Electric power system intelligent network source collaborative optimization control method, device, equipment and medium

Abstract

The invention relates to an intelligent network source collaborative optimization control method, device and equipment for a power system and a medium, and the method comprises the steps: collecting the operation data of a target power system, and triggering a two-stage collaborative optimization control strategy in response to the condition that the state quantity is greater than a preset threshold value; responding to a cross iteration optimization control strategy until the regional control deviation and the hub node voltage deviation meet a preset deviation target, and generating an active adjustment amount instruction; in response to a reactive power optimization and compensation active power distribution optimization control strategy, generating an update instruction of a hub node voltage and a distribution instruction of a compensation active power adjustment amount; and responding to the active adjustment amount instruction, the updating instruction and the distribution instruction until a preset optimization target is met. Therefore, the problems that the problem of mutual restriction cannot be solved, the coordination of active power and reactive power is difficult to realize, the operation safety and economical efficiency are influenced and the like due to the fact that the problem of bidirectional coupling cannot be effectively processed and the real-time control level is not fully considered in the related technology are solved.

Description

Technical Field

[0001] This application relates to the field of power system control technology, and in particular to a method, device, equipment and medium for intelligent grid-source collaborative optimization control of power systems. Background Technology

[0002] With increasingly interconnected power grids and continuously growing loads, the coupling effect between active and reactive power on power system operation and control is becoming increasingly significant. The independent control modes of AGC (Automatic Generation Control) and AVC (Automatic Voltage Control) fail to fully consider their interrelationship. In actual system operation, active and reactive power are coupled. When tie-line power deviates significantly from planned values, AGC regulation may cause large fluctuations in tie-line power, leading to voltage fluctuations at relevant nodes and affecting the quality of AVC control. On the other hand, load growth increases power flow on some lines, and AVC voltage regulation may cause line power changes or even exceed limits, thus interfering with the effectiveness of AGC control. The mutual constraints between the two during regulation can easily lead to voltage or frequency exceeding limits and may cause repeated adjustments, affecting the safe and economical operation of the power grid.

[0003] Among the related technologies, the main ones include: an AGC and AVC coordinated control mode based on minute-level and second-level collaboration, which takes AGC control as the core and assumes that second-level load fluctuations have little impact on AVC, and AVC coordinates AGC in one direction; and an event-driven cross-iterative optimization coordinated control strategy, which focuses on the algorithm design of the optimization layer and the prediction layer; in addition, the participation of new energy sources in AGC and AVC coordinated control has become a research hotspot.

[0004] However, in related technologies, the two-level collaborative control mode is essentially a one-way coordination of AVC to AGC, which fails to effectively handle the two-way coupling problem. Furthermore, the event-driven strategy does not adequately consider the real-time control level and lacks adaptability to scenarios with a high proportion of renewable energy access. This results in the inability to effectively resolve the mutual constraints caused by coupling, making it difficult to achieve dynamic coordination and optimization of active and reactive power, thereby affecting the operational safety and economy of the power system, which urgently needs to be improved. Summary of the Invention

[0005] This application provides a method, device, equipment, and medium for intelligent grid-source coordinated optimization control of power systems to solve the problems in related technologies, such as the inability to effectively handle bidirectional coupling issues and insufficient consideration of real-time control, which leads to the inability to effectively resolve the mutual constraints caused by coupling, making it difficult to achieve dynamic coordination of active and reactive power, and thus affecting the operational safety and economy of the power system.

[0006] The first aspect of this application provides a smart grid-source coordinated optimization control method for a power system, comprising the following steps: collecting operational data of a target power system; analyzing the state variables of the target power system based on the operational data; and triggering a two-stage coordinated optimization control strategy in response to a state variable exceeding a preset threshold, wherein the first stage is a cross-iterative optimization control strategy, and the second stage is a reactive power optimization and active power allocation compensation optimization control strategy; and triggering a cross-iterative optimization control strategy of automatic generation control and automatic voltage control in response to the cross-iterative optimization control strategy, until the regional control deviation and the central node voltage deviation meet a preset deviation. The system generates active power adjustment instructions based on the target value. In response to the reactive power optimization and compensated active power allocation optimization control strategy, it performs regional reactive power optimization and active power allocation on the target power system based on the total network active power loss and the central node voltage. This involves calculating the updated value of the central node voltage and the compensated active power adjustment, and generating update instructions for the central node voltage and allocation instructions for the compensated active power adjustment based on the updated value and the compensated active power adjustment. The optimization of the target power system is completed by responding to the active power adjustment instructions, the update instructions, and the allocation instructions until the state of the target power system meets the preset optimization target.

[0007] Through the aforementioned technical means, the embodiments of this application can, based on the operating state of the target power system, trigger a two-stage collaborative optimization control strategy in response to a state quantity exceeding a preset threshold, generate active power adjustment instructions in response to a cross-iterative optimization control strategy, and generate update instructions for central node voltage and allocation instructions for compensating active power adjustment in response to a reactive power optimization and compensated active power allocation optimization control strategy, until the preset optimization target is achieved. Thus, based on the two-stage collaborative optimization control strategy, the problem of mutual constraints is resolved, and dynamic coordination and optimization of active and reactive power are realized, thereby comprehensively improving the operational safety, economy, and renewable energy absorption capacity of the power system, providing key support for the construction of a new type of power system.

[0008] Optionally, in one embodiment of this application, the triggering of the cross-iterative optimization control strategy of automatic generation control and automatic voltage control until the regional control deviation and the central node voltage deviation meet the preset deviation target, and generating an active power adjustment command, includes: establishing an automatic generation control model based on unit output cost, system network loss, wind and solar curtailment penalties, and energy storage loss cost; establishing an automatic voltage control model based on reactive power balance and voltage control deviation; triggering the cross-iterative optimization control strategy of automatic generation control and automatic voltage control, solving the automatic generation control model and the automatic voltage control model until the regional control deviation and the central node voltage deviation meet the preset deviation target, and generating an active power adjustment command.

[0009] Through the above-mentioned technical means, the embodiments of this application can construct an automatic generation control model and an automatic voltage control model. By using a cross-iterative optimization strategy of automatic generation control and automatic voltage control, the dual models are solved until the deviation reaches the target, and active power adjustment instructions are generated. This ensures that the system can adjust the active power recovery frequency while ensuring that the voltage of the central node does not exceed the limit. Through cross-iterative calculation, the mutual influence between active power and reactive power instructions is gradually eliminated, while simultaneously meeting the control objectives of active power and reactive power.

[0010] Optionally, in one embodiment of this application, the step of performing regional reactive power optimization and active power allocation on the target power system based on the total network active power loss and the central node voltage to calculate the updated value of the central node voltage and the compensation active power adjustment amount, and generating an update instruction for the central node voltage and an allocation instruction for the compensation active power adjustment amount based on the updated value and the compensation active power adjustment amount, includes: constructing a reactive power optimization objective function and constructing reactive power optimization constraints based on the total network active power loss and the central node voltage; constructing a reactive power optimization model based on the reactive power optimization objective function and the reactive power optimization constraints; determining the number of generator sets participating in regulation based on the active power regulation capacity of the generators in the target power system, and constructing an active power allocation model; solving the reactive power optimization model to obtain the updated value of the central node voltage and the compensation active power adjustment amount; generating an update instruction for the central node voltage based on the updated value; and solving the active power allocation objective function based on the compensation active power adjustment amount to generate an allocation instruction for the compensation active power adjustment amount.

[0011] Through the above-mentioned technical means, the embodiments of this application can construct a reactive power optimization model and an active power allocation model, solve for and obtain the updated voltage value of the central node and the compensation active power adjustment amount, so as to generate update instructions and allocation instructions for the compensation active power adjustment amount, thereby ensuring the economic efficiency of system operation under the new state. During reactive power optimization, the change of system network loss leads to the generation of compensation active power adjustment amount, which is distributed and consumed within the automatic voltage control unit area, thereby avoiding repeated adjustment of the unit and realizing the coordinated optimization control of the power system.

[0012] Optionally, in one embodiment of this application, solving the reactive power optimization model to obtain the updated value of the central node voltage and the compensated active power adjustment includes: inputting the structural parameters of the target power system, and inputting the population size, maximum number of iterations, upper and lower bounds of variables, and variable dimensions to generate an initial projection search cluster and start the optimization process; obtaining the optimal solution based on residual guided projection, double random projection, weighted random projection update, and Lévy flight guided projection to obtain the updated value of the central node voltage and the compensated active power adjustment.

[0013] Through the above-mentioned technical means, the embodiments of this application can solve the regional reactive power optimization model based on the projection iterative optimization algorithm. By introducing residual guided projection, double random projection, weighted random projection update and Levy flight guided projection to guide the population to converge toward the optimal solution, the exploration ability and convergence speed are enhanced, the performance is improved and the problem of getting trapped in local optima is prevented.

[0014] Optionally, in one embodiment of this application, the expression of the active power allocation objective function is: , in, For adjustment coefficients, The marginal cost of the generator. The tracking coefficient of the generator. This is the adjustment amount of the generator in the previous operation. The regulation amount of the generator participating in active power regulation.

[0015] Through the above-mentioned technical means, the embodiments of this application can consider the economy, smoothness and rhythm of adjustment, construct a multi-objective function to optimize the active power regulation of the automatic voltage control unit, and ensure that the changes in network losses caused by reactive power optimization will not cause the automatic generation control unit to operate, thereby avoiding repeated adjustments of the unit, which is conducive to maintaining the long-term balance of regulation resources and response reliability of the system, and enhancing the robustness of the coordinated control of the power system.

[0016] A second aspect of this application provides a smart grid-source coordinated optimization control device for a power system, comprising: a data acquisition module for acquiring operational data of a target power system, analyzing the state variables of the target power system based on the operational data, and triggering a two-stage coordinated optimization control strategy in response to a state variable exceeding a preset threshold, wherein the first stage is a cross-iterative optimization control strategy, and the second stage is a reactive power optimization and active power allocation compensation optimization control strategy; and a first generation module for triggering a cross-iterative optimization control strategy of automatic generation control and automatic voltage control in response to the cross-iterative optimization control strategy, until the regional control deviation and the central node voltage deviation meet a preset deviation target. The system comprises a first generation module, which generates an active power adjustment instruction; a second generation module, which, in response to the reactive power optimization and compensated active power allocation optimization control strategy, performs regional reactive power optimization and active power allocation on the target power system based on the total network active power loss and the voltage of the central node, to calculate the updated value of the central node voltage and the compensated active power adjustment amount, and generates an update instruction for the central node voltage and an allocation instruction for the compensated active power adjustment amount based on the updated value and the compensated active power adjustment amount; and an execution module, which, in response to the active power adjustment instruction, the update instruction, and the allocation instruction, continues until the state of the target power system meets the preset optimization target, thereby completing the optimization of the target power system.

[0017] Through the aforementioned technical means, the embodiments of this application can, based on the operating state of the target power system, trigger a two-stage collaborative optimization control strategy in response to a state quantity exceeding a preset threshold, generate active power adjustment instructions in response to a cross-iterative optimization control strategy, and generate update instructions for central node voltage and allocation instructions for compensating active power adjustment in response to a reactive power optimization and compensated active power allocation optimization control strategy, until the preset optimization target is achieved. Thus, based on the two-stage collaborative optimization control strategy, the problem of mutual constraints is resolved, and dynamic coordination and optimization of active and reactive power are realized, thereby comprehensively improving the operational safety, economy, and renewable energy absorption capacity of the power system, providing key support for the construction of a new type of power system.

[0018] Optionally, in one embodiment of this application, the first generation module includes: a first construction unit, used to establish an automatic generation control model based on unit output cost, system network loss, wind and solar curtailment penalties, and energy storage loss costs; a second construction unit, used to establish an automatic voltage control model based on reactive power balance and voltage control deviation; and a first instruction generation unit, used to trigger a cross-iterative optimization control strategy of automatic generation control and automatic voltage control, solve the automatic generation control model and the automatic voltage control model, until the regional control deviation and the central node voltage deviation meet a preset deviation target, and generate an active power adjustment instruction.

[0019] Through the above-mentioned technical means, the embodiments of this application can construct an automatic generation control model and an automatic voltage control model. By using a cross-iterative optimization strategy of automatic generation control and automatic voltage control, the dual models are solved until the deviation reaches the target, and active power adjustment instructions are generated. This ensures that the system can adjust the active power recovery frequency while ensuring that the voltage of the central node does not exceed the limit. Through cross-iterative calculation, the mutual influence between active power and reactive power instructions is gradually eliminated, while simultaneously meeting the control objectives of active power and reactive power.

[0020] Optionally, in one embodiment of this application, the second generation module includes: a third construction unit, configured to construct a reactive power optimization objective function and reactive power optimization constraints based on the total network active power loss and the central node voltage, and to construct a reactive power optimization model based on the reactive power optimization objective function and the reactive power optimization constraints; a fourth construction unit, configured to determine the number of generator sets participating in regulation according to the active power regulation capacity of the generators in the target power system, and to construct an active power allocation model; a solution unit, configured to solve the reactive power optimization model to obtain the updated value of the central node voltage and the compensated active power adjustment amount; and a second instruction generation unit, configured to generate an update instruction for the central node voltage based on the updated value, solve the active power allocation objective function based on the compensated active power adjustment amount, and generate an allocation instruction for the compensated active power adjustment amount.

[0021] Through the above-mentioned technical means, the embodiments of this application can construct a reactive power optimization model and an active power allocation model, solve for and obtain the updated voltage value of the central node and the compensation active power adjustment amount, so as to generate update instructions and allocation instructions for the compensation active power adjustment amount, thereby ensuring the economic efficiency of system operation under the new state. During reactive power optimization, the change of system network loss leads to the generation of compensation active power adjustment amount, which is distributed and consumed within the automatic voltage control unit area, thereby avoiding repeated adjustment of the unit and realizing the coordinated optimization control of the power system.

[0022] Optionally, in one embodiment of this application, the solution unit includes: an initialization subunit, used to input the structural parameters of the target power system, and input the population size, maximum number of iterations, upper and lower bounds of variables, and variable dimensions to generate an initial projection search cluster and start the optimization process; and a projection subunit, used to obtain the optimal solution based on residual guided projection, double random projection, weighted random projection update, and Lévy flight guided projection to obtain the updated value of the central node voltage and the compensated active power adjustment.

[0023] Through the above-mentioned technical means, the embodiments of this application can solve the regional reactive power optimization model based on the projection iterative optimization algorithm. By introducing residual guided projection, double random projection, weighted random projection update and Levy flight guided projection to guide the population to converge toward the optimal solution, the exploration ability and convergence speed are enhanced, the performance is improved and the problem of getting trapped in local optima is prevented.

[0024] Optionally, in one embodiment of this application, the expression of the active power allocation objective function is: , in, For adjustment coefficients, The marginal cost of the generator. The tracking coefficient of the generator. This is the adjustment amount of the generator in the previous operation. The regulation amount of the generator participating in active power regulation.

[0025] Through the above-mentioned technical means, the embodiments of this application can consider the economy, smoothness and rhythm of adjustment, construct a multi-objective function to optimize the active power adjustment of the automatic voltage control unit, and ensure that the changes in network losses caused by reactive power optimization will not cause the automatic generation control unit to operate, thereby avoiding repeated adjustments of the unit, which is conducive to maintaining the long-term balance of adjustment resources and response reliability of the system, and enhancing the robustness of the coordinated control of the power system.

[0026] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the power system smart grid-source coordinated optimization control method as described in the above embodiments.

[0027] A fourth aspect of this application provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described intelligent grid-source coordinated optimization control method for power systems.

[0028] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described intelligent grid-source coordinated optimization control method for power systems.

[0029] This application embodiment can, based on the operating state of the target power system, trigger a two-stage collaborative optimization control strategy in response to a state variable exceeding a preset threshold. In response to a cross-iterative optimization control strategy, it generates active power adjustment instructions. Furthermore, in response to a reactive power optimization and compensated active power allocation optimization control strategy, it generates central node voltage update instructions and compensated active power adjustment allocation instructions, until the preset optimization target is achieved. Thus, based on the two-stage collaborative optimization control strategy, it resolves mutual constraints, achieves dynamic coordination and optimization of active and reactive power, and comprehensively improves the operational safety, economy, and renewable energy absorption capacity of the power system, providing key support for building a new type of power system. This solves the problems in related technologies where the failure to effectively handle bidirectional coupling and insufficient consideration of real-time control leads to the inability to effectively resolve mutual constraints caused by coupling, making it difficult to achieve dynamic coordination of active and reactive power, thereby affecting the operational safety and economy of the power system.

[0030] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a smart grid-source coordinated optimization control method for a power system according to an embodiment of this application; Figure 2 This is a schematic diagram of an AGC and AVC cross-iteration strategy provided according to an embodiment of this application; Figure 3 This is a flowchart of a smart grid-source coordinated optimization control method for power systems according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a smart grid-source collaborative optimization control device for a power system according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0032] Figure label: 10-Power system intelligent grid-source collaborative optimization control device; 100-Acquisition module, 200-First generation module, 300-Second generation module, 400-Execution module; 501-Memory, 502-Processor, 503-Communication interface. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The following description, with reference to the accompanying drawings, outlines a power system intelligent grid-source coordinated optimization control method, apparatus, equipment, and medium according to embodiments of this application. Addressing the issues raised in the background section regarding related technologies, the failure to effectively handle bidirectional coupling problems and insufficient consideration of real-time control leads to an inability to effectively resolve mutual constraints caused by coupling, hindering dynamic coordination of active and reactive power, and consequently affecting the operational safety and economy of the power system. This application provides a power system intelligent grid-source coordinated optimization control method. Based on the operating state of the target power system, in response to a state variable exceeding a preset threshold, a two-stage coordinated optimization control strategy is triggered. In response to a cross-iterative optimization control strategy, an active power adjustment command is generated. In response to a reactive power optimization and compensated active power allocation optimization control strategy, an update command for the central node voltage and an allocation command for compensated active power adjustment are generated until the preset optimization target is achieved. Thus, based on the two-stage coordinated optimization control strategy, the mutual constraints are resolved, achieving dynamic coordination and optimized cooperation between active and reactive power, thereby comprehensively improving the operational safety, economy, and renewable energy absorption capacity of the power system, providing crucial support for building a new type of power system. This solves the problems in related technologies, such as the inability to effectively handle bidirectional coupling and insufficient consideration of real-time control, which leads to the inability to effectively resolve the mutual constraints caused by coupling, making it difficult to achieve dynamic coordination of active and reactive power, and thus affecting the operational safety and economy of the power system.

[0035] Specifically, Figure 1 This is a flowchart illustrating a smart grid-source collaborative optimization control method for a power system provided in an embodiment of this application.

[0036] like Figure 1 As shown, the intelligent grid-source coordinated optimization control method for this power system includes the following steps: In step S101, the operating data of the target power system is collected, and the state variables of the target power system are analyzed based on the operating data. In response to the state variables being greater than a preset threshold, a two-stage collaborative optimization control strategy is triggered. The first stage is a cross-iterative optimization control strategy, and the second stage is a reactive power optimization and active power allocation compensation optimization control strategy.

[0037] It is understood that the operating data in the embodiments of this application may include grid frequency, tie line power and line power flow, and central node voltage; the state quantity may be regional control deviation and central node voltage deviation; the preset threshold may be a critical value set according to the power system safe and stable operation standard, used to determine whether the system is in an abnormal state that requires active regulation. The preset threshold may be set by those skilled in the art according to the actual situation, and no specific restrictions are made here.

[0038] In practical implementation, the embodiments of this application can monitor changes in the power grid state caused by disturbances and other factors according to their respective cycles during the actual operation of the power system. The AGC is mainly responsible for tracking the grid frequency, tie-line power, and line power flow, while the AVC mainly monitors the voltage level of central nodes. Once a relevant state quantity is detected to exceed its limit, the system will trigger cross-iterative optimization control between the AGC and AVC. This restores the state quantity to the allowable range while also considering the mutual influence during the control process, preventing a new round of limit exceedances.

[0039] Among them, AGC typically employs a fixed connection line and frequency deviation control method, through calculation. ACE (Area Control Error) is used to determine whether AGC has been activated. ACE The formula for calculation is: , in, For the deviation of the connecting line; B The system frequency deviation coefficient (MW / 0.1 Hz); This represents the power grid frequency deviation. However, since the power grid coordinated optimization control system mainly reflects static control results, and dynamic simulation is difficult to implement, it is assumed that the frequency is basically controlled within the rated value, and the system frequency deviation factor is simplified in the control objective. ACE This mainly refers to the power deviation of the tie line.

[0040] AVC typically monitors the central node voltage and determines whether the AVC has activated by calculating the central node voltage deviation. The expression is: , in, This is the current central node voltage value. This is the reference value for the central node voltage.

[0041] This application embodiment can collect core operating data such as grid frequency and tie-line power, analyze key state quantities such as regional control deviation and central node voltage deviation, and realize the on-demand triggering of two-stage collaborative optimization control strategy. By being able to perceive the grid state in real time and accurately, it ensures that the control strategy is only activated when the system state truly exceeds the limit and external intervention is required, thereby avoiding unnecessary frequent adjustments under normal operating conditions and effectively improving the power system's rapid response capability and operational stability to disturbances.

[0042] In step S102, in response to the cross-iterative optimization control strategy, the cross-iterative optimization control strategy of automatic generation control and automatic voltage control is triggered until the regional control deviation and the central node voltage deviation meet the preset deviation target, and an active power adjustment command is generated.

[0043] It is understood that the preset deviation target in the embodiments of this application can be a control accuracy standard. For example, the regional control deviation target can be set to an allowable range that approaches zero, and the central node voltage deviation target can be set to an interval close to the rated value. The preset deviation target can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0044] In actual implementation, the embodiments of this application can respond to the cross-iterative optimization control strategy, trigger the cross-iterative optimization control strategy of automatic generation control and automatic voltage control until the regional control deviation and the central node voltage deviation meet the preset deviation target, and generate an active power adjustment command.

[0045] Furthermore, the embodiments of this application can trigger a cross-iterative optimization control strategy of automatic generation control and automatic voltage control. That is, when the system needs AGC action, the AGC adjustment amount is calculated, and cross-iteration with AVC is performed during the process until the regional control deviation and the central node voltage deviation meet the preset deviation target, and an active power adjustment command is generated.

[0046] The embodiments of this application effectively resolve the control constraints caused by the coupling of active and reactive power through cross-iterative optimization, enabling the active power adjustment of AGC and the voltage regulation of AVC to form a coordinated closed loop, improving the control accuracy of regional control deviation and central node voltage deviation, providing a precise active power foundation for subsequent network-wide optimization and control, and ensuring the stability of system operation.

[0047] Optionally, in one embodiment of this application, a cross-iterative optimization control strategy of automatic generation control and automatic voltage control is triggered until the regional control deviation and the voltage deviation of the central node meet the preset deviation target, and an active power adjustment command is generated. This includes: establishing an automatic generation control model based on unit output cost, system network loss, wind and solar curtailment penalty, and energy storage loss cost; establishing an automatic voltage control model based on reactive power balance and voltage control deviation; triggering a cross-iterative optimization control strategy of automatic generation control and automatic voltage control, solving the automatic generation control model and the automatic voltage control model until the regional control deviation and the voltage deviation of the central node meet the preset deviation target, and generating an active power adjustment command.

[0048] It is understood that, in the embodiments of this application, the unit output cost can be understood as the economic cost of fuel consumption, operation and maintenance, etc., corresponding to the unit active power output of the generator set; the system network loss can be understood as the active power loss during the power grid transmission process; the wind and solar curtailment penalty can be understood as the economic loss and policy penalty cost when the output of new energy cannot be consumed and is abandoned; the energy storage loss cost can be understood as the loss cost caused by battery degradation during the charging and discharging process of energy storage equipment; the reactive power balance can be understood as the degree of balance of reactive power distribution at each node in the system, which can measure the rationality of reactive power regulation.

[0049] In actual implementation, the embodiments of this application can consider the economics of system operation, and establish an AGC control model with the goal of minimizing unit output cost, system network loss, wind and solar curtailment penalties, and energy storage loss costs, as shown in the following expression: , Among them, S AGC It is a collection of AGC (Automatic Guided Vehicle) units; It is an AGC unit i The active power adjustment; , , , These are AGC units i The active power adjustment cost, wind curtailment cost, solar curtailment cost, and energy storage loss cost; , , , These are the corresponding weighting coefficients; It is a generator set i The sensitivity of the change in active power to network loss; It is a tie line power deviation; It is an AGC unit i Current active power output; and These are AGC units i The upper and lower limits of contribution. It is the line load rate; , These are the upper and lower limits of the line load rate, respectively.

[0050] Furthermore, at the frequency control level, AGC needs to balance the power generation output and load of each node to achieve overall system supply and demand equilibrium, thereby stabilizing the grid frequency near its rated value. However, during the control solution process, local imbalances in power generation and load may exist at each node. These imbalances, accumulated across the entire network, may further increase the deviation between the frequency and the rated value. To ensure the accuracy and effectiveness of frequency regulation, frequency fluctuation constraints need to be introduced, as shown in the following formula: , In the formula, B ε is the system frequency deviation coefficient (MW / 0.1 Hz), and ε is the allowable frequency fluctuation, which is usually no more than 0.5 Hz. S D This is the set of load nodes.

[0051] Furthermore, the AVC control model primarily considers reactive power balance as a safety indicator, while aiming to minimize voltage control deviation, which is beneficial for improving system stability. The expression is shown below: , in, The control dead zone is the voltage deviation at the central node. ; w It is the safety factor; M It is the reactive power balance; C Q , C G These are the unit's reactive power adjustment. Sensitivity to changes in central node voltage and generator terminal voltage; This refers to the generator terminal voltage. , These are its upper and lower limits, respectively; For generator i The initial value of no reactive power output , These are its upper and lower limits.

[0052] Considering the mutual interference between the control commands of AGC and AVC, AGC's adjustment of active power can cause node voltage shifts, thus interfering with AVC regulation; conversely, AVC's adjustment of reactive power can affect line losses, thereby impacting the control accuracy of tie line power. This coupling effect between active and reactive power commands is difficult to coordinate using traditional discrete control methods.

[0053] like Figure 2As shown, the embodiments of this application adopt a cross-iterative control strategy to solve this problem. The core of this strategy is to solve the control model in multiple loops. Through iterative calculation, the mutual influence between active and reactive commands is gradually eliminated, thereby effectively avoiding problems such as regulation conflicts and improving the overall control coordination and execution efficiency.

[0054] The embodiments of this application can construct an automatic generation control model and an automatic voltage control model, and solve the dual models until the deviation reaches the target through a cross-iterative optimization strategy of automatic generation control and automatic voltage control, thereby generating active power adjustment instructions. This ensures that the system can adjust the active power recovery frequency while ensuring that the voltage of the central node does not exceed the limit. Through cross-iterative calculation, the mutual influence between active power and reactive power instructions is gradually eliminated, while simultaneously meeting the control objectives of active power and reactive power.

[0055] In step S103, in response to the reactive power optimization and compensated active power allocation optimization control strategy, based on the total network active power loss and the central node voltage, regional reactive power optimization and active power allocation are performed on the target power system to calculate the updated value of the central node voltage and the compensated active power adjustment amount, and based on the updated value and the compensated active power adjustment amount, update instructions for the central node voltage and allocation instructions for the compensated active power adjustment amount are generated.

[0056] It is understood that, in the embodiments of this application, the total active power loss of the entire power system can be understood as the total active power loss in the process of power transmission and conversion; regional reactive power optimization can be understood as an optimization method based on the division of the power grid into regions, by adjusting the reactive power compensation equipment and the reactive power output of generator sets in the region, to achieve a reasonable distribution of reactive power in the region; active power allocation can be understood as the process of allocating the compensation active power adjustment amount to each generator set in the region according to certain rules; compensation active power adjustment amount can be understood as the compensation adjustment amount based on the generated active power adjustment amount instruction, used to correct the active power imbalance caused by the change of active power loss of the entire network; the central node voltage update value can be understood as the target voltage value of the central node determined after reactive power optimization.

[0057] In practical implementation, this embodiment of the application can use an optimal power flow model for regional reactive power optimization. After cross-iterative optimization control, once the regional control deviation and the central node voltage deviation both meet the requirements, the active power loss of the entire network and the central node voltage are comprehensively considered. The reactive power sources of the entire network are then re-coordinated and optimized, and the voltage setpoint of the central node is updated in a timely manner to make the reactive power distribution of the system more reasonable and ensure the economic efficiency of system operation under the new state. Therefore, this optimal power flow problem will calculate the new voltage setpoint of the central node in each region and the active power regulation amount that needs to offset active power losses during the reactive power optimization process, and allocate this active power regulation amount among the AVC units in the region.

[0058] The embodiments of this application can comprehensively consider the active power loss of the entire network and the voltage deviation of the central node, re-coordinate and optimize all reactive power sources in the entire network, update the voltage setting value of the central node in a timely manner, ensure the economic operation of the system under the new state, adjust the active power output of the generator in the AVC area to compensate for the network loss changes caused by reactive power optimization, make the reactive power distribution of the system more reasonable, and improve the feasibility and effectiveness of regulation.

[0059] Optionally, in one embodiment of this application, based on the total network active power loss and the central node voltage, regional reactive power optimization and active power allocation are performed on the target power system to calculate the updated value of the central node voltage and the compensation active power adjustment amount. Based on the updated value and the compensation active power adjustment amount, update instructions for the central node voltage and allocation instructions for the compensation active power adjustment amount are generated. This includes: constructing a reactive power optimization objective function and reactive power optimization constraints based on the total network active power loss and the central node voltage; constructing a reactive power optimization model based on the reactive power optimization objective function and reactive power optimization constraints; determining the number of generator sets participating in regulation based on the active power regulation capacity of the generators in the target power system and constructing an active power allocation model; solving the reactive power optimization model to obtain the updated value of the central node voltage and the compensation active power adjustment amount; generating update instructions for the central node voltage based on the updated value; solving the active power allocation objective function based on the compensation active power adjustment amount; and generating allocation instructions for the compensation active power adjustment amount.

[0060] It is understood that the reactive power optimization constraints in the embodiments of this application may include the power flow balance equations of the active and reactive power of the power grid, the upper and lower limits of the voltage of each central node, the upper and lower limits of the active power output of generators participating in network loss compensation (AVC units), the upper and lower limits of the reactive power output of generators participating in regulation, tie line power constraints, and transmission line active power constraints; the active power regulation capacity can be understood as the range of active power output that generators can safely increase or decrease within a specified time based on the current operating point, which is usually determined by the uphill rate, downhill rate, and the current output level.

[0061] In actual implementation, the embodiments of this application can take into account the economic efficiency of the actual operation of the power system, using the minimum network loss as the objective function, as shown in the following formula: , in, For network-wide active and passive losses; N This represents the total number of nodes in the system. G ij The real part of the nodal admittance matrix; , For node voltage magnitude and phase angle.

[0062] The constraints of the optimal power flow model include the power flow balance equations for active and reactive power of the power grid, upper and lower limit constraints on the voltage of each central node, upper and lower limit constraints on the active power output of generators participating in network loss compensation (AVC units), upper and lower limit constraints on the reactive power output of generators participating in regulation, tie line power constraints, and active power constraints on transmission lines, as shown in the following equations: , , , , , , , In the formula, N This represents the total number of nodes in the system. P G , Q G This refers to the active and reactive power of the generator; P D , Q D The load consists of active and reactive power; G ij , B ij Let be the real and imaginary parts of the nodal admittance matrix; V , For node voltage magnitude and phase angle; , These are the upper and lower limits of the node voltage, respectively; , These are the upper and lower limits of the generator's active power output; , These are the upper and lower limits of the generator's reactive power output; S G For the set of generator nodes; To transmit power to the tie line; This is the upper limit of the transmission power of the tie line; This refers to the system line power; , These represent the upper and lower limits of the system line power, respectively.

[0063] Furthermore, based on the objective function and constraints, a reactive power optimization model is constructed and solved to obtain the updated value of the central node voltage and the compensation active power adjustment amount. An update command for the central node voltage is generated based on the updated value. The number of generator sets participating in the regulation is determined according to the active power regulation capacity of the generators in the target power system, and an active power allocation objective function is constructed. The active power allocation objective function is solved based on the compensation active power adjustment amount to generate an active power regulation allocation command for generators in the region.

[0064] The embodiments of this application can determine the number of generator sets participating in regulation based on the active power regulation capacity of generators in each region. S At this point, the generators participating in active power regulation must satisfy the following formula: , , in, The regulation amount of the generator participating in active power regulation; , These are the upper and lower limits of active power regulation for each generator.

[0065] Furthermore, an active power allocation objective function is constructed. Since the model is a quadratic programming problem, the active power adjustment amount of each generator participating in active power regulation in each AVC region can be quickly solved using commercial solvers such as Cplex and Gurobi. The active power allocation objective function is then solved based on the compensated active power adjustment amount to generate active power regulation allocation instructions for generators in the region.

[0066] The embodiments of this application can construct a reactive power optimization model and an active power allocation model, solve for the updated voltage value of the central node and the compensation active power adjustment amount, and generate update instructions and compensation active power adjustment amount allocation instructions to ensure the economic operation of the system under the new state. During reactive power optimization, the change of system network loss leads to the generation of compensation active power adjustment amount. The compensation active power adjustment amount is allocated and consumed within the automatic voltage control unit area, thereby avoiding repeated adjustment of the unit and realizing the coordinated optimization control of the power system.

[0067] Optionally, in one embodiment of this application, solving the reactive power optimization model to obtain the updated value of the central node voltage and the compensation active power adjustment includes: inputting the structural parameters of the target power system, and inputting the population size, maximum number of iterations, upper and lower bounds of variables, and variable dimensions to generate an initial projection search cluster and start the optimization process; obtaining the optimal solution based on residual guided projection, double random projection, weighted random projection update, and Lévy flight guided projection to obtain the updated value of the central node voltage and the compensation active power adjustment.

[0068] It is understood that the structural parameters in the embodiments of this application may include the power grid topology, line resistance and reactance, transformer turns ratio, rated parameters of generating units and reactive power equipment, etc.; the initial projection search cluster can be understood as an initial solution set generated based on the optimization algorithm, which can be used to iteratively search for the optimal solution; residual guided projection can be understood as approximating the optimal solution by continuously projecting in a multi-dimensional space; double random projection can be understood as enhancing the global search capability of the algorithm by introducing double randomness; weighted random projection update can be understood as an optimization process achieved through random weighting and adaptive adjustment; Levy flight guided projection can be understood as achieving efficient global search and local optimization by simulating the random motion of organisms in nature.

[0069] For example, embodiments of this application can use PIMO (Projection Iterative Methods based Optimizer) for solving the problem. PIMO introduces four new operators—residual guided projection, double random projection, weighted random projection update, and Lévy flight guided projection—to guide the population toward the optimal solution, thereby enhancing both exploration capability and convergence speed.

[0070] The PIMO algorithm first initiates the optimization process by initializing the population. Given a population size N and variable dimensions D, each projected agent is considered a search agent for the algorithm. The position of each agent is generated using the following mathematical expression: , in, Indicates the first m The projection search individual in the first n The position of the dimension; It is a random number between [0, 1]; , These are the variables in the th order. n The upper and lower bound vectors of dimension.

[0071] During the residual-guided projection process, the algorithm approximates the optimal solution by continuously projecting into a multidimensional space. The algorithm randomly selects two solutions with smaller residual fitness as guiding agents. and And calculate the gradient based on them, the formula for which is: , in, This is the best position at present; R , and It is a random number between [0, 1].

[0072] To update the residual guided projection more accurately, the Jacobian matrix was utilized.J Elements of the Jacobian matrix , representing the components of the objective function Relative to variables The partial derivatives of are approximately calculated as follows: , in, It is the first of the objective function n One component; It is a tiny perturbation value; These are standard basis vectors. Finally, the projection operation, incorporating gradient information, is used to update the position: , in, This is the updated new location; This is the current search agent; dynamic parameters With the current iteration number t and maximum number of iterations T The change is expressed as follows: .

[0073] The double random projection process enhances the algorithm's global search capability by introducing double randomness. The algorithm first randomly selects two indices from the particle swarm. v 3 and v 4 serves as the reference point for projection updates, as shown below: , When the algorithm uses a random decision to update the gradient, it calculates the update direction vector based on the difference between the current position and the current optimal position. g 1. The calculation formula is as follows: , If you choose to use Jacobian matrix projection for updating, then update the direction vector. g The calculation method for 2 is as follows: , in, It is the new position after the double random projection update. This double randomness ensures the diversity of the selection process and helps the algorithm explore in different directions.

[0074] Weighted stochastic projection update is an optimization process implemented through stochastic weighting and adaptive adjustment, aiming to guide particles toward the optimal solution in the solution space. This process utilizes random factors... r 7 and r 8. The weights for determining the particle position update components are calculated as follows: , The location update is based on two different projection paths. Path 1 is a randomly weighted projection, expressed as follows: , Path 2 is an adaptive correction projection, which utilizes historical positional information from the residual-guided projection stage, and its expression is as follows: , Among them, adaptive factor This is used to adjust the step size. The algorithm selects different projection paths in each dimension based on the dimension factor, increasing the dynamics of position updates.

[0075] The Levy flight-guided projection process achieves efficient global search and local optimization by simulating the random motion of organisms in nature. The trigger probability of the Levy flight projection... O Determined by the following formula: , Levi's stride z Generated by the following formula: , in, u and v These are random numbers drawn from a normal distribution; It is a random number between [0, 2]. It is a coefficient calculated using the gamma function and the sine function. The position update formula incorporates the optimal solution. (The best particle obtained in the first three steps) and the current solution And through random weights d The dynamic adjustment is performed, and the calculation formula is as follows: , in, r 9 is a random number in the range [0,1]; dynamic weights This dynamic adjustment mechanism enables the Levy flight-guided projection to achieve a smooth transition from global exploration to local convergence.

[0076] In actual implementation, the embodiments of this application can perform system initialization by inputting power system structural parameters; algorithm initialization by inputting the population size, maximum number of iterations, upper and lower bounds of variables, and variable dimensions; generating an initial projection search cluster and calculating the fitness value at each location; during residual-guided projection, the algorithm approximates the optimal solution by continuously projecting in a multi-dimensional space, when... t < T At that time, obtain dynamic parameters Different selection strategies are adopted based on residual probabilities and maximum residuals to generate gradient information, calculate the elements of the Jacobian matrix, and update the temporary agent. The double random projection process enhances the algorithm's global search capability by introducing double randomness, uniformly and randomly selecting two projection agents to update the temporary agent. Obtain two weights and update the temporary proxy. If Levi's flight projection is triggered, generate Levi's flight step size; update the temporary agent. ; Determine if the maximum number of iterations has been reached. If so, stop optimization and output the results; otherwise, return to perform residual guided projection again; Output the optimal fitness value and location information, i.e., the voltage setting value of the central node in each region. and active power regulation .

[0077] The embodiments of this application can solve the regional reactive power optimization model based on the projection iterative optimization algorithm. By introducing residual guided projection, double random projection, weighted random projection update and Levy flight guided projection to guide the population to converge toward the optimal solution, the exploration ability and convergence speed are enhanced, the performance is improved and the problem of getting trapped in local optima is prevented.

[0078] Optionally, in one embodiment of this application, the expression for the active power allocation objective function is: , in, For adjustment coefficients, The marginal cost of the generator. The tracking coefficient of the generator. This is the adjustment amount of the generator in the previous operation. The regulation amount of the generator participating in active power regulation.

[0079] It is understood that the adjustment coefficient in the embodiments of this application can be used to smoothly adjust each generator and avoid large adjustments; the tracking coefficient of the generator can be used to track the generator adjustment frequency and avoid frequent adjustments.

[0080] For example, embodiments of this application can construct an active power allocation objective function, expressed as follows: , in, This means making smooth adjustments to each generator to avoid drastic changes; The adjustment cost for each generator; The goal is to track the generator's adjustment frequency and avoid frequent adjustments. This is the adjustment coefficient, which is related to the generator's adjustment speed. Units with fast adjustment speeds can be set to a smaller value so that they can withstand larger adjustment amounts without causing excessive shocks. To optimize the marginal cost of each generator, low-cost units are encouraged to make more adjustments. The tracking coefficient for each generator is set to avoid frequent adjustments; This represents the adjustment amount of each generator in the previous operation.

[0081] The embodiments of this application can consider the economy, smoothness and rhythm of adjustment, construct a multi-objective function to optimize the active power regulation of the automatic voltage control unit, and ensure that the changes in network losses caused by reactive power optimization will not cause the automatic generation control unit to operate, thereby avoiding repeated adjustments of the unit, which is conducive to maintaining the long-term balance of regulation resources and response reliability of the system, and enhancing the robustness of the coordinated control of the power system.

[0082] In step S104, in response to the active power adjustment command, update command and allocation command, the optimization of the target power system is completed until the state of the target power system meets the preset optimization target.

[0083] It is understood that the preset optimization target in the embodiments of this application can be that the frequency and voltage meet the operational requirements. The preset optimization target can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0084] In actual implementation, the embodiments of this application can respond to active power adjustment instructions, update instructions and allocation instructions until the state of the target power system meets the preset optimization target, thereby completing the optimization of the target power system.

[0085] For example, in this embodiment, after the two-stage collaborative optimization control strategy is completed, the current instruction is issued simultaneously. The optimized AGC (Active Power Control) and AVC (Voltage Setpoint of Central Node, Active Power Distribution within the Region) instructions are sent to the AGC and AVC systems for execution. After the system executes the instruction, it continues to monitor the frequency and voltage. If the system status meets the requirements, the optimization is completed; otherwise, a new round of optimization adjustment is triggered. This achieves collaborative optimization control of the power system's AGC and AVC.

[0086] The embodiments of this application can improve the control scheme through iterative modification, so that the final operating state of the system combines safety, economy and new energy absorption capacity, which meets the development needs of new power systems. Through automated execution, manual intervention is reduced, control efficiency is improved, and the system can still operate stably under complex operating conditions, providing a reliable guarantee for the safe and economical operation of the power grid.

[0087] Specifically, it can be combined with Figure 3 As shown, the working principle of the smart grid-source coordinated optimization control method for power systems in this application is explained in detail with a specific embodiment.

[0088] like Figure 3As shown, the embodiments of this application can consider a cross-iterative control strategy that considers the mutual influence of AGC and AVC in the first stage. That is, when the system needs AGC action, the AGC adjustment amount is calculated, and cross-iteration with AVC is performed until the regional control deviation and the central node voltage deviation meet the requirements. In the second stage, a regional reactive power optimization model considering the central node voltage distribution and active power regulation allocation is used. Since the original central node voltage value may be changed after the adjustment in the first stage, the system economy may be poor. Therefore, the central node voltage of each region is optimized in this stage, and the new set value of the regional central point voltage is calculated under the latest system state. In addition, during reactive power optimization, changes in system network losses lead to active power regulation. This active power adjustment is distributed and consumed within the AVC unit region, rather than by the AGC unit. At this time, it will not affect the tie line power, that is, it will not cause AGC action, thus eliminating the repeated adjustment of AGC and AVC.

[0089] Specifically, the intelligent grid-source coordinated optimization control method for power systems proposed in this application is applied in real-world scenarios. When a significant disturbance occurs in the system, such as the loss of a large generating unit or an important line, a frequency deviation occurs. If the frequency does not recover to the normal range after the first frequency adjustment, AGC needs to be activated for secondary frequency adjustment, triggering a two-stage coordinated optimization control strategy. The first stage utilizes the cross-iterative optimization of AGC and AVC to ensure that the system restores the frequency by adjusting active power while ensuring that the voltage of the central node does not exceed the limit. The second stage utilizes regional reactive power optimization and active power redistribution to optimize the voltage setpoint of the central node of the entire network based on the new operating point after the first stage, ensuring the economic efficiency of system operation under the new state. In addition, by adjusting the active power output of generators within the AVC area to compensate for the network loss changes caused by reactive power optimization (this active power adjustment is usually small compared to the adjustment in the first stage, mainly to compensate for network loss changes), it does not affect the tie-line power and avoids repeated adjustments by AGC and AVC.

[0090] After the two-stage collaborative optimization control strategy is completed, the current command is issued. The optimized AGC (Active Power Control) and AVC commands (central node voltage setpoints and active power distribution within the region) are simultaneously sent to the AGC and AVC systems for execution. After the system executes the command, it continues to monitor the frequency and voltage. If the system status meets the requirements, the optimization is completed; otherwise, a new round of optimization adjustment is triggered. This achieves collaborative optimization control between the power system's AGC and AVC.

[0091] The intelligent grid-source coordinated optimization control method for power systems proposed in this application can, based on the operating state of the target power system, trigger a two-stage coordinated optimization control strategy when the state quantity exceeds a preset threshold. In response to a cross-iterative optimization control strategy, it generates active power adjustment instructions. In response to reactive power optimization and compensated active power allocation optimization control strategies, it generates central node voltage update instructions and compensated active power adjustment allocation instructions, until the preset optimization target is achieved. Thus, based on the two-stage coordinated optimization control strategy, it resolves mutual constraints, achieves dynamic coordination and optimization of active and reactive power, and comprehensively improves the operational safety, economy, and renewable energy absorption capacity of the power system, providing key support for building a new type of power system. This solves the problem in related technologies where the failure to effectively handle bidirectional coupling and insufficient consideration of real-time control leads to the inability to effectively resolve mutual constraints caused by coupling, making it difficult to achieve dynamic coordination of active and reactive power, thereby affecting the operational safety and economy of the power system.

[0092] Next, with reference to the accompanying drawings, the power system intelligent grid-source collaborative optimization control device proposed according to the embodiments of this application is described.

[0093] Figure 4 This is a schematic diagram of the structure of the intelligent grid-source collaborative optimization control device for power systems according to an embodiment of this application.

[0094] like Figure 4 As shown, the intelligent grid-source collaborative optimization control device 10 for the power system includes: a data acquisition module 100, a first generation module 200, a second generation module 300, and an execution module 400.

[0095] The acquisition module 100 is used to acquire the operating data of the target power system, analyze the state variables of the target power system based on the operating data, and trigger a two-stage collaborative optimization control strategy in response to the state variables being greater than a preset threshold. The first stage is a cross-iterative optimization control strategy, and the second stage is a reactive power optimization and active power allocation compensation optimization control strategy.

[0096] The first generation module 200 is used to respond to the cross-iterative optimization control strategy, trigger the cross-iterative optimization control strategy of automatic generation control and automatic voltage control until the regional control deviation and the central node voltage deviation meet the preset deviation target, and generate an active power adjustment command.

[0097] The second generation module 300 is used to respond to the reactive power optimization and compensated active power allocation optimization control strategy. Based on the active power loss of the entire network and the voltage of the central node, it performs regional reactive power optimization and active power allocation on the target power system to calculate the updated value of the central node voltage and the compensated active power adjustment amount, and generates the update instruction of the central node voltage and the allocation instruction of the compensated active power adjustment amount according to the updated value and the compensated active power adjustment amount.

[0098] The execution module 400 is used to respond to active power adjustment instructions, update instructions and allocation instructions until the state of the target power system meets the preset optimization target, and to complete the optimization of the target power system.

[0099] Optionally, in one embodiment of this application, the first generation module 200 includes: a first building unit, a second building unit, and a first instruction generation unit.

[0100] The first building unit is used to establish an automatic power generation control model based on the unit output cost, system network loss, wind and solar curtailment penalties, and energy storage loss costs.

[0101] The second building block is used to establish an automatic voltage control model based on reactive power balance and voltage control deviation.

[0102] The first instruction generation unit is used to trigger the cross-iterative optimization control strategy of automatic generation control and automatic voltage control, solve the automatic generation control model and automatic voltage control model until the regional control deviation and the central node voltage deviation meet the preset deviation target, and generate active power adjustment instructions.

[0103] Optionally, in one embodiment of this application, the second generation module 300 includes: a third construction unit, a fourth construction unit, a solving unit, an updating unit, and a second instruction generation unit.

[0104] The third building unit is used to construct a reactive power optimization objective function based on the total active power loss and the voltage of the central node, and to construct reactive power optimization constraints. Based on the reactive power optimization objective function and reactive power optimization constraints, a reactive power optimization model is constructed.

[0105] The fourth construction unit is used to determine the number of generator sets participating in regulation based on the active power regulation capacity of the generators in the target power system, and to construct an active power allocation model.

[0106] The solution unit solves the reactive power optimization model to obtain the updated value of the central node voltage and the compensation active power adjustment.

[0107] The second instruction generation unit is used to generate an update instruction for the central node voltage based on the update value, solve the active power allocation objective function based on the compensated active power adjustment, and generate an allocation instruction for the compensated active power adjustment.

[0108] Optionally, in one embodiment of this application, the solving unit includes an initialization subunit and a projection subunit.

[0109] The initialization sub-unit is used to input the structural parameters of the target power system, as well as the population size, maximum number of iterations, upper and lower bounds of variables, and variable dimensions, in order to generate an initial projection search cluster and start the optimization process.

[0110] The projection sub-unit is used to obtain the optimal solution based on residual guided projection, double random projection, weighted random projection update and Levy flight guided projection, so as to obtain the updated value of the central node voltage and the compensation active power adjustment.

[0111] Optionally, in one embodiment of this application, the expression for the active power allocation objective function is: , in, For adjustment coefficients, The marginal cost of the generator. The tracking coefficient of the generator. This is the adjustment amount of the generator in the previous operation. The regulation amount of the generator participating in active power regulation.

[0112] It should be noted that the foregoing explanation of the embodiment of the intelligent grid-source coordinated optimization control method for power systems also applies to the intelligent grid-source coordinated optimization control device of this embodiment, and will not be repeated here.

[0113] The intelligent grid-source coordinated optimization control device for power systems proposed in this application can, based on the operating state of the target power system, trigger a two-stage coordinated optimization control strategy when the state quantity exceeds a preset threshold. In response to a cross-iterative optimization control strategy, it generates active power adjustment commands. In response to reactive power optimization and compensated active power allocation optimization control strategies, it generates updates to the central node voltage and allocation commands for compensated active power adjustments, until the preset optimization target is achieved. Thus, based on the two-stage coordinated optimization control strategy, it resolves mutual constraints, achieves dynamic coordination and optimization of active and reactive power, and comprehensively improves the operational safety, economy, and renewable energy absorption capacity of the power system, providing key support for building a new type of power system. This solves the problem in related technologies where the failure to effectively handle bidirectional coupling and insufficient consideration of real-time control leads to the inability to effectively resolve mutual constraints caused by coupling, making it difficult to achieve dynamic coordination of active and reactive power, thereby affecting the operational safety and economy of the power system.

[0114] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0115] When the processor 502 executes the program, it implements the power system intelligent grid-source collaborative optimization control method provided in the above embodiments.

[0116] Furthermore, electronic devices also include: Communication interface 503 is used for communication between memory 501 and processor 502.

[0117] The memory 501 is used to store computer programs that can run on the processor 502.

[0118] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0119] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0120] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0121] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0122] This application also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described intelligent grid-source coordinated optimization control method for power systems.

[0123] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described intelligent grid-source coordinated optimization control method for power systems.

[0124] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0125] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0126] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0128] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0129] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0130] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0131] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A smart grid-source coordinated optimization control method for power systems, characterized in that, Includes the following steps: The system collects operational data of the target power system and analyzes the state variables of the target power system based on the operational data. In response to the state variables being greater than a preset threshold, a two-stage collaborative optimization control strategy is triggered. The first stage is a cross-iterative optimization control strategy, and the second stage is a reactive power optimization and active power allocation compensation optimization control strategy. In response to the cross-iterative optimization control strategy, the cross-iterative optimization control strategy of automatic generation control and automatic voltage control is triggered until the regional control deviation and the central node voltage deviation meet the preset deviation target, and an active power adjustment command is generated. In response to the reactive power optimization and compensated active power allocation optimization control strategy, based on the total network active power loss and the voltage of the central node, regional reactive power optimization and active power allocation are performed on the target power system to calculate the updated value of the central node voltage and the compensated active power adjustment amount, and based on the updated value and the compensated active power adjustment amount, update instructions for the central node voltage and allocation instructions for the compensated active power adjustment amount are generated. In response to the active power adjustment command, the update command, and the allocation command, the optimization of the target power system is completed until the state of the target power system meets the preset optimization target.

2. The method according to claim 1, characterized in that, The cross-iterative optimization control strategy of triggering automatic generation control and automatic voltage control continues until the regional control deviation and the central node voltage deviation meet the preset deviation target, generating an active power adjustment command, including: An automatic power generation control model is established based on unit output cost, system network loss, wind and solar curtailment penalties, and energy storage loss costs. An automatic voltage control model is established based on reactive power balance and voltage control deviation. A cross-iterative optimization control strategy of automatic generation control and automatic voltage control is triggered to solve the automatic generation control model and the automatic voltage control model until the regional control deviation and the central node voltage deviation meet the preset deviation target, and an active power adjustment command is generated.

3. The method according to claim 1, characterized in that, The method involves optimizing reactive power and allocating active power to the target power system based on the overall network active power loss and the central node voltage. This includes calculating the updated value of the central node voltage and the compensated active power adjustment, and generating update instructions for the central node voltage and allocation instructions for the compensated active power adjustment based on the updated value and the compensated active power adjustment. Based on the total network active power loss and the central node voltage, a reactive power optimization objective function is constructed, and reactive power optimization constraints are constructed. Based on the reactive power optimization objective function and the reactive power optimization constraints, a reactive power optimization model is constructed. The number of generator sets participating in regulation is determined based on the active power regulation capacity of the generators in the target power system, and an active power allocation model is constructed. Solve the reactive power optimization model to obtain the updated value of the central node voltage and the compensated active power adjustment amount; An update instruction for the central node voltage is generated based on the updated value. The active power allocation objective function is solved based on the compensated active power adjustment amount, and an allocation instruction for the compensated active power adjustment amount is generated.

4. The method according to claim 3, characterized in that, Solving the reactive power optimization model to obtain the updated value of the central node voltage and the compensated active power adjustment includes: Input the structural parameters of the target power system, and input the population size, maximum number of iterations, upper and lower bounds of variables, and variable dimensions to generate an initial projection search cluster and start the optimization process; The optimal solution is obtained based on residual guided projection, double random projection, weighted random projection update and Levy flight guided projection to obtain the updated value of the central node voltage and the compensated active power adjustment.

5. The method according to claim 3, characterized in that, in, The expression for the active power allocation objective function is: ， in, For adjustment coefficients, The marginal cost of the generator. The tracking coefficient of the generator. This is the adjustment amount of the generator in the previous operation. The regulation amount of the generator participating in active power regulation.

6. A smart grid-source coordinated optimization control device for a power system, characterized in that, include: The acquisition module is used to acquire the operating data of the target power system, analyze the state variables of the target power system based on the operating data, and trigger a two-stage collaborative optimization control strategy in response to the state variables being greater than a preset threshold. The first stage is a cross-iterative optimization control strategy, and the second stage is a reactive power optimization and active power allocation compensation optimization control strategy. The first generation module is used to respond to the cross-iterative optimization control strategy, trigger the cross-iterative optimization control strategy of automatic generation control and automatic voltage control until the regional control deviation and the central node voltage deviation meet the preset deviation target, and generate an active power adjustment command. The second generation module is used to respond to the reactive power optimization and compensated active power allocation optimization control strategy, and perform regional reactive power optimization and active power allocation on the target power system based on the active power loss of the entire network and the voltage of the central node, so as to calculate the updated value of the voltage of the central node and the compensated active power adjustment amount, and generate the update instruction of the voltage of the central node and the allocation instruction of the compensated active power adjustment amount according to the updated value and the compensated active power adjustment amount. The execution module is used to respond to the active power adjustment command, the update command, and the allocation command until the state of the target power system meets the preset optimization target, thereby completing the optimization of the target power system.

7. The apparatus according to claim 6, characterized in that, The first generation module includes: The first building unit is used to establish an automatic power generation control model based on the unit output cost, system network loss, wind and solar curtailment penalties, and energy storage loss costs. The second building block is used to establish an automatic voltage control model based on reactive power balance and voltage control deviation. The first instruction generation unit is used to trigger a cross-iterative optimization control strategy of automatic generation control and automatic voltage control, solve the automatic generation control model and the automatic voltage control model, until the regional control deviation and the central node voltage deviation meet the preset deviation target, and generate an active power adjustment instruction.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the power system smart grid-source coordinated optimization control method as described in any one of claims 1-5.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the power system smart grid-source collaborative optimization control method as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the power system smart grid-source collaborative optimization control method as described in any one of claims 1-5.

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