Power grid section control method and device, computer device and readable storage medium

Abstract

The application relates to a power grid section control method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring power grid operation data; acquiring constraint conditions based on the power grid operation data; constructing a linear programming model; obtaining an adjustment amount under the constraint of the constraint conditions; constructing an objective function; evaluating the adjustment amount by using the objective function under the constraint of the constraint conditions; in the case that the adjustment amount meets a preset condition, weighting the linear programming model to obtain a target linear programming model, and obtaining a target adjustment amount by solving; sorting the target adjustment amount, and attempting to select at least one target adjustment amount according to the sorting order; resetting the selected target adjustment amount to a preset value, and re-solving the linear programming model based on all the target adjustment amounts after the resetting; if the re-solving is successful, generating an adjustment instruction based on all the target adjustment amounts after the resetting; and the adjustment instruction is used for being issued to a generator set to execute power grid section control. The method can realize safe control of the power grid section.

Description

Technical Field

[0001] This application relates to the field of power grid dispatch automation technology, and in particular to a power grid section control method, device, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] In a power system, the power grid section, as a collection of transmission lines, plays a crucial role in connecting power systems in different regions. Therefore, if the power output of a power grid section exceeds safe limits, it can easily lead to instability in the power system and potentially cause widespread power outages. Thus, controlling the power output of power grid sections is indispensable.

[0003] Traditional power grid section control methods generally employ a manual pre-control mode. When manual inspections or section monitoring systems issue alarms indicating that a section has exceeded its limits, dispatchers manually adjust several generator units to maintain section stability. However, this experience-based adjustment and control method often struggles to achieve optimal allocation of adjustment economics in complex and variable operating environments, and it cannot proactively address the coordinated control problems of multiple coupled sections.

[0004] In recent years, some studies have attempted to automate cross-section control using methods such as multi-agent reinforcement learning. This method does not rely on expert experience and possesses autonomous learning capabilities; however, the decision-making process is black-box, making it difficult to strictly guarantee grid safety constraints. Furthermore, the training process is complex, and convergence is difficult to guarantee, posing significant risks when applied to practical grid control where reliability requirements are high. Existing dispatching systems have also proposed dynamic limit parameter calculation methods based on topology, but their focus is on providing dynamic and accurate boundary conditions for control rather than generating specific control strategies, and the generation of control strategies still depends on the dispatcher's experience.

[0005] Therefore, how to effectively reduce adjustment costs and improve control efficiency while achieving cross-sectional safety control is an urgent problem to be solved. Summary of the Invention

[0006] Therefore, it is necessary to provide a power grid cross-section control method, device, computer equipment, computer-readable storage medium, and computer program product that can effectively reduce regulation costs and improve control efficiency while achieving cross-section safety control, in order to address the above-mentioned technical problems.

[0007] In a first aspect, this application provides a power grid section control method, including:

[0008] Obtain power grid operation data; based on the power grid operation data, obtain the first constraint condition and the second constraint condition;

[0009] A linear programming model is constructed with the goal of minimizing the total regulation of the generator sets. Under the first constraint, the linear programming model is solved to obtain the regulation of the generator sets. An objective function is constructed with the goal of minimizing the cross-sectional over-limit, regulation time, and number of generator set regulation. Under the second constraint, the objective function is used to evaluate the regulation.

[0010] Under the condition that the adjustment amount meets the first preset condition, the linear programming model is weighted with the objective of minimizing the number of non-zero cross-section adjustment amounts to obtain the objective linear programming model. The objective linear programming model is then solved to obtain the objective adjustment amount.

[0011] Sort the target adjustment quantities and try to select at least one target adjustment quantity according to the sorting order; reset the selected target adjustment quantity to the preset value, and resolve the linear programming model based on all target adjustment quantities after the reset; if it can be solved, generate adjustment instructions based on the reset target adjustment quantities; the adjustment instructions are used to issue to the generator sets to execute grid section control.

[0012] In one embodiment, the power grid operation data includes generator set parameters, power grid section parameters, and system operation parameters;

[0013] Based on power grid operation data, the first and second constraints are obtained, including:

[0014] Based on grid section parameters, obtain section safety constraints; based on generator set parameters, obtain generator output range constraints, generator regulation time constraints, and generator ramping constraints; based on system operating parameters, obtain system power balance constraints.

[0015] The cross-sectional safety constraint, system power balance constraint, and generator ramping constraint are combined as the second constraint condition.

[0016] The cross-sectional safety constraint, the unit adjustment time constraint, and the unit output range constraint are used together as the first constraint condition.

[0017] In one embodiment, under the constraint of the first constraint, the linear programming model is solved to obtain the regulation of the generator set, including:

[0018] Under the constraints of the first constraint, solve the linear programming model to obtain the total adjustment amount that satisfies the second preset condition;

[0019] The regulation amount of the generator set is obtained based on the total adjustment amount that meets the second preset condition.

[0020] In one embodiment, when the adjustment amount meets a preset condition, the linear programming model is weighted with the objective of minimizing the number of non-zero cross-section adjustment amounts to obtain a target linear programming model. Solving the target linear programming model yields the target adjustment amount, including:

[0021] If the adjustment amount satisfies the first preset condition, the total adjustment amount that satisfies the second preset condition is added as an inequality constraint to the first constraint condition to obtain the third constraint condition.

[0022] The linear programming model is weighted with the objective of minimizing the number of non-zero cross-sectional adjustments to obtain the objective linear programming model; the objective linear programming model is solved under the constraint of the third constraint; if it can be solved, the objective adjustment amount is obtained.

[0023] In one embodiment, sorting the target adjustment amounts and attempting to select at least one target adjustment amount according to the sorting order includes:

[0024] Receive preset engineering thresholds;

[0025] Based on engineering thresholds, select the generator sets that need to be regulated from at least one generator set.

[0026] Filter out the target adjustment values ​​corresponding to the generator sets that need to be adjusted from the target adjustment values ​​and sort them; try to select at least one target adjustment value according to the sorting order.

[0027] In one embodiment, the method further includes:

[0028] Monitor the power grid's operating status;

[0029] Based on the monitoring results, determine whether the power grid operating status matches the power grid operating status that should be after the execution of the regulation command;

[0030] If there is a discrepancy, optimize the weights or objective of the objective function, or optimize the model parameters of the linear programming model; based on the optimized objective function or linear programming model, obtain adjustment instructions again.

[0031] Secondly, this application also provides a power grid section control device, comprising:

[0032] The acquisition module is used to acquire power grid operation data; based on the power grid operation data, it acquires the first constraint condition and the second constraint condition.

[0033] The first solution module is used to construct a linear programming model with the goal of minimizing the total regulation of the generator set; under the constraints of the first constraint, the linear programming model is solved to obtain the regulation of the generator set; an objective function is constructed with the goal of minimizing the cross-sectional over-limit, regulation time, and number of generator set regulation; under the constraints of the second constraint, the objective function is used to evaluate the regulation.

[0034] The second solution module is used to weight the linear programming model with the objective of minimizing the number of non-zero cross-section adjustment quantities, under the condition that the adjustment quantity meets the first preset condition, to obtain the target linear programming model, solve the target linear programming model, and obtain the target adjustment quantity.

[0035] The control module is used to sort the target adjustment quantities, attempt to select at least one target adjustment quantity according to the sorting order, reset the selected target adjustment quantity to a preset value, and resolve the linear programming model based on all target adjustment quantities after the reset; if the model can be solved, an adjustment command is generated based on all target adjustment quantities after the reset; the adjustment command is used to issue to the generator set to execute grid section control.

[0036] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0037] Obtain power grid operation data; based on the power grid operation data, obtain the first constraint condition and the second constraint condition;

[0038] A linear programming model is constructed with the goal of minimizing the total regulation of the generator sets. Under the first constraint, the linear programming model is solved to obtain the regulation of the generator sets. An objective function is constructed with the goal of minimizing the cross-sectional over-limit, regulation time, and number of generator set regulation. Under the second constraint, the objective function is used to evaluate the regulation.

[0039] Under the condition that the adjustment amount meets the first preset condition, the linear programming model is weighted with the objective of minimizing the number of non-zero cross-section adjustment amounts to obtain the objective linear programming model. The objective linear programming model is then solved to obtain the objective adjustment amount.

[0040] Sort the target adjustment quantities and try to select at least one target adjustment quantity according to the sorting order; reset the selected target adjustment quantity to the preset value, and resolve the linear programming model based on all target adjustment quantities after the reset; if it can be solved, generate adjustment instructions based on the reset target adjustment quantities; the adjustment instructions are used to issue to the generator sets to execute grid section control.

[0041] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0042] Obtain power grid operation data; based on the power grid operation data, obtain the first constraint condition and the second constraint condition;

[0043] A linear programming model is constructed with the goal of minimizing the total regulation of the generator sets. Under the first constraint, the linear programming model is solved to obtain the regulation of the generator sets. An objective function is constructed with the goal of minimizing the cross-sectional over-limit, regulation time, and number of generator set regulation. Under the second constraint, the objective function is used to evaluate the regulation.

[0044] Under the condition that the adjustment amount meets the first preset condition, the linear programming model is weighted with the objective of minimizing the number of non-zero cross-section adjustment amounts to obtain the objective linear programming model. The objective linear programming model is then solved to obtain the objective adjustment amount.

[0045] Sort the target adjustment quantities and try to select at least one target adjustment quantity according to the sorting order; reset the selected target adjustment quantity to the preset value, and resolve the linear programming model based on all target adjustment quantities after the reset; if it can be solved, generate adjustment instructions based on the reset target adjustment quantities; the adjustment instructions are used to issue to the generator sets to execute grid section control.

[0046] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0047] Obtain power grid operation data; based on the power grid operation data, obtain the first constraint condition and the second constraint condition;

[0048] A linear programming model is constructed with the goal of minimizing the total regulation of the generator sets. Under the first constraint, the linear programming model is solved to obtain the regulation of the generator sets. An objective function is constructed with the goal of minimizing the cross-sectional over-limit, regulation time, and number of generator set regulation. Under the second constraint, the objective function is used to evaluate the regulation.

[0049] Under the condition that the adjustment amount meets the first preset condition, the linear programming model is weighted with the objective of minimizing the number of non-zero cross-section adjustment amounts to obtain the objective linear programming model. The objective linear programming model is then solved to obtain the objective adjustment amount.

[0050] Sort the target adjustment quantities and try to select at least one target adjustment quantity according to the sorting order; reset the selected target adjustment quantity to the preset value, and resolve the linear programming model based on all target adjustment quantities after the reset; if it can be solved, generate adjustment instructions based on the reset target adjustment quantities; the adjustment instructions are used to issue to the generator sets to execute grid section control.

[0051] The aforementioned power grid section control method, device, computer equipment, computer-readable storage medium, and computer program product acquire power grid operation data; based on the power grid operation data, obtain first and second constraints; construct a linear programming model with the objective of minimizing the total regulation of generator units; under the constraint of the first constraint, solve the linear programming model to obtain the regulation of generator units; construct an objective function with the objective of minimizing the section over-limit, regulation time, and number of generator unit regulation; under the constraint of the second constraint, use the objective function to evaluate the regulation; when the regulation satisfies the first preset condition, weight the linear programming model with the objective of minimizing the number of non-zero section regulation to obtain the target linear programming model, and solve the target linear programming model. A linear programming model is used to obtain the target adjustment quantity. The target adjustment quantities are sorted, and at least one target adjustment quantity is selected according to the sorting order. The selected target adjustment quantity is reset to a preset value, and the linear programming model is resolved based on all target adjustment quantities after the reset. If the model can be solved, an adjustment command is generated based on the reset target adjustment quantity. The adjustment command is used to issue to the generator set to execute the grid section control. This can effectively reduce the adjustment cost and improve the control efficiency while achieving section safety control. It enables the grid section control to dynamically adapt to changes in the operating mode, calculate the optimal strategy in real time, and finally form a complete closed loop from situational awareness to optimization decision-making to automatic execution, which greatly improves the intelligence level and adaptive capability of the grid operation. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is an application environment diagram of the power grid section control method in one embodiment;

[0054] Figure 2 This is a flowchart illustrating a power grid section control method in one embodiment;

[0055] Figure 3 This is a schematic diagram of the mathematical model of a power grid section control method in one embodiment;

[0056] Figure 4 This is a flowchart illustrating the power grid section control method in another embodiment;

[0057] Figure 5 This is a structural block diagram of a power grid section control device in one embodiment;

[0058] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0060] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0061] The power grid section control method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server. Specifically, terminal 102 or server 104 implements a power grid section control method, which includes: acquiring power grid operation data; acquiring first and second constraints based on the power grid operation data; constructing a linear programming model with the goal of minimizing the total regulation of generator units; solving the linear programming model under the constraints of the first constraint to obtain the regulation of generator units; constructing an objective function with the goal of minimizing the section over-limit, regulation time, and number of generator unit regulation; evaluating the regulation using the objective function under the constraints of the second constraint; weighting the linear programming model with the goal of minimizing the number of non-zero section regulation when the regulation satisfies a first preset condition, obtaining a target linear programming model, solving the target linear programming model to obtain the target regulation; sorting the target regulation, and attempting to select at least one target regulation according to the sorting order; resetting the selected target regulation to a preset value, and resolving the linear programming model based on all the reset target regulation; if it can be solved, generating a regulation command based on the reset target regulation; and issuing the regulation command to the generator units to execute power grid section control.

[0062] Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0063] In one exemplary embodiment, such as Figure 2 As shown, a power grid section control method is provided, which is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 202 to 208. Wherein:

[0064] Step 202: Obtain power grid operation data; based on the power grid operation data, obtain the first constraint condition and the second constraint condition.

[0065] A power grid section is a collection of transmission lines used to connect power systems in different regions. Power grid operation data includes generator parameters, power grid section parameters, and system operation parameters. Generator parameters are used to accurately analyze the role of generators in power grid operation; power grid section parameters are used to clearly understand the operation of the power grid section; and system operation parameters are used to reflect the macroscopic operating status of the entire power system. The first set of constraints includes section safety constraints, system power balance constraints, and generator ramping constraints; the second set of constraints includes section safety constraints, generator regulation time constraints, and generator output range constraints.

[0066] Optionally, the power grid operating status is monitored in real time, and power grid operating data is obtained based on the monitoring results. Generator unit parameters, power grid section parameters, and system operating parameters are obtained from the current power system; based on the system operating parameters and power grid section parameters, the first constraint condition can be derived; based on the generator unit parameters and power grid section parameters, the second constraint condition can be derived.

[0067] Step 204: Construct a linear programming model with the goal of minimizing the total regulation of the generator set; under the constraints of the first constraint, solve the linear programming model to obtain the regulation of the generator set; construct an objective function with the goal of minimizing the cross-sectional over-limit, regulation time, and number of generator set regulation; under the constraints of the second constraint, use the objective function to evaluate the regulation.

[0068] The regulation of generator sets is the difference between the planned output of each generator set after regulation and its current output before regulation in the power system. The total regulation of generator sets is the L1 norm. The L1 norm can also be called the Manhattan distance or the sum of absolute values. The L1 norm is defined as the sum of the absolute values ​​of all components, and the calculation formula is:

[0069]

[0070] in, For the first The regulation quantities of each generating unit are considered. Since these regulation quantities directly determine the regulating operations of each unit that affect the power grid flow, they are treated as decision variables. By solving for the regulation quantities, the optimal unit regulation scheme can be obtained to meet the requirements of section safety, unit regulation rate limits, and regulation economy.

[0071] Among them, "cross-section exceeding the limit" represents the value exceeding the cross-section limit, indicating the severity of the exceedance. "Unit settling time" represents the longest time taken by any of the participating generator units during the entire control process; the longer the settling time, the slower the cross-section control's adjustment speed. "Number of generator units adjusted" refers to the number of generator units participating in the adjustment. The determination of this number is related to the grid scale and the specific grid cross-section control strategy, directly affecting the calculation range of the objective function and the setting of its constraints. The objective function is used to evaluate the score of the adjustment quantity.

[0072] Optionally, the objective function is:

[0073]

[0074] in, For cross-sections exceeding the limit, For unit adjustment time, Adjust the number of generating units; , and These are the weighting coefficients for cross-sectional exceedance limits, unit adjustment time, and unit adjustment quantity, respectively.

[0075] Optionally, the weighting coefficients are used to balance the impact of adjustment on costs. The larger the value, the more importance is attached to the impact of the cross-section and the limited quantity on the control target; The larger the value, the more importance is attached to the influence of the adjustment speed of cross-section control on the control target; The larger the value, the more emphasis is placed on the impact of the number of generator unit adjustments on costs. Different emphases and cost optimizations can be achieved based on actual conditions, and the weighting coefficients can be adjusted appropriately. Furthermore, since the objective function is constructed with the goal of minimizing cross-sectional exceedance limits, adjustment time, and the number of generator unit adjustments, the constraints on the objective function should include cross-sectional safety constraints for cross-sectional exceedance limits; generator unit adjustment time limits for adjustment time; and constraints on the handling range of each generator unit for the number of generator unit adjustments, i.e., the range of generator unit adjustment quantities.

[0076] For example, a linear programming model is constructed with the goal of minimizing the total regulation of the generator sets; under the constraint of the first constraint, the linear programming model is solved to obtain a set of regulation values ​​of the generator sets; an objective function is constructed with the goal of minimizing the cross-sectional limit exceedance, regulation time, and number of generator set regulation values; based on the regulation values ​​of this set of generator sets, a set of cross-sectional limit exceedance, regulation time, and number of generator set regulation values ​​can be obtained; under the constraint of the second constraint, the objective function is calculated using this set of cross-sectional limit exceedance, regulation time, and number of generator set regulation values; if it can be solved, a score for this set of regulation values ​​is obtained; if it cannot be solved, the state when all regulation values ​​are zero is returned.

[0077] Step 206: Under the condition that the adjustment amount meets the first preset condition, the linear programming model is weighted with the goal of minimizing the number of non-zero cross-section adjustment amounts to obtain the target linear programming model. The target linear programming model is then solved to obtain the target adjustment amount.

[0078] Here, the target adjustment value refers to the optimized set of adjustment values ​​obtained after evaluation. The first precondition is that the set of adjustment values ​​can solve the constraint conditions, and that it is possible to achieve this. The number of non-zero adjustment values ​​is the L0 norm. The L0 norm refers to the number of non-zero elements for a given set of adjustment values. dimensional vector The formula for calculating its L0 norm is:

[0079]

[0080] in," " indicates counting.

[0081] For example, a linear programming model is constructed with the goal of minimizing the total regulation. Under the constraint of the second constraint, the linear programming algorithm is used to solve the linear programming model to determine the minimum total regulation that satisfies all power flow limits at the over-limit sections. By expanding the constraint matrix, the minimum total regulation that satisfies all power flow limits at the over-limit sections is incorporated as a new constraint into the model as the second constraint, thus obtaining the third constraint. A minimum weighting coefficient is introduced to weight the linear programming model, approximately minimizing the number of non-zero regulation values, thus obtaining the target linear programming model. Under the constraint of the third constraint, the intermediate regulation value is obtained by calculating the target linear programming model.

[0082] Step 208: Sort the target adjustment quantities and try to select at least one target adjustment quantity according to the sorting order; reset the selected target adjustment quantity to the preset value, and resolve the linear programming model based on all target adjustment quantities after the reset; if it can be solved, generate adjustment instructions based on the reset target adjustment quantities; the adjustment instructions are used to issue to the generator set to execute grid section control.

[0083] The preset value is 0. The adjustment command is derived from mathematical model optimization calculation, enabling the unit to adjust according to the optimal scheme. The target adjustment amount is the generator unit adjustment amount that meets multiple optimization objectives. The optimization objectives are: elimination of cross-sectional power flow safety over-limit, total adjustment amount close to the minimum value, minimum number of units involved in adjustment, and adjustment range of each unit conforming to actual operating limits.

[0084] For example, the absolute values ​​of the target adjustment quantities are sorted in descending order, and at least one target adjustment quantity is iteratively selected from all target adjustment quantities according to the sorting order; the selected target adjustment quantity is reset to a preset value, and the linear programming model is resolved for all target adjustment quantities after the reset; if the model can be solved, an adjustment command is generated based on the reset target adjustment quantity; the adjustment command is used to issue to the generator set to perform grid section control; if the model cannot be solved, the state of all target adjustment quantities not reset to the preset value is returned.

[0085] Optionally, the generator set adjustment amount, which is similar to the daily cross-section control work of the dispatcher, can be compared with the target adjustment amount to verify the grid cross-section control strategy through simulation experiments.

[0086] In the aforementioned power grid section control method, power grid operation data is acquired; based on the power grid operation data, first and second constraints are obtained; a linear programming model is constructed with the objective of minimizing the total regulation of generator units; under the constraint of the first constraint, the linear programming model is solved to obtain the regulation of generator units; an objective function is constructed with the objective of minimizing section overruns, regulation time, and the number of generator unit regulation; under the constraint of the second constraint, the objective function is used to evaluate the regulation; when the regulation satisfies the first preset condition, the linear programming model is weighted with the objective of minimizing the number of non-zero section regulation to obtain the target linear programming model; the target linear programming model is solved to obtain the target regulation. The process involves: sorting the target adjustment quantities and attempting to select at least one target adjustment quantity according to the sorting order; resetting the selected target adjustment quantity to a preset value; and resolving the linear programming model for all target adjustment quantities based on the reset value; if a solution is found, generating adjustment instructions based on the reset target adjustment quantities; and issuing adjustment instructions to generator units to execute grid section control. This effectively reduces adjustment costs and improves control efficiency while achieving safe section control, enabling grid section control to dynamically adapt to changes in operating modes, calculate the optimal strategy in real time, and ultimately form a complete closed loop from situational awareness to optimization decision-making to automatic execution, greatly improving the intelligence level and adaptability of grid operation.

[0087] In one embodiment, the power grid operation data includes generator set parameters, power grid section parameters, and system operation parameters. Based on the power grid operation data, a first constraint and a second constraint are obtained, including: obtaining section safety constraints based on the power grid section parameters; obtaining unit output range constraints, unit regulation time constraints, and generator ramping constraints based on the generator set parameters; obtaining system power balance constraints based on the system operation parameters; using the section safety constraints, system power balance constraints, and generator ramping constraints together as the second constraint; and using the section safety constraints, unit regulation time constraints, and unit output range constraints together as the first constraint.

[0088] The generator unit parameters include the adjustable range, ramp rate limit, current output, minimum output, and maximum output of each unit; the grid section parameters include the current power, safety limit, and limit exceedance of each section; and the system operating parameters include the current system frequency and load level. The adjustable range of each unit represents the boundary of the generator unit's adjustment capability during the adjustment process; the ramp rate limit is the limitation on the generator unit's adjustment speed; the current output reflects the real-time operating status of the generator unit; the maximum and minimum output define the upper and lower limits of the generator unit's output; the current power of each section is real-time monitoring data, reflecting the current operating load of the section; the safety limit is an important indicator for ensuring the safe operation of the section; the limit exceedance is used to determine whether the section exceeds the safety range and the degree of exceedance; the current system frequency and load level reflect the macroscopic operating status of the entire power system; the system active power is an important indicator of the normal operation of power equipment; and the load level is an indicator affecting the output demand of the units.

[0089] Optionally, section safety constraints are obtained based on the current power and safety limits of each section. The section safety constraints are as follows: for each unit... exist:

[0090]

[0091] in, For the first The cross-section at the first The power at any given moment is used to reflect the load situation of the cross section at different stages in real time, and is a key indicator for judging whether the cross section can operate safely. For the first The safety limit for each cross-section is the upper limit to ensure the safe operation of the cross-section. By setting this constraint, it is ensured that the power of the cross-section will not exceed the safe range at any stage, thereby maintaining the safe and stable operation of the power grid.

[0092] Sectional safety constraints are manifested in the second constraint condition as Based on the PTDF matrix, the cross-sectional safety constraints of the first constraint condition are constructed, and the power flow restrictions of each over-limit cross section are transformed into a system of linear inequalities.

[0093] Optionally, since the preset section control time limit is to complete section control within 15 minutes, to ensure successful regulation and reserve time for handling emergencies, 10 minutes is generally chosen. Therefore, the unit regulation time constraint is: for all generator units, there exists... .

[0094] Optionally, based on the ramp rate limit, generator ramp constraints considering the actual operating characteristics of the units are determined. Based on the current system frequency and load level, the total active power balance of the system before and after adjustment is ensured using equality constraints. Based on the current output, maximum output, and minimum output, unit output range constraints are obtained. Specifically, the unit output range constraints are for each unit... ,exist:

[0095]

[0096] in, For the unit The current output adjustment value, For the unit Minimum output, unit The maximum output. The current output adjustment value is the unit's maximum output. The incremental adjustment output value in this section control calculation; unit The minimum output is the lower limit of the unit's output, determined by the characteristics of the unit equipment, the operating requirements of the power system, and the maximum 10-minute regulation time. If the unit's output is below this lower limit, it may cause the unit to malfunction or affect the stable power supply of the power system. The maximum output is the upper limit of the unit's output, which is set based on the unit's equipment performance, power system operation requirements, and a maximum adjustment time of 10 minutes. Exceeding this limit may damage the unit's equipment and affect the safe operation of the power system.

[0097] In this embodiment, by accurately acquiring power grid operation data and scientifically and rationally determining the first and second constraints based on this data, a solid foundation is laid for the subsequent construction of the objective function and linear programming model. The first and second constraints can also dynamically adapt to changes in the power grid operation mode.

[0098] In one embodiment, under the constraint of a first constraint, solving a linear programming model to obtain the adjustment amount of the generator set includes: under the constraint of the first constraint, solving a linear programming model to obtain a total adjustment amount that satisfies a second preset condition; and based on the total adjustment amount that satisfies the second preset condition, obtaining the adjustment amount of the generator set.

[0099] Among them, the total adjustment amount that satisfies the second preset condition is the minimum total adjustment amount that satisfies all constraints.

[0100] In this embodiment, by accurately solving the linear programming model under the strict constraints of the first constraint, it can be ensured that the total adjustment amount obtained not only meets the requirements of the safe and stable operation of the power grid, but also minimizes the adjustment range as much as possible, thereby reducing the adjustment cost.

[0101] In one embodiment, when the adjustment amount meets a preset condition, the linear programming model is weighted with the objective of minimizing the number of non-zero section adjustment amounts to obtain a target linear programming model. The target linear programming model is then solved to obtain the target adjustment amount. This includes: when the adjustment amount meets a first preset condition, the total adjustment amount that meets a second preset condition is added as an inequality constraint to the first constraint condition to obtain a third constraint condition; the linear programming model is weighted with the objective of minimizing the number of non-zero section adjustment amounts to obtain the target linear programming model; the target linear programming model is solved under the constraint of the third constraint condition; if it can be solved, the target adjustment amount is obtained.

[0102] The third constraint is obtained by extending the constraint matrix to incorporate the total adjustment limit obtained from the linear programming model as a new constraint into the second constraint. When weighting the linear programming model with the objective of minimizing the number of non-zero section adjustments, the weights are minimal weighting coefficients, which can be set to... Using minimal weighting coefficients can minimize the preference for the number of units involved in the adjustment, while preserving the solvability of linear programming.

[0103] For example, under the constraint of the third constraint, the objective linear programming model is solved; if it can be solved, the objective adjustment amount is obtained; if it cannot be solved, the objective adjustment amount is returned to the numerical state of the adjustment amount after the objective function evaluation.

[0104] In this embodiment, when the adjustment amount meets the first preset condition, the total adjustment amount that meets the second preset condition is added as an inequality constraint to the first constraint condition to obtain the third constraint condition; the linear programming model is weighted with the goal of minimizing the number of non-zero section adjustment amounts to obtain the target linear programming model; the target linear programming model is solved under the constraint of the third constraint condition; if it can be solved, the target adjustment amount is obtained, which further optimizes the adjustment strategy, reduces unnecessary adjustment actions, and thus effectively reduces adjustment costs and improves control efficiency while ensuring the safe and stable operation of the power grid.

[0105] In one embodiment, sorting the target adjustment amounts and attempting to select at least one target adjustment amount according to the sorting order includes: receiving a preset engineering threshold; based on the engineering threshold, filtering out the generator sets that need to be adjusted from at least one generator set; filtering out the target adjustment amounts corresponding to the generator sets that need to be adjusted from the target adjustment amounts and sorting them; and attempting to select at least one target adjustment amount according to the sorting order.

[0106] The preset engineering threshold is a practical engineering threshold, and its specific value can be set to... This is used to identify the set of units that actually need adjustment.

[0107] In this embodiment, a preset engineering threshold is received; based on the engineering threshold, the generator sets that need to be adjusted are selected from at least one generator set; the intermediate adjustment amounts corresponding to the generator sets that need to be adjusted are selected from the intermediate adjustment amounts and sorted in descending order of adjustment amount; based on the sorting results, an iterative trial mechanism is used to optimize the intermediate adjustment amounts to obtain the target adjustment amount. This can accurately locate the generator sets that need to be adjusted, avoid unnecessary adjustment operations, effectively reduce adjustment costs, ensure that the target adjustment amount can meet the needs of grid section control to the greatest extent, and improve the control effect.

[0108] In one embodiment, the method further includes: monitoring the power grid operating status; determining, based on the monitoring results, whether the power grid operating status matches the power grid operating status that should exist after the execution of the regulation command; if they do not match, optimizing the weights or objective of the objective function, or optimizing the model parameters of the linear programming model; and obtaining the regulation command again based on the optimized objective function or linear programming model.

[0109] For example, since the power system is a complex and dynamic system, it is affected by various factors, such as weather changes and load fluctuations. The actual operating conditions may deviate from the grid operation predicted by this application in response to regulation commands. By monitoring the grid operating status in real time, once a deviation is detected, the parameters of the objective function or linear programming model are adjusted. For example, the unit regulation rate limit parameters are adjusted according to actual unit performance changes; the weighting coefficients in the objective function are adjusted according to new load demands; and the optimization objective is reset according to the actual situation, such as appropriately relaxing economic requirements while prioritizing grid security in certain special circumstances.

[0110] In this embodiment, by monitoring the power grid's operating status in real time and optimizing the weights or objectives of the objective function or adjusting the model parameters of the linear programming model when deviations are found between the actual operating conditions and expectations, the objective function and the linear programming model can dynamically adapt to changes in the power grid's operating mode. This ensures that the power grid section control method maintains high control accuracy and effectiveness under different operating scenarios, effectively addressing the impact of various uncertainties on power grid operation.

[0111] Next reference Figure 3 and Figure 4 The present application’s power grid section control method will be illustrated with a specific embodiment.

[0112] like Figure 3As shown, the power grid section control method of this application structurally transforms a complex power grid control problem into a solvable mathematical optimization problem. The data collection layer is fundamental, providing precise input to the multi-stage mathematical programming model. The multi-stage mathematical programming model is the core engine, mathematically modeling the actual section safety and regulation rate problems along with economic efficiency using decision variables, objective functions, and constraints. The final output and application layer provides decision support for power grid operation.

[0113] Specifically, the detailed process of the power grid section control method in this application is as follows: Figure 4 As shown.

[0114] First, collect power grid operation data. Based on the power grid parameter data, the required constraints can be derived and it can be confirmed whether there are any non-compliant sections among all generating units in the power grid.

[0115] Secondly, if it is confirmed that there are no illegal cross sections, all adjustment quantities are set to 0; if it is confirmed that there are illegal cross sections, a phased linear programming model calculation is performed.

[0116] Phase 1: Constructing a linear programming model with the objective of minimizing the total adjustment (L1 norm). Under the constraints of cross-sectional safety constraints, system power balance constraints, and generator ramping constraints constructed based on the PTDF matrix, the linear programming model is solved to select the minimum total adjustment that satisfies all over-limit cross-sectional power flow limits. Based on the total adjustment, a set of regulation values ​​is obtained. An objective function is constructed with the objective of minimizing the cross-sectional over-limit amount, regulation time, and number of generator regulation units. Under the constraints of the second condition, the objective function is used to evaluate the regulation amount, ensuring that the set of regulation amounts satisfies all constraints.

[0117] The second stage involves weighting the linear programming model to approximately minimize the non-zero adjustment quantity (L0 norm). By expanding the constraint matrix, the minimum total adjustment quantity that satisfies all power flow limits at the out-of-limit sections is selected as a new constraint and added to the model. The model is then solved again using these constraints to obtain the target adjustment quantity.

[0118] Greedy post-processing: An engineering threshold is set to identify the set of generating units that actually need adjustment. A subset of generating units that do not participate in the adjustment are selected, and their corresponding adjustment values ​​are deleted. After deletion, the units are sorted in descending order based on their adjustment values, and an iterative trial mechanism is used to gradually fix the adjustment amount of more units to 0. In each iteration, the linear programming model is resolved to verify whether the cross-sectional safety constraints and the total adjustment limit can still be satisfied while reducing the number of participating units. This process continues until an optimal solution is found that satisfies the following multiple optimization objectives: elimination of cross-sectional power flow safety exceedances, total adjustment amount approaching the minimum, minimum number of participating generating units, and adjustment range of each unit conforming to actual operating limits.

[0119] Finally, based on the final optimization results, regulation commands for each generating unit are generated and issued to the corresponding generator units for execution. These commands specify the exact regulation amounts for each unit at different stages. Upon receiving the commands, the generator units adjust their output according to the requirements, thereby achieving the goal of controlling the power output of the grid section. Furthermore, the grid's operating status is continuously monitored, and when discrepancies are found between the actual situation and the model predictions, the model parameters and optimization objectives are adjusted promptly to ensure effective control.

[0120] The effectiveness of regulation commands can be verified using an IEEE 39-bus standard power system with 10 generating units. The IEEE 39-bus standard power system comprises multiple generating units and numerous electrical loads, resulting in a complex grid structure and large cross-sectional transmission capacity. Initial operating conditions are set for peak load periods, where the grid cross-sectional power is close to its rated capacity, and some units experience significant output fluctuations. Parameter settings include a rated cross-sectional capacity of 600MW, with different values ​​set for unit regulation time constraints based on unit type, and the unit output range also set according to unit characteristics. The limit for exceeding the cross-sectional capacity is generally taken as 0.9 times the rated capacity, i.e., 600 × 0.9 = 540MW.

[0121] The generator set adjustment was performed in a manner similar to manual adjustment, based on the dispatcher's daily cross-section control work, and the adjustment values ​​are shown in Table 1.

[0122] The power grid section control method of this application is used to regulate the generator set, and the target regulation amount is shown in Table 2.

[0123] By comparing the generator set adjustment quantities shown in Table 1 with the target adjustment quantities shown in Table 2, we obtain the comparison of manual adjustment and automatic adjustment section control of the power grid section control method of this application, as shown in Table 3.

[0124] Table 1

[0125]

[0126] Table 2

[0127]

[0128] Table 3

[0129]

[0130] As shown in Tables 1-3 above, under the same initial operating conditions and parameter settings, two sections still exceeded the limit after adjustment using a manual-like adjustment method. However, after adopting the grid section control method of this application, all 12 sections did not exceed the limit, and the number of regulating units was reduced from 10 to 6. Therefore, the grid section control method of this application is superior to the manual-like method in terms of section control effect and unit regulation cost.

[0131] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0132] Based on the same inventive concept, this application also provides a power grid section control device for implementing the power grid section control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more power grid section control device embodiments provided below can be found in the limitations of the power grid section control method described above, and will not be repeated here.

[0133] In one exemplary embodiment, such as Figure 5 As shown, a power grid section control device 500 is provided, including: an acquisition module 502, a first solution module 504, a second solution module 506, and a control module 508, wherein:

[0134] The acquisition module 502 is used to acquire power grid operation data; based on the power grid operation data, it acquires the first constraint condition and the second constraint condition.

[0135] The first solution module 504 is used to construct a linear programming model with the goal of minimizing the total regulation of the generator set; under the constraint of the first constraint, the linear programming model is solved to obtain the regulation of the generator set; an objective function is constructed with the goal of minimizing the cross-sectional over-limit, regulation time, and number of generator set regulation; under the constraint of the second constraint, the objective function is used to evaluate the regulation.

[0136] The second solution module 506 is used to weight the linear programming model with the objective of minimizing the number of non-zero cross-section adjustment quantities when the adjustment quantity meets the first preset condition, to obtain the target linear programming model, solve the target linear programming model, and obtain the target adjustment quantity.

[0137] The control module 508 is used to sort the target adjustment quantities, attempt to select at least one target adjustment quantity according to the sorting order, reset the selected target adjustment quantity to a preset value, and resolve the linear programming model based on all target adjustment quantities after the reset; if it can be solved, an adjustment command is generated based on the reset target adjustment quantities; the adjustment command is used to issue to the generator set to execute grid section control.

[0138] In one embodiment, the acquisition module is further configured to acquire cross-sectional safety constraints based on grid cross-sectional parameters; acquire generator output range constraints, generator regulation time constraints, and generator ramping constraints based on generator parameters; acquire system power balance constraints based on system operating parameters; use the cross-sectional safety constraints, system power balance constraints, and generator ramping constraints together as a second constraint condition; and use the cross-sectional safety constraints, generator regulation time constraints, and generator output range constraints together as a first constraint condition.

[0139] In one embodiment, the first solving module is further configured to solve a linear programming model under the constraints of the first constraint condition to obtain the total adjustment amount that satisfies the second preset condition; and to obtain the adjustment amount of the generator set based on the total adjustment amount that satisfies the second preset condition.

[0140] In one embodiment, the second solution module is further configured to, when the adjustment amount satisfies the first preset condition, add the total adjustment amount that satisfies the second preset condition as an inequality constraint to the first constraint condition to obtain the third constraint condition; weight the linear programming model with the objective of minimizing the number of non-zero cross-section adjustment amounts to obtain the target linear programming model; solve the target linear programming model under the constraint of the third constraint condition; if it can be solved, the target adjustment amount is obtained.

[0141] In one embodiment, the control module is further configured to receive a preset engineering threshold; based on the engineering threshold, select the generator sets that need to be adjusted from at least one generator set; select the target adjustment amounts corresponding to the generator sets that need to be adjusted from the target adjustment amounts and sort them; and attempt to select at least one target adjustment amount according to the sorting order.

[0142] In one embodiment, the power grid section control device further includes a monitoring module, which is used to monitor the power grid operating status; based on the monitoring results, determine whether the power grid operating status is consistent with the power grid operating status that should be after the adjustment command is executed; if they are inconsistent, optimize the weights or objectives of the objective function, or optimize the model parameters of the linear programming model; and based on the optimized objective function or linear programming model, obtain the adjustment command again.

[0143] Each module in the aforementioned power grid section control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0144] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores power grid operation data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a power grid section control method.

[0145] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0146] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0147] Obtain power grid operation data; based on the power grid operation data, obtain the first constraint condition and the second constraint condition;

[0148] A linear programming model is constructed with the goal of minimizing the total regulation of the generator sets. Under the first constraint, the linear programming model is solved to obtain the regulation of the generator sets. An objective function is constructed with the goal of minimizing the cross-sectional over-limit, regulation time, and number of generator set regulation. Under the second constraint, the objective function is used to evaluate the regulation.

[0149] Under the condition that the adjustment amount meets the first preset condition, the linear programming model is weighted with the objective of minimizing the number of non-zero cross-section adjustment amounts to obtain the objective linear programming model. The objective linear programming model is then solved to obtain the objective adjustment amount.

[0150] Sort the target adjustment quantities and try to select at least one target adjustment quantity according to the sorting order; reset the selected target adjustment quantity to the preset value, and resolve the linear programming model based on all target adjustment quantities after the reset; if it can be solved, generate adjustment instructions based on the reset target adjustment quantities; the adjustment instructions are used to issue to the generator sets to execute grid section control.

[0151] In one embodiment, when the processor executes the computer program, it further implements the following steps: obtaining cross-sectional safety constraints based on grid cross-sectional parameters; obtaining unit output range constraints, unit regulation time constraints, and generator ramping constraints based on generator unit parameters; obtaining system power balance constraints based on system operating parameters; using the cross-sectional safety constraints, system power balance constraints, and generator ramping constraints together as a second constraint condition; and using the cross-sectional safety constraints, unit regulation time constraints, and unit output range constraints together as a first constraint condition.

[0152] In one embodiment, when the processor executes the computer program, it further performs the following steps: under the constraints of the first constraint, solving the linear programming model to obtain the total adjustment amount that satisfies the second preset condition; and based on the total adjustment amount that satisfies the second preset condition, obtaining the adjustment amount of the generator set.

[0153] In one embodiment, when the processor executes the computer program, it further performs the following steps: if the adjustment amount satisfies the first preset condition, the total adjustment amount that satisfies the second preset condition is added as an inequality constraint to the first constraint condition to obtain the third constraint condition; the linear programming model is weighted with the goal of minimizing the number of non-zero section adjustment amounts to obtain the target linear programming model; the target linear programming model is solved under the constraint of the third constraint condition; if it can be solved, the target adjustment amount is obtained.

[0154] In one embodiment, when the processor executes the computer program, it further performs the following steps: receiving a preset engineering threshold; based on the engineering threshold, selecting generator sets that need to be regulated from at least one generator set; selecting the target regulation amounts corresponding to the generator sets that need to be regulated from the target regulation amounts and sorting them; and attempting to select at least one target regulation amount according to the sorting order.

[0155] In one embodiment, when the processor executes the computer program, it further performs the following steps: monitoring the power grid operating status; based on the monitoring results, determining whether the power grid operating status matches the power grid operating status that should exist after executing the regulation command; if they do not match, optimizing the weights or objective of the objective function, or optimizing the model parameters of the linear programming model; and based on the optimized objective function or linear programming model, obtaining the regulation command again.

[0156] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0157] Obtain power grid operation data; based on the power grid operation data, obtain the first constraint condition and the second constraint condition;

[0158] A linear programming model is constructed with the goal of minimizing the total regulation of the generator sets. Under the first constraint, the linear programming model is solved to obtain the regulation of the generator sets. An objective function is constructed with the goal of minimizing the cross-sectional over-limit, regulation time, and number of generator set regulation. Under the second constraint, the objective function is used to evaluate the regulation.

[0159] Under the condition that the adjustment amount meets the first preset condition, the linear programming model is weighted with the objective of minimizing the number of non-zero cross-section adjustment amounts to obtain the objective linear programming model. The objective linear programming model is then solved to obtain the objective adjustment amount.

[0160] Sort the target adjustment quantities and try to select at least one target adjustment quantity according to the sorting order; reset the selected target adjustment quantity to the preset value, and resolve the linear programming model based on all target adjustment quantities after the reset; if it can be solved, generate adjustment instructions based on the reset target adjustment quantities; the adjustment instructions are used to issue to the generator sets to execute grid section control.

[0161] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: obtaining cross-sectional safety constraints based on grid cross-sectional parameters; obtaining unit output range constraints, unit regulation time constraints, and generator ramping constraints based on generator unit parameters; obtaining system power balance constraints based on system operating parameters; using the cross-sectional safety constraints, system power balance constraints, and generator ramping constraints together as a second constraint condition; and using the cross-sectional safety constraints, unit regulation time constraints, and unit output range constraints together as a first constraint condition.

[0162] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: under the constraints of the first constraint, solving the linear programming model to obtain the total adjustment amount that satisfies the second preset condition; and based on the total adjustment amount that satisfies the second preset condition, obtaining the adjustment amount of the generator set.

[0163] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: if the adjustment amount satisfies the first preset condition, the total adjustment amount that satisfies the second preset condition is added as an inequality constraint to the first constraint condition to obtain the third constraint condition; the linear programming model is weighted with the goal of minimizing the number of non-zero section adjustment amounts to obtain the target linear programming model; the target linear programming model is solved under the constraint of the third constraint condition; if it can be solved, the target adjustment amount is obtained.

[0164] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: receiving a preset engineering threshold; based on the engineering threshold, selecting generator sets that need to be regulated from at least one generator set; selecting and sorting the target regulation amounts corresponding to the generator sets that need to be regulated from the target regulation amounts; and attempting to select at least one target regulation amount according to the sorting order.

[0165] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: monitoring the power grid operating status; based on the monitoring results, determining whether the power grid operating status matches the power grid operating status that should exist after the execution of the regulation command; if they do not match, optimizing the weights or objective of the objective function, or optimizing the model parameters of the linear programming model; and based on the optimized objective function or linear programming model, obtaining the regulation command again.

[0166] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0167] Obtain power grid operation data; based on the power grid operation data, obtain the first constraint condition and the second constraint condition;

[0168] A linear programming model is constructed with the goal of minimizing the total regulation of the generator sets. Under the first constraint, the linear programming model is solved to obtain the regulation of the generator sets. An objective function is constructed with the goal of minimizing the cross-sectional over-limit, regulation time, and number of generator set regulation. Under the second constraint, the objective function is used to evaluate the regulation.

[0169] Under the condition that the adjustment amount meets the first preset condition, the linear programming model is weighted with the objective of minimizing the number of non-zero cross-section adjustment amounts to obtain the objective linear programming model. The objective linear programming model is then solved to obtain the objective adjustment amount.

[0170] Sort the target adjustment quantities and try to select at least one target adjustment quantity according to the sorting order; reset the selected target adjustment quantity to the preset value, and resolve the linear programming model based on all target adjustment quantities after the reset; if it can be solved, generate adjustment instructions based on the reset target adjustment quantities; the adjustment instructions are used to issue to the generator sets to execute grid section control.

[0171] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: obtaining cross-sectional safety constraints based on grid cross-sectional parameters; obtaining unit output range constraints, unit regulation time constraints, and generator ramping constraints based on generator unit parameters; obtaining system power balance constraints based on system operating parameters; using the cross-sectional safety constraints, system power balance constraints, and generator ramping constraints together as a second constraint condition; and using the cross-sectional safety constraints, unit regulation time constraints, and unit output range constraints together as a first constraint condition.

[0172] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: under the constraints of the first constraint, solving the linear programming model to obtain the total adjustment amount that satisfies the second preset condition; and based on the total adjustment amount that satisfies the second preset condition, obtaining the adjustment amount of the generator set.

[0173] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: if the adjustment amount satisfies the first preset condition, the total adjustment amount that satisfies the second preset condition is added as an inequality constraint to the first constraint condition to obtain the third constraint condition; the linear programming model is weighted with the goal of minimizing the number of non-zero section adjustment amounts to obtain the target linear programming model; the target linear programming model is solved under the constraint of the third constraint condition; if it can be solved, the target adjustment amount is obtained.

[0174] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: receiving a preset engineering threshold; based on the engineering threshold, selecting generator sets that need to be regulated from at least one generator set; selecting and sorting the target regulation amounts corresponding to the generator sets that need to be regulated from the target regulation amounts; and attempting to select at least one target regulation amount according to the sorting order.

[0175] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: monitoring the power grid operating status; based on the monitoring results, determining whether the power grid operating status matches the power grid operating status that should exist after the execution of the regulation command; if they do not match, optimizing the weights or objective of the objective function, or optimizing the model parameters of the linear programming model; and based on the optimized objective function or linear programming model, obtaining the regulation command again.

[0176] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0177] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0178] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0179] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method of controlling a power grid section, characterized by, The method includes: Acquire power grid operation data; based on the power grid operation data, acquire a first constraint and a second constraint; the power grid operation data includes generator set parameters, power grid cross-sectional parameters, and system operation parameters; acquiring the first and second constraint conditions includes: acquiring cross-sectional safety constraints based on the power grid cross-sectional parameters; acquiring generator set output range constraints, generator set adjustment time constraints, and generator ramp-up constraints based on the generator set parameters; acquiring system power balance constraints based on the system operation parameters; using the cross-sectional safety constraints, system power balance constraints, and generator ramp-up constraints together as the first constraint; using the cross-sectional safety constraints, generator set adjustment time constraints, and generator set output range constraints together as the second constraint. A linear programming model is constructed with the goal of minimizing the total adjustment of the generator set; under the constraint of the first constraint, the linear programming model is solved to obtain the total adjustment that satisfies the second preset condition; based on the total adjustment that satisfies the second preset condition, the adjustment amount of the generator set is obtained; an objective function is constructed with the goal of minimizing the cross-sectional over-limit, adjustment time, and number of generator set adjustments; under the constraint of the second constraint, the objective function is used to evaluate the adjustment amount. If the adjustment amount satisfies the first preset condition, the total adjustment amount satisfying the second preset condition is added as an inequality constraint to the first constraint to obtain the third constraint; the linear programming model is weighted with the goal of minimizing the number of non-zero section adjustment amounts to obtain the target linear programming model; the target linear programming model is solved under the constraint of the third constraint; if it can be solved, the target adjustment amount is obtained. The target adjustment quantities are sorted, and at least one target adjustment quantity is selected according to the sorting order. The selected target adjustment quantity is reset to a preset value, and the linear programming model is resolved based on all target adjustment quantities after the reset. If the model can be solved, an adjustment command is generated based on all target adjustment quantities after the reset. The adjustment command is used to issue to the generator set to perform grid section control.

2. The method of claim 1, wherein, The step of sorting the target adjustment quantities and attempting to select at least one target adjustment quantity according to the sorting order includes: Receive preset engineering thresholds; Based on the engineering threshold, select the generator sets that need to be adjusted from the at least one generator set; The target adjustment values ​​corresponding to the generator sets that need to be adjusted are selected from the target adjustment values ​​and sorted; at least one target adjustment value is selected according to the sorting order.

3. The method of claim 1, wherein, The method further includes: Monitor the power grid's operating status; Based on the monitoring results, determine whether the power grid operating status matches the power grid operating status that should exist after the execution of the regulation command; If there is a discrepancy, optimize the weights or objective of the objective function, or optimize the model parameters of the linear programming model; based on the optimized objective function or linear programming model, obtain adjustment instructions again.

4. A power grid section control device, characterized by The device includes: An acquisition module is used to acquire power grid operation data; based on the power grid operation data, acquire a first constraint condition and a second constraint condition; the power grid operation data includes generator set parameters, power grid cross-sectional parameters, and system operation parameters; acquiring the first constraint condition and the second constraint condition includes: acquiring cross-sectional safety constraints based on the power grid cross-sectional parameters; acquiring generator set output range constraints, generator set adjustment time constraints, and generator ramping constraints based on the generator set parameters; acquiring system power balance constraints based on the system operation parameters; using the cross-sectional safety constraints, system power balance constraints, and generator ramping constraints together as the first constraint condition; and using the cross-sectional safety constraints, generator set adjustment time constraints, and generator set output range constraints together as the second constraint condition. The first solution module is used to construct a linear programming model with the objective of minimizing the total adjustment of the generator set; under the constraint of the first constraint, solve the linear programming model to obtain the total adjustment that satisfies the second preset condition; based on the total adjustment that satisfies the second preset condition, obtain the adjustment amount of the generator set; construct an objective function with the objective of minimizing the cross-sectional over-limit, adjustment time, and number of generator set adjustments; and under the constraint of the second constraint, use the objective function to evaluate the adjustment amount. The second solution module is used to, when the adjustment amount satisfies the first preset condition, add the total adjustment amount that satisfies the second preset condition as an inequality constraint to the first constraint condition to obtain the third constraint condition; weight the linear programming model with the objective of minimizing the number of non-zero cross-section adjustment amounts to obtain the target linear programming model; solve the target linear programming model under the constraint of the third constraint condition; if it can be solved, the target adjustment amount is obtained. The control module is used to sort the target adjustment quantities, attempt to select at least one target adjustment quantity according to the sorting order, reset the selected target adjustment quantity to a preset value, and resolve the linear programming model based on all target adjustment quantities after the reset; if it can be solved, generate an adjustment command based on all target adjustment quantities after the reset; the adjustment command is used to issue to the generator set to perform grid section control.

5. The apparatus of claim 4, wherein, The control module is also used for: Receive preset engineering thresholds; Based on the engineering threshold, select the generator sets that need to be adjusted from the at least one generator set; The target adjustment values ​​corresponding to the generator sets that need to be adjusted are selected from the target adjustment values ​​and sorted. Try to select at least one target adjustment amount in the sorting order.

6. The apparatus of claim 4, wherein, The device further includes a monitoring module, which is used for: Monitor the power grid's operating status; Based on the monitoring results, determine whether the power grid operating status matches the power grid operating status that should exist after the execution of the regulation command; If there is a discrepancy, optimize the weights or objective of the objective function, or optimize the model parameters of the linear programming model; based on the optimized objective function or linear programming model, obtain adjustment instructions again. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

9. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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