Distributed new energy active power automatic control method and device based on multi-source cooperative control framework, computer equipment and storage medium

Abstract

The embodiment of the invention relates to the field of power systems, and provides a distributed new energy active power automatic control method and device based on a multi-source cooperative control framework, computer equipment and a storage medium. Constructing to obtain a multi-objective optimization model; solving the multi-objective optimization model by adopting a parallel optimization algorithm with an elitism strategy to obtain an optimized power distribution scheme; and based on a high-speed communication technology and an inverter accurate modulation technology, executing a control instruction associated with the optimized power distribution scheme. By adopting the method, the power control effect of the distributed new energy power system can be improved.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, device, computer equipment, and storage medium for automatic control of distributed renewable energy active power based on a multi-source collaborative control framework. Background Technology

[0002] In the field of power system technology, with the in-depth advancement of low-carbon technologies, distributed renewable energy sources, represented by photovoltaics and wind power, are being integrated into distribution networks at an unprecedented speed and scale. This profound transformation of the energy structure has led to the evolution of distribution networks from traditional single-source radial passive networks into complex active networks with multiple sources and bidirectional power flow. Against this backdrop, the inherent characteristics of distributed renewable energy sources pose a severe challenge to the active power control of the power grid.

[0003] First, renewable energy output exhibits significant randomness and volatility. Photovoltaic power generation changes instantaneously with solar irradiance, while wind power output is affected by wind speed fluctuations, with output power potentially fluctuating dramatically on timescales ranging from seconds to minutes. This strong uncertainty makes traditional power control methods based on deterministic models ineffective. Second, distributed renewable energy devices are numerous and scattered, exhibiting high heterogeneity. A typical regional distribution network may connect dozens of inverters from different manufacturers, models, and with different control characteristics, resulting in significant differences in response characteristics, communication protocols, and control precision. This heterogeneity makes it difficult to apply a unified control strategy, while optimizing each device individually faces a combinatorial explosion of computational complexity. Furthermore, the high proportion of renewable energy integration makes power balance and power quality maintenance in the distribution network exceptionally complex, with issues such as frequency deviation and voltage exceeding limits becoming increasingly prominent, severely impacting grid stability and operational efficiency.

[0004] In summary, traditional power control technologies in distributed renewable energy power systems suffer from poor power control performance. Summary of the Invention

[0005] This application provides a method, apparatus, computer equipment, and storage medium for automatic control of active power of distributed renewable energy based on a multi-source collaborative control framework. More specifically, this application provides a method, apparatus, computer equipment, computer storage medium, and computer program product for automatic control of active power of distributed renewable energy based on a multi-source collaborative control framework, which can improve the power control effect of distributed renewable energy power systems.

[0006] In a first aspect, embodiments of this application provide a distributed new energy active power automatic control method based on a multi-source collaborative control framework, including:

[0007] A multi-objective optimization model is constructed based on the interrelated preset optimization objectives in the distributed new energy power system.

[0008] The multi-objective optimization model is solved using a parallel optimization algorithm with an elite retention strategy to obtain the optimized power allocation scheme;

[0009] Based on high-speed communication technology and inverter precision modulation technology, control commands associated with the optimized power allocation scheme are executed; wherein, the control commands are used to control the active power of the distributed new energy power system.

[0010] Secondly, embodiments of this application provide a distributed renewable energy active power automatic control device based on a multi-source cooperative control framework, which has the function of implementing the distributed renewable energy active power automatic control method based on the multi-source cooperative control framework provided in the first aspect above. The function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, and the modules can be software and / or hardware.

[0011] In one possible design, the device includes:

[0012] The model building module is used to construct a multi-objective optimization model based on the interrelated preset optimization objectives in the distributed new energy power system.

[0013] The model solving module is used to solve the multi-objective optimization model using a parallel optimization algorithm with an elite retention strategy to obtain the optimized power allocation scheme;

[0014] The instruction execution module is used to execute control instructions associated with the optimized power allocation scheme based on high-speed communication technology and inverter precision modulation technology; wherein, the control instructions are used to control the active power of the distributed new energy power system.

[0015] In another aspect, this application provides a computer device including at least one connected processor and a memory, wherein the memory is used to store program code, and the processor is used to call the program code in the memory to execute the methods described in the above aspects.

[0016] In another aspect, embodiments of this application provide a computer storage medium including instructions that, when executed on a computer, cause the computer to perform the methods described in the above aspects.

[0017] In another aspect, this application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods described in the above aspects.

[0018] Compared to traditional power control methods in conventional technologies, the technical solution of this application optimizes multiple conflicting objectives simultaneously in terms of the control model, overcoming the limitations of objective optimization, improving economy, safety, and renewable energy absorption capacity, and enhancing overall operating efficiency. In terms of the optimization algorithm, the algorithm possesses global search capabilities and parallel computing advantages, resulting in faster computation speed, improved convergence, and the ability to efficiently handle nonlinear and high-dimensional problems to meet real-time control requirements. Regarding control accuracy, it achieves precise issuance and execution of power setpoints, reducing command delays and errors, and significantly improving control accuracy and synchronization. Therefore, it comprehensively improves the power control effect of distributed renewable energy power systems. Attached Figure Description

[0019] Figure 1 This is an application environment diagram from one embodiment;

[0020] Figure 2 This is a flowchart illustrating a distributed new energy active power automatic control method based on a multi-source collaborative control framework in one embodiment.

[0021] Figure 3 Here is a flowchart of the optimization solution in one embodiment;

[0022] Figure 4 This is a flowchart illustrating the control command issuance and execution process in one embodiment.

[0023] Figure 5 This is a schematic diagram of model predictive control rolling optimization in one embodiment;

[0024] Figure 6 This is an overall technical solution architecture diagram provided in one embodiment of this application;

[0025] Figure 7 This is a structural block diagram of a distributed new energy active power automatic control device based on a multi-source collaborative control framework in one embodiment;

[0026] Figure 8 This is an internal structural diagram of a computer device in one embodiment;

[0027] Figure 9 This is a diagram of the internal structure of a computer device in another embodiment. Detailed Implementation

[0028] The terms "first," "second," etc., used in the embodiments of this application 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 described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. The division of modules appearing in the embodiments of this application is only a logical division. In actual applications, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not performed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interface, and the indirect coupling or communication connection between modules may be electrical or other similar forms. None of these are limited in the embodiments of this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed among multiple circuit modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of this application.

[0029] Figure 1 As shown in the application environment diagram of one embodiment, this application provides a distributed new energy active power automatic control method based on a multi-source cooperative control framework, which can be applied to, for example... Figure 1 In the application scenario shown, terminal 102 communicates with server 104 via a network.

[0030] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0031] It should be noted that the terminal 102 involved in the embodiments of this application can be a wired terminal or a wireless terminal, and can be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. The wireless terminal can communicate with one or more core networks via a wireless access network, and the wireless terminal can be a mobile terminal, such as a mobile phone or a computer with a mobile terminal.

[0032] Figure 2This is a flowchart illustrating a distributed new energy active power automatic control method based on a multi-source collaborative control framework in one embodiment, as shown below. Figure 2 As shown, the distributed new energy active power automatic control method based on a multi-source cooperative control framework provided in this application embodiment, which can be simply referred to as the power control method provided in this application, includes:

[0033] S201. Based on the pre-defined optimization objectives that are interconnected in the distributed new energy power system, a multi-objective optimization model is constructed.

[0034] The interconnected preset optimization objectives refer to the objectives of maximizing overall power generation efficiency, minimizing grid fluctuations, and minimizing wind and solar curtailment rates. The multi-objective optimization model refers to the mathematical model used in this embodiment for automatic active power control. By constructing and solving this model, an implementation scheme for automatic active power control can be obtained.

[0035] S202 employs a parallel optimization algorithm with an elite retention strategy to solve the multi-objective optimization model, resulting in an optimized power allocation scheme.

[0036] Elite retention strategy refers to the optimization mechanism that retains the optimal solution during iteration; parallel optimization algorithm is an algorithm that uses multiple computing units to solve the problem simultaneously to speed up the process; the optimized power allocation scheme is the scheme obtained after solving the problem.

[0037] S203, based on high-speed communication technology and inverter precision modulation technology, executes control commands associated with the optimized power distribution scheme.

[0038] Among them, high-speed communication technology refers to a power-specific communication technology designed in this application that provides low-latency and high-reliability transmission of control commands.

[0039] Among them, inverter precision modulation technology is a technology that achieves precise active power tracking through proportional-integral-derivative control (PID) algorithm and pulse width modulation (PWM) algorithm.

[0040] Among them, the control commands are used to control the active power of the distributed new energy power system.

[0041] Compared to traditional power control methods in conventional technologies, this embodiment first constructs a multi-objective optimization model based on the interconnected preset optimization objectives in the distributed renewable energy power system. Then, a parallel optimization algorithm with an elite retention strategy is used to solve the multi-objective optimization model, resulting in an optimized power allocation scheme. Finally, based on high-speed communication technology and inverter precise modulation technology, control commands associated with the optimized power allocation scheme are executed. In terms of the control model, this embodiment simultaneously optimizes multiple conflicting objectives, overcoming the limitations of objective optimization and improving economy, safety, and renewable energy absorption capacity, thereby enhancing overall operating efficiency. Regarding the optimization algorithm, it possesses global search capabilities and parallel computing advantages, resulting in faster computation speed, improved convergence, and the ability to efficiently handle nonlinear and high-dimensional problems to meet real-time control requirements. In terms of control accuracy, it achieves precise issuance and execution of power setpoints, reducing command delays and errors, and significantly improving control accuracy and synchronization. Therefore, it comprehensively improves the power control effect of the distributed renewable energy power system.

[0042] Optionally, in some embodiments of this application, each preset optimization objective includes the objective of maximizing total power generation efficiency, the objective of minimizing grid fluctuations, and the objective of minimizing wind and solar curtailment rates. Based on the interrelated preset optimization objectives in the distributed new energy power system, a multi-objective optimization model is constructed, including: using a weighted sum method to transform the multi-objective optimization problem corresponding to the objective of maximizing total power generation efficiency, the objective of minimizing grid fluctuations, and the objective of minimizing wind and solar curtailment rates into a single-objective optimization problem, thereby obtaining the optimization objective of the multi-objective optimization model; determining voltage constraints, line capacity constraints, and equipment capacity constraints as constraints of the multi-objective optimization model; and initializing the model parameters of the multi-objective optimization model composed of optimization objectives and constraints to obtain the initialized multi-objective optimization model.

[0043] Among these, the highest overall power generation efficiency target refers to maximizing the total output power of all distributed renewable energy units. Minimizing grid fluctuations means minimizing the variation in total system power over two consecutive control cycles to smooth the output curve. Minimizing wind and solar curtailment rates means minimizing the wasted usable renewable energy power due to regulation.

[0044] The constraints of the multi-objective optimization model include voltage constraints, line capacity constraints, and equipment capability constraints; parameter initialization refers to the process of completing the process through a systematic data flow, providing accurate input to the optimization model.

[0045] For example, the power control method provided in this application includes the steps of: establishing a multi-objective optimization model, specifically including: 1) defining the optimization objective; 2) integrating constraints; and 3) initializing model parameters.

[0046] Establishing a multi-objective optimization model is the foundation and primary step in achieving precise active power control. The core objective of this step is to construct a mathematical model that can simultaneously consider multiple optimization objectives, transforming the complex active power control problem into a computable optimization problem. Its technical implementation relies on multi-objective genetic algorithm modeling techniques, which, by defining the optimization objective function and constraints, provide clear guidance for the subsequent optimization process.

[0047] 1) Optimize the target definition

[0048] Defining the optimization objectives is the core task of the entire model building process, and its core is to determine the multiple key performance indicators that need to be optimized for active power control. This embodiment, considering the characteristics of distributed renewable energy access, selects three interrelated but potentially conflicting optimization objectives: maximizing overall power generation efficiency, minimizing grid fluctuations, and minimizing wind and solar curtailment rates. These objectives comprehensively cover economic, stability, and environmental requirements.

[0049] First, the goal of maximizing overall power generation efficiency aims to maximize the overall power output of renewable energy plants and minimize energy losses. This is achieved by optimizing the power setpoints of each unit to ensure the highest possible total output power within grid constraints. Second, the goal of minimizing grid fluctuations aims to smooth the renewable energy output curve and reduce the impact on grid frequency and voltage. This is achieved by minimizing the variance or gradient of power changes, thereby improving grid stability. Finally, the goal of minimizing wind and solar curtailment rates aims to minimize the renewable energy generation wasted due to regulation and improve grid integration capacity. This is achieved by optimizing power allocation to reduce power curtailment.

[0050] The specific objective function is defined as follows:

[0051] ①Maximize overall power generation efficiency: The goal is to maximize the total output power of all distributed renewable energy units. Its mathematical expression is:

[0052] (1);

[0053] in, This is the active power setting value for the i-th new energy unit.

[0054] ② Minimize grid fluctuations: The objective is to minimize the variation in total system power over two consecutive control cycles to smooth the output curve. Its mathematical expression is:

[0055] (2);

[0056] in, This is the set value for the i-th unit at the current time. This represents the actual output value at the previous moment.

[0057] ③ Minimize wind and solar curtailment rates: The goal is to minimize the wasted usable renewable energy power due to regulation. Its mathematical expression is: (3);

[0058] in, The maximum available power of the i-th unit at the current moment (derived from ultra-short-term power prediction).

[0059] Finally, the multi-objective goal is transformed into a single objective through weighting coefficients:

[0060] (4);

[0061] Among them, w1, w2, and w3 are weight coefficients that are greater than zero, reflecting the degree of preference for different objectives.

[0062] When defining the objective function, the system uses a weighted sum method to transform the multiple optimization objectives into a single-objective optimization problem as shown in formula (4). This single-objective function comprehensively considers the total power generation efficiency, grid fluctuations, and wind and solar curtailment rates, and flexibly adjusts the priority of different objectives through weight coefficients w1, w2, and w3. The mathematical expression of the objective function is constructed and updated based on real-time collected power data, ultra-short-term forecast data, and equipment parameters, ensuring the consistency between the optimization model and the actual grid operating state, thereby improving the accuracy and practicality of the control strategy.

[0063] 2) Constraint Integration

[0064] Constraint integration is a crucial step in model building, aiming to incorporate the safety constraints of power grid operation into the optimization model. This embodiment considers constraints such as voltage upper and lower limits, frequency deviation, line capacity, and equipment capability. These constraints serve as boundary conditions for the optimization problem, ensuring the feasibility and safety of the solution.

[0065] First, voltage constraints require that the voltage at each node be within the allowable range. According to the "Power Quality - Supply Voltage Deviation" standard (GB / T 12325-2008), the voltage deviation for three-phase supply of 20kV and below is ±7% of the nominal voltage. Therefore, this method sets the voltage constraint as follows: . and This effectively avoids overvoltage or undervoltage problems. The system calculates the voltage distribution and sets the constraint inequality based on the power grid topology and real-time monitoring data. Secondly, frequency constraints require the system frequency deviation to be controlled within a standard range, typically reflected in the power balance equation. Finally, equipment constraints include maximum inverter output and ramp rate limits to ensure safe operation of the equipment.

[0066] This application integrates the safety constraints of power grid operation into the model in the form of inequalities, mainly including:

[0067] ① Voltage constraint: For all nodes j.

[0068] ② Line capacity constraints: For all lines k.

[0069] ③ Equipment capacity constraints: ,as well as (Climb rate constraint).

[0070] Model parameter initialization is achieved in real time through a data acquisition and monitoring system. Current output Power grid topology and equipment parameters The model is then constructed. These constraints are integrated into the optimization model through mathematical inequalities or equations. The genetic algorithm automatically satisfies these constraints during the optimization process, avoiding the generation of invalid solutions.

[0071] 3) Model parameter initialization

[0072] Model parameter initialization is completed through a systematic data flow, providing accurate input for model optimization.

[0073] First, the system collects real-time data on equipment parameters such as the current output of each unit, the maximum available power derived from ultra-short-term power forecasts, the grid topology, and the inverter ramp rate through sensors, data acquisition and monitoring systems, and an IoT platform deployed in the renewable energy units. Then, the collected raw data undergoes standardization, dimension unification, and outlier removal preprocessing, converting it into a vector or matrix format directly usable by the optimization algorithm. Finally, the processed parameter set is injected into the optimization model with defined objective functions and constraints, completing the model instantiation. This process ensures that the optimization model always makes decisions based on the latest grid conditions, laying a reliable data foundation for subsequent efficient and accurate optimization.

[0074] In summary, by organically combining the three stages of target definition, constraint integration, and model parameter initialization, this step constructs a comprehensive and accurate multi-objective optimization model. This model provides a clear direction and boundary for subsequent genetic algorithm optimization, ensuring the scientific validity and feasibility of the control strategy.

[0075] Optionally, in some embodiments of this application, a parallel optimization algorithm with an elite retention strategy is used to solve a multi-objective optimization model to obtain an optimized power allocation scheme. This includes: initializing the solution set and evaluating the performance of the multi-objective optimization model to obtain an initialized solution set and performance evaluation value; executing optimization operators based on the performance evaluation value to iteratively update the initialized solution set; and outputting the optimized power allocation scheme if the iterative update process meets a preset convergence condition.

[0076] Among them, the solution set initialization is the process of generating the initial power control strategy, and the initialized solution set is the result of this process.

[0077] Among them, performance evaluation refers to the quantitative scoring of the merits of the power allocation scheme represented by each candidate solution in the solution set, and the performance evaluation value is the result of the implementation of this process.

[0078] Among them, performing optimization operators and iteratively updating the initialized solution set refers to iteratively updating the solution set through operations such as selection, crossover, and mutation.

[0079] The iterative update process satisfies the preset convergence condition, which means that the number of iterations reaches the upper limit, or the performance evaluation value of the optimal solution improves by less than a set threshold in multiple consecutive generations, thus determining that the algorithm has converged; the optimized power allocation scheme is the solution obtained after solving.

[0080] For example, the power control method provided in this application includes: parallel optimization solution of the control strategy, specifically including: 1) solution set initialization and performance evaluation; 2) optimization operator execution; 3) parallel computing acceleration; 4) convergence judgment and output.

[0081] After successfully establishing a multi-objective optimization model, this application aims to efficiently solve for the optimal control strategy. Its core task is to solve the problem of rapid optimization in a complex solution space. The key technology lies in employing a parallel optimization algorithm with an elitist retention strategy, which searches for optimal or near-optimal solutions within a distributed computing framework through a systematic iterative improvement process.

[0082] Figure 3 This is a flowchart of the optimization solution in one embodiment; its detailed process is as follows: Figure 3 As shown below, in conjunction with Figure 3 Further explanation is needed.

[0083] 1) Deset initialization and performance evaluation

[0084] Solution set initialization is the starting step in the optimization solution process, and its core task is to generate an initial set of power control strategies. Each strategy represents a possible power allocation scheme, consisting of power setpoints for multiple renewable energy units. The size of the solution set is determined based on the problem complexity, typically containing 50-200 candidate solutions. Initialization methods employ random generation or heuristic generation based on historical data to ensure the diversity and coverage of the solution set. An initial solution set of size N is randomly generated, with each candidate solution represented as a vector. This represents a complete power allocation scheme.

[0085] The power setpoint refers to the active power target value for each distributed renewable energy unit within the jurisdiction, output through the parallel optimization solution step of the control strategy. This data originates from the final output of the optimization solution process and is a globally optimized power allocation vector that has undergone multi-objective optimization and constraint verification. Specifically, this vector... Each component in , which is the optimal active power setting value calculated for the i-th new energy unit.

[0086] Example: Consider a regional distribution network containing three distributed renewable energy units. The solution set size is set to N=100, and each candidate solution is a three-dimensional vector. During initialization, the system randomly generates power setpoints: In [0,5]MW, In [0,3]MW, 100 diverse initial power allocation schemes are formed by randomly selecting values ​​within the range of [0,8]MW.

[0087] Furthermore, performance evaluation is a core step connecting the optimization model and the solution process. Its task is to quantitatively score the merits of the power allocation scheme represented by each candidate solution in the solution set. This process specifically includes: first, reading the numerical encoding of the candidate solution, i.e., a vector containing the power setpoints of all distributed renewable energy units. Then, the system substitutes this vector into a pre-established multi-objective optimization model for calculation. The calculation process not only includes solving the weighted overall objective function value but also rigorously verifying whether the solution vector satisfies all safety constraints integrated into the model, including node voltage limits, line transmission capacity, and equipment ramp-up capabilities. To efficiently handle constraints, the system uses a penalty function method, transforming the degree of constraint violation into a penalty term, which is added to the original objective function value to constitute the final performance evaluation value of the candidate solution.

[0088] Specifically, for a total objective function value of If a candidate solution violates certain constraints, its performance evaluation value is... The calculation formula is:

[0089] (5);

[0090] Where λ is a maximal positive penalty factor. Let $\frac{ ...

[0091] Example: Selecting candidate solutions An evaluation was conducted. Calculations were performed. The verification revealed that the voltage at node 2 was 0.91 pu (below the lower limit of 0.93 pu), and the wind farm power change of 0.7 MW exceeded the ramping limit of 0.5 MW / min. Calculate Σ. Taking λ=1000, we get This solution was eliminated due to its poor evaluation value. The candidate solution X=[3.8, 2.5, 6.9] that satisfies all constraints is... =0.92, and was retained due to its high evaluation value.

[0092] 2) Optimize operator execution

[0093] The optimization operator execution is the core of the solution process, and its task is to iteratively update the solution set through operations such as selection, crossover, and mutation. First, based on performance evaluation values, a tournament selection method is used to select basic solutions from the current solution set: three candidate solutions are randomly selected each time, and the one with the best performance evaluation value is retained. Then, the basic solutions are recombined through a crossover operation. The crossover operation uses a simulated binary crossover method, with a crossover probability set to 0.8 based on the conventional value range of genetic algorithms and the requirements of global search capability for this problem, to effectively explore the solution space. On this basis, a mutation operation is performed to enhance diversity. The mutation operation uses a multinomial mutation method, with a probability set to 0.1 based on typical settings while also considering local development needs, aiming to introduce appropriate random perturbation. To maintain the solution quality during the iteration process, an elite retention mechanism is adopted, directly retaining the two solutions with the highest performance evaluation values ​​in each generation into the next generation.

[0094] 3) Parallel computing acceleration

[0095] To meet the computational speed requirements of real-time control, this application employs a parallel computing architecture to accelerate the optimization solution process. This process is implemented through the following specific steps:

[0096] The system first divides the complete candidate solution set into several subsets of similar size. Each subset contains multiple candidate solutions, with the number of subsets matching the number of available computing units. These subsets are assigned to different computing units for parallel processing. Each computing unit independently executes the complete optimization process on its assigned subset, including solution set initialization, performance evaluation, and optimization operator execution. Each computing unit performs local optimization according to the aforementioned method, including selecting candidate solutions using a tournament selection method, performing simulated binary crossover and polynomial mutation operations, and implementing an elite retention strategy. After completing a specified number of local optimizations, the system performs an information exchange operation. Each computing unit selects the candidate solutions with the highest performance evaluation values ​​in its current subset and exchanges these superior solutions with other computing units. The receiving unit replaces its locally performing candidate solutions with these external superior solutions. Through this periodic information exchange, the diversity of solutions is maintained, and the propagation of superior characteristics is promoted.

[0097] Through the aforementioned mechanisms of parallel partitioning, independent optimization, and periodic switching, the system significantly improves computational efficiency while maintaining optimization quality, ensuring the timeliness requirements of real-time control. This parallel acceleration method is fully integrated with the aforementioned optimization solution process, and all operation steps are based on the established optimization method.

[0098] 4) Convergence judgment and output

[0099] Convergence determination and output are the final steps in the optimization process. The system continuously monitors the evolution of the solution set. When preset convergence conditions are met, such as reaching the upper limit of the number of iterations, or the performance evaluation value of the optimal solution improving by less than a set threshold over several consecutive generations, the algorithm is considered to have converged. At this point, the system outputs the power allocation scheme with the highest performance evaluation value in the current solution set, serving as the basis for formulating the final control command. This mechanism ensures the comprehensive optimality of the output solution under various constraints, providing a reliable strategy foundation for subsequent precise control.

[0100] In summary, this step achieves efficient solution of the control strategy through the coordinated work of four stages: solution set initialization, operator optimization, parallel computation, and convergence judgment. This mechanism not only improves computation speed but also handles multi-objective and nonlinear problems, providing an optimized strategy for precise control.

[0101] Optionally, in some embodiments of this application, based on high-speed communication technology and inverter precision modulation technology, the control command associated with the optimized power allocation scheme is executed, including: converting the power setpoint corresponding to the optimized power allocation scheme into a control command that meets the instruction format recognizable by the device, and sending the control command through a secure channel with integrity verification and access control mechanisms; executing the control command and adjusting the output power of the inverter according to the sent control command; obtaining the execution result of the control command and feeding the execution result back to the control system.

[0102] Converting control commands into a format recognizable by the device involves standardization, encapsulation into a standard data frame structure, and then adding a timestamp and cyclic redundancy check (CRC) code. Issuing control commands refers to distributing them through a secure channel with integrity verification and access control mechanisms.

[0103] For example, the power control method provided in this application includes: precise issuance and execution of control commands, specifically including: 1) command encapsulation and secure transmission; 2) precise modulation of the inverter; and 3) execution status feedback.

[0104] After obtaining the optimized power allocation scheme, this invention aims to ensure the accurate execution of control commands. The core task is to solve the problems of delay and error in command issuance and execution. Its key technology lies in employing high-speed communication and inverter precision modulation technology to achieve millimeter-level precise control.

[0105] Figure 4 This is a flowchart of the control command issuance and execution process in one embodiment; the control command issuance and execution process is as follows: Figure 4 As shown below, in conjunction with Figure 4 Further explanation is needed.

[0106] 1) Instruction encapsulation and secure transmission

[0107] Command encapsulation and secure transmission are the primary steps in issuing control commands. Their task is to convert power setpoints into a device-recognizable command format and transmit them through a secure channel with integrity verification and access control mechanisms. This secure channel relies on a dedicated power communication network (fiber optic or 5G) to ensure the isolation, real-time performance, and reliability of command transmission. The specific implementation steps are as follows: First, the system standardizes the power setpoints of each renewable energy unit obtained through optimization. Second, the power setpoints are encapsulated into a standard data frame structure according to the IEC 61850 GOOSE protocol specification. Then, a millisecond-precision timestamp and a cyclic redundancy check code for integrity verification are added to each data frame. Finally, the encapsulated command frames are transmitted to each renewable energy unit controller through the secure channel, ensuring an end-to-end transmission delay of less than 20ms.

[0108] 2) Inverter Precision Modulation

[0109] Precise modulation of the inverter is the core of the control execution, and its task is to enable the inverter to accurately adjust its output power according to the command. The specific execution process is as follows: First, the inverter controller parses the received power setpoint command; second, it calculates the required modulation parameters through the built-in PID control algorithm; then, it uses pulse width modulation (PWM) technology to adjust the duty cycle of switching devices such as IGBTs in real time; finally, through closed-loop control, the actual output power of the inverter tracks the setpoint within 100ms, and the steady-state error is controlled within 1% of the rated power. At the same time, parameters such as device temperature and DC voltage are monitored in real time to ensure that the operation is within a safe range.

[0110] 3) Execution status feedback

[0111] Execution status feedback is a crucial part of the control closed loop, its task being to provide real-time feedback of command execution results to the control system. The specific implementation process is as follows: First, each renewable energy unit synchronously collects operating parameters such as actual output power, AC voltage, and current through high-precision sensors; second, the collected data is encapsulated into standard communication messages, with device identifiers and timestamps added; then, it is uploaded to the central controller via the power communication network; finally, the central controller parses the feedback data and calculates indicators such as control deviation and execution success rate.

[0112] After receiving the actual operating parameters uploaded by each new energy unit, the central controller performs the following calculation and analysis process:

[0113] ① Control deviation calculation:

[0114] Instantaneous absolute deviation: First, calculate the instantaneous power deviation of each new energy unit i at time t, using the following formula: (6);

[0115] in, The issued power setting value, This represents the actual output power collected by the sensor.

[0116] Statistical deviation: Within an evaluation period T (e.g., the past hour), calculate the average absolute deviation of all units, which serves as the core indicator of the overall system control accuracy. The formula is:

[0117] (7);

[0118] (N is the total number of cells, and T is the number of sampling points within the period)

[0119] ② Execution success rate assessment:

[0120] The system sets an allowable error band δ for power control (e.g., 2% of rated power). For each command issuance and execution within an evaluation cycle T: if If so, the control operation is considered successful; If so, the control execution is deemed to have failed.

[0121] The execution success rate is the ratio of the number of successful executions to the total number of control operations within a given period, expressed by the following formula:

[0122] (8);

[0123] The calculated average absolute deviation and execution success rate, along with the original power setpoint and actual values, are recorded and stored. This quantified performance data is transmitted in real time to the system performance monitoring and parameter adjustment module in the step "Prediction-Based Rolling Optimization and Adaptation," serving as the core basis for evaluating historical control effects and dynamically adjusting optimization model weights or algorithm parameters.

[0124] In summary, this step, through the organic combination of instruction encapsulation, precise modulation, and status feedback, ensures the accurate execution of control instructions. This mechanism reduces instruction transmission and execution errors, improving control precision and system reliability.

[0125] Optionally, in some embodiments of this application, the method further includes: within each preset forecast period, obtaining forecast data corresponding to the future preset forecast period from the meteorological service platform and the load forecasting system; and importing the forecast data into a multi-objective optimization model to update the maximum available power parameter and load parameter in the multi-objective optimization model.

[0126] The preset forecast period can be 15 minutes, and the future preset forecast period refers to the next 15 minutes. The forecast data can include ultra-short-term wind and solar power forecast sequences and load forecast data.

[0127] In the step of updating the maximum available power parameter and load parameter in the multi-objective optimization model, the updated parameters can be the equipment parameters such as the current output of each unit in real time, the maximum available power derived from the ultra-short-term power prediction, the grid topology, and the inverter ramp rate, which are collected in "3) Model parameter initialization" in the above embodiment.

[0128] Optionally, in some embodiments of this application, the method further includes: cyclically executing the process of model building, optimization solution and instruction issuance of a multi-objective optimization model within a preset control period.

[0129] The preset control cycle can be 5 minutes. The process of building the multi-objective optimization model, solving the optimization problem, and issuing instructions is executed cyclically. This refers to the entire process of cyclically executing the steps of "building the multi-objective optimization model", "parallel optimization of the control strategy", and "precise issuance and execution of control instructions".

[0130] Optionally, in some embodiments of this application, the method further includes: comprehensively evaluating the control effect of historical periods within a preset performance monitoring period to obtain a comprehensive evaluation result; and optimizing the weight coefficients and key parameters in the multi-objective optimization model based on the comprehensive evaluation result.

[0131] The preset performance monitoring cycle refers to the time period corresponding to the system's periodic comprehensive evaluation of the control effect of historical periods, which can be 1 hour.

[0132] The key performance indicators used in the comprehensive evaluation results include the average value of the overall objective function, the constraint violation rate, and the instruction execution success rate.

[0133] For example, as can be seen from the above embodiments, the power control method provided in this application includes: prediction-based rolling optimization and adaptation, specifically including: 1) prediction data integration; 2) rolling time window optimization; 3) system performance monitoring and parameter adjustment.

[0134] To achieve continuous optimization and adaptive operation of the system in dynamic environments, this application constructs a closed-loop optimization mechanism based on a model predictive control framework. This mechanism ensures that the control strategy always matches the actual state of the system by integrating predictive data, performing rolling optimization, and making online feedback adjustments.

[0135] Figure 5 This is a schematic diagram of model predictive control rolling optimization in one embodiment, and its core principle is as follows: Figure 5 As shown below, in conjunction with Figure 5 Further explanation.

[0136] 1) Predictive data integration

[0137] Forecast data integration is the foundation for implementing forward-looking control. At the start of each control cycle, the system acquires ultra-short-term wind and solar power forecast sequences and load forecast data for the next 15 minutes from the meteorological service platform and the load forecast system. This high-precision forecast data is imported into the optimization model in real time to update the maximum available power parameters and load parameters in the model. This allows optimization decisions to anticipate future changes in the system state, thereby significantly improving the forward-looking nature and accuracy of the control strategy.

[0138] 2) Optimization of rolling time window

[0139] Rolling time window optimization is the core execution step of this adaptive control mechanism. The prediction time domain for rolling time window optimization is set to 15 minutes, the control time domain is set to 5 minutes, and the control cycle is synchronized with the time window, both being 5 minutes. The system starts a new control cycle at a fixed time interval T=5 minutes, cyclically executing the entire process of model building, optimization solution, and command issuance.

[0140] Specifically, at each rolling time point, based on the latest real-time grid state and system predictions within the future prediction domain, the system restarts and executes the "parallel optimization solution of the control strategy" process (i.e., using a genetic algorithm with an elitist retention strategy), thereby outputting an optimal control sequence. The system executes only the first control command in this sequence corresponding to the current time point, and in the next cycle, it performs rolling optimization again based on the new state and prediction information. This "prediction-optimization-execution-rolling" mechanism enables the system to continuously track the optimal operating trajectory and promptly correct control deviations caused by prediction errors or environmental disturbances.

[0141] 3) System performance monitoring and parameter adjustment

[0142] System performance monitoring and parameter adjustment are crucial for ensuring long-term stable and optimized operation. The system periodically performs a comprehensive evaluation of the control effect over historical periods, for example, once per hour. Key performance indicators used in the evaluation include the average value of the overall objective function, constraint violation rate, and command execution success rate. Based on the evaluation results, the system automatically adjusts the weight coefficients in the optimization model or the key parameters of the optimization algorithm online. Specifically, if the evaluation finds that the power grid fluctuation index deviates from the expected range for a long period, the corresponding weight coefficient w2 is increased accordingly; if the algorithm's convergence speed remains poor, the crossover rate or mutation rate is appropriately increased to enhance the global search capability. This closed-loop parameter adaptive mechanism effectively improves the control system's adaptability to different operating scenarios, ensuring optimal overall performance in long-term operation.

[0143] In summary, this step, through the coordinated operation of three stages—predictive data integration, rolling optimization, and system performance monitoring and parameter adjustment—constitutes a complete closed-loop control system with predictive, optimization, execution, and self-learning capabilities. This mechanism significantly enhances the system's robustness against uncertainty, ensuring continuous, efficient, and stable operation in dynamically changing environments.

[0144] Figure 6 This is a diagram illustrating the overall technical solution architecture provided in one embodiment of this application. This application aims to construct a multi-source collaborative precision active power control framework to address the core pain points of distributed renewable energy active power control. For example... Figure 6As shown, the overall process of this method begins with the establishment of a multi-objective optimization model, followed by parallel evolutionary optimization of the control strategy, ultimately achieving precise issuance and rolling optimization of control commands. Its core idea is to combine genetic algorithms with model predictive control, achieving fine-grained control of active power and multi-source coordination through a closed-loop process of "modeling-optimization-execution-adaptation".

[0145] Figure 6 The architecture shown clearly illustrates the sequential relationship and data flow of the main steps in this application. Combined with the above embodiments, it can be seen that one technical implementation process of the power control method provided in this application may include: establishing a multi-objective optimization model is fundamental, defining optimization objectives and constraints; parallel evolutionary optimization of the control strategy is the core, using a genetic algorithm for rapid optimization; precise issuance and execution of control commands are crucial, ensuring accurate command delivery; prediction-based rolling optimization and adaptation are guarantees, achieving continuous optimization. Ultra-short-term forecast data from the cloud and real-time grid status serve as inputs, supporting control decisions at the edge. Specific steps and implementation principles are detailed in the above embodiments and will not be repeated here.

[0146] The technical research process and other technical details of this application are described below with reference to a specific embodiment.

[0147] In traditional technologies, the most similar implementations to this invention are mainly reflected in the following aspects.

[0148] (1) The first approach is a control method based on fixed rules, such as allocation according to capacity ratio or priority control according to the voltage level of the access point. This method allocates active power to distributed renewable energy sources through preset simple rules, achieving fast but coarse control.

[0149] (2) The second approach is a control strategy based on traditional optimization algorithms, such as deterministic optimization methods like linear programming or quadratic programming. This approach establishes a single-objective optimization model to solve for the optimal power setpoint under the constraints of the power grid, achieving relatively accurate but slow-responding control.

[0150] (3) The third approach is a control system based on a centralized-distributed hybrid architecture. This approach attempts to calculate the global optimization target at the center and execute control commands locally, thereby achieving a combination of global optimization and local execution through hierarchical control.

[0151] (4) In terms of specific implementation, the closest existing technology is a distributed new energy active power control method based on model predictive control (MPC). This scheme establishes a linearized model of the power distribution system, aims to minimize network loss and voltage deviation, solves the optimization problem in each control cycle, and sends the results to each new energy unit.

[0152] (5) Another related technology is an active power control method based on sensitivity analysis. This method calculates the sensitivity coefficient of each new energy node to the power of the critical line, allocates the adjustment task according to the sensitivity, and achieves rapid power balance.

[0153] These existing technical solutions provide feasible technical paths for those skilled in the art in terms of control architecture, optimization methods, and execution mechanisms, but they all have obvious limitations, as follows:

[0154] (1) The control method based on fixed rules has significant defects. Its control logic is rigid and cannot adapt to the dynamic changes in new energy output and load demand. When the system operating state deviates from the preset operating conditions, the method cannot self-adjust, resulting in a significant decrease in control effect and even causing new safety and stability problems. The method can only achieve coarse power allocation and cannot meet the control accuracy requirements in scenarios with a high proportion of new energy access.

[0155] (2) Although control strategies based on traditional optimization algorithms achieve the optimization objectives to a certain extent, their computational efficiency is low, making it difficult to meet the stringent timeliness requirements of real-time control. When dealing with multi-objective and nonlinear optimization problems, such methods often get stuck in local optima and cannot obtain globally optimal control strategies. In addition, traditional optimization algorithms rely too heavily on model accuracy. In practical engineering applications, due to the prevalence of model errors and parameter uncertainties, the theoretical optimization results are greatly reduced in actual execution.

[0156] (3) In engineering practice, the control system based on the centralized-distributed hybrid architecture has exposed the core problem of poor command synchronization. Due to the differences in communication delay and device response time, the timing of the execution of control commands by each distributed unit is not synchronized, resulting in power oscillations during system transients, which seriously affects the stability and accuracy of the control system.

[0157] (4) Specifically, the most similar model predictive control (MPC) method has limitations in three main aspects: First, the optimization objective is singular, usually only considering individual indicators such as network loss or voltage deviation, and cannot take into account multiple objectives such as economy, safety and new energy consumption. Second, the computational complexity is high, and solving the optimization problem in each control cycle takes a long time, making it difficult to adapt to the rapid control requirements of minute-level or even shorter time scales. Third, the model dependence is strong, and the requirements for prediction accuracy and system parameter accuracy are too high, which leads to a decline in control performance due to uncertainties in practical applications.

[0158] (5) Although the sensitivity analysis-based method has a fast calculation speed, its control accuracy is limited. It can only achieve approximate optimization and cannot achieve fine power allocation. At the same time, the method lacks forward adjustment capability and cannot pre-optimize the control strategy based on predictive information.

[0159] Based on this, in order to overcome the shortcomings of the prior art, this application provides a distributed new energy active power control method with high control accuracy, fast response speed, ability to collaboratively optimize multiple objectives and adaptive capability, also known as a distributed new energy active power automatic control method based on a multi-source collaborative control framework, or a distributed new energy precise active power automatic control method based on a multi-source collaborative control framework, as detailed below.

[0160] The power control method provided in this application overcomes the limitations of single-objective optimization, achieving multi-objective coordinated optimization of economy, safety, and renewable energy absorption capacity; it solves the problems of low computational efficiency and easy getting trapped in local optima in traditional optimization algorithms by using an efficient parallel evolutionary algorithm to achieve rapid global optimization in complex solution spaces; it solves the problem of asynchronous control command issuance and execution by ensuring control accuracy and consistency through high-precision communication and execution mechanisms; and it solves the problem of poor system adaptability by introducing a predictive and rolling optimization mechanism, enabling the system to proactively respond to fluctuations and maintain continuous optimal operation.

[0161] The power control method provided in this application offers the following advantages: First, a multi-objective collaborative optimization modeling technique is offered: a multi-objective optimization model is established that comprehensively considers total power generation efficiency, grid fluctuation minimization, and wind / solar curtailment rate, achieving collaborative optimization of multiple conflicting objectives through weighting coefficients. Second, a parallel and efficient optimization solution technique is offered: a genetic algorithm with an elite retention strategy, combined with a parallel computing architecture, is used to achieve rapid global optimization of the control strategy in a complex solution space. Third, an end-to-end precise control technique is offered: precise power setpoint issuance and execution are achieved through high-speed communication networks and precise modulation of power converters, ensuring the synchronization and accuracy of control commands. Fourth, a rolling optimization and adaptive technique is offered: combining a model predictive control framework and ultra-short-term forecast data, continuous adaptive operation of the system is achieved through rolling time window optimization and system performance monitoring.

[0162] Furthermore, the power control method provided in this application offers several approaches. One is a multi-objective optimization-based active power control method, comprising: establishing a multi-objective optimization model that comprehensively considers total power generation efficiency, grid fluctuations, and wind / solar curtailment rates; and transforming the multi-objective model into a single objective for solution through weighting coefficients. Another is a genetic algorithm-based control strategy optimization method, employing a genetic algorithm with an elite retention strategy, achieving efficient optimization of the control strategy through solution set initialization, optimization operator execution, and parallel computation. A third is a method for precise execution of control commands, achieving precise power setpoint issuance and tracking through command encapsulation and secure transmission, precise inverter modulation, and execution status feedback. Finally, a rolling optimization method based on model predictive control is provided, integrating predictive data, optimizing the rolling time window, and monitoring and adjusting system performance to form a closed-loop adaptive control system.

[0163] The power control method provided in this application exhibits several significant advantages compared to existing technologies. Regarding the control model, this application employs a multi-objective optimization method, simultaneously optimizing multiple conflicting objectives. This overcomes the limitations of single-objective optimization in existing technologies, achieving a synergistic improvement in economy, safety, and renewable energy absorption capacity, thereby enhancing overall operational efficiency. In terms of the optimization algorithm, this application's algorithm possesses global search capabilities and parallel computing advantages. Compared to traditional optimization algorithms, it boasts faster computation speed and better convergence, efficiently handling nonlinear and high-dimensional problems and meeting real-time control requirements. Regarding control accuracy, this application utilizes high-speed communication and precise modulation technology to achieve accurate power setpoint issuance and execution, reducing command latency and errors, and significantly improving control accuracy and synchronization. In terms of adaptability, this application integrates a model predictive control framework, employing ultra-short-term forecast data for rolling optimization. This enables the system to proactively respond to renewable energy output and load fluctuations, enhancing robustness and adaptability. Ultimately, at the system architecture level, this application constructs a complete closed-loop control system from modeling, optimization, execution to adaptation. Compared with the discrete solutions of existing technologies, it achieves precise active power control through multi-source collaboration, providing efficient and reliable technical support for the large-scale integration of distributed new energy sources.

[0164] It should be noted that any technical feature in any of the above embodiments provided in this application is also applicable to any of the following embodiments provided in this application, and similar details will not be repeated hereafter.

[0165] Figure 7 Here is a structural block diagram of a distributed new energy active power automatic control device based on a multi-source collaborative control framework in one embodiment, with reference to... Figure 7 The device includes:

[0166] The model building module 701 is used to build a multi-objective optimization model based on the interrelated preset optimization objectives in the distributed new energy power system.

[0167] The model solving module 702 is used to solve the multi-objective optimization model using a parallel optimization algorithm with an elitist retention strategy to obtain the optimized power allocation scheme;

[0168] The instruction execution module 703 is used to execute control instructions associated with the optimized power allocation scheme based on high-speed communication technology and inverter precision modulation technology; wherein, the control instructions are used to control the active power of the distributed new energy power system.

[0169] In this embodiment of the application, based on, as follows Figure 7 The connections between the modules or units shown in the diagram can improve the power control effect of the distributed new energy power system through the cooperation between these modules or units.

[0170] In another embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, it includes a processor, memory, input / output interfaces, and a communication interface. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface is connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores relevant data. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. The computer program can be executed by the processor to implement the various methods described in the above embodiments.

[0171] In yet another embodiment, a computer device is provided, such as a terminal, whose internal structure diagram may be as follows: Figure 9As shown, it includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. The computer program can be executed by the processor to implement the various methods described in the above embodiments.

[0172] Those skilled in the art will understand that Figure 8 and Figure 9 The structure shown is only a block diagram of a part of the structure related to the solution of this application, and does not constitute a limitation on the computer device on which the solution of this application is applied. It may also include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements, in order to realize the function of the terminal or server.

[0173] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0174] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the systems, devices, equipment, modules or units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, devices, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only 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; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0176] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0177] Furthermore, the functional modules in the various embodiments of this application 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 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 be stored in a computer-readable storage medium.

[0178] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0179] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium, or a semiconductor medium (e.g., a solid-state drive), etc.

[0180] The technical solutions provided by the embodiments of this application have been described in detail above. Specific examples have been used in the embodiments of this application to illustrate the principles and implementation methods of the embodiments of this application. The description of the above embodiments is only for the purpose of helping to understand the methods and core ideas of the embodiments of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments of this application. Therefore, the content of this specification should not be construed as a limitation on the embodiments of this application.

Claims

1. A distributed active power automatic control method based on a multi-source cooperative control framework, characterized in that, The method comprises: Based on the interrelated preset optimization objectives in the distributed new energy power system, a multi-objective optimization model is constructed; A parallel optimization algorithm with an elite reservation strategy is used to solve the multi-objective optimization model to obtain an optimized power distribution scheme; Based on high-speed communication technology and precise inverter modulation technology, control instructions associated with the optimized power distribution scheme are executed; wherein the control instructions are used to control the active power of the distributed new energy power system.

2. The method of claim 1, wherein, The preset optimization objectives include the highest total power generation efficiency objective, the minimum grid fluctuation minimization objective, and the lowest wind and light curtailment rate objective. Based on the interrelated preset optimization objectives in the distributed new energy power system, a multi-objective optimization model is constructed, which comprises: The multi-objective optimization problem corresponding to the highest total power generation efficiency objective, the minimum grid fluctuation minimization objective, and the lowest wind and light curtailment rate objective is converted into a single-objective optimization problem using the weighted sum method to obtain the optimization objective of the multi-objective optimization model; Voltage constraints, line capacity constraints, and device capacity constraints are determined as constraint conditions of the multi-objective optimization model; The multi-objective optimization model formed by the optimization objective and the constraint condition is subjected to model parameter initialization to obtain the initialized multi-objective optimization model.

3. The method of claim 1, wherein, The multi-objective optimization model is solved using a parallel optimization algorithm with an elite reservation strategy to obtain an optimized power distribution scheme, which comprises: The multi-objective optimization model is subjected to solution set initialization and performance evaluation to obtain an initialized solution set and a performance evaluation value; According to the performance evaluation value, optimization operators are executed to iteratively update the initialized solution set; If the iterative updating process meets a preset convergence condition, the optimized power distribution scheme is output.

4. The method of claim 1, wherein, Based on high-speed communication technology and precise inverter modulation technology, control instructions associated with the optimized power distribution scheme are executed, which comprises: The power set value corresponding to the optimized power distribution scheme is converted into the control instruction in a format recognizable by the device, and the control instruction is issued through a secure channel with integrity verification and access control mechanism; The control instruction is executed to adjust the output power of the inverter according to the issued control instruction; The execution result of the control instruction is obtained and fed back to the control system.

5. The method of claim 1, wherein, The method further comprises: In each preset prediction period, future prediction data corresponding to the preset prediction period are obtained from a meteorological service platform and a load prediction system; The prediction data are imported into the multi-objective optimization model to update the maximum available power parameter and the load parameter in the multi-objective optimization model.

6. The method of claim 1, wherein, The method further comprises: In a preset control period, the processes of model establishment, optimization solving, and instruction issuing of the multi-objective optimization model are cyclically executed.

7. The method of claim 1, wherein, The method further comprises: In a preset performance monitoring period, the control effect of a historical period is comprehensively evaluated to obtain a comprehensive evaluation result; According to the comprehensive evaluation result, the weight coefficient and the key parameter in the multi-objective optimization model are optimized.

8. A distributed active power automatic control device based on a multi-source cooperative control framework, characterized in that, The device comprises: A model construction module is configured to construct a multi-objective optimization model based on each preset optimization target associated with each other in the distributed new energy power system; A model solution module is configured to solve the multi-objective optimization model by using a parallel optimization algorithm with an elite reservation strategy to obtain an optimized power distribution scheme. An instruction execution module is configured to execute control instructions associated with the optimized power distribution scheme based on high-speed communication technology and precise inverter modulation technology, wherein the control instructions are used to control active power of the distributed new energy power system.

9. A computer device, comprising: The computer device comprises: at least one processor, a memory; The memory is configured to store program code, and the processor is configured to call the program code stored in the memory to execute the method of any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer device comprises: at least one processor, a memory; The memory is configured to store program code, and the processor is configured to call the program code stored in the memory to execute the method of any one of claims 1 to 7. The computer device comprises: at least one processor, a memory; The memory is configured to store program code, and the processor is configured to call the program code stored in the memory to execute the method of any one of claims 1 to 7.

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