, renewable energy output and line flow data in real time, and dynamically evaluates the flexibility supply and demand status in combination with load forecast error. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 Flow chart of the method of the present invention;

[0076] Figure 2 A network topology diagram with six flexible lines constructed in one embodiment of the present invention;

[0077] Figure 3 This is a percentage line load diagram of a flexible circuit using LS-1 and DNT-1 according to an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0079] Example 1

[0080] This embodiment discloses a smart grid optimization control method based on dynamic network topology reconstruction. The method is as follows: Figure 1 As shown, the steps are as follows:

[0081] Step S1: Acquire actual power grid data and establish a mixed integer linear programming optimization model with the goal of minimizing the total power cost.

[0082] In step S1, the objective function of the mixed integer linear programming optimization model is:

[0083]

[0084] Among them, T is the time set, I is the substation set, c it represents the electricity cost of substation i at time t, P it is the output power of substation i at time t.

[0085] The constraints of the mixed integer linear programming optimization model include power balance constraints, line thermal capacity constraints, voltage phase angle relationship constraints, and topology radial constraints; among them,

[0086] The power balance constraint is:

[0087]

[0088] Among them, K(n-) and K(n+) represent the set of non-flexible lines with bus n as the starting point and end point, respectively. kt is the power flow of line k at time t, d nt is the load demand of bus n at time t;

[0089] The circuit heat capacity constraint is:

[0090]

[0091] Among them, J lt is a binary variable, indicating the switch state of the flexible circuit l at time t, Rating k and Rating l are the thermal capacity limits of lines k and l, respectively;

[0092] Voltage phase angle relationship constraints include constraints on the relationship between non-flexible line power flow and bus phase angle and constraints on the relationship between flexible line power flow and bus phase angle.

[0093] The constraint on the relationship between the power flow of non-flexible lines and the bus phase angle is:

[0094]

[0095] Among them, x k is the reactance of line k, θ mt is the voltage phase angle of busbar m at time t, θ nt is the voltage phase angle of bus n at time t.

[0096] The constraint on the relationship between the flexible line power flow and the bus phase angle is:

[0097]

[0098] Where M is a constant used to relax the constraints of unclosed circuits;

[0099] The topological radial constraints are:

[0100]

[0101] Among them, N n is the total number of buses, N s is the total number of substations.

[0102] When the actual data of the monitored power grid line does not meet any constraint, it is determined that the power grid current is out of limit and the network topology of the power grid needs to be reconstructed.

[0103] Specifically, it can be expressed as follows:

[0104] When the line load rate is greater than 95% (the actual power flow of the line exceeds its thermal capacity limit), the overloaded line is disconnected, the standby line is closed, and the load is transferred to the lightly loaded area;

[0105] When the node voltage deviation exceeds ±3% (the node voltage deviation exceeds the safety range), the voltage deviation is reduced to within ±2% by closing the flexible circuit or adjusting the loop closing device.

[0106] Step S2: Screening the optimal deployment location of the loop closing device based on the actual power grid data, active power flow sensitivity coefficient method and injection transfer distribution factor.

[0107] The process of selecting the optimal deployment location for the loop closure includes:

[0108] The active power flow sensitivity coefficient method is used to calculate the sensitivity of each line to the power change of the ring closing device;

[0109] The calculation expression of the active power flow sensitivity coefficient method is:

[0110]

[0111] Among them, A p is the sensitivity coefficient, P ij is the power of the ring closing device on line j at node i;

[0112] Construct an injection transfer distribution factor matrix to quantify the transmission capacity of each node's flexibility resources to the line flow;

[0113] The process of constructing the injection transfer distribution factor matrix includes: calculating the transmission coefficient of the node power change in each line to the line flow, and constructing the transfer distribution factor matrix using the transmission coefficient, where:

[0114] The calculation expression of the transfer coefficient is:

[0115]

[0116] Where ΔP i is the net load fluctuation of the i-th node, ΔP ij is the line power flow from node i to line j, ISDF is the transfer coefficient;

[0117]

[0118] Among them, P load，i is the real-time load of node i, P DER，i Real-time output of renewable energy for node i, is the net load of node i at time t;

[0119] The transmission capacity of each node's flexibility resources to the line flow is the absolute value of the transfer distribution factor matrix.

[0120] Based on the sensitivity of each line to the power change of the ring closing device and the transmission capacity of each node's flexibility resources for the line power flow, the comprehensive weight of each line is calculated, and the optimal line is obtained as the optimal deployment location of the ring closing device through weight sorting;

[0121] The calculation expression of comprehensive weight is:

[0122] W l =α·|A p |+β·|ISDF|

[0123] Among them, α and β are preset weight coefficients, A p is the sensitivity coefficient, and ISDF is the transfer coefficient.

[0124] Step S3: collect the node load, renewable energy output, line flow and voltage deviation data of the actual power grid in real time, and calculate the net load fluctuation range of each power grid node.

[0125] The calculation expression of the net load fluctuation range of each grid node is:

[0126] F i up / down =ΔP i ±w i

[0127] Among them, w i is the forecast error used to quantify flexibility demand, ΔP i is the net load fluctuation of the i-th node;

[0128]

[0129] Among them, P load，i is the real-time load of node i, is the measured real-time output of renewable energy at node i, is the net load of node i at time t, ΔP i is the net load fluctuation of the i-th node, ΔP ij is the line power flow from node i to line j, P DER，i is the real-time output of renewable energy at node i predicted based on historical data.

[0130] Step S4: Evaluate the supply and demand status of grid line flexibility. If the grid line flexibility demand exceeds the supply or the power flow exceeds the limit, dynamically optimize and calculate the optimal power regulation amount of the loop closing device and the optimal grid network topology through a mixed integer linear programming optimization model.

[0131] The calculation expression of the grid line flexibility demand is:

[0132]

[0133] in, is the flexibility transmission coefficient of node i to line i, F i up is the flexibility requirement of node i;

[0134] The calculation expression for the grid line flexibility supply is:

[0135]

[0136] Among them, A p is the power flow sensitivity coefficient of line k, ΔP opt，k is the power adjustment of line k.

[0137] Step S5: deploy the loop closing device according to the optimal deployment position and optimal power regulation amount of the loop closing device, change the grid connection structure according to the optimal grid network topology, and optimize and control the actual grid.

[0138] Step S6, obtaining the grid performance after the deployment of the loop closing device through power flow calculation. If any grid performance is lower than a preset threshold, return to step S1.

[0139] The grid performance after the deployment of the ring closing device includes:

[0140] Grid loss reduction rate: the percentage change in total grid loss before and after the loop closing device is connected;

[0141] Congestion relief rate: the ratio of the number of line flow exceeding the limit or the reduction of the flow amplitude;

[0142] Voltage stability index: node voltage deviation range.

[0143] Below, based on real-life situations, examples are given to illustrate the use of the above method.

[0144] First, high-precision measurement equipment is required, including Class 0.2 smart meters that comply with the GB / T17215-2008 standard. These meters offer a ±0.2% measurement accuracy, enabling precise measurement of power consumption at each node. A phasor measurement unit (PMU) with a time synchronization error of no more than 1 microsecond is also required, ensuring microsecond-level time synchronization accuracy. For control equipment, smart switches with a rated current of 630A or higher are selected. These switches utilize vacuum arc extinguishing technology, have a breaking capacity of 20kA, a mechanical lifespan of over 10,000 cycles, and an electric operating mechanism lifespan of 5,000 cycles. Built-in temperature sensors monitor contact temperature in real time, supporting both remote control and local manual operation.

[0145] The communication network utilizes a dual-channel redundant design. The primary channel uses single-mode optical fiber with a transmission rate of 100Mbps, while the backup channel utilizes 5G wireless communication with latency controlled to less than 20ms. The communication protocol adheres to the internationally recognized IEC61850 standard and supports GOOSE and SV message transmission, ensuring real-time and reliable data transmission.

[0146] Then the implementation of dynamic reconstruction. As the core content of this method, the implementation process of dynamic reconstruction includes three key links: data acquisition, optimization calculation and control execution. The network topology diagram of the basic flexible circuit used is shown in Figure 2 Data collection utilizes a hierarchical strategy, with load data updated every minute, with no fewer than 128 sampling points per cycle. PMU data is refreshed every 10 cycles (200ms) and includes complete voltage, current amplitude, and phase angle information. Switch status is monitored in real time, with any changes immediately reported. All collected data undergoes a rigorous CRC-16 checksum verification algorithm with a checksum polynomial of 0x8005. If data anomalies are detected, the system automatically replaces them with the sliding average of the previous three minutes to ensure data continuity and system stability.

[0147] The construction of mixed integer linear programming optimization is the key link of this system, which mainly includes two aspects: variable definition and constraint setting. In terms of variable definition, binary variable J is used. lt represents the state of the flexible circuit l at time t (1 = closed, 0 = open), the continuous variable P kt Indicates the power flow of line k at time t. In terms of constraints, several important constraints are set:

[0148] Power balance: ∑P in -∑P out =d nt ±5% allows for a measurement error of ±5%;

[0149] Line capacity constraint - 0.95 Rating k ≤P kt ≤1.05·Rating k Set a 5% safety margin;

[0150] Voltage constraint is controlled within the range of 0.95-1.05pu;

[0151] Ensure that the network always maintains a hub-and-spoke structure.

[0152] Loop closing device control. Loop closing device control includes two important parts: deployment location selection and regulation strategy. Deployment location selection follows a three-level priority principle: the first priority is the tie line connecting different substations; the second priority is the line with a historical load rate exceeding 80%; the third priority is the trunk line with a length of more than 5km. Phase-shifting transformer regulation uses advanced control algorithms. Where Δθ represents the phase adjustment amount, ΔP opt is the power to be adjusted obtained by optimization calculation, x l is the line reactance value, V i and V j The voltage amplitude at each end of the line is measured in real time. When the line load exceeds 95%, the voltage deviation exceeds ±3%, or the phase angle difference exceeds 10°, the loop closing device is automatically triggered for precise adjustment.

[0153] Performance verification. System performance verification includes two important aspects: economic indicators and safety indicators. In terms of economic indicators, the cost savings rate is required to reach more than 5% (benchmark load scenario), the investment payback period is not more than 5 years, and the internal rate of return is more than 12%. In terms of safety indicators, the voltage deviation is required to be controlled within the range of 0.95-1.05pu (detected once per minute), the line load rate is not more than 105% (detected once every 15 minutes), the protection action time is controlled within 0.5 seconds (daily test), and the power supply reliability index SAIDI is not more than 1.5 hours / year. A complete exception handling mechanism is also established in this embodiment, adopting a three-level response strategy: when the communication interruption time is less than 5 minutes, the local cache strategy is enabled; when the interruption time is between 5-30 minutes, switch to the backup communication channel; when the interruption time exceeds 30 minutes, the preset safety topology is started.

[0154] Operation and maintenance management. The construction of an operation and maintenance management system is an important guarantee for ensuring the long-term stable operation of the system. The system has established a complete operation and maintenance system covering three aspects: daily maintenance, regular inspections, and document management. In terms of daily maintenance, SQL queries are executed daily to check data integrity to ensure that the data integrity rate is not less than 90%; real-time monitoring of CPU, memory, and storage usage; and hierarchical processing of various types of alarm information. In terms of regular inspections, intelligent switches detect the number of mechanical operations and contact wear every month; phase-shifting transformers detect the DC resistance and insulation performance of the windings every quarter; and communication terminals test the signal strength and data transmission quality every week. In terms of document management, operation records are kept for more than 5 years, optimization results are kept for more than 1 year, abnormal events are permanently archived, and maintenance records are kept for 2 years after the equipment is scrapped to ensure that all operations are traceable.

[0155] Example 2

[0156] This embodiment, based on the aforementioned Example 1, discloses a specific application of a smart grid optimization control method based on dynamic network topology reconstruction in urban distribution network transformation. The system in this embodiment utilizes the control method of Example 1 and is specifically optimized for the concentrated commercial load characteristics, achieving significant results in practical applications.

[0157] System Configuration: A flexible line switching sequence was implemented for the 13-node distribution network renovation project. Key equipment included: 0.2S-class smart meters supporting the DL / T645-2007 protocol; PMUs with ±1μs synchronization accuracy supporting the IEEE C37.118 standard; smart switches with a rated current of 630A and a breaking capacity of 20kA; and industrial-grade switches supporting the IEEE 802.3 standard. The system was initially configured with six flexible lines, located at key locations such as Node 5 to Node 6, Node 6 to Node 19, Node 18 to Node 19, Node 10 to Node 38, Node 9 to Node 10, and Node 37 to Node 38. These lines were selected with full consideration of the network structure and load distribution.

[0158] Operational Results: After three months of trial operation, the system demonstrated excellent operational results. During peak hours (8:00-11:00, 18:00-21:00), the system performed an average of eight reconfiguration operations daily, achieving a 12.7% cost savings rate, keeping the maximum line load factor below 98%, and a voltage compliance rate of 99.98%. During normal hours (11:00-18:00), the system performed an average of four reconfiguration operations daily, achieving a 6.2% cost savings rate, a maximum line load factor of 85%, and a voltage compliance rate of 99.99%. During off-peak hours (21:00-8:00 the following day), the system performed an average of two reconfiguration operations daily, achieving a 3.1% cost savings rate, a maximum line load factor of 72%, and a voltage compliance rate of 100%. These data fully demonstrate the system's adaptability and optimization effectiveness under varying load conditions.

[0159] Special processing: To meet the special needs of industrial applications, the system has added a number of specialized functions. In terms of harmonic control, a strict constraint of THD ≤ 5% is set, and an active power filter (APF) with a compensation capacity of 200A is configured. The harmonic detection accuracy reaches the IEC 61000-4-7 Class A standard. In terms of impact load processing, a power ramp rate limiting function is configured. A supercapacitor energy storage device with a capacity of 500kWh was installed, and a scientific load-grading switching strategy was set up to effectively solve various problems caused by industrial loads.

[0160] Security: The system's security features a multi-layered design. Regarding network security, TLS 1.3 is used for communication encryption, a RBAC-based permissions management model is implemented, and a machine learning-based abnormal behavior recognition system is also included. Regarding physical security, the control cabinet meets IP54 protection and is equipped with dual backup power supplies, including a 4-hour UPS and a diesel generator. Environmental monitoring devices, such as temperature, humidity, and smoke, are also installed to ensure a safe and reliable operating environment.

[0161] Test and Verification: The system has passed a rigorous test and verification procedure. In terms of functional testing, single-line overload testing and multi-line fault testing were completed to verify the system's self-healing ability under various fault conditions. In terms of performance testing, a 100-node stress test showed that the solution time was controlled within 120 seconds, and a large data volume test proved that the system supports a data processing capacity of 10,000 points per second. In terms of reliability testing, it has passed a number of rigorous tests, including a 72-hour continuous operation test, a communication interruption simulation test, and a power switching test. The flexible line percentage line load diagram is shown in the following figure. Figure 3 All test results show that all indicators of the system meet or exceed the design requirements and have high reliability and practicality.

[0162] The implementation of this invention fully considers the various requirements of practical engineering applications. Through the smart grid optimization and control method based on dynamic network topology reconstruction in Example 1 and the specific application solution in Example 2, a complete technical approach is provided for the dynamic reconstruction of distribution networks. This smart grid optimization and control method based on dynamic network topology reconstruction achieves significant economic benefits while ensuring safety and reliability. Simulation results under three different load conditions are shown in Table 1.

[0163] Table 1 Simulation results under three different load conditions

[0164] Somewhere Total cost ($) LS-1 111，024 LS-2 122，126 LS-3 133，229

[0165] This shows that the system achieves a cost savings rate of 5-12% and improves line load balancing by over 35%, demonstrating its significant engineering application value and broad market prospects. The specific implementation process and effectiveness verification of the embodiment provide a reliable practical foundation and technical support for the promotion and application of this technology.

[0166] Example 3

[0167] Based on Example 1, this embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs include instructions for executing the aforementioned smart grid optimization control method based on dynamic network topology reconstruction.

[0168] At the hardware level, the electronic device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above-mentioned smart grid optimization control method based on dynamic network topology reconstruction. Of course, in addition to software implementation, the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0169] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0170] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0171] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A smart grid optimization control method based on dynamic network topology reconstruction, characterized in that: The method process includes: Obtain actual power grid data and establish a mixed integer linear programming optimization model with the goal of minimizing total electricity cost; Based on actual grid data, the optimal deployment location of the loop closing device is selected based on the active power flow sensitivity coefficient method and the injection transfer distribution factor; Real-time data collection of actual grid node loads, renewable energy output, line flow, and voltage deviation is used to calculate the net load fluctuation range of each grid node and assess the supply and demand of grid line flexibility. If the demand for grid line flexibility exceeds the supply or the flow exceeds the limit, a mixed integer linear programming optimization model is used to dynamically optimize and calculate the optimal power regulation of the loop closing device and the optimal grid network topology. Deploy the loop closing device based on its optimal deployment location and optimal power regulation, change the grid connection structure based on the optimal grid network topology, and optimize the control of the actual grid; The grid performance after the loop closing device is deployed is calculated. If any grid performance is lower than the preset threshold, the above process is repeated.

2. The smart grid optimization control method based on dynamic network topology reconstruction according to claim 1 is characterized in that: The objective function of the mixed integer linear programming optimization model is: Among them, T is the time set, I is the substation set, c it represents the electricity cost of substation i at time t, P it is the output power of substation i at time t.

3. The smart grid optimization control method based on dynamic network topology reconstruction according to claim 2 is characterized in that: The constraints of the mixed integer linear programming optimization model include power balance constraints, line thermal capacity constraints, voltage phase angle relationship constraints and topology radial constraints; wherein, The power balance constraint is: Among them, K(n-) and K(n+) represent the set of non-flexible lines with bus n as the starting point and end point, respectively. kt is the power flow of line k at time t, d nt is the load demand of bus n at time t; The circuit heat capacity constraint is: Among them, J lt is a binary variable, indicating the switch state of the flexible circuit l at time t, Rating k and Rating l are the thermal capacity limits of lines k and l, respectively; The voltage phase angle relationship constraints include the constraints on the relationship between the non-flexible line power flow and the bus phase angle and the constraints on the relationship between the flexible line power flow and the bus phase angle. The constraint on the relationship between the power flow of the non-flexible line and the bus phase angle is: Among them, x k is the reactance of line k, θ mt is the voltage phase angle of busbar m at time t, θ nt is the voltage phase angle of bus n at time t; The constraint on the relationship between the flexible line power flow and the bus phase angle is: Where M is a constant used to relax the constraints of unclosed circuits; The topological radial constraint is: Among them, N n is the total number of buses, N s is the total number of substations.

4. The smart grid optimization control method based on dynamic network topology reconstruction according to claim 3 is characterized in that: When the actual data of the monitored power grid line does not meet any constraint, it is determined that the power grid current is out of limit.

5. The smart grid optimization control method based on dynamic network topology reconstruction according to claim 1, characterized in that: The grid performance after the deployment of the ring closing device includes: Grid loss reduction rate: the percentage change in total grid loss before and after the loop closing device is connected; Congestion relief rate: the ratio of the number of line flow exceeding the limit or the reduction of the flow amplitude; Voltage stability index: node voltage deviation range.

6. The smart grid optimization control method based on dynamic network topology reconstruction according to claim 1, characterized in that: The process of screening the optimal deployment position of the ring closing device includes: The active power flow sensitivity coefficient method is used to calculate the sensitivity of each line to the power change of the ring closing device; Construct an injection transfer distribution factor matrix to quantify the transmission capacity of each node's flexibility resources to the line flow; According to the sensitivity of each line to the power change of the ring closing device and the transmission capacity of each node flexibility resource to the line flow, the comprehensive weight of each line is calculated, and the optimal line is obtained as the optimal deployment position of the ring closing device through weight sorting.

7. The smart grid optimization control method based on dynamic network topology reconstruction according to claim 6, characterized in that: The calculation expression of the active power flow sensitivity coefficient method is: Among them, A p is the sensitivity coefficient, P ij is the power of the ring closing device on line j at node i.

8. The smart grid optimization control method based on dynamic network topology reconstruction according to claim 6 is characterized in that: The process of constructing the injection transfer distribution factor matrix includes: calculating the transmission coefficient of the node power change in each line to the line flow, and constructing the transfer distribution factor matrix using the transmission coefficient, wherein: The calculation expression of the transfer coefficient is: Where ΔP i is the net load fluctuation of the i-th node, ΔP ij is the line power flow from node i to line j, ISDF is the transfer coefficient; Among them, P load,i is the real-time load of node i, P DER,i Real-time output of renewable energy for node i, is the net load of node i at time t; The transmission capacity of the flexibility resources of each node for the line flow is the absolute value of the transfer distribution factor matrix.

9. The smart grid optimization control method based on dynamic network topology reconstruction according to claim 6, characterized in that: The calculation expression of the comprehensive weight is: W l =α·|A p |+β·|ISDF| Among them, α and β are preset weight coefficients, A p is the sensitivity coefficient, and ISDF is the transfer coefficient.

10. The smart grid optimization control method based on dynamic network topology reconstruction according to claim 1, characterized in that: The calculation expression of the net load fluctuation range of each grid node is: F i up / down =ΔP i ±w i Among them, w i is the forecast error used to quantify flexibility demand, ΔP i is the net load fluctuation of the i-th node; Among them, P load,i is the real-time load of node i, is the measured real-time output of renewable energy at node i, is the net load of node i at time t, ΔP i is the net load fluctuation of the i-th node, ΔP ij is the line power flow from node i to line j, P DER,i is the real-time output of renewable energy at node i predicted based on historical data; The calculation expression of the grid line flexibility demand is: in, is the flexibility transmission coefficient of node i to line i, F i up is the flexibility requirement of node i; The calculation expression for the grid line flexibility supply is: Among them, A p is the power flow sensitivity coefficient of line k, ΔP opt,k is the power adjustment of line k.

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