35 results about "Linear optimization problem" patented technology

The invention is suitable for the technical field of integrated energy microgrid scheduling, and provides a power distribution network random distributed robust optimization scheduling method of an integrated energy microgrid group, comprising the following steps: constructing a DN structure comprising a plurality of microgrids; the DN day-ahead scheduling optimization model containing the IEMGs pays attention to operation cost and constraint conditions; for the DN, the problem of non-convex nonlinear optimization including line loss is solved; for the IEMG, the model is used for processing a convex optimization problem considering WT and PV output power uncertainty; power exchange is carried out between the DN and the IEMG through a connecting line, so that a double-layer model framework is constructed to analyze the system; the solving process is a double-layer optimization iteration process, an outer layer obtains an interaction power value between the DN and the IEMG by using an ATC method, and an inner layer uses Camp; the CG method solves the problem of random distribution robust optimization of IEMG. According to the method, the carbon emission and the operation cost of the system can be effectively reduced, meanwhile, stable operation of the DN is ensured, and a theoretical basis is provided for efficient utilization of ammonia energy in the DN.

The invention discloses a vehicle kinetic parameter identification method based on constrained nonlinear optimization, which comprises the following steps: establishing a vehicle two-degree-of-freedom transverse kinetic model based on a small-slip-angle tire and a road cross slope, acquiring a vehicle historical driving data measurement value, acquiring a to-be-estimated vehicle kinetic parameter, and identifying the to-be-estimated vehicle kinetic parameter according to the to-be-estimated vehicle kinetic parameter. Constructing a constrained nonlinear optimization problem about the yaw velocity, solving to obtain an estimated value sequence of the kinetic parameters of the vehicle, carrying out statistics on the sequence, and taking a statistical median as a final estimated value; on one hand, a vehicle transverse dynamics model including a road cross slope is adopted; on the other hand, the constraint of vehicle parameters is considered, a nonlinear optimization problem is constructed, and the vehicle front axle distance (the longitudinal distance from a front axle to the vehicle mass center), the vehicle rear axle distance (the longitudinal distance from a rear axle to the vehicle mass center), the front wheel cornering stiffness, the rear wheel cornering stiffness and the vehicle total mass are identified at the same time.

The invention discloses a micro-grid emergency power supply capability assessment method based on an aggregation operation domain. The method comprises the following steps: firstly, establishing an aggregation operation domain general model based on Enry hub; according to the decomposable characteristic of the model, constructing an aggregation operation domain model based on partial decomposition of time domain coupling and time domain decoupling; for the time domain decoupling part, obtaining upper and lower power boundaries of multi-cycle decoupling by solving a linear optimization problem; for the time domain coupling part, based on a homomorphic polyhedron aggregation operation domain solving algorithm, the time domain coupling part model with the energy storage property is approximated by optimizing a basic homomorphic polyhedron, a scaling factor and a translation factor at the same time; and finally, constructing a microgrid emergency power supply capability evaluation system, solving the model and calculating evaluation indexes to obtain a final evaluation result. The method provided by the invention proves that the aggregation operation domain can be equivalently modeled as a time domain decoupling and time domain coupling partial decomposition model, and the method can be effectively applied to various scenes such as power distribution network fault recovery and the like.

The invention discloses a hydrogen energy storage capacity configuration and port comprehensive energy system multi-energy coupling collaborative optimization method, and belongs to the field of energy system control and optimal configuration, and the method comprises the steps: constructing a hydrogen-containing port comprehensive energy optimization system model comprising a hydrogen energy storage capacity configuration model and a port operation cost minimization model; and determining the hydrogen energy dissatisfaction cost of the port operator according to the foreground theoretical model and the potential economic loss evaluation of the hydrogen energy accident risk. According to the hydrogen energy storage capacity configuration, the operation cost function and the hydrogen energy dissatisfaction cost of the port operator, establishing a hydrogen energy storage capacity configuration and port integrated energy system multi-energy coupling collaborative optimization model including minimization of the sum of the port operation cost and the port operator dissatisfaction cost; and converting a nonlinear optimization problem into a linear optimization problem. The method effectively balances the operation cost of the port and the subjective worries of the port operator, thereby reducing the operation cost while maintaining the acceptable level of the satisfaction of the operator.

The invention discloses a multi-parameter collaborative optimization method for deterministic grinding and polishing of a limited space robot, and the method comprises the steps: bringing a material removal amount prediction model, a grinding and polishing track, a robot posture and dynamic error compensation into a unified optimization process through a multi-step collaborative optimization framework; the nonlinear coupling influence of robot attitude adjustment and feeding speed fluctuation on removal amount distribution is reduced, the cooperative problems of removal amount out-of-tolerance, coverage uniformity and interference avoidance caused by multi-parameter coupling in a narrow flow channel are solved, finally, the high-dimensional nonlinear optimization problem is decomposed through an alternating direction multiplier method, the convergence efficiency is remarkably improved, and the convergence efficiency is improved. And it is ensured that the machined surface meets the preset tolerance requirement, and high-precision deterministic grinding and polishing under the limited space are achieved.

The invention discloses a real-time solution reconstruction planning method for a man-machine interaction rope traction parallel robot, which belongs to the field of robot configuration planning, and comprises the following steps of: 1, constructing kinematics and dynamics models according to the position relationship between a rope leading-out point and a movable platform, and establishing an admittance model; 2, according to the dynamics and admittance model, a hyperplane movement method is used for representing a force feasible condition, and an optimization problem objective function is set according to the force feasible condition; 3, expressing a rope leading-out point solving problem as a nonlinear optimization problem through constraint, approximating the nonlinear optimization problem as a linear optimization problem through linear approximation, and solving an approximate optimal solution of the linear optimization problem; and 4, an artificial potential field is set according to dynamic characteristics, the approximate optimal solution is corrected, the solved rope leading-out point position is not located at the boundary of a solution space while the solving speed is guaranteed, and reconstruction planning of real-time solving of the robot rope leading-out point is completed. According to the method, the solving instantaneity and the man-machine interaction stability can be ensured.

The present invention discloses a method for online identification of the dynamic parameters of an articulated serial robot, comprising the following steps: S1. For the articulated serial industrial robot to be identified, a robot dynamic model that takes friction into account is established; S2. The dynamic model of the articulated serial industrial robot is linearized; S3. An optimal excitation trajectory is designed for the identification experiment, using a five-term finite Fourier series to obtain the excitation trajectory by solving a constrained nonlinear optimization problem; S4. The serial robot runs the excitation trajectory while simultaneously collecting the position information of each joint of the robot and the driving current of each joint at each moment, and online calculating the parameters to be identified at each moment. The present invention can perform online identification of robot dynamic parameters, improves the efficiency of robot dynamic parameter identification, and can be used in industrial scenarios such as robot mobile handling, polishing and grinding, and heavy-load hydraulics.

Traditional algorithms for solving constrained optimization problems are complicated to implement, difficult to interpret, and require significant computational resources. Disclosed embodiments convert constrained optimization problems into parametric optimization problems, in which at least a subset of the constraints are converted into parametric quadratic penalty (PQP) terms that each depends on a translational parameter. The parametric optimization problem may be used for optimization in a power system (e.g., for optimal power flow, economic dispatch, etc.). When solving the parametric optimization problem, the translational parameters are updated to ensure convergence. The parametric optimization problem can be solved with reduced computational expense, using only a linear equation solver to solve a sequence of primal variables only, thereby reducing computational complexity and expense. In addition, the disclosed embodiments provide a means to incorporate constraints into machine-learning algorithms. The disclosed algorithmic framework also provides interpretability and insights for analysis.

The present invention discloses a method and device for jointly optimizing and controlling scalable adjustable resources, including: considering the dynamic process of cluster response, constructing multi-dimensional response performance parameters of adjustable resources, and solving the comprehensive response performance parameters of adjustable resources; considering the impact of response uncertainty on the response performance of adjustable resources, constructing a response performance analysis model; based on the response performance analysis model, decomposing and extracting features from the historical time series data of relevant parameters in the model through data analysis; according to the extracted feature set, obtaining the parameters of the adjustable resource response performance analysis model at multiple time nodes; considering the difference in adjustable resource response performance, determining the objective function of the joint optimization control of adjustable resources for VPP to participate in peak shaving and frequency modulation; determining the constraint conditions for the joint optimization control of adjustable resources for VPP to participate in peak shaving and frequency modulation; substituting the relevant parameters of the model, quickly solving the above linear optimization problem through Cplex software, and adjusting the electricity price structure and electricity load of users based on the solution results to reduce the transmission risk of transmission lines.

The application belongs to the technical field of power systems, and relates to a carbon emission flow constraint linearization method for low-carbon scheduling of power systems. The method comprises the following steps: establishing a power carbon emission flow model based on power flow direction decomposition to obtain power flow direction constraints and node carbon flow balance constraints for coupling carbon flow calculation; introducing auxiliary state variables and processing the power flow direction constraints by using a large M method; constructing a carbon flow balance equation simplification model based on piecewise linearization and a large M relaxation method; based on the power flow direction constraint and node carbon flow balance constraint linearization method, establishing a low-carbon economic scheduling model of the power system coupled with carbon flow calculation, and converting the model into a linear optimization problem for solving. The application converts the original nonlinear problem into a linear optimization programming problem, improves the solving universality of the low-carbon economic scheduling method of the power system containing carbon flow calculation, can guarantee good carbon accounting accuracy, and significantly improves the model solving efficiency.

The invention relates to the technical field of data processing, in particular to a real-time data query method and system, and the method comprises the steps: obtaining an initial population comprising a plurality of chromosomes; wherein the chromosomes are in one-to-one correspondence with the query plans; in the initial population, determining a fitness value of each chromosome of the plurality of chromosomes; selecting a parent chromosome from the plurality of chromosomes based on the fitness of each chromosome; performing crossover operation on the parent chromosomes to generate offspring chromosomes; performing mutation operation on the offspring chromosomes to obtain updated offspring chromosomes; and determining a target query plan based on the fitness value corresponding to the updated progeny chromosome. According to the method, the optimization of real-time data query is realized, the problem of multi-constraint nonlinear optimization can be efficiently solved, the data access efficiency is further improved, and the hardware resource demand is reduced.

This invention discloses a cost- and reliability-aware profit optimization method for a heterogeneous multi-server system. The method includes: establishing a heterogeneous multi-server system model to obtain expected service revenue and multi-server system utilization; establishing a soft error reliability model to formulate the application domain investment selection and profit maximization problem as a constrained nonlinear optimization problem; utilizing an iterative method based on a penalty function to obtain the optimal multi-server system configuration, minimum cost, and maximum profit for any single application domain, and converting the profit and configuration optimization problem of the heterogeneous multi-server system into a 0-1 knapsack problem; and utilizing a dynamic programming-based algorithm and a greedy-based algorithm to solve the optimal application domain investment strategy and the corresponding maximum profit. This method can consider budget constraints and the soft error reliability of service requests while configuring the heterogeneous multi-server system configuration, thereby improving cloud service quality and provider profits.

The present invention discloses a real-time solution and reconfiguration planning method for a rope-pulled parallel robot with human-machine interaction, belonging to the field of robot configuration planning. The method comprises the following steps: 1. constructing a kinematic and dynamic model based on the positional relationship between the rope index point and the moving platform, and establishing an admittance model; 2. using the hyperplane movement method based on the dynamic and admittance models to express the force feasibility condition, and setting the optimization problem objective function according to the force feasibility condition; 3. constraining the rope index point solution problem to be expressed as a nonlinear optimization problem, approximating the nonlinear optimization problem to a linear optimization problem through linear approximation, and solving the approximate optimal solution of the linear optimization problem; and 4. correcting the approximate optimal solution based on an artificial potential field set according to the dynamic characteristics, while ensuring solution speed and ensuring that the obtained rope index point position does not lie at the boundary of the solution space, thereby completing the reconfiguration planning of the robot's real-time solution of the rope index point. This method can ensure real-time solution performance and human-machine interaction stability.

The invention discloses a power distribution network low-carbon high-quality scheduling method, system, equipment and medium considering distributed robust optimization segmented affine, and belongs to the technical field of power distribution network scheduling, and the method comprises the steps: taking the minimization of the carbon emission of the product of the main network carbon potential and the net load power as a target function; a Wasserstein distance is adopted to measure the deviation between historical data and real distribution, and multiple types of operation constraints are considered at the same time; the above distribution robust optimization model is converted and solved by adopting a piecewise affine theory, an original nonlinear optimization problem is converted into a linear optimization problem based on a simplex dominant uncertainty set, and the model solving efficiency and robustness are improved. According to the method, the uncertainty of new energy output and carbon potential is effectively dealt with through Wasserstein distance constraint, and the robustness is high; by means of segmented affine transformation, nonlinear robust optimization is simplified into linear optimization, and the solving speed is remarkably increased.

The application discloses a multi-parameter collaborative optimization method for deterministic polishing of a robot in a confined space, and the method is characterized in that: through a multi-step collaborative optimization framework, a material removal amount prediction model, a polishing track, a robot posture and dynamic error compensation are integrated into a unified optimization process, the nonlinear coupling influence of robot posture adjustment and feed speed fluctuation on the removal amount distribution is reduced, the collaborative problems of removal amount out-of-tolerance, coverage uniformity and interference avoidance caused by multi-parameter coupling in a narrow flow channel are solved, and finally, the high-dimensional nonlinear optimization problem is decomposed through an alternating direction multiplier method, the convergence efficiency is significantly improved, the processing surface can meet the preset tolerance requirement, and the deterministic polishing with high precision in the confined space is realized.

A controller for controlling an operation of a robot to execute a task is provided. The controller comprises a memory configured to store a set of dynamic movement primitives (DMPs) associated with the task. The set of DMPs comprise a set of at least two dynamical systems: a function representing point attractor dynamics and a forcing function corresponding to a learned demonstration of the task. The controller comprises a processor configured to transform the set of DMPs to a set of constrained DMPs (CDMPs) by determining a perturbation function associated with the forcing function. The perturbation function is associated with a set of operational constraints. The processor is further configured to solve, a non-linear optimization problem for the set of CDMPs based on the set of operational constraints and generate, a control input for controlling the robot for executing the task, based on the solution.

The invention relates to the technical field of power grid planning, in particular to a high-performance parallel automatic differential method, system, equipment and medium for main distribution micro power flow nonlinear optimization, and by adopting the method provided by the invention, modeling is carried out based on a main distribution micro power flow nonlinear optimization problem, and an expression diagram is constructed; considering that a large number of isomorphic constraints exist in the problem, the constructed expression graphs are subjected to grouping management, and an automatic differential calculation function pointer used for calculating each group of instances is generated for a plurality of obtained expression groups. And finally, based on the constructed automatic differential calculation function pointer, introducing an OpenMP parallel framework, and carrying out automatic differential parallel calculation on each expression group. Based on the experimental result of opfbenchmark, the method provided by the invention improves the calculation efficiency of automatic differentiation by 47% in the main distribution micro nonlinear optimization problem.

The application provides a sequential linearization solving method and device for network-constructed energy storage optimal control problems, comprising: establishing a system frequency dynamic characteristic representation model and an energy storage dynamic characteristic representation model; constructing a model prediction optimal control framework for network-constructed energy storage, considering the coupling characteristics of system frequency dynamics and energy storage dynamics, and optimizing the optimal control strategy under the minimum cost of network-constructed energy storage; analyzing the linearization constraint conditions of the model prediction optimal control framework, and realizing the linearization representation of the system frequency dynamics and the energy storage dynamics; analyzing the linearization objective function of the model prediction optimal control framework, and realizing the linearization mapping between the optimization objective and the system state and the energy storage state; constructing a sequential linearization solving framework, converting the nonlinear optimization problem of the optimal control framework into a group of iterative and progressive linear optimization problems, and realizing the fast and stable convergence of the algorithm through an acceptance rejection mechanism and a credible domain updating mechanism.

The present invention relates to a riveting quality optimization method based on a multi-objective particle swarm algorithm and PCA. The riveting quality optimization method based on the multi-objective particle swarm algorithm and PCA effectively evaluates the impact of different parameter combinations on a response target by performing nonlinear transformations on defined parameters in the multi-objective particle swarm algorithm and using a Taguchi experiment. Principal component analysis is performed on the signal-to-noise ratio obtained from the Taguchi experiment using PCA. The fitness values of the particles are calculated based on the data obtained from the principal component analysis as a fitness function of the multi-objective particle swarm algorithm. This method can simplify high-dimensional data, extract the main information components, reduce the complexity of the problem, and make the optimization process more efficient. The principal component analysis can extract the nonlinear components that affect the signal-to-noise ratio, thereby helping the multi-objective particle swarm algorithm better handle nonlinear optimization problems.

The embodiment of the invention provides a time-varying optimization solving method based on a robust direct discrete recurrent neural network, and the method comprises the specific steps: representing an actual physical system as a time-varying optimization problem with nonlinear equality constraints through a mathematical modeling method; according to a Lagrange multiplier method, converting the time-varying optimization problem into a nonlinear equation system; establishing an error function according to the nonlinear equation system; constructing a robust direct discrete recurrent neural network solver in combination with the error function; and analyzing by using the robust direct discrete recurrent neural network solver to obtain an optimal solution of the time-varying optimization problem. According to the method, nonlinear equality constraint, direct discretization and noise suppression are considered at the same time, higher solving precision is achieved, the method can be directly applied to digital hardware, higher anti-noise robustness of the method can be reflected in various noise environments, and the method can be widely applied to the technical field of time-varying nonlinear optimization problems.

The invention belongs to the technical field of power systems, and relates to a power system low-carbon dispatching-oriented carbon emission flow constraint set linearization method. The method comprises the following steps: establishing an electric power carbon emission flow model based on tidal current direction decomposition to obtain tidal current direction constraint and node carbon flow balance constraint for coupling carbon flow calculation; introducing an auxiliary state variable, and processing a power flow direction constraint by adopting a large M method; constructing a carbon flow balance equation simplified model based on piecewise linearization and a large M relaxation method; and based on a power flow direction constraint and node carbon flow balance constraint linearization method, establishing a power system low-carbon economic dispatching model coupled with carbon flow calculation, converting the power system low-carbon economic dispatching model into a linear optimization problem, and solving the linear optimization problem. According to the method, an original nonlinear problem is converted into a linear optimization planning problem, the solving universality of the power system low-carbon economic dispatching method containing carbon flow calculation is improved, good carbon accounting accuracy can be guaranteed, and the model solving efficiency is remarkably improved.

The present invention discloses a method for optimizing energy storage operation strategies that takes into account charging and discharging losses and real-time electricity prices. The method comprises the following steps: establishing an optimization model for user energy storage system operations; determining optimal charging and discharging periods based on electricity price curves; determining the charge and discharge states of the energy storage system during each period based on the optimal charging and discharging periods; converting the optimization model into a linear programming model based on the energy storage system charge and discharge states; and finally solving for an operation strategy that maximizes the operational efficiency of the energy storage system. The present invention converts the original model into a linear programming model, thereby simplifying an NP-hard combinatorial optimization problem into a linear optimization problem. This significantly improves the computational speed of the optimization process and can be conveniently run on resource-limited embedded industrial computers deployed on-site in energy storage systems.

The invention provides a laser processing reflector predictive control method based on a dimension expansion mapping dynamical model, and belongs to the field of laser processing reflector motion control, and the method comprises the following steps: S1, providing a dynamical model based on neural network dimension expansion mapping in combination with a neural network and a linear model, so as to describe the double-axis dynamic characteristics of a laser processing reflector; s2, identifying parameters in the dimension expansion mapping dynamical model by solving an optimization problem based on input and output data; s3, designing a linear quadratic programming problem based on the dimension expansion mapping dynamical model and the control target; and S4, solving a quadratic programming problem to obtain a predictive controller with nonlinear feed-forward compensation capability. The method mainly solves the problem of trajectory tracking of the biaxial laser processing reflector under high-frequency sampling, and can solve the problem of insufficient tracking precision caused by nonlinearity of a reflector driver and the problem of difficulty in solving the nonlinear optimization problem under rapid sampling.

This invention discloses a method and apparatus for optimizing the design of a space-based natural fly-around trajectory for a medium-to-high orbit target, belonging to the field of spacecraft orbital dynamics and optimization design technology. It includes: establishing a dynamic model of a medium-to-high orbit satellite considering complex perturbations; generating initial values ​​for the fly-around trajectory using a dual-mode approach combining direct position offset and CW equation analysis; constructing a multi-objective weighted optimization objective function, jointly constraining the root mean square of the relative distance difference, the extreme difference of the relative distance, and the mean absolute value of the difference in the relative distance interval time to evaluate trajectory morphological stability; setting three-dimensional coordinate constraints in the target satellite orbital coordinate system to avoid invalid solutions, and using the interior point method to solve the constrained nonlinear optimization problem; and achieving trajectory correction through dynamic model extrapolation, quantitative evaluation of performance indicators, and iterative optimization. This invention overcomes the defect of large extrapolation errors of the CW equation under complex perturbation environments by combining an accurate dynamic model with optimization methods, resulting in a natural fly-around trajectory with high accuracy and long holding time.

The invention provides a steering and torque distribution integrated control method for a four-wheel independent drive vehicle based on nonlinear predictive control, which comprises the following steps of: firstly, establishing a nonlinear state-space equation of the four-wheel independent drive vehicle, and establishing an optimal control problem based on the state-space equation; and then the optimal control problem of the original continuous time system is converted into a nonlinear optimization problem by using a forward Euler method and a multiple targeting method, and the singularity of the nonlinear optimization problem is effectively eliminated and the convexity of the nonlinear optimization problem is improved by adding a regular term in an optimization target. Finally, a moderate-scale sequential quadratic programming mode is designed, and compared with the prior art adopting hierarchical nonlinear predictive control, an open source solver IPOPT and the like, the control precision and the solving speed can be effectively improved.

The application provides a power and price segmentation declaration method and device for a wind storage power station participating in a forward market, wherein the method comprises the following steps: constructing a forward market declaration model of the wind storage power station based on an initial energy of the storage and a wind power output-out clearing price scenario set, a target function of the forward market declaration model comprising a current period net income and a next period storage energy value function; converting the forward market declaration model into a segmented linear optimization problem about accumulated declaration power; and solving the segmented linear optimization problem by using a dynamic programming algorithm to obtain a stepwise power and price declaration curve. The method generates a stepwise power and price declaration curve for the wind storage power station under the premise of not depending on information of other market subjects, not depending on accurate probability distribution assumptions, and not needing to call an external commercial optimization solver, and the stepwise power and price declaration curve takes into account current income, power shortage risk and future storage value, and has high calculation efficiency and strong engineering implementability, and the economic income and operation reliability of the wind storage power station in the forward market are improved.

The application relates to a robot data-driven control method and device based on an iterative algorithm. The method comprises the following steps: constructing a control strategy of a robot dynamics model according to a linear gain matrix to be optimized and a dimension-up characteristic function; the dimension-up characteristic function is used for mapping an original state space of the robot to a high-dimensional characteristic space in a Koopman operator framework, so that a nonlinear optimization problem of the control strategy is converted into a high-dimensional linear optimization problem; a Q function is defined; the Q function is converted; and data samples of z=(s, u) are used to iteratively calculate an updated value h of the matrix in a data-driven manner i The linear gain matrix is calculated, so that the control strategy of the robot dynamics model is obtained. The method can avoid introducing model errors and can calculate a more optimal control strategy.

The invention discloses a large-size high-uniformity coil design method based on adaptive PSOGWO fusion optimization, and the method comprises the steps: carrying out the Taylor expansion of a central magnetic field, and converting the solving of the axial position of a coil into a multivariable nonlinear optimization problem; and under geometric and unified manufacturing constraints, a self-adaptive PSOGWO fusion optimization algorithm is provided to obtain the optimal axial position of each pair of coils. According to the algorithm, particle swarm global exploration and grey wolf hunting type local mining are combined, and adaptive factors and weight proportions are introduced, so that the search precision and robustness in a complex multi-peak problem are improved, and the convergence stability and the local extremum resistance of the algorithm are improved. Under the background of a large-size coil, the uniform interval and the center uniformity of the coil designed by the novel optimization algorithm are obviously superior to those of a traditional coil and a coil designed by a single intelligent algorithm. The design method is suitable for scenes of SERF atom magnetometer compensation / modulation coils and the like, and can be used in cooperation with a multi-channel measurement scheme to improve the spatial resolution.

The present invention relates to a frequency offset estimation method for average consensus-based clock synchronization, and belongs to the technical field of wireless sensor networks. According to the method, in combination with distributed one-way broadcast characteristics, solving of maximum likelihood estimation is converted into a linear optimization problem, and a relative frequency offset estimation value is obtained by adopting an iterative method. By applying the estimation value to the compensation of logic clock parameter between nodes, an effect of keeping logic clocks of network nodes consistent can be achieved. According to the present invention, distribution characteristics of communication time delay are fully considered, accurate relative frequency offset estimation can be implemented, so the synchronization precision of average consensus-based clock synchronization is effectively improved, the maximum likelihood estimation solving is performed by adopting the iterative method, an estimation algorithm is simplified, and storage overhead is reduced.

The invention discloses a virtual power plant optimal configuration method and system based on a hunting optimization algorithm, and the method comprises the steps: constructing a virtual power plant model according to the operation characteristics of distributed energy resources; according to the virtual power plant model, constructing an optimization model with the goal of minimizing the operation cost of the virtual power plant and the unsupplied electric quantity in extreme weather; according to the optimization model, performing iterative optimization on the position and capacity of the virtual power plant in the radial power distribution system by using a hunting optimization algorithm to obtain an optimal configuration scheme; and performing scheduling control on the virtual power plant according to the optimal configuration scheme. According to the method, a hunting optimization algorithm is adopted, the preying behavior of a hunter is simulated, global search and local development capabilities are balanced through a position updating mechanism, and the high-dimensional nonlinear optimization problem in virtual power plant configuration is effectively solved. Compared with a traditional method, the method can remarkably improve the toughness of the intelligent micro-grid under the extreme fault condition, reduces the operation cost, and improves the power supply reliability.