A Power Grid Planning Method Based on Digital Twin Technology

Abstract

The present invention relates to the field of electric power planning, and discloses a power grid planning method based on digital twin technology, the method comprising the following steps: Step 1: Establishing a power grid planning framework based on digital twin technology; Step 2: Establishing a power grid planning trigger mechanism based on an indicator system; Step 3: Establishing a power grid planning model and solving it using a digital twin platform. The present invention constructs a power grid planning framework based on digital twins, proposes a trigger mechanism with an indicator system as the core, establishes a model of power grid planning under a market environment, and solves it using a digital twin platform, thereby using digital twin technology to solve the problems of slow response and poor accuracy in power grid planning under the current market environment, and promotes the transformation of power grid planning from on-time planning to on-demand planning.

Description

Technical Field

[0001] The present invention relates to the field of electric power planning, and specifically to a power grid planning method based on digital twin technology. Background Art

[0002] With the further liberalization of the power market, the economic factors and types of control equipment in the power grid operation planning are increasing. The traditional annual time-based planning can no longer adapt to the rapid changes in the power grid, and the traditional planning simulation tools can no longer provide efficient solutions with frequent data interaction and dynamic self-update. Digital twins have the characteristics of real-time interaction, holographic replication, and closed-loop feedback. Its emergence and application provide new ideas for solving this problem. Summary of the invention

[0003] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a power grid planning method based on digital twin technology.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] A power grid planning method based on digital twin technology, the method comprising the following steps:

[0006] Step 1: Establish a power grid planning framework based on digital twin technology;

[0007] Step 2: Establish a power grid planning trigger mechanism based on the indicator system;

[0008] Step 3: Establish a power grid planning model and solve it using the digital twin platform.

[0009] Furthermore, the power grid planning framework based on digital twin technology established in step 1 is as follows:

[0010] The power grid planning framework based on digital twin technology is mainly divided into physical layer, perception layer, data layer, simulation layer and application layer; the physical layer exists in the actual physical power grid; the data layer and simulation layer are located in the twin power grid.

[0011] Furthermore, the establishment of a power grid planning trigger mechanism based on an indicator system in step 2 includes the following steps:

[0012] Step 2.1: Establish a trigger mechanism for grid planning. The twin grid obtains data from the physical grid and stores it in the data layer.

[0013] Step 2.2: Establish an indicator system in the digital twin environment, and establish economic indicators and reliability indicators;

[0014] Step 2.3: Comprehensive evaluation of indicators.

[0015] Furthermore, the economic indicators include:

[0016] Transmission efficiency per unit asset:

[0017]

[0018] In the formula, C1 is the transmission efficiency index per unit asset, Q y is the annual power transmission, p trans is the transmission price, F is the total assets;

[0019] Network loss rate:

[0020]

[0021] In the formula, Q m is the total annual power generation of unit m, M is the number of units connected to the high-voltage power grid, Q s is the total amount of electricity sold by the transmission grid;

[0022] Flow distribution index:

[0023]

[0024] Where L is the total number of lines to be calculated, P l is the power flow through line l, P lmax is the transmission capacity of line l;

[0025] Furthermore, the reliability index includes:

[0026] Blockage indicators:

[0027]

[0028] In the formula, r i is the node price of the i-th node, and N is the number of system nodes. For a running system, the above formula is used to calculate the blocking index based on historical data;

[0029] The evaluation system also needs to simulate the future congestion situation and obtain the future congestion index C″4, which is combined with C′4 obtained from historical data.

[0030] C″4 is calculated according to the following formula:

[0031]

[0032] In the formula, E(r i ) is the expected value of the congestion price obtained by market simulation for each node;

[0033] The final blocking index C4 is obtained by combining C′4 and C″4 according to the following formula:

[0034] C4=γ1C′4+γ2C″4 (6)

[0035] In the formula, γ1 and γ2 are weight coefficients, which are determined and adjusted according to the actual situation. The market simulation of future situations is carried out using the Monte Carlo method;

[0036] Low battery expectation:

[0037]

[0038] Where S is the set of all system states where load shedding may occur, p s is the probability of system state s occurring; P cs is the load shedding amount under system state s; T is the time length, generally calculated in years; the Monte Carlo simulation method is used to obtain the expected value of power shortage;

[0039] Proportion of new energy installed capacity:

[0040]

[0041] Where C6 is the ratio of new energy installed capacity to total installed capacity; M k is the installed capacity of new energy; M l is the total installed capacity of the system.

[0042] Furthermore, in step 3, the power grid planning model is established and solved using the digital twin platform in the following manner:

[0043] Step 3.1: Establish a power grid planning model:

[0044]

[0045] st

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] In the formula, is a 0-1 variable, which takes 1 if the kth link between node i and node j exists or is established, otherwise it takes 0; is the number of initial lines in the network. When there is one initial line between node i and node j Take 1, is the construction cost of the kth line between node i and node j; P gi is the sum of the outputs of all generators connected to node i, P di is the sum of all loads connected to node i; P gimax , P gimin They are the upper and lower limits of generator output respectively; is the power flow of the kth line between node i and node j; is the upper capacity limit of the kth line between node i and node j; x ij is the reactance value between node i and node j; M is the linearization operator, which is generally a larger integer; S L is the set of all lines; S N is the set of all nodes; Formula (10) is the node balance constraint; Formula (11) and Formula (12) linearize the model, and Formula (13) is the line capacity constraint; when the line is not built, that is is 0, and formula (13) constrains the line flow to 0; when the line is selected for construction, that is, is 1, and the right-hand side terms of equations (11) and (12) are 0, and the equation constraint of the line flow is obtained; equation (14) is the generator capacity constraint;

[0053] Step 3.2: Due to the different indicators in the trigger mechanism, the reasons for the activation of the trigger mechanism are also different. Considering that the activation of the trigger mechanism is due to the blocking indicator exceeding the relevant threshold, the formula (9) is modified as follows:

[0054]

[0055] In the formula, α cong is the blocking index influence coefficient, C4 is the value of the blocking index, β ij is the line weighting coefficient, P ij is the line flow. ij The value selection principle is that when the remaining capacity of line ij is larger, β ij The smaller;

[0056] Step 3.3: Power grid planning solution steps based on digital twin technology;

[0057] Step 3.4: Use the digital twin platform to solve the problem.

[0058] Furthermore, the steps of solving the power grid planning based on digital twin technology in step 3.3 are:

[0059] Step 3.3.1: Obtain data from the physical power grid, including power grid equipment parameters, power supply equipment parameters, load parameters, power grid operation data, and geographic information;

[0060] Step 3.3.2: Use the acquired data to predict the future power grid and calculate the evaluation index of power grid planning based on the prediction results. The evaluation system includes economic evaluation and reliability evaluation;

[0061] Step 3.3.3: Determine whether the triggering conditions for grid planning are met based on the obtained indicators. If the triggering conditions are met, calculate the grid planning in the twin grid. Stop when the conditions are met and feed the results back to the physical grid for reference by on-site engineering personnel.

[0062] Furthermore, the method for solving the problem using the digital twin platform in step 3.4 is:

[0063] Step 3.4.1: Create an Azure digital twin instance, build and instantiate models of power grid planning lines, generators, loads, nodes, etc. based on the Digital Twins Definition Language;

[0064] Step 3.4.2: Build the corresponding device in the IoT center and receive telemetry from the actual physical device through sensors;

[0065] Step 3.4.3: Use Azure Event Hub to trigger Azure Functions to receive upstream telemetry data and update the digital twin.

[0066] Step 3.4.4: Based on Azure Function, determine whether the trigger mechanism is met according to the parameters of each twin in Digital Twins and perform relevant calculations for power grid planning, feed back the results to the IoT center, and interact with the actual physical devices.

[0067] Beneficial effects of the present invention:

[0068] The present invention constructs a power grid planning framework based on digital twins, proposes a trigger mechanism with an indicator system as the core, establishes a model for power grid planning under a market environment, and uses a digital twin platform for solving the problems of slow response and poor accuracy in power grid planning under the current market environment, thereby using digital twin technology to solve the problems of slow response and poor accuracy in power grid planning under the current market environment, and promote the transformation of power grid planning from on-time planning to on-demand planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present invention will be further described below in conjunction with the accompanying drawings.

[0070] Figure 1 This is a schematic diagram of the power grid planning framework based on digital twins;

[0071] Figure 2 It is a schematic diagram of the trigger mechanism of power grid planning;

[0072] Figure 3 It is a step in grid planning based on digital twin;

[0073] Figure 4 It is the grid planning implementation logic based on the Azure digital twin platform;

[0074] Figure 5 It is the result of power grid planning based on the Azure platform. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] like Figure 1-Figure 5 As shown, a power grid planning method based on digital twin technology comprises the following steps:

[0077] Step 1: Establish a power grid planning framework based on digital twin technology;

[0078] The digital twin-based power grid planning framework is a framework structure consisting of a physical power grid, a twin power grid, and interactive channels.

[0079] Attached Figure 1 This is a schematic diagram of the power grid planning framework based on digital twins. The framework is mainly divided into the physical layer, perception layer, data layer, simulation layer and application layer. The physical layer exists in the actual physical power grid, reflecting the physical entities, the link relationship between power grid elements and providing standardized data interfaces for physical elements. The data layer and simulation layer are located in the twin power grid. The data layer pre-processes the information transmitted and provides data support for the simulation layer. The power grid planning trigger mechanism with the indicator system as the core will serve as a switch between the data layer and the simulation layer. When the indicator reaches the set threshold, the data layer and the simulation layer will be connected. The simulation layer predicts and deduce based on the data of the data layer, and makes plans. The perception layer and the application layer are bridges connecting the physical power grid and the twin power grid, realizing the interaction between the physical power grid and the twin power grid.

[0080] Grid planning based on digital twins has the following three characteristics:

[0081] (1) Holographic replication: Based on the massive sensors installed in the physical power grid, various parameters of the physical power grid are identified, and a twin power grid that is exactly the same as the physical power grid is constructed in the digital world. The twin power grid can fully perceive and dynamically monitor the status of the physical power grid, thereby achieving holographic digital description and accurate information expression.

[0082] (2) Real-time interaction: In the digital world, based on the virtual image of holographic replication, the information of power grid lines, nodes, power sources and loads can be reflected in the twin power grid in real time. The twin power grid can realize the modeling simulation and algorithm solution of power grid planning. At the same time, from virtual to real, the simulation results in the twin power grid are fed back to the physical power grid in real time to guide the planning of the physical power grid, forming a complex twin interactive system with virtual-real integration and virtual-real collaboration.

[0083] (3) Closed-loop feedback: Different from conventional simulation software that only has a simulation process, the power grid planning framework based on digital twins forms a closed-loop flow of information from data acquisition, transmission, and conversion in the physical world to data processing, model simulation, algorithm solving in the digital world, and then to result feedback and precise planning in the physical world.

[0084] Step 2: Establish a power grid planning trigger mechanism based on the indicator system;

[0085] Step 2.1: Establish a trigger mechanism for grid planning;

[0086] In the power grid planning framework based on digital twins, as the "switch" between the data layer and the simulation layer, the trigger mechanism plays a vital role in the digital world. Traditional power grid planning is "on-time planning", and planning work is carried out regularly, including historical data collection, planning work, and planning results update, which is investment-intensive and time-consuming. Therefore, under this power grid planning framework, a trigger mechanism is proposed as a key link between the simulation layer and the data layer. The trigger mechanism is proposed to speed up the feedback speed of the twin power grid and the physical power grid, which is significantly different from the traditional on-time planning based on an annual unit, realizes on-demand planning, and improves the efficiency of power grid planning.

[0087] Attached Figure 2This is a schematic diagram of the trigger mechanism for power grid planning. The twin power grid obtains data from the physical power grid and stores it in the data layer. The trigger mechanism extracts indicators based on the data in the data layer and predicts the relevant data of the future power grid. Based on the constructed indicator system, the relevant indicators of the future power grid are calculated, and indicators such as economy and reliability are used to determine whether the power grid needs a new round of planning. The indicator threshold is set based on historical data and human experience. When the relevant indicators in the indicator system exceed the threshold, the "switch" between the data layer and the simulation layer is turned on to plan the power grid and feed the results back to the physical power grid; if the indicator does not reach the relevant threshold, the data layer continuously obtains data from the physical power grid and updates the indicator. Through the establishment of the trigger mechanism, the traditional power grid "on-time planning" can be improved to "on-demand planning" under the digital twin framework, greatly improving planning efficiency and reducing resource consumption.

[0088] Step 2.2: Establish an indicator system in the digital twin environment;

[0089] 1) Economic indicators

[0090] Unit asset transmission efficiency: Unit asset transmission efficiency refers to the benefits that each unit of assets can bring, and is an economic indicator to measure the input-output efficiency of power grid enterprises. If this indicator is too low, it means that the input-output efficiency of the existing power grid is low, and the entity can consider re-planning the power grid to improve the unit asset transmission efficiency. This indicator is calculated as follows:

[0091]

[0092] In the formula, C1 is the transmission efficiency index per unit asset, Q y is the annual power transmission, p trans is the transmission price, F is the total assets;

[0093] Network loss rate: The network loss rate is the proportion of power loss in the power transmission process to the total power consumption, and is an important indicator for measuring the economic operation of the power grid. The network loss rate is calculated according to the following formula:

[0094]

[0095] In the formula, Q m is the total annual power generation of unit m, M is the number of units connected to the high-voltage power grid, Q s is the total amount of electricity sold by the transmission grid;

[0096] Flow distribution index: The flow distribution index is the average of the difference between the line transmission capacity and the absolute value of the line flow as a percentage of the transmission capacity. The index reflects the rationality and coordination of the power grid flow distribution. The index is calculated as follows:

[0097]

[0098] Where L is the total number of lines to be calculated, P l is the power flow through line l, P lmax is the transmission capacity of line l;

[0099] 2) Reliability indicators

[0100] Blockage index: Whether the transmission line can transmit electric energy within the limit is the most basic requirement of the power grid. In the market environment, the distribution of power flow will be affected by the bids of the power generation and consumption side. The system node price obtained by the market clearing result can measure the blockage of the transmission line. Therefore, a blockage index based on node price is proposed, and the standard deviation of node price is used as an indicator to measure the severity of system blockage, which is calculated according to the following formula:

[0101]

[0102] In the formula, r i is the node price of the i-th node, and N is the number of system nodes. For a running system, the above formula is used to calculate the blocking index based on historical data;

[0103] The evaluation system also needs to simulate the future congestion situation and obtain the future congestion index C″4, which is combined with C′4 obtained from historical data.

[0104] C″4 is calculated according to the following formula:

[0105]

[0106] In the formula, E(r i ) is the expected value of the congestion price obtained by market simulation for each node;

[0107] The final blocking index C4 is obtained by combining C′4 and C″4 according to the following formula:

[0108] C4=γ1C′4+γ2C″4 (6)

[0109] In the formula, γ1 and γ2 are weight coefficients, which are determined and adjusted according to the actual situation. The market simulation of future situations is carried out using the Monte Carlo method;

[0110] Expected value of power shortage: The expected value of power shortage is the average value of power loss caused by load power outage due to insufficient power supply in a period of time. The expected value of power shortage is calculated according to the following formula:

[0111]

[0112] Where S is the set of all system states where load shedding may occur, p s is the probability of system state s occurring; Pcs is the load shedding amount under system state s; T is the time length, generally calculated in years; the Monte Carlo simulation method is used to obtain the expected value of power shortage;

[0113] Proportion of installed capacity of new energy: When the proportion of new energy power generation exceeds a certain proportion, the original power grid structure's ability to absorb clean energy and resist risks will decline. At this time, a new round of power grid planning is needed in a timely manner to ensure the absorption of new energy and the reliable operation of the power grid. To this end, the proportion of installed capacity of new energy is proposed in the power grid planning evaluation system. The specific calculation method is shown in the following formula:

[0114]

[0115] Where C6 is the ratio of new energy installed capacity to total installed capacity; M k is the installed capacity of new energy; M l is the total installed capacity of the system.

[0116] Step 2.3: Comprehensive evaluation of indicators;

[0117] At present, the main methods used for comprehensive evaluation include fuzzy comprehensive evaluation method, envelope method, hierarchical analysis method, entropy weight method, etc. The hierarchical analysis method can make full use of expert experience to rank the index system by weight, but this method is highly subjective and the result is not objective and convincing. The entropy weight method uses the information of the original data to determine the weight according to the amount of information contained in the index. It is an objective weighting method. The method of the present invention combines the two evaluation methods for evaluation.

[0118] Step 3: Establish a power grid planning model and solve it using the digital twin platform.

[0119] Step 3.1: Establish a power grid planning model:

[0120] After the trigger mechanism is turned on as a "switch", the simulation layer needs to plan the power grid based on the data from the data layer and the simulation model. The power grid planning model usually takes the minimum line cost as the goal and considers the relevant constraints for modeling.

[0121]

[0122] st

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129] In the formula, is a 0-1 variable, which takes 1 if the kth link between node i and node j exists or is established, otherwise it takes 0; is the number of initial lines in the network. When there is one initial line between node i and node j Take 1, is the construction cost of the kth line between node i and node j; P gi is the sum of the outputs of all generators connected to node i, P di is the sum of all loads connected to node i; P gimax , P gimin They are the upper and lower limits of generator output respectively; is the power flow of the kth line between node i and node j; is the upper capacity limit of the kth line between node i and node j; x ij is the reactance value between node i and node j; M is the linearization operator, which is generally a larger integer; S L is the set of all lines; S N is the set of all nodes; Formula (10) is the node balance constraint; Formula (11) and Formula (12) linearize the model, and Formula (13) is the line capacity constraint; when the line is not built, that is is 0, and formula (13) constrains the line flow to 0; when the line is selected for construction, that is, is 1, and the right-hand side terms of equations (11) and (12) are 0, and the equation constraint of the line flow is obtained; equation (14) is the generator capacity constraint;

[0130] Step 3.2: Due to the different indicators in the trigger mechanism, the reasons for the activation of the trigger mechanism are also different. Considering that the activation of the trigger mechanism is due to the blocking indicator exceeding the relevant threshold, the formula (9) is modified as follows:

[0131]

[0132] In the formula, α cong is the blocking index influence coefficient, C4 is the value of the blocking index, β ij is the line weighting coefficient, P ij is the line flow. ij The value selection principle is that when the remaining capacity of line ij is larger, β ij The smaller;

[0133] Step 3.3: Steps for solving power grid planning based on digital twin technology, see attached Figure 3 ;

[0134] Step 3.3.1: Obtain data from the physical power grid, including power grid equipment parameters, power supply equipment parameters, load parameters, power grid operation data, and geographic information;

[0135] Step 3.3.2: Use the acquired data to predict the future power grid and calculate the evaluation index of power grid planning based on the prediction results. The evaluation system includes economic evaluation and reliability evaluation;

[0136] Step 3.3.3: Determine whether the triggering conditions for grid planning are met based on the obtained indicators. If the triggering conditions are met, calculate the grid planning in the twin grid. Stop when the conditions are met and feed the results back to the physical grid for reference by on-site engineering personnel.

[0137] Step 3.4: Use the digital twin platform to solve;

[0138] Azure Digital Twins is a service-oriented platform launched by Microsoft that can be used to create a knowledge graph based on a digital model of the entire environment. The Azure Digital Twins platform provides a systematic solution. The advantage of using the platform is that it can create a comprehensive digital model of the entire environment, track the past of any networked environment, and predict and simulate the future. At the same time, it can break the island phenomenon in the networked environment, overcome the shortcomings of previous simulation software that cannot interact with the physical world, and form a closed-loop feedback. The present invention uses the Azure Digital Twins platform to verify the power grid planning framework based on digital twins. The overall implementation logic is shown in the attached figure. Figure 4 .

[0139] Step 3.4.1: Create an Azure digital twin instance, build and instantiate models of power grid planning lines, generators, loads, nodes, etc. based on the Digital Twins Definition Language;

[0140] Step 3.4.2: Build the corresponding device in the IoT center and receive telemetry from the actual physical device through sensors;

[0141] Step 3.4.3: Use Azure Event Hub to trigger Azure Functions to receive upstream telemetry data and update the digital twin.

[0142] Step 3.4.4: Based on Azure Function, determine whether the trigger mechanism is met according to the parameters of each twin in Digital Twins and perform relevant calculations for power grid planning, feed back the results to the IoT center, and interact with the actual physical devices.

[0143] The Garver-6 node system was solved using the Azure platform. The results are attached. Figure 5 .

[0144] For the Garver-6 node system planning results, new lines 2-3(2), 2-6(2), 3-5(1), and 4-6(2) are built.

[0145] According to calculations, a total of 7 new lines were actually built, with a total cost of 1.8 million yuan. Through a power grid planning method based on digital twin technology proposed in this application, the planning was triggered in advance in the on-demand planning, and the congestion surplus in the second year was calculated in two stages to be 519,600 yuan. The results of the on-time planning were the construction of new lines 2-6(2), 3-5(2), and 4-6(2), with a total of 6 new lines built at a total cost of 1.6 million yuan and 527,300 yuan. On-demand planning takes into account the impact of the market, increases construction costs in exchange for the relief of congestion, and the planning can be implemented in advance, which is more advantageous in responding to system trend changes.

[0146] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A power grid planning method based on digital twin technology, characterized in that: The method comprises the following steps: Step 1: Establish a power grid planning framework based on digital twin technology; Step 2: Establish a power grid planning trigger mechanism based on the indicator system; Step 3: Establish a power grid planning model and solve it using the digital twin platform; The power grid planning framework based on digital twin technology established in step 1 is as follows: The power grid planning framework based on digital twin technology is mainly divided into physical layer, perception layer, data layer, simulation layer and application layer; the physical layer exists in the actual physical power grid; the data layer and simulation layer are located in the twin power grid; The step 2 of establishing a power grid planning trigger mechanism based on an indicator system includes the following steps: Step 2.1: Establish a trigger mechanism for grid planning. The twin grid obtains data from the physical grid and stores it in the data layer. Step 2.2: Establish an indicator system in the digital twin environment, and establish economic indicators and reliability indicators; Step 2.3: Comprehensive evaluation of indicators; The economic indicators include: Transmission efficiency per unit asset: In the formula, C1 is the transmission efficiency index per unit asset, Q y is the annual power transmission, p trans is the transmission price, F is the total assets; Network loss rate: In the formula, Q m is the total annual power generation of unit m, M is the number of units connected to the high-voltage power grid, Q s is the total amount of electricity sold by the transmission grid; Flow distribution index: Where L is the total number of lines to be calculated, P l is the power flow through line l, P lmax is the transmission capacity of line l; The reliability indicators include: Blockage indicators: In the formula, r i is the node price of the i-th node, N is the number of system nodes, and for a running system, the above formula is used to calculate the blocking index based on historical data; The evaluation system also needs to simulate the future congestion situation and obtain the future congestion index C″4, which is combined with C′4 obtained from historical data. C″4 is calculated according to the following formula: In the formula, E(r i ) is the expected value of the congestion price obtained by market simulation for each node; The final blocking index C4 is obtained by combining C′4 and C″4 according to the following formula: C4=γ1C′4+γ2C″4 (6) In the formula, γ1 and γ2 are weight coefficients, which are determined and adjusted according to the actual situation. The market simulation of future situations is carried out using the Monte Carlo method. Low battery expectation: Where S is the set of all system states where load shedding may occur, p s is the probability of system state s occurring; P cs is the load shedding amount under system state s; T is the time length, calculated in years; the Monte Carlo simulation method is used to obtain the expected value of power shortage; Proportion of new energy installed capacity: Where C6 is the ratio of new energy installed capacity to total installed capacity; M k is the installed capacity of new energy; M l is the total installed capacity of the system; In step 3, the power grid planning model is established and solved using the digital twin platform in the following manner: Step 3.1: Establish a power grid planning model: st In the formula, is a 0-1 variable, which takes 1 if the kth link between node i and node j exists or is established, otherwise it takes 0; is the number of initial lines in the network. When there is one initial line between node i and node j Take 1, is the construction cost of the kth line between node i and node j; P gi is the sum of the outputs of all generators connected to node i, P di is the sum of all loads connected to node i; P gimax , P gimin They are the upper and lower limits of generator output respectively; is the power flow of the kth line between node i and node j; is the upper capacity limit of the kth line between node i and node j; x ij is the reactance value between node i and node j; M is the linearization operator, which takes a larger integer; S L is the set of all lines; S N is the set of all nodes; Formula (10) is the node balance constraint; Formula (11) and Formula (12) linearize the model, and Formula (13) is the line capacity constraint; when the line is not built, that is is 0, and formula (13) constrains the line flow to 0; when the line is selected for construction, that is, is 1, and the right-hand side terms of equations (11) and (12) are 0, and the equation constraint of the line flow is obtained; equation (14) is the generator capacity constraint; Step 3.2: Due to the different indicators in the trigger mechanism, the reasons for the activation of the trigger mechanism are also different. Considering that the activation of the trigger mechanism is due to the blocking indicator exceeding the relevant threshold, the formula (9) is modified as follows: In the formula, α cong is the blocking index influence coefficient, C4 is the value of the blocking index, β ij is the line weighting coefficient, P ij is the line flow, β ij The value selection principle is that when the remaining capacity of line ij is larger, β ij The smaller; Step 3.3: Power grid planning solution steps based on digital twin technology; Step 3.4: Use the digital twin platform to solve; The steps for solving the power grid planning based on digital twin technology in step 3.3 are as follows: Step 3.3.1: Acquire data from the physical power grid, including power grid equipment parameters, power supply equipment parameters, load parameters, power grid operation data, and geographic information; Step 3.3.2: Use the acquired data to predict the future power grid and calculate the evaluation index of power grid planning based on the prediction results. The evaluation system includes economic evaluation and reliability evaluation; Step 3.3.3: Determine whether the triggering conditions of the power grid planning are met based on the obtained indicators. If the triggering conditions are met, calculate the power grid planning in the twin power grid. When the conditions are met, stop and feed back the results to the physical power grid for reference by on-site engineering personnel. The method for solving the problem using the digital twin platform in step 3.4 is: Step 3.4.1: Establish an Azure digital twin instance, build and instantiate the line, generator, load, and node models of the power grid planning based on the digital twin language; Step 3.4.2: Build the corresponding device in the IoT center and receive telemetry from the actual physical device through sensors; Step 3.4.3: Use the Azure Event System to trigger an Azure function to receive upstream telemetry data and update the digital twin; Step 3.4.4: Based on Azure Function, determine whether the trigger mechanism is met according to the parameters of each twin in Digital Twins and perform relevant calculations for power grid planning, feed back the results to the IoT center, and interact with the actual physical devices.

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