Power grid control method, storage medium and electronic equipment

Abstract

The invention discloses a power grid control method, a storage medium and electronic equipment. The method relates to the technical field of power system intelligent control, and comprises the following steps: obtaining operation demands of a target power grid, the operation demands comprising a power demand, a frequency demand and an energy utilization efficiency demand; based on the operation demand, optimizing the initial control strategy of the target power grid to obtain a target control strategy; the target control strategy is sent to an edge server, so that the edge server generates a target control instruction for controlling the target power grid, and the target control instruction is obtained by performing time delay compensation on an initial control instruction generated based on the target control strategy. According to the invention, the technical problems of low power grid operation efficiency and insufficient stability caused by inconsistency of communication delay and multi-layer targets are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control of power systems, in particular to a power grid control method, a storage medium and an electronic device. BACKGROUND

[0002] With the increasing proportion of new energy power generation (such as wind energy and solar energy) in the power grid, the operation characteristics of the power grid are undergoing significant changes. These changes mainly manifest in high volatility, high time variability and high uncertainty, which brings unprecedented challenges to the stable operation of the power grid. The obvious deficiencies of the related technology in power grid control mainly manifest in the following points:

[0003] Based on centralized architecture and single-layer control logic, the dynamic characteristic changes brought by large-scale access of new energy have not been fully considered. Therefore, in the face of the reality of new energy grid connection, the control method in the related technology exposes a series of deficiencies, which in actual operation are manifested as inadaptability to communication delay, weakening of control ability of system frequency and voltage stability, and increase of regulation and control difficulty of multi-agent collaborative operation. In terms of communication delay, the related technology mostly adopts a fixed delay model, which is inadequate in a dynamically changing communication network environment. In terms of system frequency and voltage stability control, the related technology adopts fuzzy control to rely on expert knowledge to build a rule base, which is prone to output power out-of-limit or frequency instability in complex power grid environment. In terms of multi-agent collaborative operation, the method in the related technology mainly manifests as insufficient smoothness of inter-layer control mode switching, insufficient effectiveness of delay compensation, and insufficient precision of constraint projection. The existence of these problems leads to the fact that, in actual operation, when the instructions of central dispatch are transmitted to the edge device, due to the lack of real-time information synchronization and constraint consistency guarantee, the response of the edge device may deviate from the expectation of the central dispatch, and even in extreme cases, the local optimization strategy of the edge may conflict with the global goal, leading to excessive power support, transmission channel congestion and other instability phenomena, threatening the safe operation of the entire system. Although the related technology can improve the automation and intelligence level of the power grid to some extent in the field of power grid control, it fails to solve the problems of communication delay and multi-layer target inconsistency, resulting in low efficiency and instability of the power grid.

[0004] At present, no effective solution has been proposed for the above problems. SUMMARY

[0005] The embodiments of the present application provide a power grid control method, a storage medium and an electronic device to at least solve the technical problems of low efficiency and instability of the power grid caused by communication delay and multi-layer target inconsistency.

[0006] According to an aspect of some embodiments of the present application, there is provided a power grid control method, comprising: obtaining an operation requirement of a target power grid, wherein the operation requirement comprises a power requirement, a frequency requirement and an energy utilization efficiency requirement, the power requirement is used to indicate a power balance of the target power grid, the frequency requirement is used to indicate a frequency balance of the target power grid, and the energy utilization efficiency requirement is used to indicate a state of charge balance of the target power grid; optimizing an initial control strategy of the target power grid based on the operation requirement to obtain a target control strategy; and sending the target control strategy to an edge server, so that the edge server generates a target control instruction for controlling the target power grid, wherein the target control instruction is obtained by performing time delay compensation on an initial control instruction generated based on the target control strategy.

[0007] According to another aspect of some embodiments of the present application, there is also provided a power grid control method, comprising: receiving a target control strategy sent by a central server, wherein the target control strategy is obtained by optimizing an initial control strategy of a target power grid, and the initial control strategy is obtained based on an operation requirement of the target power grid, wherein the operation requirement at least comprises a power requirement, a frequency requirement and an energy utilization efficiency requirement; generating an initial control instruction of the target power grid based on the target control strategy; performing time delay compensation on the initial control instruction to obtain a target control instruction of the target power grid; and controlling the target power grid based on the target control instruction.

[0008] According to another aspect of some embodiments of the present application, there is also provided a power grid control device, comprising: an operation requirement obtaining module, configured to obtain an operation requirement of a target power grid, wherein the operation requirement comprises a power requirement, a frequency requirement and an energy utilization efficiency requirement, the power requirement is used to indicate a power balance of the target power grid, the frequency requirement is used to indicate a frequency balance of the target power grid, and the energy utilization efficiency requirement is used to indicate a state of charge balance of the target power grid; a target control strategy determining module, configured to optimize an initial control strategy of the target power grid based on the operation requirement to obtain a target control strategy; and a target control strategy sending module, configured to send the target control strategy to an edge server, so that the edge server generates a target control instruction for controlling the target power grid, wherein the target control instruction is obtained by performing time delay compensation on an initial control instruction generated based on the target control strategy.

[0009] According to another aspect of the present invention, a power grid control device is also provided, comprising: a target control strategy receiving module, configured to receive a target control strategy sent by a central server, wherein the target control strategy is obtained by optimizing an initial control strategy of a target power grid, the initial control strategy being based on the operating requirements of the target power grid, wherein the operating requirements include at least power requirements, frequency requirements, and energy utilization efficiency requirements; an initial control command determining module, configured to generate an initial control command for the target power grid based on the target control strategy; a target control command determining module, configured to perform time delay compensation on the initial control command to obtain a target control command for the target power grid; and a target power grid control module, configured to control the target power grid based on the target control command.

[0010] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium storing a plurality of instructions adapted for loading by a processor and executing any one of the power grid control methods described herein.

[0011] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any of the power grid control methods described above.

[0012] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of any of the power grid control methods described herein.

[0013] In this embodiment of the invention, the operating requirements of the target power grid are obtained, including power requirements, frequency requirements, and energy utilization efficiency requirements. Power requirements indicate the power balance of the target power grid, frequency requirements indicate the frequency balance, and energy utilization efficiency requirements indicate the state of charge balance. Based on these operating requirements, the initial control strategy of the target power grid is optimized to obtain a target control strategy. This target control strategy is then sent to an edge server, which generates target control instructions for controlling the target power grid. The target control instructions are obtained by compensating for the time delay of the initial control instructions generated based on the target control strategy. This achieves the goal of precisely controlling the target power grid by optimizing the initial control strategy based on the power grid's operating requirements through a central server, generating the target control strategy, and transmitting it to the edge server. Upon receiving the target control strategy, the edge server generates initial control instructions and performs time delay compensation to obtain the target control instructions. This improves the power grid's operating efficiency and stability, thereby solving the technical problems of low power grid operating efficiency and insufficient stability caused by communication delays and inconsistencies among multiple targets. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0015] Figure 1 This is a flowchart of a power grid control method according to an embodiment of the present invention;

[0016] Figure 2 This is a flowchart of another power grid control method according to an embodiment of the present invention;

[0017] Figure 3 This is a schematic diagram of an optional time delay compensation and power output dynamic response according to an embodiment of the present invention;

[0018] Figure 4 This is a flowchart of an optional power grid control method according to an embodiment of the present invention;

[0019] Figure 5 This is a schematic diagram of an optional overall structure of a power grid control system according to an embodiment of the present invention;

[0020] Figure 6 This is a schematic diagram of an optional central control ring structure according to an embodiment of the present invention;

[0021] Figure 7 This is a schematic diagram of an optional edge control ring structure according to an embodiment of the present invention;

[0022] Figure 8 This is a schematic diagram of a power grid control device according to an embodiment of the present invention;

[0023] Figure 9 This is a schematic diagram of another power grid control device according to an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention will be explained below:

[0027] The policy gradient algorithm refers to an algorithm that can update the policy using data that does not perfectly match the current policy. The policy gradient algorithm can handle datasets generated by old policies or any other policies, not just those generated by the current policy.

[0028] Model predictive control (MMC) is an advanced control strategy primarily used to handle constrained, multivariable, and dynamic systems. It predicts the system's behavior over a future period based on a mathematical model and uses optimization algorithms to find the optimal control sequence to minimize the prediction error or cost function.

[0029] According to an embodiment of the present invention, a method embodiment for power grid control is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] Figure 1 This is a flowchart of a power grid control method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0031] Step S102: Obtain the operating requirements of the target power grid, wherein the operating requirements include power requirements, frequency requirements and energy utilization efficiency requirements. The power requirements are used to indicate the power balance of the target power grid, the frequency requirements are used to indicate the frequency balance of the target power grid, and the energy utilization efficiency requirements are used to indicate the state of charge balance of the target power grid.

[0032] Optionally, acquiring and meeting the operational requirements of the target power grid is a crucial step in achieving stable and efficient operation. These operational requirements include, but are not limited to, being set based on the real-time status of the target power grid, predicted load, generation, and grid security standards. Meeting power requirements means ensuring a real-time balance between power supply and demand, a fundamental prerequisite for maintaining stable operation of the target power grid. This avoids problems such as frequency fluctuations and voltage instability caused by power imbalances, thereby guaranteeing the quality of power service. Frequency balance is critical to the security of the target power grid, as even small frequency changes can affect the operating efficiency of power equipment and even cause equipment failures. This is especially true in modern target power grids with a high proportion of renewable energy integration, where frequency fluctuations pose a significant challenge. Meeting frequency requirements helps improve the reliability of the target power grid. Optimizing energy utilization efficiency helps improve the overall operational efficiency of the target power grid, reduces energy waste, and is also important for balancing the supply and demand relationship of the target power grid and addressing the uncertainties of renewable energy.

[0033] Step S104: Based on operational requirements, optimize the initial control strategy of the target power grid to obtain the target control strategy.

[0034] Optionally, by optimizing the initial control strategy, the actual operating state of the target power grid can be aligned with the expected operating requirements, ultimately yielding the target control strategy. Through this optimization process, the target power grid can operate more efficiently and stably.

[0035] In one optional embodiment, based on operational requirements, the initial control strategy of the target power grid is optimized to obtain a target control strategy, including: determining the power requirement as follows: at any given time, the difference between the total power provided by the generation side and the total power consumed by the consumption side of the target power grid does not exceed a preset power deviation; determining the frequency requirement as follows: at any given time, the difference between the frequency of the target power grid and a preset frequency does not exceed a preset frequency deviation; determining the energy utilization efficiency requirement as follows: at any given time, the difference between the state of charge of the energy storage devices in the target power grid and a preset state of charge does not exceed a preset state of charge deviation; determining the optimization objective based on the power requirement, frequency requirement, and energy utilization efficiency requirement; determining constraints, wherein the constraints include at least: the transmission power of the lines of the target power grid does not exceed a preset transmission power, the actual output of the generator is within a preset output range, the actual charging power of the energy storage devices does not exceed a preset charging power, the actual discharging power of the energy storage devices does not exceed a preset discharging power, and the actual frequency deviation of the target power grid is within a preset frequency deviation; and optimizing the initial control strategy based on the optimization objective and constraints to obtain the target control strategy.

[0036] Optionally, firstly, the operational requirements of the target power grid are defined, which define the targets for power balance, frequency stability, and energy efficiency. These requirements can be represented by functions. Next, to convert the operational requirements into an optimizable form, a weighted quadratic objective function can be designed to determine the optimization objective. This objective integrates the power, frequency, and energy efficiency requirements, each quantifying the gap between the operational requirements and the actual state. Furthermore, the constraints on the target power grid's operation need to be considered during optimization. These constraints ensure the feasibility of the optimized target control strategy, avoiding strategies that exceed equipment capacity or disrupt the stability of the target power grid. Finally, the constraints can be transformed into penalty factors using the Lagrange multiplier method, ensuring that the optimized target control strategy conforms to all constraints.

[0037] In one optional embodiment, the optimization objective is determined based on the power requirement, frequency requirement, and energy utilization efficiency requirement, including: determining the weight values ​​corresponding to each of the power requirement, frequency requirement, and energy utilization efficiency requirement; and performing a weighted summation operation based on the power requirement, frequency requirement, energy utilization efficiency requirement, and their respective weight values ​​to obtain the optimization objective.

[0038] Optionally, weight values ​​can be introduced to adjust the priorities of power demand, frequency demand, and energy efficiency demand. The weight design is based on real-time operating scenarios; for example, frequency demand has the highest priority during normal operation, while the weight values ​​of power demand or energy efficiency demand are dynamically adjusted during peak load periods or when the energy storage state of charge is abnormal, to strengthen power demand or optimize energy efficiency. Based on power demand, frequency demand, and energy efficiency demand, the optimization objective can be determined as follows: ,in, This represents the function value of the objective at any given time. This represents the power demand at any given moment. This represents the frequency requirement at any given time. Let t represent the energy efficiency requirement at any given time. The weight value representing power demand. The weight value representing the frequency demand. This represents the weight value for energy utilization efficiency requirements. This multi-objective optimization strategy enables precise control of the target power grid's operating state.

[0039] In one optional embodiment, the initial control strategy is optimized based on the optimization objective and constraints to obtain a target control strategy, including: optimizing the initial control strategy based on the constraints and optimization objective to obtain a reference control strategy; determining a feasible region projection set, wherein the feasible region projection set is used to indicate the safe operating range of the reference control strategy; and determining the target control strategy based on the feasible region projection set and the reference control strategy.

[0040] Optionally, the reference control strategy provides preliminary guidance for the next control action and forms the basis for the target control strategy. The target control strategy can be obtained using optimization algorithms in the following manner: First, based on the characteristics of the target power grid, a suitable model is selected. For example, for a target power grid containing a large amount of wind and solar energy, a nonlinear model integrating physical mechanisms and data correction can be used to comprehensively capture the dynamic characteristics of the target power grid; while for simpler target power grids, a linearized state-space model is sufficient. In the model, state variables include, but are not limited to, bus voltage, grid frequency, and the state of charge of energy storage devices; control variables include, but are not limited to, the charging and discharging power of energy storage devices and the output of distributed power sources. Simultaneously, the model must embed constraints. Next, model predictive control is implemented in the model, setting a prediction period that covers multiple scheduling execution cycles. Within the prediction period, optimization algorithms (including but not limited to the interior-point method) are used to solve the optimization objective function. At the end of each scheduling execution cycle, only the first control command in the prediction period is executed. Then, after acquiring new real-time data, prediction and optimization are repeated, forming a rolling "prediction-optimization-execution" pattern to ensure that the strategy can adapt to changes in the state of the target power grid in real time. Furthermore, a deep reinforcement learning agent can be introduced through the policy gradient algorithm to form a closed-loop collaboration with model predictive control. The deep reinforcement learning agent collects the deviation between the model's predicted results and the actual operating results to learn and optimize the control strategy online. The weight matrix correction and prediction error compensation coefficients output by the policy gradient algorithm are used to adjust the parameters in the model, achieving dynamic optimization of the control strategy. The state space of the deep reinforcement learning can be represented by a high-dimensional state vector. The action space design can use multi-dimensional discrete variables, allowing the agent to perform fine-grained control across these dimensions. Based on the state space and action space, the reward function can be determined as follows: r = Where r represents the reward function, Indicates the cost of the target power grid. Indicates the weight of costs. Indicates the frequency deviation of the target power grid. Weight of frequency deviation This indicates a penalty for violating the constraints. This represents the weight of the penalty for violating constraints. The weight can be dynamically adjusted based on the real-time load scenario of the target power grid, ensuring that the control strategy achieves optimal balance under different operating conditions. Regularly verifying the reward function parameters helps the strategy adapt to the diversity and dynamism of power grid operation, significantly reducing scheduling errors, accelerating strategy convergence, and improving overall operating efficiency. Through the synergistic effect of model predictive control and deep reinforcement learning algorithms, the target power grid can achieve efficient response and dynamic adaptation to complex power grid environments, ultimately optimizing the reference control strategy. Furthermore, the determination of the target control strategy requires further verification and adjustment based on the reference control strategy using a feasible region projection set. Specifically, by projecting the reference control strategy onto a pre-calculated feasible region set, it is ensured that even under dynamic and uncertain operating environments, the output of the control strategy always remains within the safe operating range.

[0041] In one optional embodiment, determining the feasible region projection set includes: obtaining an initial superset, wherein any point in the initial superset represents a control strategy; optimizing the initial superset based on preset security constraints to obtain an optimized superset, wherein the preset security constraints include at least: the transmission current of the lines and transformers of the target power grid does not exceed a preset current threshold, the voltage of any node in the target power grid is within a preset voltage range, and the temperature of the lines during normal operation does not exceed a preset temperature threshold; repeating the optimization process until a preset termination condition is reached, wherein the preset termination condition includes at least all control strategies in the optimized superset satisfying the preset security constraints; and obtaining the feasible region projection set based on the optimized superset obtained when the preset termination condition is reached.

[0042] Optionally, determining the feasible region projection set ensures that none of the implemented strategies will cause the target power grid to operate outside the safe range. The initial superset is a set within the control strategy parameter space, containing all possible control strategies. Based on preset safety constraints, optimization algorithms (including but not limited to the bisection method) can be used to progressively filter and adjust the initial superset, ensuring that all candidate control strategies strictly adhere to the preset safety constraints during execution, thus forming an optimized superset. In each iteration, valid points that satisfy the preset safety constraints are selected, while invalid points that violate the preset safety constraints are projected back into the safe region to adjust their parameters to conform to the preset safety constraints. This optimization process is repeated until all control strategies in the optimized superset fully satisfy the preset safety constraints, i.e., under the preset safety constraints, the initial superset and the optimized superset completely overlap, at which point the preset termination condition is met. When the optimization process reaches the preset termination condition, the resulting optimized superset is the feasible region projection set. Each control strategy in the feasible region projection set satisfies all preset safety constraints and can be safely applied to the operation control of the target power grid.

[0043] In one optional embodiment, determining a target control strategy based on a feasible region projection set and a reference control strategy includes: if the feasible region projection set includes the reference control strategy, determining the reference control strategy as the target control strategy; if the feasible region projection set does not include the reference control strategy, determining the target control strategy based on a preset projection strategy, wherein the preset projection strategy is used to project the reference control strategy onto the nearest point within the feasible region projection set.

[0044] Optionally, the target control strategy can be determined by monitoring whether the reference control strategy is within the feasible region projection set. If the reference control strategy is already within the feasible region projection set, meaning it satisfies all preset safety constraints, then this reference control strategy can be directly determined as the target control strategy to guide the operation of the target power grid. If the reference control strategy is not within the feasible region projection set, it means that some parameters of the reference control strategy exceed the limits of safe operation. In this case, a preset projection strategy needs to be adopted. The preset projection strategy is used to project the reference control strategy that exceeds the safe operation range to the nearest point within the feasible region projection set, thereby adjusting the strategy parameters to meet the preset safety constraints. This process ensures that even in the face of complex and variable operating environments, the control strategy can be corrected to a safe and feasible range.

[0045] Step S106: The target control strategy is sent to the edge server, which generates target control instructions for controlling the target power grid. The target control instructions are obtained by time delay compensation of the initial control instructions generated based on the target control strategy.

[0046] Optionally, in the two-layer control architecture of the target power grid, the central server is responsible for global prediction and optimization, generating the target control strategy, while the edge servers generate and execute specific control commands based on the received target control strategy. The target control strategy obtained by the central server through optimization algorithms needs to be sent to the edge servers via a high-speed and reliable communication network. The edge servers are located in the target power grid closer to the actual control points (including but not limited to power sources, load equipment, and energy storage equipment). After receiving the target control strategy, the edge servers generate initial control commands. After generating the initial control commands, the edge servers further compensate for the time delay of the initial control commands. This time delay compensation can be performed through a time delay compensation model. This is because there is a certain delay in communication between the central server and the edge servers, which may affect the timeliness and effectiveness of the control commands. The time delay compensation mechanism adjusts the execution timing or parameters of the control commands based on the real-time estimation of the communication delay to offset the impact of communication delay on the control effect. For example, if communication delays cause control commands to arrive at the edge server later than expected, delay compensation can advance or adjust the execution of the commands, ensuring that the commands act on the power grid at the appropriate time and in the appropriate form, avoiding control failures or performance degradation caused by timing misalignments. The control commands after delay compensation become the target control commands, which can more accurately and timely guide the operation of the power grid, ensuring that the target control strategy achieves the expected optimization effect in actual execution.

[0047] The execution entity of the above steps S102 to S106 can be a central server. Through the above steps S102 to S106, the central server can optimize the initial control strategy based on the power grid operation requirements, generate the target control strategy, and transmit the target control strategy to the edge server. After receiving the target control strategy, the edge server generates the initial control command and performs time delay compensation to obtain the target control command, thereby achieving the purpose of precise control of the target power grid. This achieves the technical effect of improving the power grid operation efficiency and stability, and solves the technical problems of low power grid operation efficiency and insufficient stability caused by communication delay and inconsistency of multi-level targets.

[0048] According to an embodiment of the present invention, another embodiment of a power grid control method is provided. Figure 2 This is a flowchart of a power grid control method according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0049] Step S202: Receive the target control strategy sent by the central server. The target control strategy is obtained by optimizing the initial control strategy of the target power grid. The initial control strategy is based on the operating requirements of the target power grid, which include at least power requirements, frequency requirements, and energy utilization efficiency requirements.

[0050] Optionally, firstly, the central server, based on the target power grid's power demand, frequency demand, and energy efficiency demand, weights the initial control strategy by setting corresponding weight values ​​for each demand, to obtain the optimization objective. Simultaneously, constraints are defined, including at least line transmission power limits, generator output range, energy storage device charging and discharging power limits, and frequency deviation range, to ensure the feasibility of the control strategy. Next, the central server uses optimization algorithms (model predictive control and deep reinforcement learning algorithms) to optimize the initial control strategy, aiming to minimize the function value of the optimization objective while satisfying the constraints. This optimization process first determines a reference control strategy, then iteratively optimizes it, combining this with the feasible region projection set, to determine the final target control strategy. The feasible region projection set can be obtained iteratively from the initial superset using a bisection method, retaining only control strategies that satisfy preset safety constraints (line transmission current not exceeding a preset threshold, node voltage within a preset range, and line operating temperature not exceeding a preset limit). During the optimization process, if the reference control strategy is already within the feasible region projection set, meaning it satisfies all preset safety constraints, then this reference control strategy can be determined as the final target control strategy. However, if the reference control strategy does not fall within the feasible region projection set, it means that the reference control strategy may exceed the boundaries of safe operation in some aspects. In this case, the preset projection strategy will be activated, projecting the reference control strategy to the nearest point within the feasible region projection set, thereby adjusting the strategy parameters to ensure that it both optimizes the objective and does not violate safety constraints. The final target control strategy is then sent from the central server to the edge server. This transmission process is completed through a communication network to ensure that control commands arrive in a timely manner, guiding the operation of the edge devices.

[0051] Step S204: Based on the target control strategy, generate the initial control command for the target power grid.

[0052] Optionally, after receiving the target control strategy, the edge server collects real-time operational data of the target power grid. This operational data includes, but is not limited to, actual load, real-time output of renewable energy, and equipment status. The edge server needs to combine the central target control strategy with the real-time operational data to ensure that the generation of initial control commands both follows the central global optimization objectives and can respond promptly to local disturbances. Based on the target control strategy and real-time operational data, the edge server generates initial control commands. These initial control commands specifically target various control points in the target power grid, including, but not limited to, adjustments to generator output, settings for the charging and discharging power of energy storage devices, and adjustment strategies for smart loads. The initial control commands aim to translate the target control strategy into directly executable operational commands.

[0053] Step S206: Perform time delay compensation on the initial control command to obtain the target control command of the target power grid.

[0054] Optionally, considering the communication latency between the central server and the edge server, after generating the initial control command, the edge server will apply latency compensation to adjust the execution time and parameters of the initial control command in order to make up for the impact of latency, thereby obtaining the target control command and ensuring the accuracy and timeliness of the control action.

[0055] In one optional embodiment, delay compensation is applied to the initial control command to obtain the target control command for the target power grid. This includes: determining an error value based on a delay compensation model, wherein the delay compensation model is used to compensate for the communication delay between the central server and the edge server, and the error value represents the deviation between the preset output operating state and the actual output operating state of the target power grid, wherein the output operating state includes power output, frequency level, and energy utilization efficiency; updating the initial compensation gain in the delay compensation model based on the error value to obtain an updated compensation gain, wherein the compensation gain is a parameter for compensating for communication delay; and performing delay correction on the initial control command based on the updated compensation gain to obtain the target control command.

[0056] Optionally, the introduction of a time-delay compensation model aims to address the control signal synchronization problem caused by communication delays between the central server and edge servers, ensuring phase consistency of multi-layer control signals and thus improving grid operation stability. The time-delay compensation model can be based on the model reference adaptive control principle and employ a dual-ended Smith predictor architecture. The edge server monitors the error value in real time—the deviation between the preset output operating state and the actual output operating state of the target grid. The time-delay compensation model can update the initial compensation gain. The dynamic update mechanism allows the model to estimate time delay changes in real time and adjust the initial compensation gain to instantly correct the phase of the control signal, obtaining the updated compensation gain. This ensures that multi-layer control signals remain synchronized even with communication delays, reducing control command execution deviations caused by time delays. Furthermore, in the face of model mismatch caused by external interference (including but not limited to network jitter and data packet loss), an adaptive law k(t)=γ(t) can be designed. u(t) rapidly adapts to time delay fluctuations, where k(t) represents the updated compensation gain at any given time, γ(t) represents the learning rate at any given time, and u(t) represents the error value at any given time. Finally, based on the updated compensation gain, the edge server performs time delay correction on the initial control commands received from the central server, generating target control commands. These target control commands ensure that control actions are executed at the optimal time to minimize error values ​​and achieve stable operation of the target power grid. Figure 3This is a schematic diagram of an optional time delay compensation and dynamic response of power output according to an embodiment of the present invention. The diagram shows that without time delay compensation (no compensation), the output power fluctuation is large and the oscillation frequency is high. Conversely, with time delay compensation (compensated in this embodiment), the output power fluctuation is significantly reduced and the oscillation frequency trend is flat. This indicates that time delay compensation can effectively reduce control errors caused by communication delays, improve the response speed and accuracy of the target power grid to disturbances, and thus enhance the stability and reliability of the target power grid.

[0057] Step S208: Control the target power grid based on the target control command.

[0058] Optionally, the delayed target control commands are sent to the corresponding actual control points (including but not limited to power sources, load devices, and energy storage devices) for actual control operations. The actual control points adjust their operating states according to the target control commands to achieve real-time control of the target power grid. After executing the target control commands, the edge server sends the actual target power grid operating state and control effect as feedback information back to the central server via a low-latency communication link. Based on this feedback information, the central server verifies and optimizes the next control strategy, forming a closed-loop optimization process of "prediction-correction-feedback-re-prediction". Controlling the target power grid based on target control commands involves not only command transmission from global optimization to local execution, but also real-time response from edge servers and closed-loop optimization from the central server, ensuring the efficient, stable, and safe operation of the target power grid.

[0059] The execution entity of the above steps S202 to S208 can be an edge server. Through the above steps S202 to S208, the initial control strategy based on the power grid operation requirements can be optimized by the central server, a target control strategy can be generated, and the target control strategy can be transmitted to the edge server. After receiving the target control strategy, the edge server generates an initial control command and performs time delay compensation to obtain the target control command, thereby achieving the purpose of precise control of the target power grid. This achieves the technical effect of improving the power grid operation efficiency and stability, and solves the technical problems of low power grid operation efficiency and insufficient stability caused by communication delay and inconsistency of multi-level targets.

[0060] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 4 This is a flowchart of an optional power grid control method according to an embodiment of the present invention, such as... Figure 4 As shown, the method includes:

[0061] S1: Monitoring Data / Predictive Input: Collect real-time monitoring data from the target power grid, including but not limited to the actual output of the power source, the real-time power consumption of the load equipment, the state of charge of the energy storage equipment, and the predicted values ​​of the load and renewable energy output within a predetermined period of time, to provide basic information for subsequent optimization of the initial control strategy.

[0062] S2: Construction of unified objective function: Based on the operational requirements of the target power grid, construct the optimization objective. The specific implementation process is the same as the previous embodiment, and will not be repeated here.

[0063] S3: Constraint Set / Feasible Region Definition: Define the constraints of the target power grid, such as power transmission limits, equipment operating range and frequency deviation limits. The specific implementation process is the same as the previous embodiments, and will not be repeated here.

[0064] S4: Multi-level optimization solution (central / edge): Based on the constraints and optimization objectives, the initial control strategy is optimized to obtain the target control strategy. In the central control loop (central server), model predictive control is used to perform global rolling optimization solution to generate the target control strategy. In the edge control loop (edge ​​server), the target control strategy of the central control loop is responded to. The specific implementation process is the same as the previous embodiment, and will not be repeated here.

[0065] S5: Feasible Region Projection and Time Delay Correction: During the optimization process in step S4, the feasible region projection set is used to ensure that the obtained target control strategy meets the preset safety constraints. Furthermore, based on the target control strategy, the edge control loop generates target control commands for controlling the target power grid. These target control commands are obtained by compensating for the time delay of the initial control commands. The specific implementation process is the same as in the aforementioned embodiments and will not be repeated here.

[0066] S6: Control signal output (plan / instruction): Based on the target control instruction, output control signal to control the target power grid. The specific implementation process is the same as the previous embodiment, and will not be repeated here.

[0067] S7: Monitoring Feedback / Effect Evaluation: After the target control command is executed, the edge control loop uses the actual power grid operating status as feedback information and returns it to the central control loop via the communication link. Based on the feedback information, the central control loop evaluates the control effect and optimizes the control strategy for the next operation, forming a closed-loop optimization process. The specific implementation process is the same as in the aforementioned embodiments and will not be repeated here.

[0068] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 5This is a schematic diagram of an optional overall structure of a power grid control system according to an embodiment of the present invention. The diagram shows the central server, i.e., the central control ring (dispatch center), the central server, i.e., the edge control ring (microgrid server), and the interaction and control flow between sources, loads, and storage. The central control ring is located at the top layer of the power grid control system and acts as the global coordination and optimization center. It uses optimization algorithms to generate a cross-layer consistent objective function (unified objective) and a global optimization plan (plan distribution) based on the overall operating status of the power grid and forecast data. The central control ring is responsible for formulating long-term dispatch strategies, considering the global power balance, frequency stability, and energy utilization rate of the power grid, to ensure the economic efficiency and stability of system operation. The edge control ring is the middle layer of the power grid control system and communicates directly with the actual operating units such as sources, loads, and storage. The edge control ring receives the unified objective and plan from the central control ring and, combined with real-time monitoring data of the power grid, such as load changes, renewable energy generation status, and energy storage state of charge, performs rapid local corrections and responses. When executing local control, the edge control loop synchronously applies a time delay compensation mechanism to ensure the phase accuracy and timing synchronization of control signals, and to constrain the optimized solution within a safe operating range through feasible region projection (feasible region constraints). In the diagram, sources, loads, and storage represent renewable energy generation (such as photovoltaic and wind power), loads (such as industrial and park electricity), and energy storage devices (such as battery energy storage systems), respectively. Sources, loads, and storage (actual control points) are directly managed by the edge control loop, receiving local control commands and adjusting their own operating states, such as charging and discharging strategies and output levels, according to the commands to achieve real-time response and optimized control of the power grid.

[0069] Figure 6This is a schematic diagram of an optional central control loop structure according to an embodiment of the present invention. The diagram illustrates the internal structure and workflow of the central control loop. First, the monitoring data aggregation / cleaning step is responsible for collecting and preprocessing real-time monitoring data from different source, load, and energy storage ends. The data aggregation process integrates the scattered, raw monitoring data into system-level operating status information, while data cleaning is used to remove noise and outliers, ensuring data quality in subsequent prediction and optimization processes. Next, the central control loop uses a predictive model (load / output) to optimize the initial control strategy to obtain the target control strategy. During the optimization process, constraint management (safety / regulation) is responsible for sorting out and maintaining the preset conditions for grid operation, ensuring that the control strategy formulated by the central loop does not cause the system operation to exceed its physical boundaries. The model predictive control optimizer (model predictive control) performs rolling time-domain optimization based on the input of the predictive model, achieving dynamic adjustment and optimization of the control strategy by solving the optimization objective function. The strategy updater (deep reinforcement learning / parameter assimilation) combines deep reinforcement learning and parameter assimilation techniques to adjust and optimize the parameters of the predictive model online. Finally, through the parameter / plan distribution interface, the central control loop sends the optimization results and target control strategies to the edge control loops.

[0070] Figure 7This is a schematic diagram of an optional edge control loop structure according to an embodiment of the present invention. The diagram illustrates the internal structure and workflow of the edge control loop. First, the edge control loop detects disturbances or state changes in the local power grid in real time, such as sudden increases in load, fluctuations in renewable energy generation, and equipment failures. It quickly identifies the source of the disturbance and quantifies the degree of disturbance, providing accurate local operating status information for subsequent control actions. Next, to address the uncertainty of communication delay, the delay compensation model in the edge control loop performs adaptive delay compensation to generate target control commands. By dynamically estimating the communication delay between the central control loop and the edge control loop and adjusting the phase of the control signal in real time, it ensures that even with changes in network delay, the control commands can remain synchronized with the actual operating state, reducing control timing errors and improving system response speed and stability. The feasible region projection (feasible region constraint) ensures that the target control strategy is within preset safety constraints. The edge control loop includes various local control algorithms (local controllers), such as proportional-integral-derivative control, model predictive control, and deep reinforcement learning-based control strategies, used in conjunction with the central control loop to optimize and obtain the target control strategy. Furthermore, the edge control loop is directly connected to the actual control points in the power grid, i.e., the execution units (power sources, energy storage devices, and load devices). It sends the compensated target control commands to the actual control points, guiding them to adjust their operating states to achieve rapid response to disturbances and maintain the stability of the power grid control system. Finally, after the actual control points operate according to the received target control commands, the edge control loop collects the execution results and feedback information on the actual power grid state, and sends the feedback information back to the central control loop as input for the next optimization, forming a closed-loop control process of "prediction-execution-feedback-re-optimization," continuously improving control accuracy and the performance of the power grid control system.

[0071] This embodiment can achieve at least one of the following effects: (1) Cross-layer consistency control: Through the central-edge dual-ring structure, the integrated central control and multi-time-scale coordinated control of source-load-storage-schedule are realized, taking into account the global optimization of the system and the local fast response, and eliminating the problem of inconsistent objectives of each layer in the hierarchical control. (2) Time delay adaptive compensation: Through dynamic time delay compensation, the communication delay can be identified online, the control signal can be automatically corrected, the time consistency of cross-layer information can be guaranteed, and the system stability and response speed can be significantly improved. (3) Feasible domain guarantee and security: Through feasible domain projection, the security of the control strategy is guaranteed. (4) Intelligent self-learning: The adaptive control strategy combining reinforcement learning and model predictive control is adopted. The parameters can be adaptively adjusted according to different operating scenarios to achieve self-learning and self-optimization. (5) Engineering deployability: This embodiment can be embedded in the dispatch center and microgrid control terminal, supporting modular deployment and distributed collaborative operation. Under the condition of 100ms communication delay, the system frequency offset is kept within ±0.05hz, and the energy utilization efficiency is improved by 8.7%, which has good practicality and promotion.

[0072] This embodiment also provides a power grid control device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0073] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described power grid control method is also provided. Figure 8 This is a schematic diagram of the structure of a power grid control device according to an embodiment of the present invention, such as... Figure 8 As shown, the aforementioned power grid control device includes: an operation demand acquisition module 800, a target control strategy determination module 802, and a target control strategy transmission module 804, wherein:

[0074] The operation requirement acquisition module 800 is used to acquire the operation requirements of the target power grid. The operation requirements include power requirements, frequency requirements and energy utilization efficiency requirements. The power requirements are used to indicate the power balance of the target power grid, the frequency requirements are used to indicate the frequency balance of the target power grid, and the energy utilization efficiency requirements are used to indicate the state of charge balance of the target power grid.

[0075] The target control strategy determination module 802 is connected to the operation requirement acquisition module 800 and is used to optimize the initial control strategy of the target power grid based on the operation requirements to obtain the target control strategy.

[0076] The target control strategy sending module 804 is connected to the target control strategy determining module 802 and is used to send the target control strategy to the edge server. The edge server generates target control instructions for controlling the target power grid. The target control instructions are obtained by performing time delay compensation on the initial control instructions generated based on the target control strategy.

[0077] According to an embodiment of the present invention, another embodiment of an apparatus for implementing the above-described power grid control method is also provided. Figure 9 This is a schematic diagram of the structure of a power grid control device according to an embodiment of the present invention, such as... Figure 9 As shown, the above-mentioned power grid control device includes: a target control strategy receiving module 900, an initial control command determining module 902, a target control command determining module 904, and a target power grid control module 906, wherein:

[0078] The target control strategy receiving module 900 is used to receive the target control strategy sent by the central server. The target control strategy is obtained by optimizing the initial control strategy of the target power grid. The initial control strategy is based on the operating requirements of the target power grid, which include at least power requirements, frequency requirements and energy utilization efficiency requirements.

[0079] The initial control command determination module 902 is connected to the target control strategy receiving module 900 and is used to generate the initial control command of the target power grid based on the target control strategy.

[0080] The target control command determination module 904 is connected to the initial control command determination module 902 and is used to perform time delay compensation on the initial control command to obtain the target control command of the target power grid.

[0081] The target power grid control module 906 is connected to the target control command determination module 904 and is used to control the target power grid based on the target control command.

[0082] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0083] It should be noted that the aforementioned operation requirement acquisition module 800, target control strategy determination module 802, and target control strategy sending module 804 correspond to steps S102 to S106 in the embodiments, and the aforementioned target control strategy receiving module 900, initial control command determination module 902, target control command determination module 904, and target power grid control module 906 correspond to steps S202 to S208 in the embodiments. The instances and application scenarios implemented by the above modules and their corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0084] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0085] The aforementioned power grid control device may also include a processor and a memory. The aforementioned operation requirement acquisition module 800, target control strategy determination module 802, target control strategy sending module 804, etc., are all stored in the memory as program modules, and the processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0086] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0087] The aforementioned power grid control device may also include a processor and a memory. The aforementioned operation requirement acquisition module 800, target control strategy determination module 802, target control strategy sending module 804, target control strategy receiving module 900, initial control instruction determination module 902, target control instruction determination module 904, and target power grid control module 906 are all stored as program modules in the memory. The processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0088] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0089] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program is running, it controls the device containing the non-volatile storage medium to execute any of the aforementioned power grid control methods.

[0090] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0091] Optionally, during program execution, the device containing the non-volatile storage medium performs the following functions: acquiring the operating requirements of the target power grid, including power requirements, frequency requirements, and energy efficiency requirements. The power requirements indicate the power balance of the target power grid, the frequency requirements indicate the frequency balance of the target power grid, and the energy efficiency requirements indicate the state of charge balance of the target power grid; optimizing the initial control strategy of the target power grid based on the operating requirements to obtain a target control strategy; and sending the target control strategy to an edge server, whereby the edge server generates target control instructions for controlling the target power grid, wherein the target control instructions are obtained by time-delay compensation of the initial control instructions generated based on the target control strategy.

[0092] Optionally, during program execution, the device containing the non-volatile storage medium performs the following functions: receiving a target control strategy sent by a central server, wherein the target control strategy is obtained by optimizing the initial control strategy of the target power grid, and the initial control strategy is based on the operating requirements of the target power grid, wherein the operating requirements include at least power requirements, frequency requirements, and energy utilization efficiency requirements; generating initial control commands for the target power grid based on the target control strategy; performing time delay compensation on the initial control commands to obtain the target control commands for the target power grid; and controlling the target power grid based on the target control commands.

[0093] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the above-described power grid control methods.

[0094] According to an embodiment of this application, an embodiment of a computer program product is also provided. Optionally, in this embodiment, the computer program product includes a computer program that, when executed by a processor, implements the steps of any of the above-described power grid control methods.

[0095] Optionally, when the aforementioned computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following method steps: obtaining the operating requirements of the target power grid, wherein the operating requirements include power requirements, frequency requirements, and energy utilization efficiency requirements, wherein the power requirements are used to indicate the power balance of the target power grid, the frequency requirements are used to indicate the frequency balance of the target power grid, and the energy utilization efficiency requirements are used to indicate the state of charge balance of the target power grid; optimizing the initial control strategy of the target power grid based on the operating requirements to obtain a target control strategy; and sending the target control strategy to an edge server, wherein the edge server generates target control instructions for controlling the target power grid, wherein the target control instructions are obtained by time delay compensation of the initial control instructions generated based on the target control strategy.

[0096] Optionally, when the aforementioned computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following method steps: receiving a target control strategy sent by a central server, wherein the target control strategy is obtained by optimizing an initial control strategy of the target power grid, the initial control strategy being based on the operational requirements of the target power grid, wherein the operational requirements include at least power requirements, frequency requirements, and energy utilization efficiency requirements; generating initial control instructions for the target power grid based on the target control strategy; performing time delay compensation on the initial control instructions to obtain target control instructions for the target power grid; and controlling the target power grid based on the target control instructions.

[0097] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the above-described power grid control methods.

[0098] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0099] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0101] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0102] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0103] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0104] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A power grid control method, characterized in that, include: The operation requirements of the target power grid are obtained, wherein the operation requirements include power requirements, frequency requirements and energy utilization efficiency requirements, wherein the power requirements are used to indicate the power balance of the target power grid, the frequency requirements are used to indicate the frequency balance of the target power grid, and the energy utilization efficiency requirements are used to indicate the state of charge balance of the target power grid. Based on the aforementioned operational requirements, the initial control strategy of the target power grid is optimized to obtain the target control strategy; The target control strategy is sent to an edge server, which generates a target control command to control the target power grid. The target control command is obtained by performing time delay compensation on the initial control command generated based on the target control strategy.

2. The method according to claim 1, characterized in that, The optimization of the initial control strategy of the target power grid based on the operational requirements to obtain the target control strategy includes: The power demand is defined as follows: at any given time, the difference between the total power provided by the generation side of the target power grid and the total power consumed by the consumption side does not exceed a preset power deviation. The frequency requirement is determined as follows: at any given time, the difference between the frequency of the target power grid and the preset frequency does not exceed the preset frequency deviation; The energy utilization efficiency requirement is determined as follows: at any given time, the difference between the state of charge of the energy storage device in the target power grid and the preset state of charge does not exceed the preset state of charge deviation. Based on the power requirement, the frequency requirement, and the energy utilization efficiency requirement, the optimization objective is determined; The constraints are determined, including at least the following: the transmission power of the target power grid lines does not exceed the preset transmission power; the actual output of the generator is within the preset output range; the actual charging power of the energy storage device does not exceed the preset charging power; the actual discharging power of the energy storage device does not exceed the preset discharging power; and the actual frequency deviation of the target power grid is within the preset frequency deviation. Based on the optimization objective and the constraints, the initial control strategy is optimized to obtain the target control strategy.

3. The method according to claim 2, characterized in that, The process of determining optimization objectives based on the power requirement, the frequency requirement, and the energy utilization efficiency requirement includes: Determine the weight values ​​corresponding to the power requirement, the frequency requirement, and the energy utilization efficiency requirement; The optimization objective is obtained by performing a weighted summation operation based on the power requirement, the frequency requirement, the energy utilization efficiency requirement, and their respective weight values.

4. The method according to claim 2, characterized in that, The optimization of the initial control strategy based on the optimization objective and the constraints to obtain the target control strategy includes: Based on the constraints and the optimization objective, the initial control strategy is optimized to obtain a reference control strategy; Determine a feasible domain projection set, wherein the feasible domain projection set is used to indicate the safe operating range of the reference control strategy; The target control strategy is determined based on the feasible domain projection set and the reference control strategy.

5. The method according to claim 4, characterized in that, The determination of the feasible region projection set includes: Obtain an initial superset, wherein any point in the initial superset represents a control strategy; Based on preset security constraints, the initial superset is optimized to obtain an optimized superset. The preset security constraints include at least the following: the transmission current of the lines and transformers of the target power grid does not exceed a preset current threshold, the voltage of any node in the target power grid is within a preset voltage range, and the temperature of the lines during normal operation does not exceed a preset temperature threshold. The optimization process is repeated until a preset termination condition is reached, wherein the preset termination condition includes at least all control strategies within the optimization superset satisfying the preset security constraint. The feasible region projection set is obtained based on the optimized superset obtained when the preset termination condition is met.

6. The method according to claim 4, characterized in that, The step of determining the target control strategy based on the feasible region projection set and the reference control strategy includes: If the feasible domain projection set includes the reference control strategy, the reference control strategy is determined to be the target control strategy. If the reference control strategy is not included in the feasible domain projection set, the target control strategy is determined based on a preset projection strategy, wherein the preset projection strategy is used to project the reference control strategy onto the nearest point within the feasible domain projection set.

7. A power grid control method, characterized in that, include: The system receives a target control strategy sent by a central server. The target control strategy is obtained by optimizing the initial control strategy of the target power grid. The initial control strategy is based on the operating requirements of the target power grid, which include at least power requirements, frequency requirements, and energy utilization efficiency requirements. Based on the target control strategy, the initial control command for the target power grid is generated; Time delay compensation is applied to the initial control command to obtain the target control command for the target power grid; The target power grid is controlled based on the target control command.

8. The method according to claim 7, characterized in that, The step of performing time delay compensation on the initial control command to obtain the target control command for the target power grid includes: Based on the time delay compensation model, an error value is determined. The time delay compensation model is used to compensate for the communication delay between the central server and the edge server. The error value represents the deviation between the preset output operating state and the actual output operating state of the target power grid. The output operating state includes power output, frequency level and energy utilization efficiency. Based on the error value, the initial compensation gain in the delay compensation model is updated to obtain the updated compensation gain, wherein the compensation gain is a parameter for compensating the communication delay; Based on the updated compensation gain, the initial control command is time-delayed to obtain the target control command.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions adapted for loading by a processor and executing the power grid control method according to any one of claims 1 to 8.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the power grid control method according to any one of claims 1 to 8.

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