Power transaction matching method and system based on multi-objective optimization

Abstract

The application relates to the technical field of multi-objective optimization, and provides a power transaction matching method and system based on multi-objective optimization, which solves the problems of low transaction efficiency and poor user power consumption satisfaction in the existing power grid blocked area. The method comprises the following steps: acquiring historical bidding data, load label information and audio monitoring signals of users in a power grid area, identifying a high-elasticity user cluster based on the bidding data and the load label; detecting a hidden blocked position through an audio signal and generating an impedance anomaly coefficient matrix; inputting the impedance matrix and the high-elasticity user cluster into a direct current optimal power flow model, taking the minimum blocked cost and the maximum user satisfaction as the target, and solving the transaction matching scheme by using a Lagrange dual decomposition algorithm; and adjusting the operation parameters of the power transmission line according to the power distribution demand in the scheme, so that the actual transmission power can accurately match the demand. The application improves the transaction efficiency and the user power consumption satisfaction in the power grid blocked area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-objective optimization, and in particular to a power transaction matching method and system based on multi-objective optimization. BACKGROUND

[0002] In the power grid transmission capacity limited area, the power market transaction needs to consider the power grid operation safety and economic benefits. With the large-scale grid connection of renewable energy and the deepening of power market reform, the power grid congestion problem is becoming increasingly prominent, and an intelligent matching method that can dynamically balance the transmission capacity limitation and market transaction demand is urgently needed to improve the channel utilization rate and market operation efficiency.

[0003] The current more advanced solution adopts a congestion management method based on node marginal price, establishes an association model between line transmission capacity and node price, combines historical operation data to predict congestion, and uses a linear programming method to optimize the transaction clearing result. This method reflects the degree of line congestion by adjusting the node price to guide market participants to adjust their transaction behavior.

[0004] This solution has limited perception of the physical operation state of the power grid and mainly relies on historical statistical data, making it difficult to capture the hidden abnormal state of the power grid equipment in a timely manner. In the scenario of rapid changes in user demand, the optimization method based on linear programming lacks flexibility and is prone to produce suboptimal solutions, affecting the overall market efficiency. SUMMARY

[0005] The present application provides a power transaction matching method and system based on multi-objective optimization to solve the problems of low transaction efficiency and poor user power consumption satisfaction in the existing technology in the congestion area of the power grid.

[0006] In a first aspect, the present application provides a power transaction matching method based on multi-objective optimization, comprising:

[0007] Obtaining user historical bidding data, load label information and audio monitoring signals in a power grid area;

[0008] Based on the user historical bidding data and the load label information, identifying a high elasticity user cluster;

[0009] Detecting the hidden congestion position in the power grid area through the audio monitoring signal, and generating a resistance abnormality coefficient matrix based on the hidden congestion position;

[0010] Inputting the resistance abnormality coefficient matrix and the high elasticity user cluster into a pre-trained direct current optimal power flow model to minimize the multi-objective function of the power grid congestion cost and maximize the user price satisfaction, and outputting a transaction matching scheme using the Lagrange dual decomposition algorithm;

[0011] Adjust operating parameters of the power transmission lines in the power grid region based on the power distribution demand corresponding to the transaction matching scheme, so that the actual transmission power of the power transmission lines matches the power distribution demand.

[0012] Optionally, the impedance anomaly coefficient matrix and the high-elasticity user cluster are input into a pre-trained direct current optimal power flow model to minimize a multi-objective function with the target of maximizing user electricity price satisfaction, and a Lagrange dual decomposition algorithm is used to output a transaction matching scheme, including:

[0013] In the direct current optimal power flow model, a line power constraint is constructed, and an element value of the impedance anomaly coefficient matrix is multiplied by a reference transmission capacity of a corresponding power transmission line to obtain a dynamic power transmission limit value of each power transmission line;

[0014] In the direct current optimal power flow model, a node power adjustment constraint is constructed, and bid data of the high-elasticity user cluster is converted into a price elasticity coefficient of a corresponding node, and the price elasticity coefficient is used as a node power adjustment coefficient;

[0015] Based on the dynamic power transmission limit value and the node power adjustment coefficient, a multi-objective function is established with the target of minimizing a power grid congestion cost and maximizing user electricity price satisfaction, the power grid congestion cost is calculated according to a load rate of the power transmission line under the dynamic power transmission limit value, and the user electricity price satisfaction is calculated according to an acceptance degree of the bid under the node power adjustment coefficient;

[0016] Based on the multi-objective function, the line power constraint, and the node power adjustment constraint, an augmented Lagrange function is constructed, and a Lagrange dual decomposition algorithm is used to solve the augmented Lagrange function to generate a transaction matching scheme.

[0017] Optionally, based on the multi-objective function, the line power constraint, and the node power adjustment constraint, an augmented Lagrange function is constructed, and a Lagrange dual decomposition algorithm is used to solve the augmented Lagrange function to generate a transaction matching scheme, including:

[0018] The power grid congestion cost and the user electricity price satisfaction are combined into a multi-objective function through a preset weight coefficient;

[0019] A first Lagrange multiplier is introduced for the line power constraint, and a second Lagrange multiplier is introduced for the node power adjustment constraint, and the multi-objective function, the line power constraint with the first Lagrange multiplier, and the node power adjustment constraint with the second Lagrange multiplier are combined to form an augmented Lagrange function;

[0020] The augmented Lagrange function is decomposed into two sub-problems by using a Lagrange dual decomposition algorithm, wherein a first sub-problem is to solve a line power allocation value satisfying the dynamic power transmission limit by fixing the second Lagrange multiplier, and a second sub-problem is to solve a node price and electricity consumption allocation value satisfying the node power adjustment coefficient by fixing the first Lagrange multiplier;

[0021] The first sub-problem and the second sub-problem are solved to generate a transaction matching scheme.

[0022] Optionally, the solving the first sub-problem and the second sub-problem to generate a transaction matching scheme comprises:

[0023] The first sub-problem and the second sub-problem are alternately iterated and solved, and each iteration performs the following operations:

[0024] The first sub-problem is solved to obtain an updated line power allocation value;

[0025] The second sub-problem is solved based on the updated line power allocation value to obtain an updated electricity consumption allocation value;

[0026] The first Lagrange multiplier is updated according to a difference between the updated line power allocation value and the dynamic power transmission limit;

[0027] The second Lagrange multiplier is updated according to a difference between the updated electricity consumption allocation value and an expected electricity consumption, the expected electricity consumption being calculated based on the node power adjustment coefficient;

[0028] When the line power allocation value and the electricity consumption allocation value have a change amount less than a preset change amount threshold in two consecutive iterations, a final line power allocation value, a node price setting value, and an electricity consumption allocation value are output as a transaction matching scheme.

[0029] Optionally, the identifying a high-elasticity user cluster based on the user historical bidding data and the load label information comprises:

[0030] The power grid area is divided into a plurality of geographical sub-areas, each geographical sub-area corresponding to a plurality of time periods;

[0031] For each time period of each geographical sub-area, a bidding response curve is extracted from the user historical bidding data;

[0032] According to the bidding response curve, a price sensitivity index of each user is calculated;

[0033] Users with a price sensitivity index greater than a preset sensitivity threshold are aggregated to form a high-elasticity user cluster.

[0034] Optionally, the detecting the implicit congestion position in the power grid area based on the acoustic monitoring signal and generating an impedance anomaly coefficient matrix based on the implicit congestion position comprises:

[0035] Obtaining a reference acoustic wave frequency characteristic of a key node in the power grid area;

[0036] Calculating a waveform propagation time difference of the acoustic monitoring signal relative to the reference acoustic wave frequency characteristic;

[0037] Marking a key node with a waveform propagation time difference exceeding a preset time difference threshold as an implicit congestion position;

[0038] Based on the implicit congestion position, calculating an impedance deviation of a corresponding power transmission channel;

[0039] According to the impedance deviation, an impedance anomaly coefficient matrix is generated in combination with a power grid node topology relationship.

[0040] Optionally, the adjusting the operating parameters of the power transmission line in the power grid area based on the power distribution demand corresponding to the transaction matching scheme comprises:

[0041] Obtaining operating parameters of the power transmission line, the operating parameters including a real-time current value and a voltage phase angle;

[0042] According to the power distribution demand corresponding to the transaction matching scheme, a target line transmission power is calculated;

[0043] Based on the target line transmission power and a preset standard voltage value, a target current value is obtained, a difference between the target current value and the real-time current value is calculated, and a voltage phase angle adjustment amount is generated according to a proportional relationship between the difference and a preset current-voltage adjustment amount;

[0044] According to the voltage phase angle adjustment amount, the corresponding voltage phase angle is adjusted.

[0045] In a second aspect, the application provides a power transaction matching system based on multi-objective optimization, comprising:

[0046] An obtaining module is configured to obtain user historical bidding data, load label information, and an acoustic monitoring signal in a power grid area;

[0047] An identification module is configured to identify a high-elasticity user cluster based on the user historical bidding data and the load label information;

[0048] A detection module is configured to detect an implicit congestion position in the power grid area based on the acoustic monitoring signal and generate an impedance anomaly coefficient matrix based on the implicit congestion position;

[0049] An input module is configured to input the impedance anomaly coefficient matrix and the high-elasticity user cluster into a pre-trained direct current optimal power flow model, to minimize a multi-objective function of power grid congestion cost and maximize user electricity price satisfaction, and to output a transaction matching scheme by using a Lagrange dual decomposition algorithm.

[0050] An adjustment module is configured to adjust operation parameters of a power transmission line in the power grid region based on a power distribution demand corresponding to the transaction matching scheme, so that an actual transmission power of the power transmission line matches the power distribution demand.

[0051] In a third aspect, the present application provides a computing device including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the power transaction matching method based on multi-objective optimization according to any one of the first aspect.

[0052] In a fourth aspect, the present application provides a computer storage medium storing computer program instructions, wherein the computer program instructions are executed by a processor to implement the power transaction matching method based on multi-objective optimization according to any one of the first aspect.

[0053] In the present application, a power transaction matching method based on multi-objective optimization is provided, which includes: obtaining historical bidding data of users in a power grid region, load label information, and audio monitoring signals; identifying a high-elasticity user cluster based on the historical bidding data of users and the load label information; detecting a hidden congestion position in the power grid region through the audio monitoring signals, and generating an impedance anomaly coefficient matrix based on the hidden congestion position; inputting the impedance anomaly coefficient matrix and the high-elasticity user cluster into a pre-trained direct current optimal power flow model, to minimize a multi-objective function of power grid congestion cost and maximize user electricity price satisfaction, and outputting a transaction matching scheme by using a Lagrange dual decomposition algorithm; and adjusting operation parameters of a power transmission line in the power grid region based on a power distribution demand corresponding to the transaction matching scheme, so that an actual transmission power of the power transmission line matches the power distribution demand.

[0054] The technical scheme provided by the present application has the following beneficial effects:

[0055] The present application establishes a complete power grid operation and market transaction database, providing data support for subsequent analysis. Accurately locates user groups sensitive to electricity price changes, providing target objects for demand side response. Finds potential physical transmission bottlenecks in the power grid, and quantifies the degree of line impedance anomaly. Establishes a collaborative optimization model considering power grid physical constraints and market demand. Achieves efficient solution of multi-objective optimization problems and obtains a transaction scheme that takes into account economy and safety. Ensures that the actual power transmission matches the transaction scheme accurately, and guarantees the safe operation of the power grid.

[0056] Further, the application also multiplies the element values of the impedance anomaly coefficient matrix with the line reference transmission capacity to obtain dynamic power transmission limits, converts high-elasticity user cluster quotation data into node power adjustment coefficients, and establishes a multi-objective function considering line load rate and user quotation acceptance based on the two key parameters, solves by constructing an augmented Lagrangian function and using a dual decomposition algorithm, and finally generates a transaction matching scheme.

[0057] Moreover, the method realizes collaborative optimization of power grid physical constraints and market transaction demand, dynamically adjusts line transmission capacity and node power adjustment parameters to reduce congestion costs and improve user satisfaction while ensuring safe operation of the power grid, and provides a more scientific and reasonable matching scheme for power market transactions.

[0058] These and other aspects of the present application will become more apparent from the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0060] Figure 1 A flowchart of a power transaction matching method based on multi-objective optimization provided by an embodiment of the present application;

[0061] Figure 2 A structural schematic diagram of a power transaction matching system based on multi-objective optimization provided by an embodiment of the present application;

[0062] Figure 3 A structural schematic diagram of a computing device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0063] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0064] In some of the flowcharts described in the specification and claims of the present application and in the above description of the drawings, a plurality of operations are included which occur in a particular order, but it should be clearly understood that the operations can be performed in the order in which they appear herein or in parallel, and the serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these flowcharts can include more or fewer operations, and the operations can be performed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. herein are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do "first" and "second" represent different types.

[0065] The current power transaction matching method mainly relies on historical data and static models to optimize power grid operation, although it can handle regular congestion problems, but it is difficult to accurately identify the implicit abnormal state of power grid equipment. For example, local aging or poor contact of transmission lines often do not immediately cause failures, but gradually affect power transmission efficiency, and existing methods cannot capture these potential risks in real time. In addition, the response behavior of users to electricity prices is usually simplified as fixed parameters, which is difficult to adapt to the rapid changes in market demand, leading to deviation of the optimization results from reality, increasing the risk of power grid operation and reducing market transaction efficiency.

[0066] To solve the above problems, the present application proposes a power transaction matching method based on multi-objective optimization, which dynamically adjusts the transaction strategy by fusing real-time monitoring data of the power grid and analysis of user bidding behavior. Specifically, this method uses audio signal detection to identify implicit abnormalities in the power grid, and combines historical bidding data of users to identify high-response groups sensitive to electricity prices. These information is input into the optimization model to automatically balance congestion costs and user satisfaction under the premise of ensuring power grid safety. This method breaks through the limitations of traditional static models, can not only discover potential power grid problems in time, but also accurately match user demand, effectively solving the optimization deviation problem caused by information lag and simplified assumptions in existing technologies, and improving the reliability and economy of power transactions.

[0067] The technical solutions in the embodiments of the present application will be described clearly and completely in the embodiments of the present application combined with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0068] Figure 1 A flowchart of a power transaction matching method based on multi-objective optimization provided by the embodiments of the present application is shown in Figure 1 The method comprises:

[0069] Step 101: Obtain user historical bidding data, load label information, and acoustic monitoring signals in the power grid area.

[0070] In step 101, user historical bidding data refers to the power price declared by the power user in the past trading period and the record data of the used power, reflecting the sensitivity of the user to the power price and the characteristics of the power usage behavior. In this application, the user power usage mode is analyzed. The load label information represents the classification data for identifying the power usage characteristics of the user, including user type, power equipment composition, and other information. In this application, it is used to assist in identifying the characteristics of the user group. The acoustic monitoring signal refers to the acoustic vibration signal collected by the ultrasonic sensor installed on the power grid equipment, which is used to detect the mechanical state change inside the power equipment, such as transformer winding looseness or insulator aging, and other physical abnormalities.

[0071] In the embodiments of the present application, first, the user's bidding record in the past multiple trading periods is called from the power grid database, including the power usage declaration data at different price levels. At the same time, the load characteristic label of the user is obtained, such as the classification information of industrial users, commercial users, etc. The acoustic signal collection device is deployed at the key nodes of the power grid to record the acoustic vibration signal of the equipment in real time. After the three types of data are summarized, a complete data basis is provided for subsequent analysis.

[0072] For example, taking a certain regional power grid as an example, the bidding records of industrial user A and commercial user B in the region in the past 30 days are obtained, wherein user A declares a power usage of 100 megawatts at a price of 0.6 yuan, and reduces to 80 megawatts at 0.7 yuan. User B uses 50 megawatts at 0.8 yuan, and reduces to 40 megawatts at 0.9 yuan. At the same time, it is obtained that the load label of user A is "interruptible production", and the load label of user B is "constant load operation". The acoustic sensors installed at nodes X and Y collect the reference acoustic signal with a frequency of 50 kHz.

[0073] Step 102: Identify a high-elasticity user cluster based on the user historical bidding data and the load label information.

[0074] In step 102, the high-elasticity user cluster refers to a user group that is sensitive to price changes and can flexibly adjust power usage behavior. It is formed by analyzing the historical bidding curve with a price sensitivity coefficient greater than a threshold value (such as ±5% power usage change / ±10% price fluctuation), for example, interruptible production manufacturing enterprises in an industrial park.

[0075] In the embodiments of the present application, the bidding data of each user is processed to calculate the power usage adjustment amplitude when the price changes, and the price sensitivity coefficient is obtained. Combined with the load label information, users with a sensitivity coefficient exceeding a set threshold value are screened out. These users are grouped according to geographical location and power usage characteristics to form multiple high-elasticity user groups.

[0076] For example, the price sensitivity coefficient of user A is (100-80) / 100÷(0.7-0.6) / 0.6=1.2, and that of user B is 0.5. Assuming that the threshold is 0.8, user A is included in the high elasticity cluster. In combination with the load label of "interruptible production", it is determined that the user can flexibly adjust the production plan during the peak period of electricity price.

[0077] Step 103: detecting the implicit congestion position in the power grid area through the audio monitoring signal, and generating an impedance anomaly coefficient matrix based on the implicit congestion position.

[0078] In step 103, the implicit congestion position refers to potential fault points in the power grid that have not caused obvious power outage but have affected the power transmission efficiency, which is specifically manifested as an abnormal increase in line impedance but has not reached the action threshold of the protection device. Such congestion cannot be found through conventional monitoring but can be reflected through the abnormal characteristics of sound wave propagation. The impedance anomaly coefficient matrix represents a mathematical matrix reflecting the degree of impedance anomaly between nodes in the power grid. The matrix elements represent the impedance deviation of the corresponding line.

[0079] In the embodiments of the present application, the collected audio signal is compared with the reference signal to calculate the waveform propagation time difference. The node with a time difference exceeding the threshold value is marked as an implicit congestion position. The impedance deviation of the corresponding line is calculated according to the time difference, and the impedance anomaly coefficient matrix is generated according to the topology of the power grid.

[0080] For example, the sound wave propagation time difference between nodes X and Y is 0.2 milliseconds, which exceeds the threshold value of 0.15 milliseconds. The calculated impedance deviation is 0.2x0.5=0.1. A 3x3 matrix is generated, and 0.1 is filled in the X-Y and Y-X positions, and 0 is filled in the remaining positions.

[0081] Step 104: inputting the impedance anomaly coefficient matrix and the high elasticity user cluster into a pre-trained direct current optimal power flow model to minimize the multi-objective function of the congestion cost of the power grid and maximize the user price satisfaction, and outputting a transaction matching scheme by using the Lagrange dual decomposition algorithm.

[0082] In step 104, the direct current optimal power flow model represents a mathematical model for power system optimization, which is used in the present application to coordinate the physical constraints of the power grid and the market transaction demand. The transaction matching scheme refers to the power transaction execution plan obtained by optimization calculation, which includes the power distribution value of each transmission line, the electricity price setting value of each node, and the electricity consumption distribution value of the high elasticity user cluster. The scheme realizes the dual objectives of reducing the congestion cost and improving the user satisfaction while meeting the physical safety constraints of the power grid, and is the result of the coordinated optimization of power grid operation and market transaction.

[0083] In the embodiments of the present application, the impedance matrix element value is multiplied by the line reference capacity to obtain the dynamic power limit value. The bid data of the user cluster is converted into a node power adjustment coefficient. A double-objective function considering line load rate and user satisfaction is established, and the optimal transaction scheme is solved by Lagrange algorithm.

[0084] For example, the line X-Y reference capacity is 100 MW, multiplied by the impedance coefficient 0.1 to obtain the dynamic limit value 90 MW. The adjustment coefficient of user A is 1.2. The objective function is established: min(line load rate) + max(user satisfaction), and the solution is obtained: the X-Y line distribution power is 88 MW, and the user A power consumption is 79 MW.

[0085] Step 105: Based on the power distribution demand corresponding to the transaction matching scheme, adjust the operating parameters of the transmission line in the power grid area to match the actual transmission power of the transmission line with the power distribution demand.

[0086] In step 105, the operating parameter adjustment refers to adjusting the working state of the power grid equipment to make the actual operation consistent with the planned scheme.

[0087] In the embodiments of the present application, according to the power distribution value in the scheme, the voltage phase angle that needs to be adjusted is calculated. The phase angle adjustment is performed through the power grid control device to make the actual transmission power of the line reach the target value. The adjustment effect is verified to ensure the stable operation of the system.

[0088] For example, to achieve the target of 88 MW of X-Y line, the voltage phase angle of node X is adjusted by 0.1 radian. The actual power is monitored to rise to 89 MW, and the error is within the allowable range, and the adjustment is completed.

[0089] The method realizes the safe and economic matching of power transaction by comprehensively analyzing the physical state of the power grid and the power consumption characteristics of users. It can not only timely find the hidden dangers of the power grid, but also accurately respond to market demand, and improve the operation efficiency and market vitality of the power system.

[0090] In order to solve the problem of power transaction optimization in the blocked area of the power grid, in some embodiments, step 104: the impedance anomaly coefficient matrix and the high elasticity user cluster are input into the pre-trained DC optimal power flow model, and a multi-objective function with the target of minimizing the power grid congestion cost and maximizing the user price satisfaction is adopted. The Lagrange dual decomposition algorithm outputs the transaction matching scheme, including:

[0091] Step 201: constructing line power constraints in the DC optimal power flow model, and multiplying the element value of the impedance anomaly coefficient matrix with the corresponding reference transmission capacity of the transmission line to obtain the dynamic power transmission limit value of each transmission line.

[0092] In step 201, the line power constraint refers to the limitation that the power transmission of each transmission line in the power system cannot exceed its maximum allowable value, which in this application is specifically manifested as the real-time transmission power of each line cannot exceed its dynamic power transmission limit value, which ensures the safety of the power grid operation. The reference transmission capacity refers to the maximum theoretical transmission capacity of the transmission line under standard operating conditions, which is determined by the physical characteristics of the line (such as conductor cross-sectional area, voltage level, etc.), and is usually determined by the power grid planning and design department according to equipment parameters and operating experience as a reference value for line capacity calculation. The dynamic power transmission limit value refers to the upper limit of the power considering the actual transmission capacity limitation of the line, which is obtained by multiplying each element value in the impedance abnormality coefficient matrix with the corresponding line reference capacity, reflecting the change of transmission capacity of the line due to hidden congestion.

[0093] In the embodiments of the present application, the reference transmission capacity data of each line of the power grid is first obtained, and then the value of the corresponding line position in the impedance abnormality coefficient matrix is multiplied by the reference capacity to obtain the maximum allowed transmission power of each line under the current state as the constraint condition for subsequent optimization calculation.

[0094] Step 202: constructing a node power regulation constraint in the DC optimal power flow model, and converting the bidding data of the high elasticity user cluster into a price elasticity coefficient of the corresponding node, taking the price elasticity coefficient as the node power regulation coefficient.

[0095] In step 202, the node refers to all nodes included in the DC optimal power flow model, not limited to the key nodes for detecting hidden congestion positions. The node power regulation constraint refers to the requirement that the electricity consumption allocation of each node must meet the price response characteristics of the node user group in power trading optimization, which is specifically manifested as the deviation limitation between the actual electricity consumption of the node and the expected electricity consumption calculated based on the price elasticity coefficient. The bidding data of the high elasticity user cluster refers to the electricity consumption data of the selected user group sensitive to price changes at different price levels in historical transactions, which is derived from the user declaration records of the power trading platform and reflects the electricity consumption behavior characteristics of the user group. The node power regulation coefficient is a parameter reflecting the response intensity of users to price changes, which is calculated by analyzing the historical bidding data of the high elasticity user cluster and is used to quantify the flexible adjustment ability of different node users in trading.

[0096] In the embodiments of the present application, for each power grid node, the bidding curve data of the connected high elasticity users is extracted, the electricity consumption adjustment ratio of these users when the price changes is calculated, and the power regulation coefficient of the node is converted, which is used to balance the user satisfaction in the subsequent optimization model.

[0097] Step 203: Based on the dynamic power transmission limit and the node power adjustment coefficient, a multi-objective function is established to minimize the grid congestion cost and maximize the user electricity price satisfaction, the grid congestion cost is calculated according to the transmission line load rate under the dynamic power transmission limit, and the user electricity price satisfaction is calculated according to the acceptance of the offer under the node power adjustment coefficient.

[0098] In step 203, the multi-objective function includes two optimization objectives of grid congestion cost and user electricity price satisfaction, the former is calculated based on the deviation of actual line power from dynamic limit, and the latter is evaluated according to the matching degree of actual user electricity consumption and declared amount.

[0099] In the embodiments of the present application, an optimization model considering line transmission safety and user electricity demand is established, and the two objectives are integrated into a unified evaluation standard by mathematical method, which provides a basis for subsequent solution.

[0100] Step 204: Based on the multi-objective function, the line power constraint and the node power adjustment constraint, an augmented Lagrangian function is constructed, and the augmented Lagrangian function is solved by using Lagrange dual decomposition algorithm to generate a transaction matching scheme.

[0101] In step 204, the augmented Lagrangian function is an extended form of the original optimization problem by introducing constraint conditions, and the complex problem is decomposed into multiple sub-problems for iterative solution by dual decomposition algorithm. The expression of the augmented Lagrangian function is: wherein represents the optimization variable vector, represents the Lagrange multiplier vector, represents the penalty coefficient, represents the original objective function, represents the constraint condition function.

[0102] In the embodiments of the present application, the optimization problem containing double objectives is converted into the form of Lagrangian function, and the line power distribution and node electricity consumption distribution are alternately solved by decomposition and coordination method until the optimal solution satisfying all constraint conditions is obtained.

[0103] The following is a specific example:

[0104] In the inter-provincial tie-line regional power grid, based on the obtained user A offer data and the node X-Y impedance abnormality coefficient 0.1, first, the line X-Y reference capacity 100 MW is multiplied by the impedance coefficient 0.1 to obtain the dynamic power transmission limit 90 MW. The price sensitivity coefficient 1.2 of user A is used as the power regulation coefficient of node X. When establishing the multi-objective optimization function, the power grid congestion cost is calculated by squaring the ratio of the actual power of the line to the dynamic limit, wherein the load rate calculation formula of the line X-Y is P_XY / 90, and P_XY represents the actual transmission power of the line X-Y; the user satisfaction is calculated by the matching degree of the power consumption Q_X of node X and the declared amount Q_X0, and the specific expression is 1-|Q_X-Q_X0| / Q_X0. The two objectives are combined into a unified objective function according to the weights 0.7 and 0.3: 0.7×(P_XY / 90)^2+0.3×[1-|Q_X-80| / 80]. The augmented Lagrangian function L=0.7×(P_XY / 90)^2+0.3×[1-|Q_X-80| / 80]+λ(P_XY-90)+ρ / 2(P_XY-90)^2 is constructed, wherein λ is the Lagrange multiplier, and ρ is the penalty coefficient. Through the alternating optimization method, P_XY=88 MW is obtained by fixing λ, and Q_X=79 MW is obtained by fixing P_XY, and the multiplier λ is updated as λ=λ+ρ(P_XY-90)=0+0.1×(88-90)=-0.2. After three iterations, the power of the line X-Y is stabilized at 89 MW, and the power consumption of node X is 79.5 MW, which meets the convergence condition. The final output transaction scheme is: the line X-Y is allocated 89 MW, the node X consumes 79.5 MW corresponding to the electricity price 0.682 yuan, the line X-Z is allocated 60 MW, and the node Y consumes 45 MW corresponding to the electricity price 0.75 yuan.

[0105] In the embodiments of the present application, by dynamically adjusting the power grid operation parameters and market transaction strategies, the optimization of resource allocation is realized under the premise of ensuring the safety of the power grid, the congestion risk is controlled, and the user power consumption satisfaction is improved, thereby providing effective decision support for the operation of the power market.

[0106] In order to further improve the optimization effect of the power transaction matching scheme, in some embodiments, step 204: based on the multi-objective function, the line power constraint and the node power regulation constraint, an augmented Lagrangian function is constructed, and a Lagrange dual decomposition algorithm is used to solve the augmented Lagrangian function to generate a transaction matching scheme, comprising:

[0107] Step 301: combining the power grid congestion cost and the user electricity price satisfaction through a preset weight coefficient into a multi-objective function.

[0108] In step 301, the preset weight coefficient refers to a proportion parameter artificially set to balance the importance of the two targets of grid congestion cost and user price satisfaction, and the value thereof is determined according to grid operation demand and market strategy.

[0109] In the embodiment of the application, the relative importance of congestion cost control and user satisfaction improvement is first determined, and then the mathematical expressions of the two targets are linearly combined according to the set proportion to form a new comprehensive evaluation function, thereby providing a unified standard for subsequent optimization.

[0110] In step 302, a first Lagrange multiplier is introduced for the line power constraint, and a second Lagrange multiplier is introduced for the node power adjustment constraint, and the multi-target function, the line power constraint with the first Lagrange multiplier and the node power adjustment constraint with the second Lagrange multiplier are combined to form an augmented Lagrange function.

[0111] In step 302, the first Lagrange multiplier is an adjustment parameter associated with the line power constraint, which is used to process the transmission capacity limit in the optimization process. The second Lagrange multiplier is an adjustment parameter associated with the node power adjustment constraint, which is used to process the user demand response characteristic.

[0112] In the embodiment of the application, a corresponding multiplier variable is introduced for each constraint condition, and the product of these multipliers and the respective constraint conditions is added as an additional term to the target function, and a quadratic penalty term of constraint deviation is added, thereby constructing an augmented Lagrange function more conducive to optimization.

[0113] In step 303, the augmented Lagrange function is decomposed into two sub-problems by using the Lagrange dual decomposition algorithm, wherein the first sub-problem is to solve the line power distribution value satisfying the dynamic power transmission limit by fixing the second Lagrange multiplier, and the second sub-problem is to solve the node price and power consumption distribution value satisfying the node power adjustment coefficient by fixing the first Lagrange multiplier.

[0114] In step 303, the first sub-problem is to optimize the grid line power distribution under the condition that the user side adjustment parameter is fixed. The second sub-problem is to optimize the user power consumption and price under the condition that the grid side power distribution is fixed.

[0115] In the embodiment of the application, the Lagrange dual principle is used to decompose the complex joint optimization problem into two relatively independent sub-problems, and the solving process is simplified by fixing part of the variables alternately. This decomposition and coordination method reduces the optimization difficulty.

[0116] In step 304, the first sub-problem and the second sub-problem are solved to generate a transaction matching scheme.

[0117] In the embodiments of the present application, two sub-problems are solved alternately repeatedly, and the multiplier parameters are updated after each iteration, so that the solution gradually approaches the optimal value, and the iteration is stopped when the result changes are small enough, and the final determined transaction matching scheme is output.

[0118] The following is a specific example:

[0119] In the case of a cross-province tie-line regional power grid, based on the established unified objective function 0.7 times the actual power P_XY of the line X-Y and the dynamic limit 90 megawatts, the square plus 0.3 times 1 minus the actual electricity consumption Q_X of node X minus the declared amount 80 megawatts, the absolute value divided by 80 megawatts, start to build the augmented Lagrangian function. First, introduce the first Lagrange multiplier λ initial value 0.5 for the line X-Y power constraint, and introduce the second Lagrange multiplier μ initial value 0.4 for the node X power adjustment constraint, and build the complete augmented Lagrangian function expression as 0.7 times P_XY divided by 90 square plus 0.3 times 1 minus Q_X minus 80 absolute value divided by 80 plus λ times P_XY minus 90 plus 0.1 divided by 2 times P_XY minus 90 square plus μ times Q_X minus 80 times 1.2 plus 0.05 divided by 2 times Q_X minus 80 times 1.2 square, wherein 0.1 and 0.05 are the penalty coefficients of the line and node constraints respectively. In the first iteration, fix μ as 0.4, solve the first sub-problem to get the line X-Y power allocation value P_XY equal to 87 megawatts, which is obtained by optimizing the algorithm to minimize the objective function under the condition of meeting the line power constraint. Then fix λ as 0.5, solve the second sub-problem to get the node X electricity consumption Q_X equal to 78.5 megawatts and the corresponding price 0.685 yuan, which is calculated according to the user A's bid curve and power adjustment coefficient 1.2. According to the results of the first iteration, update the first Lagrange multiplier λ new value equal to the original value 0.5 plus the penalty coefficient 0.1 times the power difference 87 minus 90 to get 0.2, and update the second Lagrange multiplier μ new value equal to the original value 0.4 plus the penalty coefficient 0.05 times the electricity consumption difference 78.5 minus 80 times 1.2 to get 0.34. After three such iterations, the line X-Y power stabilizes at 88.5 megawatts, and the node X electricity consumption stabilizes at 79.2 megawatts, and the continuous iteration variation of both is less than the set threshold, and the final output transaction matching scheme is that the line X-Y is allocated 88.5 megawatts, the node X is used 79.2 megawatts corresponding to the price 0.678 yuan, the line X-Z is allocated 60.5 megawatts, and the node Y is used 45.3 megawatts corresponding to the price 0.748 yuan.

[0120] In the embodiment of the present application, the method effectively solves the coordination problem between the physical constraints of the power grid and the market transaction demand through a scientific decomposition coordination mechanism, realizes the dual goals of congestion cost control and user satisfaction improvement while ensuring the safe operation of the power grid, and provides reliable optimization decision support for power market operation.

[0121] To further improve the solving efficiency and accuracy of the transaction matching scheme, in some embodiments, step 304: solving the first sub-problem and the second sub-problem to generate a transaction matching scheme, comprises:

[0122] Alternately solving the first sub-problem and the second sub-problem, each iteration performs the following operations:

[0123] Step 401: solving the first sub-problem to obtain updated line power distribution values.

[0124] In step 401, the updated line power distribution values refer to the power distribution results of each transmission line recalculated after each iteration, which reflects the optimal power distribution of the power grid side under the current constraint condition.

[0125] In the embodiment of the present application, under the condition of fixed user side parameters, the power distribution scheme satisfying the line power constraint is calculated by an optimization algorithm to obtain a new set of line power values.

[0126] Step 402: based on the updated line power distribution values, solving the second sub-problem to obtain updated power consumption distribution values.

[0127] In step 402, the updated power consumption distribution values refer to the user power consumption data re-optimized based on the latest line power distribution results, which embodies the optimal power consumption arrangement under the current power grid operation state.

[0128] In the embodiment of the present application, using the latest obtained line power data, the power consumption distribution and corresponding electricity price of each node are recalculated under the premise of ensuring the safety of the power grid, to obtain the optimal response scheme of the user side.

[0129] Step 403: updating the first Lagrange multiplier according to the difference between the updated line power distribution values and the dynamic power transmission limit value.

[0130] In step 403, the updating of the first Lagrange multiplier refers to the process of adjusting the multiplier value according to the difference between the actual line power and the limit value, which is used to dynamically balance the strictness of the constraint condition.

[0131] In the embodiment of the present application, by comparing the difference between the newly calculated line power and its limit value, the multiplier size is adjusted according to the preset rule, so that the next iteration can better meet the line power constraint.

[0132] Step 404: updating the second Lagrange multiplier according to the difference between the updated electricity consumption allocation value and the expected electricity consumption, the expected electricity consumption being calculated based on the node power adjustment coefficient.

[0133] In step 404, the updating of the second Lagrange multiplier refers to the process of adjusting the multiplier value according to the difference between the actual electricity consumption and the expected value, for dynamically balancing the user demand response characteristics.

[0134] In the embodiments of the present application, by comparing the difference between the newly calculated electricity consumption and the expected value predicted based on the user response characteristics, the multiplier size is adjusted according to the preset rule, so that the next iteration can better meet the user demand.

[0135] Step 405: when the line power allocation value and the electricity consumption allocation value have a variation in the two consecutive iterations less than a preset variation threshold, output the final line power allocation value, node electricity price setting value and electricity consumption allocation value as the transaction matching scheme.

[0136] In step 405, the preset variation threshold refers to the critical standard for judging whether the iterative calculation converges, and when the variation of the two consecutive calculation results is less than the standard, it is considered that a satisfactory solution has been obtained.

[0137] In the embodiments of the present application, the iterative variation of the line power and the electricity consumption is continuously monitored, and the calculation is stopped when the variation of both is small enough, and the final determined transaction matching scheme is output.

[0138] The following is a specific example:

[0139] In the case of the inter-provincial tie-line regional power grid, based on the previous iteration results, a new round of iteration calculation is started from the current values of the line X-Y power 88.5 MW and the node X power consumption 79.2 MW. First, fix the second Lagrange multiplier μ as 0.35, solve the first sub-problem to obtain the updated line X-Y power allocation value 89 MW, which is calculated by minimizing the objective function 0.7 times P_XY divided by 90 squared plus 0.2 times P_XY minus 90 plus 0.1 divided by 2 times P_XY minus 90 squared, where P_XY represents the line X-Y power. Then fix the first Lagrange multiplier λ as 0.18, solve the second sub-problem to obtain the updated node X power consumption 79.5 MW and the corresponding electricity price 0.68 yuan, which is calculated according to the expression 1 minus the absolute value of Q_X minus 80 divided by 80 plus 0.35 times Q_X minus 80 times 1.2 plus 0.05 divided by 2 times Q_X minus 80 times 1.2 squared, where Q_X represents the node X power consumption. According to the difference 1 MW between the newly obtained line power 89 MW and the dynamic limit value 90 MW, update the first Lagrange multiplier according to the formula λ_new = 0.18 + 0.1 × 1 = 0.28. According to the difference 0.5 MW between the node X power consumption 79.5 MW and the expected value 80 MW, update the second Lagrange multiplier according to the formula μ_new = 0.35 + 0.05 × 0.5 × 1.2 = 0.38. It is monitored that the line power changes from 88.5 MW to 89 MW, with a change of 0.5 MW, which is less than the preset threshold 1 MW, and the power consumption changes from 79.2 MW to 79.5 MW, with a change of 0.3 MW, which is less than the preset threshold 0.5 MW, satisfying the convergence condition. The final output transaction matching scheme is that the line X-Y is allocated 89 MW, the node X power consumption is 79.5 MW corresponding to the electricity price 0.68 yuan, the line X-Z is allocated 60 MW, and the node Y power consumption is 45 MW corresponding to the electricity price 0.75 yuan.

[0140] In the embodiments of the present application, through the systematic iterative optimization process, the transaction scheme ensures that the physical constraints of the power grid and the demand response characteristics of the users are satisfied at the same time, achieving the best balance between safety and economy, and providing a scientific and reliable decision basis for power market operation.

[0141] In order to more accurately identify the user group sensitive to electricity price changes, in some embodiments, step 102: based on the user historical bidding data and the load label information, a high elasticity user cluster is identified, including:

[0142] Step 501: divide the power grid region into a plurality of geographical sub-regions, each geographical sub-region corresponding to a plurality of time periods.

[0143] In step 501, the geographical sub-region refers to a local power supply range divided according to the power grid topology and user distribution characteristics, and the time period division refers to dividing a day into multiple time intervals for analyzing the time characteristics of user power consumption behavior.

[0144] In the embodiments of the present application, first, the entire service area is divided into several sub-areas according to the distribution of power grid nodes and the power supply range, and each sub-area contains several power consumption nodes, and then 24 hours of a day are divided into multiple time periods to form a space-time grid for subsequent analysis.

[0145] Step 502: For each time period of each geographical sub-region, extract the bidding response curve from the user historical bidding data.

[0146] In step 502, the bidding response curve refers to a curve composed of user power consumption declaration data at different electricity price levels, reflecting the electricity price sensitivity of the user.

[0147] In the embodiments of the present application, for each time period of each sub-area, the power consumption declaration records of each user at different electricity prices are extracted from the historical transaction database, and the electricity price-power consumption change curve of each user is drawn to intuitively display the bidding response mode of the user.

[0148] Step 503: Calculate the electricity price sensitivity index of each user according to the bidding response curve.

[0149] In step 503, the electricity price sensitivity index is a numerical value quantifying the response intensity of the user to the change of electricity price, which is obtained by calculating the ratio of the power consumption change rate of the user to the electricity price change rate.

[0150] In the embodiments of the present application, based on the bidding response curve of each user, the relative ratio of the adjustment amplitude of the power consumption to the adjustment amplitude of the electricity price when the electricity price changes is calculated to obtain a quantitative index reflecting the price sensitivity degree of the user.

[0151] Step 504: Aggregate users with the electricity price sensitivity index greater than a preset sensitivity threshold to form a high elasticity user cluster.

[0152] In step 504, the preset sensitivity threshold is a critical value for distinguishing high and low elasticity users determined according to historical data analysis.

[0153] In the embodiments of the present application, the calculated sensitivity index of each user is compared with the set threshold, and users with an index value higher than the threshold are selected and grouped according to their sub-area and time period to form multiple high elasticity user groups.

[0154] The following is a specific example:

[0155] In the case of a cross-provincial tie-line regional power grid, the power grid region is first divided into three geographical sub-regions: sub-region X contains node X and surrounding users, sub-region Y contains node Y and surroundings, and sub-region Z is the other region. Each day is divided into three periods: peak period 8:00-11:00, flat period 11:00-17:00, and valley period 17:00-22:00. For the peak period of sub-region X, the bidding response curve of industrial user A is extracted from historical data. This user reports 100 megawatts at a price of 0.6 yuan, 90 megawatts at a price of 0.65 yuan, and 80 megawatts at a price of 0.7 yuan. According to these data, the price sensitivity index of user A is calculated as (100-80) / 100 ÷ (0.7-0.6) / 0.6 = 1.2. At the same time, the bidding data of commercial user B is extracted, which reports 50 megawatts at 0.8 yuan, 48 megawatts at 0.85 yuan, and 40 megawatts at 0.9 yuan. The sensitivity index is calculated as (50-40) / 50 ÷ (0.9-0.8) / 0.8 = 0.5. Set the sensitivity threshold to 0.8, so user A is included in the high elasticity user cluster, and user B is not included. Combined with the "interruptible production" load label of user A, it is confirmed that the user has the ability to flexibly adjust the production plan when the electricity price fluctuates. In the peak period of sub-region Y, another high elasticity user C is identified by the same method, with a sensitivity index of 1.0. The final peak period high elasticity user cluster includes user A in sub-region X and user C in sub-region Y. The electricity consumption characteristic data of these users will be used in the subsequent transaction optimization model.

[0156] In the embodiments of the present application, the method accurately identifies the user group sensitive to price changes through systematic spatio-temporal division and quantitative analysis, providing accurate demand side response objects for subsequent power trading optimization, and effectively improving the targeting and effectiveness of transaction matching.

[0157] In order to solve the problem of insufficient detection accuracy of implicit congestion of power grid, in some embodiments, step 103: detecting the location of the implicit congestion in the power grid region through the audio monitoring signal, and generating an impedance anomaly coefficient matrix based on the location of the implicit congestion, comprises:

[0158] Step 601: Obtain the reference sound wave frequency characteristics of the key nodes in the power grid region.

[0159] In step 601, the reference sound wave frequency characteristics refer to the vibration characteristics of the sound waves of a specific frequency generated by the power grid equipment in the normal state, including frequency size and waveform mode parameters.

[0160] In the embodiments of the present application, first, when the power grid is normally running, the standard sound wave signal of the equipment is collected by the sound wave sensor installed on the key node, and the frequency and waveform characteristics are recorded as the reference for subsequent detection.

[0161] Step 602: Calculate the waveform propagation time difference of the acoustic monitoring signal relative to the reference acoustic wave frequency characteristic.

[0162] In step 602, the waveform propagation time difference refers to the difference in transmission time between the actual monitored acoustic wave signal and the reference signal, reflecting the change in acoustic wave propagation speed caused by abnormal device state.

[0163] In the embodiments of the present application, the real-time collected acoustic wave signal is compared with the reference signal in waveform, and the time difference used by the signal from sending to receiving is calculated. This time difference can indirectly reflect the physical state change inside the device.

[0164] Step 603: Mark the key node with a waveform propagation time difference exceeding a preset time difference threshold as a hidden blockage position.

[0165] In step 603, the preset time difference threshold is a standard value determined according to a large amount of experimental data, which is used as a critical standard for judging whether the device has an abnormal state.

[0166] In the embodiments of the present application, the calculated time difference is compared with the pre-set safety threshold. The node exceeding the threshold is determined to have a hidden blockage problem and needs to be paid special attention and handled.

[0167] Step 604: Calculate the impedance deviation amount of the corresponding power transmission channel based on the hidden blockage position.

[0168] In step 604, the power transmission channel refers to the power transmission path connecting the key nodes in the power grid area, which is physically composed of one or more "power transmission lines". Each power transmission line is a specific implementation carrier of the power transmission channel. The power grid area includes a complete network composed of all power transmission channels and their associated devices. The impedance deviation amount is a parameter quantifying the degree of decline in line transmission capacity, which is calculated through the conversion relationship between time difference and impedance.

[0169] In the embodiments of the present application, according to the physical relationship model between acoustic wave time difference and line impedance, the detected time difference value is converted into the corresponding impedance change amount, which is used to evaluate the degree of decline in line transmission capacity.

[0170] Step 605: Generate an impedance anomaly coefficient matrix according to the impedance deviation amount and in combination with the topological relationship of the power grid nodes.

[0171] In the embodiments of the present application, according to the actual connection topology of the power grid, the impedance deviation of each line is filled into the corresponding position of the matrix to generate a complete impedance abnormality distribution diagram, providing data support for subsequent power flow calculation. The specific implementation process is as follows: first, according to the wiring diagram of the power grid, the physical connection relationship of each node is determined, and a matrix element position is created for each directly connected node pair; then the detected impedance deviation is filled into the matrix position of the corresponding node pair, and the matrix elements of the node pairs not directly connected are set to zero; finally, the symmetry of the matrix is ensured, that is, the values of node A to B and node B to A are the same. Taking the inter-provincial tie-line area as an example, it is detected that there is hidden congestion (impedance deviation 0.08 p.u.) in the channel from node 3 to node 5, 0.08 is filled in the 3rd row and 5th column and 5th row and 3rd column positions of the 3×3 matrix, and 0 is filled in the positions between the non-directly connected nodes (such as 1-3, 2-4, etc.). Finally, a symmetric matrix reflecting the impedance abnormality distribution of the whole network is generated, which will be used for dynamic line capacity correction in subsequent power flow calculation.

[0172] The following is a specific example:

[0173] In the case of the inter-provincial tie-line area power grid, first, the reference acoustic characteristic data of node X and node Y under normal operating conditions is obtained, where the reference frequency is 50 kHz and the standard propagation time is 1.2 milliseconds. The actual propagation time from node X to Y is collected by the real-time acoustic monitoring system as 1.4 milliseconds, and the wave propagation time difference is calculated as 1.4 minus 1.2 equaling 0.2 milliseconds. This value exceeds the preset safety threshold of 0.15 milliseconds, so the line between nodes X-Y is marked as a position with hidden congestion. According to the conversion relationship between acoustic time difference and impedance change, impedance deviation equals time difference multiplied by conversion coefficient 0.5, and 0.2 multiplied by 0.5 equals 0.1 is calculated. Combined with the node connection relationship of the regional power grid, node X is connected to Y and Z, node Y is connected to X and Z, and node Z is connected to X and Y, a 3×3 impedance abnormality coefficient matrix is generated. In the matrix, the position representing the connection between node X and Y is the first row and second column, and the position representing the connection between node Y and X is the second row and first column, and the impedance deviation 0.1 calculated is filled in, and the positions representing the nodes not directly connected including the first row and third column, the second row and third column, the third row and first column, and the third row and second column are all filled in 0, and the main diagonal line positions first row and first column, second row and second column, and third row and third column are filled in 0 by convention, and finally the matrix formed completely reflects the impedance abnormality distribution of the regional power grid.

[0174] In the embodiments of the present application, the method realizes accurate positioning and quantitative evaluation of hidden congestion of the power grid through acoustic detection technology, provides reliable power grid state data for subsequent power trading optimization, and effectively improves the safety and feasibility of the trading scheme.

[0175] To ensure the accurate matching of the transaction scheme and the actual power grid operation, in some embodiments, step 105: based on the power distribution demand corresponding to the transaction matching scheme, adjusting the operation parameters of the transmission lines in the power grid region, including:

[0176] Step 701: obtaining the operation parameters of the transmission line, the operation parameters including real-time current value and voltage phase angle.

[0177] In step 701, the operation parameters of the transmission line refer to physical quantity data reflecting the real-time working state of the line, including the current size flowing through the line and the phase angle difference of the voltage waveform at both ends.

[0178] In the embodiments of the present application, the current measurement value and voltage phase data of each transmission line are collected in real time by the power grid monitoring system to obtain the first-hand information of the current operation state of the power grid.

[0179] Step 702: calculating the target line transmission power according to the power distribution demand corresponding to the transaction matching scheme.

[0180] In step 702, the power distribution demand corresponding to the transaction matching scheme refers to the power value to be transmitted by each transmission line and the electricity consumption distribution plan of each node determined by optimization calculation, which is derived from the line power distribution value, node electricity price and electricity consumption distribution result output by the Lagrange dual decomposition algorithm solving the multi-objective function. The target line transmission power refers to the planned transmission power value of the line determined according to the transaction matching scheme, which reflects the specific requirements of the transaction scheme on the operation of the power grid.

[0181] In the embodiments of the present application, the power distribution values of each line are extracted from the generated transaction matching scheme as the target basis for the operation adjustment of the power grid.

[0182] Step 703: based on the target line transmission power and the preset standard voltage value, obtaining the target current value, calculating the difference between the target current value and the real-time current value, and generating the voltage phase angle adjustment amount according to the proportional relationship between the difference and the preset current and voltage adjustment amount.

[0183] In step 703, the target current value refers to the theoretical current value of the line calculated by the power formula according to the target transmission power of the line in the transaction matching scheme, combined with the standard voltage and power factor of the power grid, for comparison with the actual current to determine the adjustment amount. The difference refers to the deviation of the target current value from the real-time current value, reflecting the difference between the actual operation and the transaction scheme. The proportional relationship refers to the conversion coefficient between the current change amount and the required voltage phase angle adjustment amount determined by the characteristics of the power grid (such as 0.05 radian phase angle adjustment per 100 ampere current difference), which is used to convert the current difference into specific phase angle adjustment instructions. The voltage phase angle adjustment amount refers to the change value of the voltage waveform phase difference that needs to be adjusted to achieve the target power, which is obtained by proportional conversion of the current difference.

[0184] In the embodiments of the present application, the theoretical current value is first calculated according to the target power and the standard voltage, then the difference is obtained by comparing with the actual current, and finally the phase angle to be adjusted is determined according to the preset current-phase angle conversion relationship.

[0185] Step 704: Adjust the voltage phase angle according to the voltage phase angle adjustment amount.

[0186] In step 704, the voltage phase angle adjustment refers to the operation of changing the relative time position of the node voltage waveform by the power grid control device, thereby changing the power distribution of the line.

[0187] In the embodiments of the present application, the calculated phase angle adjustment amount is sent to the power grid adjustment device to perform accurate adjustment of the specified node voltage phase.

[0188] The following is a specific example:

[0189] In the case of a cross-provincial tie-line regional power grid, the target transmission power of line X-Y is 88 MW based on the transaction matching scheme. First, the real-time operating parameters of the line are obtained: the measured current value is 450 amperes, the voltage phase angle of node X is 0 radian, and the voltage phase angle of node Y is -0.1 radian. According to the target power of 88 MW and the preset standard voltage of 220 kV, considering the power factor of 0.95, the target current value is calculated by the power calculation formula where P is the power, U is the voltage, I is the current, and φ is the power factor angle. amperes. The difference between the target current of 245 amperes and the real-time current of 450 amperes is 205 amperes. According to the preset adjustment proportion of 0.05 radian phase angle adjustment per 100 amperes current difference, the voltage phase angle to be adjusted is 205 / 100 x 0.05 ≈ 0.102 radian. After adjusting the voltage phase angle of node X from 0 radian to 0.102 radian, the actual transmission power of line X-Y is monitored to be increased to 88.8 MW, with an error of 0.8 MW from the target value of 88 MW, which verifies the effectiveness of the adjustment.

[0190] In the embodiment of the present application, the method ensures the effective implementation of the transaction scheme in the actual power grid through accurate power grid parameter adjustment, realizes the coordination and unity of the power market transaction and the physical operation of the power grid, and guarantees the safe and stable operation of the power system.

[0191] Figure 2 A structure diagram of a power transaction matching system based on multi-objective optimization provided in the embodiment of the present application is shown in FIG. 1, which includes: Figure 2

[0192] The acquisition module 21 is configured to acquire historical bidding data of users, load label information, and audio monitoring signals in a power grid region.

[0193] The identification module 22 is configured to identify a high-elasticity user cluster based on the historical bidding data of users and the load label information.

[0194] The detection module 23 is configured to detect a hidden congestion position in the power grid region through the audio monitoring signals, and generate an impedance abnormality coefficient matrix based on the hidden congestion position.

[0195] The input module 24 is configured to input the impedance abnormality coefficient matrix and the high-elasticity user cluster into a pre-trained direct current optimal power flow model, to minimize a multi-objective function of a power grid congestion cost and maximize user price satisfaction, and output a transaction matching scheme by using a Lagrange dual decomposition algorithm.

[0196] The adjustment module 25 is configured to adjust operating parameters of a power transmission line in the power grid region based on a power distribution demand corresponding to the transaction matching scheme, so that an actual transmission power of the power transmission line matches the power distribution demand.

[0197] Figure 2 The power transaction matching system based on multi-objective optimization can perform the power transaction matching method based on multi-objective optimization as shown in the embodiments. Figure 1 The implementation principle and technical effects of the power transaction matching method based on multi-objective optimization are not described again. The specific operation modes of each module and unit of the power transaction matching system based on multi-objective optimization in the above embodiments have been described in detail in the embodiments related to the method, and will not be described in detail here.

[0198] In one possible design, Figure 2 The power transaction matching system based on multi-objective optimization in the embodiment shown in FIG. 1 can be implemented as a computing device, as shown in FIG. 1, which can include a storage component 31 and a processing component 32. Figure 3

[0199] ​​The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0200] The processing component 32 is configured to execute the above-described Figure 1 The embodiment provides a power transaction matching method based on multi-objective optimization.

[0201] The processing component 32 can include one or more processors to execute the computer instructions to complete all or part of the steps in the above-described method. Of course, the processing component can also be one or more Application-Specific Integrated Circuit (ASIC), Digital Signal Process (DSP), Digital Signal Process Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor or other electronic component, which is configured to execute the above-described method.

[0202] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage devices or their combinations, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0203] Of course, the computing device can also include other components, such as input / output interface, display component, communication component, etc.

[0204] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0205] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices.

[0206] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component, the storage component, and the like can be a basic server resource rented or purchased from the cloud computing platform.

[0207] The embodiment of the application further provides a computer storage medium storing a computer program, and the computer program can implement the above-mentioned Figure 1 The embodiment shown in the figure is a power transaction matching method based on multi-objective optimization.

[0208] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-mentioned system, device and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described here.

[0209] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0210] Through the description of the foregoing embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and a necessary general hardware platform, and of course, it can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0211] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A power trading matching method based on multi-objective optimization, characterized in that, include: Acquire historical user pricing data, load tag information, and audio monitoring signals within the power grid area; Based on the user's historical pricing data and the load tag information, identify highly elastic user clusters; The location of hidden blockages in the power grid area is detected by the audio monitoring signal, and an impedance anomaly coefficient matrix is ​​generated based on the location of the hidden blockages. The impedance anomaly coefficient matrix and the highly resilient user cluster are input into a pre-trained DC optimal power flow model. A multi-objective function with the goal of minimizing grid congestion costs and maximizing user electricity price satisfaction is used to output a transaction matching scheme using the Lagrange dual decomposition algorithm. Based on the power allocation demand corresponding to the transaction matching scheme, the operating parameters of the transmission lines in the power grid area are adjusted so that the actual transmission power of the transmission lines matches the power allocation demand. The step of identifying highly elastic user clusters based on the user's historical pricing data and the load tag information includes: The power grid area is divided into multiple geographical sub-regions, and each geographical sub-region corresponds to multiple time periods; For each time period in each geographical sub-region, extract the quote response curve from the user's historical quote data; Based on the quoted price response curve, calculate the electricity price sensitivity index for each user; Users whose electricity price sensitivity index is greater than a preset sensitivity threshold are aggregated to form a highly elastic user cluster; The step of detecting hidden blockage locations within the power grid area using the audio monitoring signal and generating an impedance anomaly coefficient matrix based on the hidden blockage locations includes: Obtain the reference acoustic frequency characteristics of key nodes within the power grid area; Calculate the waveform propagation time difference of the audio monitoring signal relative to the frequency characteristics of the reference sound wave; Key nodes whose waveform propagation time difference exceeds a preset time difference threshold are marked as hidden blockage locations; Based on the location of the hidden blockage, the impedance deviation of the corresponding transmission channel is calculated; Based on the impedance deviation and the topology of the power grid nodes, an impedance anomaly coefficient matrix is ​​generated.

2. The power trading matching method based on multi-objective optimization according to claim 1, characterized in that, The process involves inputting the impedance anomaly coefficient matrix and the highly resilient user cluster into a pre-trained DC optimal power flow model. This model employs a multi-objective function aimed at minimizing grid congestion costs and maximizing user electricity price satisfaction. The Lagrange dual decomposition algorithm is used to output a transaction matching scheme, including: In the DC optimal power flow model, line power constraints are constructed, and the element values ​​of the impedance anomaly coefficient matrix are multiplied by the reference transmission capacity of the corresponding transmission line to obtain the dynamic power transmission limit of each transmission line. In the DC optimal power flow model, node power adjustment constraints are constructed, and the bidding data of the highly elastic user cluster is converted into the price elasticity coefficient of the corresponding node, and the price elasticity coefficient is used as the node power adjustment coefficient. Based on the dynamic power transmission limit and the node power adjustment coefficient, a multi-objective function is established with the goal of minimizing grid congestion cost and maximizing user electricity price satisfaction. The grid congestion cost is calculated based on the transmission line load rate under the dynamic power transmission limit, and the user electricity price satisfaction is calculated based on the price acceptance rate under the node power adjustment coefficient. Based on the multi-objective function, the line power constraint, and the node power adjustment constraint, an augmented Lagrangian function is constructed, and the augmented Lagrangian function is solved using the Lagrangian dual decomposition algorithm to generate a transaction matching scheme.

3. The power trading matching method based on multi-objective optimization according to claim 2, characterized in that, The process involves constructing an augmented Lagrangian function based on the multi-objective function, the line power constraint, and the node power adjustment constraint, and then solving the augmented Lagrangian function using the Lagrangian dual decomposition algorithm to generate a transaction matching scheme, including: The grid congestion cost and the user's electricity price satisfaction are combined into a multi-objective function using preset weighting coefficients; A first Lagrange multiplier is introduced for the line power constraint, and a second Lagrange multiplier is introduced for the node power adjustment constraint. The multi-objective function, the line power constraint with the first Lagrange multiplier, and the node power adjustment constraint with the second Lagrange multiplier are combined to form an augmented Lagrange function. The augmented Lagrange function is decomposed into two subproblems using the Lagrange dual decomposition algorithm. The first subproblem is to solve for the line power allocation value that satisfies the dynamic power transmission limit by fixing the second Lagrange multiplier. The second subproblem is to solve for the node electricity price and electricity consumption allocation value that satisfies the node power adjustment coefficient by fixing the first Lagrange multiplier. Solve the first subproblem and the second subproblem to generate a transaction matching scheme.

4. The power trading matching method based on multi-objective optimization according to claim 3, characterized in that, Solving the first subproblem and the second subproblem to generate a transaction matching scheme includes: The first subproblem and the second subproblem are solved iteratively, with the following operations performed in each iteration: Solve the first subproblem to obtain the updated line power allocation values; Based on the updated line power allocation value, solve the second sub-problem to obtain the updated power consumption allocation value; The first Lagrange multiplier is updated based on the difference between the updated line power allocation value and the dynamic power transmission limit. The second Lagrange multiplier is updated based on the difference between the updated power consumption allocation value and the expected power consumption, wherein the expected power consumption is calculated based on the node power adjustment coefficient. When the changes in the line power allocation value and the electricity consumption allocation value in two consecutive iterations are both less than the preset change threshold, the final line power allocation value, node electricity price setting value, and electricity consumption allocation value are output and used as the transaction matching scheme.

5. The power trading matching method based on multi-objective optimization according to claim 1, characterized in that, The step of adjusting the operating parameters of transmission lines within the power grid area based on the power allocation demand corresponding to the transaction matching scheme includes: The operating parameters of the transmission line are obtained, including real-time current value and voltage phase angle; Calculate the target line transmission power based on the power allocation requirements corresponding to the transaction matching scheme; Based on the target line transmission power and the preset standard voltage value, the target current value is obtained, the difference between the target current value and the real-time current value is calculated, and the voltage phase angle adjustment is generated according to the difference and the preset proportional relationship between current and voltage adjustment amount. Adjust the corresponding voltage phase angle according to the voltage phase angle adjustment amount.

6. A power trading matching system based on multi-objective optimization, characterized in that, include: The acquisition module is used to acquire historical user price data, load tag information, and audio monitoring signals within the power grid area; The identification module is used to identify highly elastic user clusters based on the user's historical pricing data and the load tag information; The detection module is used to detect the location of hidden blockages in the power grid area through the audio monitoring signal, and generate an impedance anomaly coefficient matrix based on the location of the hidden blockages. The input module is used to input the impedance anomaly coefficient matrix and the highly resilient user cluster into a pre-trained DC optimal power flow model, which is a multi-objective function with the goal of minimizing grid congestion costs and maximizing user electricity price satisfaction, and outputs a transaction matching scheme using the Lagrange dual decomposition algorithm. The adjustment module is used to adjust the operating parameters of the transmission lines in the power grid area based on the power distribution demand corresponding to the transaction matching scheme, so as to make the actual transmission power of the transmission lines match the power distribution demand. The step of identifying highly elastic user clusters based on the user's historical pricing data and the load tag information includes: The power grid area is divided into multiple geographical sub-regions, and each geographical sub-region corresponds to multiple time periods; For each time period in each geographical sub-region, extract the quote response curve from the user's historical quote data; Based on the quoted price response curve, calculate the electricity price sensitivity index for each user; Users whose electricity price sensitivity index is greater than a preset sensitivity threshold are aggregated to form a highly elastic user cluster; The step of detecting hidden blockage locations within the power grid area using the audio monitoring signal and generating an impedance anomaly coefficient matrix based on the hidden blockage locations includes: Obtain the reference acoustic frequency characteristics of key nodes within the power grid area; Calculate the waveform propagation time difference of the audio monitoring signal relative to the frequency characteristics of the reference sound wave; Key nodes whose waveform propagation time difference exceeds a preset time difference threshold are marked as hidden blockage locations; Based on the location of the hidden blockage, the impedance deviation of the corresponding transmission channel is calculated; Based on the impedance deviation and the topology of the power grid nodes, an impedance anomaly coefficient matrix is ​​generated.

7. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a power trading matching method based on multi-objective optimization as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a power trading matching method based on multi-objective optimization as described in any one of claims 1 to 5.

Images