253 results about "Random optimization" patented technology

Provided in embodiments of the present disclosure are a method for scheduling workloads of a data center in an electricity market environment, and a device. In the embodiments of the present disclosure, a multi-stage random optimization solution that allows a data center to participate in a multi-level electricity market is provided. The types and data dependency relationship of workloads are taken into consideration, and a load scheduling model, in a time dimension, for the workloads is constructed; on the basis of electricity source information of the data center, a multi-stage electricity cost optimization model is constructed, so as to comprehensively consider the exogenic uncertainty of an electricity price and carbon emissions and the endogenic uncertainty of the workloads; on the basis of the multi-stage electricity cost optimization model, multi-stage optimization solving is performed on the load scheduling model, so as to obtain a load scheduling scheme and an electricity purchase strategy. Therefore, a more accurate and more reliable estimation is made concerning the electricity flexibility of the data center, such that the electricity cost of the data center can be reduced, support is provided for the reduction of carbon emissions, and the consumption of renewable energy sources is encouraged, thereby achieving the energy use flexibility of the data center in the multi-level electricity market environment.

The invention relates to the technical field of flour processing, and discloses an intelligent control method and system for a flour workshop, and the method comprises the following steps: collecting raw material characteristic data and equipment operation state data in real time through a sensor, calculating the state variable of the current system according to the collected data, and calculating the state variable of the current system according to the current state variable. Performing rolling optimization on a control strategy in a future time domain, dynamically adjusting grinding parameters according to a rolling optimization result, correcting a prediction model according to a data deviation between real-time acquired data and nonlinear model prediction data, and calculating an optimal control parameter combination based on a stochastic optimization theory; the system comprises a sensing module, a control module, an execution module, a feedback correction module and a random optimization module. By adopting the technical scheme that a high-precision sensor is combined with intelligent control, comprehensive automation and accurate control of the flour production process are achieved. The stability and consistency of flour quality are remarkably improved, and quality fluctuation is avoided.

The invention discloses a feeder voltage adjustable potential multi-target evaluation method and device considering a flexible resource operation domain. The method comprises the following steps: constructing a flexible resource adjustment operation constraint model; establishing a deterministic model of node flexible resource operation domain evaluation considering the adjustment cost; in consideration of output of renewable energy sources in nodes and source load uncertainty of loads, a multi-scene stochastic optimization method is adopted to convert a deterministic model into a node flexible resource operation domain stochastic evaluation model, and upper and lower adjustable ranges of node power, baseline power and segmented unit adjustment cost after flexible resource aggregation are obtained; and finally, comprehensively considering feeder network constraint, adjustment cost after flexible resource aggregation and feeder source load prediction, and constructing a multi-target evaluation stochastic optimization model of the adjustable potential of the feeder voltage with minimum node voltage deviation and network power loss to obtain a quantized adjustable range of the feeder voltage.

The invention discloses a new energy medium and long term stochastic programming method and system coupled with an electro-hydrogen water regulation power supply, and the method comprises the steps: building a power supply planning model with the minimum planning operation cost as an optimization target; bringing the new energy power supply and energy storage adjusting equipment operation model and the new energy consumption rate upper and lower limit constraint expression into a power supply planning model; in consideration of the randomness of fluctuation at two sides of a source and a load in power supply planning, based on the obtained power system operation scene set S and the power supply planning model, dividing into an investment decision-making layer and an operation simulation layer according to a time period influenced by a decision, and constructing a power supply planning two-stage stochastic optimization model based on a typical scene; and solving the power supply planning two-stage stochastic optimization model under a medium and long term planning framework, and determining the investment planning installed capacity of the adjusting power supply and the new energy in the planning level year. According to the invention, economy of medium and long term planning and power supply capacity configuration of new energy can be improved, and reasonable consumption and flexible adjustment of new energy electric power are promoted.

The invention relates to a coking coal blending method based on intelligent optimization, and the method comprises the following steps: S1, collecting historical feed coal mass ratio data and corresponding coke quality data, carrying out data cleaning preprocessing and data standardization, and analyzing the correlation between each coke quality parameter and a feed coal quality parameter; s2, performing hyper-parameter optimization of a base algorithm in combination with a correlation analysis result and historical data, determining each layer of base algorithm composition of a multi-algorithm multi-layer superposition model, and obtaining a coke parameter prediction model set; and S3, according to the coke quality constraint, on the basis of the coke parameter prediction model set, establishing a multi-constraint single-target random optimization model, and searching a lowest cost ratio scheme. According to the method, manual coal blending is converted into intelligent coal blending, the model is trained according to historical data, the proportioning scheme is automatically generated through the model, the manual participation degree is reduced, the process is simplified, and the proportioning cost and the proportioning time are lower.

The invention discloses a water-grain-energy-ecology association system collaborative configuration method considering unconventional water utilization, which comprises the following steps: firstly, constructing a water-grain-energy-ecology association system collaborative configuration model, and quantitatively analyzing the interaction relationship among water resource distribution, agricultural production, energy utilization and ecological protection; a vine copula method is adopted to quantify a complex interdependence relationship among a plurality of random variables; vine copula and a multi-stage stochastic optimization method are integrated to solve the model, and the influence of the brackish water utilization rate, the joint default probability and the incoming water level on the WFEE association system is analyzed. According to the method, cooperative configuration of unconventional and conventional water resources is considered, the pressure of shortage of regional water resources is relieved, and the complex mutual influence relationship among the subsystems is reflected.

The invention discloses a current inversion method for a steel tape armored multi-core cable, and the method comprises the steps: S1, setting a plurality of measurement points at a to-be-measured part of a to-be-measured multi-core cable, building a polar coordinate system with the geometric center of the to-be-measured multi-core cable as an original point, and constructing a position coefficient matrix, a conductor current matrix, and an attenuation coefficient matrix; s2, assigning an initial value to the unknown variable, and calculating to obtain an initial conductor current matrix and an initial attenuation coefficient matrix; s3, searching a local optimal solution of the attenuation coefficient matrix through a random optimization method; s4, constructing a linear equation set taking the conductor current matrix as an unknown variable to solve the conductor current matrix; and S5, after the sampling value of the magnetic induction intensity component is updated, solving the linear equation set, and calculating to obtain the current value of each conductor of the current cable. According to the method, on the basis of the time-invariant characteristic of the magnetization curve, the undetermined coefficient of the approximation function is solved by using the stochastic optimization algorithm, the nonlinear attenuation coefficient of the steel tape armoring of the target cable is solved, the influence of the steel tape armoring on the current inversion result is corrected, and the measurement efficiency and precision can be improved.

The invention relates to the technical field of data processing, and particularly provides a low-altitude aircraft power supply facility cooperative control method, system and device and a medium, and the method comprises the steps: predicting the renewable energy power generation power of a power supply facility and the charging load power of a low-altitude aircraft based on obtained multi-source information, and generating a prediction scene set representing uncertainty; based on the prediction scene set, through a multi-time scale optimization strategy, solving a scheduling model with optimal operation cost as a target, and successively obtaining a pre-scheduling plan of a day-ahead stage and a real-time control instruction of an intra-day stage; the multi-time scale optimization strategy comprises random optimization based on a scene and feedback optimization based on a rolling time domain; according to the real-time control instruction, power distribution is conducted on the hybrid energy storage system, and a power type energy storage unit preferentially responds to the transient power requirement for charging of the low-altitude aircraft. The comprehensive operation cost is effectively reduced, and particularly, the service life of equipment is prolonged through quantitative energy storage degradation.

The invention discloses an optimization method and system for aviation equipment storage and a medium, and the method comprises the steps: collecting multi-dimensional dynamic data, carrying out the fusion cleaning and structural feature extraction, and generating a high-dimensional feature set; outputting a dynamic demand prediction value and uncertainty measurement by using a hybrid intelligent prediction model fusing time sequence prediction and ensemble learning; calculating an optimal inventory control parameter through a stochastic optimization model in combination with the aviation material key grade and the stockout loss cost; and generating an inventory operation instruction according to the optimal inventory control parameter and the real-time inventory state, and outputting a strategy report with confidence evaluation and key influence factor analysis. According to the method, multi-dimensional accurate prediction of the aerial material demand and dynamic optimization of the inventory strategy are realized, the inventory control precision and the resource utilization efficiency are remarkably improved, the stockout risk and the overstocked cost are effectively reduced, and the reliability and the economical efficiency of aviation equipment guarantee are enhanced.

The invention discloses a joint optimization method for task unloading and power control in a satellite edge computing network, and provides a time delay-energy consumption balance optimization framework oriented to dynamic task arrival aiming at the long-term random optimization problem in satellite edge computing task unloading. Through fusion of Lyapunov optimization and a virtual queue mechanism, the long-term delay constraint is successfully converted into a time slot level optimization target which can be executed online, and a self-adaptive decision process is realized. And aiming at the generated non-convex optimization problem, constructing a robust solution algorithm under a PDD framework, and integrating secondary transformation and an SCA technology to realize efficient solution. Simulation experiments show that the method can obviously reduce the energy consumption in a random task arrival scene, and strictly meets the delay constraint requirement at the same time.

The invention relates to the technical field of array antennas, in particular to a sparse linear array beam forming method and system with unit rotation and mutual coupling considered. Array elements are obtained, Euler angle derivation is carried out on rotation of the array elements around the z axis, and a radiation electric field after rotation is obtained; setting a beam forming target according to the electric field of the radiation field; according to a beam forming target, optimizing the position, the rotation angle and the excitation phase of an array element by adopting a random optimization algorithm, and generating an initial beam scheme; an active directional diagram is extracted from the initial beam scheme according to full-wave simulation, the active directional diagram is combined with a CAL-SAPSO algorithm to optimize the array element position, the rotation angle and the excitation phase, and a final array element directional diagram is generated; according to the invention, by introducing array element rotation and considering a mutual coupling effect, efficient beamforming of the sparse wiring array is realized, the radiation characteristic of the antenna array can be adjusted more finely, the peak side lobe level and the cross polarization level are effectively reduced, and the overall performance of the system is improved.

The invention discloses a day-ahead optimal scheduling method considering a system inertia supply out-of-limit risk, and belongs to the technical field of day-ahead scheduling. Comprising the following steps: calculating to obtain a system inertia demand lower limit according to system frequency change rate constraint and frequency lowest point constraint, and calculating to obtain a system inertia demand upper limit by making a new energy station and an energy storage power station equivalent to a thermal power generating unit when the system is in the maximum load; the method comprises the following steps: considering inertia supporting capacities of a conventional unit, a wind power plant station and network construction energy storage, and constructing a risk driving index considering insufficient maximum inertia supply and excess minimum inertia supply of a system; constructing a two-stage stochastic optimization model according to the objective function and the constraint condition of the day-ahead stage and the objective function and the constraint condition of the real-time stage; and solving the two-stage stochastic optimization model, and verifying the economical efficiency of the scheduling scheme and the supply capability of the system inertia. According to the invention, the system is guaranteed to have sufficient inertia supply capability in the day-ahead stage, and the safety and economy of the system are guaranteed.

The invention discloses a flexible climbing joint clearing method and system based on stochastic optimization, and a related device, and belongs to the technical field of power system optimization scheduling, and the method comprises the steps: obtaining clearing boundary data, and completing verification; based on the historical data of the new energy predicted power and the load power predicted value, generating a random scene considering the wind and light uncertainty, and performing scene reduction; and inputting the verified clearing boundary data and the reduced random scene considering the wind and light uncertainty into a pre-constructed flexible climbing and electric energy, standby and frequency modulation combined clearing optimization model, and solving to obtain a clearing result of flexible climbing, electric energy, standby and frequency modulation. According to the method, coupling mutual exclusion relation characteristics of electric energy, flexible climbing, standby and frequency modulation in the aspects of unit capacity, unit climbing capacity and the like are combined, optimal configuration of resources is achieved, uncertain factors of a complete link of power production scheduling are represented in a refined mode, and robustness of clearing results of electric energy, flexible climbing, standby and frequency modulation can be improved to a great extent.

The invention discloses an LNG cold energy gradient utilization general modeling and optimizing method and system for multi-energy micro-grid energy management, and the method comprises the steps: considering the transfer coupling and loss of cold energy based on the gradient utilization purpose and equipment parameters, and building a utilization general model for each stage of the cold energy gradient utilization process; the method comprises the following steps: establishing a multi-energy micro-grid energy optimization model containing cold energy gradient utilization, and carrying out energy management cooperation on a cold energy gradient utilization link and a multi-energy micro-grid based on a multi-energy micro-grid architecture and distributed energy characteristics by taking system economic carbon reduction operation as an optimization target; and converting the energy optimization model into a two-stage stochastic optimization model based on multiple scenes and solving the two-stage stochastic optimization model according to the uncertainty of the source load and the operation characteristics of the multi-energy micro-grid equipment to obtain a multi-energy micro-grid energy management decision of cold energy gradient utilization. According to the invention, integration and collaboration are carried out on a universal model for gradient utilization of cold energy and energy consumption management of the multi-energy micro-grid, and an energy management decision with economic low-carbon property and high energy efficiency property is obtained through optimization.

The invention discloses a self-adaptive photovoltaic energy storage comprehensive optimization method and system, and belongs to the technical field of photovoltaic energy storage optimization control. According to the method, a system operation model is established based on historical and real-time operation data of a photovoltaic unit, an energy storage unit and a load side, a hybrid robust-stochastic optimization framework is introduced, a collaborative optimization model with energy balance and system stability as targets is constructed, and an initial control parameter set is solved; in the system operation process, a power fluctuation mode and energy storage response characteristics are identified through a multi-layer agent algorithm, and a dynamic energy storage control instruction is generated and corrected; and according to the cross-cycle change trend of the operation performance index, adaptively adjusting the weight and step length of the control parameter, and realizing strategy evolution and dynamic closed-loop optimization. According to the invention, cooperative operation of photovoltaic and energy storage can be realized under complex operation conditions, and the energy utilization rate and the operation stability are improved.

The invention discloses an agricultural irrigation load-containing intelligent farm virtual power plant model and an optimal scheduling method, and belongs to the field of power systems, and the model comprises the steps of constructing a typical scene based on an affinity propagation clustering algorithm, constructing an agricultural-power coupling relation model, and constructing a farm virtual power plant operation cost calculation model. The optimal scheduling method can achieve the operation effects of single-field stabilization, multi-field peak shifting, production guaranteeing and overall optimization, calculates the optimal regulation and scheduling scheme of the flexible resources of the intelligent farm virtual power plant according to the obtained typical scene operation random optimization strategy by using the regulation and control total cost model of the flexible resources of the intelligent farm virtual power plant, and achieves the optimal regulation and scheduling of the flexible resources of the intelligent farm virtual power plant. And obtaining a day-ahead scheduling strategy. According to the method, the regulation and control level of the intelligent farm virtual power plant can be effectively improved, distributed energy resources are effectively integrated and optimized, the energy utilization efficiency is improved, and the farm virtual power plant is promoted to develop in a more environment-friendly, efficient and flexible direction, so that the sustainable development target of an electric power-agriculture coupling system is achieved.

The invention belongs to the technical field of power distribution network operation optimization, and particularly relates to a multi-microgrid-containing regional power distribution network self-discipline collaborative operation optimization method which comprises the following specific steps: S1, data acquisition and preprocessing; s2, establishing a model; s3, a self-discipline cooperative control strategy; s4, solving by an optimization algorithm; and S5, real-time monitoring and adjustment are carried out. And through self-discipline cooperative operation optimization, an information interaction and cooperation mechanism among the micro-grids is constructed, and resource sharing and advantage complementation of the multi-micro-grid system are realized. A distributed collaborative optimization algorithm and a self-discipline control strategy are adopted, and dependence on centralized communication is reduced. The uncertainty of output and load demand of the distributed power supply is fully considered by utilizing technologies such as model predictive control and stochastic optimization. According to the scheme, a plurality of optimization objectives such as economy, environment and reliability are comprehensively considered, and the optimal operation strategy is solved by adopting a multi-objective collaborative optimization algorithm.

The invention discloses an automatic segmentation method and system based on a deep foundation pit point cloud model in the technical field of deep foundation pit modeling, and aims to solve the optimization problem of an existing deep foundation pit modeling technology. The method comprises the following steps: carrying out model simplification on an obtained deep foundation pit point cloud model by adopting a dynamic threshold voxel reduction algorithm to obtain a simplified point cloud model; dividing the underground diaphragm wall and the foundation pit ground for the simplified point cloud model by using a geometric constraint random optimization algorithm to obtain an underground diaphragm wall set and a foundation pit ground set; filtering the underground diaphragm wall and the foundation pit ground in the simplified point cloud model according to the underground diaphragm wall set and the foundation pit ground set to obtain a preliminarily filtered point cloud model; performing support layer division on the preliminarily filtered point cloud model by using a geometric constraint random optimization algorithm to obtain a support layer set; and carrying out independently supported point cloud division on the support layer set by adopting a dynamic radius domain division algorithm to obtain an independently supported point cloud set.

The invention discloses a 9Ni steel process optimization method based on artificial intelligence, and relates to the technical field of steel production, and the 9Ni steel process optimization method comprises the following steps: collecting multi-source process parameters and optimization target parameters; performing feature processing on the collected data to generate target feature data; constructing a 9Ni steel performance multi-target prediction model based on the target feature data, and training the model until convergence; and constructing a multi-objective optimization function by utilizing the converged 9Ni steel performance multi-objective prediction model, and solving the multi-objective optimization function by adopting a stochastic optimization algorithm under an operation constraint condition and outputting an optimal process parameter combination. According to the method, deep learning modeling, cluster cleaning, feature engineering construction, deep learning and a random optimization strategy are comprehensively applied, a data-driven intelligent process optimization system for the 9Ni steel manufacturing process is formed, the achievement rate of target performance indexes can be remarkably increased, the parameter trial and error period is shortened, and the implementation efficiency of the 9Ni steel manufacturing process is improved. And intelligent adjustment and cost optimization of a process path can be realized.

Random key optimization is used to generate sequencing plans. An optimization problem is obtained to generate a sequencing plan. The optimization problem may be described according to nodes that represent decision variables of the optimization problem. A random optimization key technique may be applied to determine a vector of respective values for the nodes. The vector of respective values of the nodes may be decoded according to a sequence planning constraint to generate the sequencing plan and determine a fitness score for the sequencing plan.

The invention discloses a network-side independent energy storage stochastic optimization configuration method based on multi-market transaction operation, and the method comprises the following steps: building an independent energy storage optimization configuration model participating in multi-market transaction operation, obtaining a minimum system total cost target function, and calculating a stage constraint condition of the minimum system total cost target function; an original problem is divided into a main problem and a plurality of sub-problems, a double-loop iteration solving algorithm is established to solve loop iteration between the main problem and the plurality of sub-problems in combination with an independent energy storage optimal configuration model, and an optimal independent energy storage configuration scheme and an optimal system operation strategy are obtained. The method comprehensively considers the participation of energy storage in multi-market transaction operation such as peak-valley arbitrage, frequency modulation and leasing, achieves the effect of improving the comprehensive income and utilization rate of energy storage, and solves the problems that in the prior art, single market transaction is performed from the adjustment demand of a single subject, and the transaction efficiency is poor. The energy storage utilization rate is low; and the investment cost recovery period is long.

The invention provides a scene-based random optimization method for micro-grid energy and flexibility scheduling, and belongs to the technical field of micro-grid optimization scheduling. The method comprises the following specific steps: minimizing the expected cost of the micro-grid as a target; setting a power balance constraint in the micro-grid according to a relation among photovoltaic power generation capacity, power load power consumption, battery energy storage charging and discharging power and power loss in the micro-grid internal transmission and conversion process in the micro-grid; determining a flexible service constraint of the micro-grid according to the flexible service model and the battery energy storage model; predicting and updating by using a rolling time domain method, modeling uncertainty of the micro-grid by combining scene generation and scene reduction technologies, determining a day-ahead prediction error of the micro-grid, and constructing a random optimization model of energy and flexibility of the micro-grid; based on a flexible intra-day framework, solving a random optimization model of micro-grid energy and flexibility according to a scene at different time stages, and obtaining a flexible scheduling strategy; meanwhile, the stability and efficiency of the micro-grid are considered.

The invention relates to a polarization reconfigurable array antenna beam forming method using a polarization selection dictionary matrix, and aims to solve the problems that an existing reconfigurable array polarization selection algorithm is low in calculation efficiency, high in parameter sensitivity and incapable of achieving beam scanning. The method comprises the following steps: firstly, giving an expected main polarization direction and a cross polarization direction; then, according to the polarization state which can be provided by each unit, constructing a polarization selection dictionary matrix used for selecting a unit polarization mode; then, according to the polarization selection dictionary matrix and a given polarization direction, constructing a corresponding main polarization beam matrix and a cross polarization beam matrix; and finally, converting a non-convex polarization selection problem into a convexity problem for solving. Compared with an existing stochastic optimization algorithm in the field of polarization reconfigurable array beam forming, the method has higher efficiency and better stability, so that the method has higher practical value.

The invention relates to a power grid side energy storage two-stage locating and sizing method in a sea wind access scene considering the influence of temperature on a tide safety domain. The method comprises the following steps: adopting a temperature trend prediction model combining LSTM and TCN; a Gaussian mixture model is adopted to calculate a temperature variable capacitance expectation coefficient, and a power flow safety domain is reconstructed; establishing a random optimization scene set of source-load space-time coupling by adopting a typical source-load daily trajectory clustering method; establishing a power distribution network energy storage locating and sizing planning layer model taking the maximum comprehensive utility as a target; a power distribution network energy storage locating and sizing operation layer model with the minimum voltage fluctuation quadratic sum as the target is established; constructing a DistFlow multi-period optimal power flow model under the sea wind access scene; and solving the planning layer by adopting second-order cone relaxation, and carrying out secondary optimization on a charge-discharge and capacity configuration strategy on the operation layer by adopting a target cascade algorithm on the basis of anchoring a locating and sizing decision variable of the planning layer. According to the method, planning and operation targets can be more effectively coordinated, and comprehensive optimal configuration of power grid side energy storage is realized.

The invention discloses a dynamic optimization method and system for the carrying capacity of a power distribution network. The method comprises the steps that demand response models of different types of users are established through a piecewise linear function; generating a large amount of load and distributed resource output scenes by using a Monte Carlo simulation method, and selecting a typical scene from the generated large amount of scenes by using a K-means clustering algorithm; dynamically evaluating a bearing capacity evaluation result through a power distribution network bearing capacity index; constructing a multi-objective optimization model according to the economical efficiency objective, the reliability objective and the user satisfaction objective; a random optimization mechanism is adopted, weighted averaging is carried out on the optimal solutions in all the scenes, and a final optimization objective function is obtained; solving an optimization problem in a rolling time domain through a rolling optimization strategy to obtain a real-time optimization result; and designing and implementing a user incentive mechanism to obtain a final solution for dynamically optimizing the carrying capacity of the power distribution network. The bearing capacity of the power distribution network can be remarkably improved, and optimization of the bearing capacity of the power distribution network is achieved.

The invention relates to the technical field of intelligent building control, in particular to a sunshade optimization method based on environmental parameter reverse modeling, and the method comprises the steps: 1, collecting dynamic parameters; 2, light field reverse modeling is carried out; 3, reversely solving a sunshade configuration, constructing an optimization function by taking improvement of an indoor photo-thermal environment as a target, inverting a regulation and control action region of a sunshade component according to the position of a highlight region, and iteratively solving a target state set of the sunshade component by adopting a random optimization algorithm; step 4, interference effect compensation: detecting light intensity deviation of interference areas of adjacent components, and generating dynamic compensation amount of the sunshade component; and 5, generating a control instruction, fusing the target state set and the dynamic compensation amount to generate a final control parameter, and converting the final control parameter into an execution mechanism driving instruction according to mechanical transmission characteristics. According to the sunshade optimization method, the energy efficiency of the building is improved, the comfort and adaptability of the building are enhanced, and the method has high practical application prospects and market value.

The invention belongs to the technical field of new energy power generation, and particularly relates to a multistage collaborative random optimization scheduling method, device and equipment and a medium. The method comprises the steps of establishing a robust constraint for global new energy prediction uncertainty, and establishing a chance constraint for local new energy prediction uncertainty; for prediction error distribution of different areas, a kernel density estimation method is adopted to establish a global and local new energy prediction error probability distribution model, and the maximum value of a certain quantile of global and local new energy prediction errors is selected; and a global robust and local random multi-region power system random optimization scheduling model is constructed, a bipartite iteration method is adopted to optimize opportunity parameters of establishment of reserve capacity constraints of each region, and the target of economical optimization operation of the multi-region interconnection system is achieved.

The invention relates to a power system low-carbon scheduling method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: constructing a source load uncertainty set of wind power and loads in a power system; constructing a system operation constraint based on robust optimization according to the source load uncertainty set, an adjustment strategy of each adjustment resource on source load fluctuation, a power balance constraint, a wind power operation constraint, an adjustment resource operation constraint and a line transmission constraint; constructing a reference cost function according to the reference cost parameter, constructing an adjustment expected cost based on random optimization according to the source load uncertainty set, and obtaining a scheduling cost function according to the reference cost function and the adjustment expected cost; constructing a scheduling model according to the system operation constraint and the scheduling cost function; and carrying out optimization solution on the scheduling model by taking the minimum scheduling cost function as an optimization target to obtain a scheduling strategy parameter, and scheduling the power system. By adopting the method, safe and economical operation of the power system can be effectively guaranteed.

The invention provides a power distribution network mobile energy storage optimization configuration method and equipment considering fault evolution under typhoon disasters, and belongs to the field of power distribution network mobile energy storage optimization configuration. The method comprises the following steps: firstly, establishing a typhoon disaster space-time evolution model according to typhoon weather prediction information, and constructing a space-time characteristic matrix of a distribution line fault rate; then determining a fragile line based on the space-time characteristic matrix of the line fault rate, and building a pre-disaster mobile energy storage pre-scheduling model to prepare for rapid recovery of a post-disaster system; then, a mobile energy storage optimal configuration and operation strategy in a probability scene is searched by adopting random optimization to minimize the economic loss of the affected power distribution network during a typhoon disaster, and a random optimization model of mobile energy storage optimal configuration and operation is established; the flexibility of mobile energy storage is fully utilized to provide staged power support for the power distribution network during typhoon disasters, the problem that safe and reliable power utilization of the power distribution network is difficult to guarantee under typhoon extreme disasters is effectively solved, and the elasticity of the power distribution network is improved.

The invention discloses an unmanned aerial vehicle cruise path planning method for multi-priority dynamic coverage, and the method comprises the steps: carrying out the sorting of the detection priorities of different parts of a region according to the traversal period constraint, carrying out the high-frequency cruise detection of key parts, and carrying out the low-frequency cruise detection of general parts; secondly, according to the priority ranking, recursively constructing a series of cyclic cruise path planning schemes by adopting a stochastic optimization algorithm, so that the region is dynamically covered and meets corresponding traversal period constraints; and finally, analyzing and evaluating the cruise path planning scheme from two aspects of redundancy and priority, and outputting an optimal planning scheme. Compared with a traditional path planning method, cyclic cruise detection is carried out according to the priority, the whole area can be dynamically covered with emphasis, the cooperative cruise detection efficiency of the unmanned aerial vehicle is effectively improved, and the area change is accurately mastered; in addition, the cooperative cruise path can be adaptively adjusted according to the priority change, unmanned detection resources are efficiently utilized, and the method flexibly adapts to the dynamic coverage demand change.