342 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 provides an integrated energy system optimal scheduling method based on multiple time scales, which comprises the following steps of: constructing a radix uncertainty set, constructing a day-ahead robust optimization model based on the radix uncertainty set, inputting day-ahead one-hour-level power prediction data of an integrated energy system into the day-ahead robust optimization model, and performing optimal scheduling on the day-ahead robust optimization model. Obtaining a day-ahead scheduling scheme of the integrated energy system; generating an intra-day random scene set, constructing an intra-day stochastic optimization model based on the intra-day stochastic scene set, acquiring intra-day power prediction data of the integrated energy system by taking 15 minutes as a period, inputting the intra-day stochastic optimization model to obtain an intra-day optimization scheduling scheme, and repeating the steps until optimization scheduling of all periods in the day is completed; and generating a real-time random scene set, constructing a real-time random optimization model based on the real-time random scene set, and inputting the real-time 5-minute-level power prediction data of the integrated energy system into the real-time random optimization model to obtain a real-time scheduling scheme of the integrated energy system.

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 an integrated energy system optimization method based on hierarchical uncertainty, and the method comprises the following steps: taking an error between historical power generation data and historical prediction power generation data of an integrated energy system as a historical prediction error, and constructing a prediction error fuzzy set according to the historical prediction error, constructing a distribution robust optimization model based on the prediction error fuzzy set; obtaining the day-ahead one-hour power prediction data of the integrated energy system, and inputting the day-ahead one-hour power prediction data into the distributed robust optimization model to obtain a day-ahead scheduling scheme of the integrated energy system; the method comprises the following steps: constructing an intra-day random scene set, constructing a random optimization model based on the intra-day random scene set, acquiring intra-day power prediction data based on a day-ahead scheduling scheme by taking 15 minutes as a period, inputting the intra-day power prediction data into the random optimization model to obtain an intra-day scheduling scheme, and repeating the steps until optimization scheduling of all periods in the day is completed. And obtaining a final scheduling scheme of the integrated energy system.

The invention relates to the technical field of mobile edge computing, in particular to a wind power plant inspection method and system fused with mobile edge computing. According to the working mode of the inspection robot and the distribution condition of the wind turbine, the meteorological monitoring equipment, the sensor and the micro base station, a wind energy intelligent control inspection network is constructed; based on a wind energy intelligent control inspection network, a double-layer multi-robot auxiliary edge calculation model is constructed, a Lyapunov optimization technology is adopted to solve a long-term energy consumption steady-state constraint problem of a stochastic optimization problem, and the problem is converted into a deterministic optimization problem; and applying a DDPG reinforcement learning algorithm to obtain an unloading decision and a calculation task allocation scheme of the inspection robot. According to the method, the computing resources of the inspection robot are saved, and the data processing efficiency is remarkably improved. By optimizing the routing inspection path and task allocation, the mobile energy consumption and task processing time delay are minimized, so that the overall benefit of routing inspection work is improved, and safe and stable operation of the wind power plant is ensured.

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 relates to a stochastic optimization method for capacity configuration of an off-grid wind and light storage system. The method comprises the following steps: constructing a system mathematical model by considering equipment constraint and power balance; considering the uncertainty of wind speed and illumination intensity and performing probability distribution fitting by using a parameter estimation method; constructing a stochastic optimization algorithm by using an expected value model and a Monte Carlo method; a non-dominated sorting genetic algorithm is used for carrying out multi-objective optimization calculation on economic benefits and carbon emission; a new method for evaluating the permeability of renewable energy sources is provided by combining a Monte Carlo method definition, and investment decision suggestions are given by taking the new method as an index. The problem that the renewable energy source permeability of the off-grid energy system with uncertainty lacks specific quantification is solved. According to the method, the uncertainty of renewable energy sources is considered, compared with a deterministic method, the method has higher robustness, and the system uncertainty caused by volatility is effectively solved.

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 an electric power system look-ahead scheduling decision-making method and device based on two-stage robust optimization. The method comprises the following steps: constructing a robust optimization model according to each regulation and control resource characteristic; decomposing the robust optimization model into a main problem and a sub-problem in a mixed integer form through a CCG algorithm and a KKT condition, and solving the main problem and the sub-problem to obtain a look-ahead scheduling plan in a future period; and scheduling based on the look-ahead scheduling plan. Two-stage robust optimization is adopted, a decision maker is required to realize scheme optimization in a worst scene of uncertain variables according to the requirement of robust optimization, and when the scheme optimization is applied to a power system optimization scheduling model containing an uncertain power supply, a scheduling scheme meeting an optimization target is required to be searched in a scene with a maximum prediction error. Compared with random optimization in the prior art, the robust optimization obviously meets the requirements of actual intra-day scheduling.

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 relates to a genetic algorithm-based mixed coal washing coal blending structure optimization method. Compared with the prior art, the defect that an optimal coal blending scheme is difficult to determine in a mixed coal blending process is overcome. The method comprises the following steps: constructing a multi-target stochastic optimization model of the washed coal blending structure of the mixed coal; setting a target parameter and a value range of the target parameter for optimizing the multi-target stochastic optimization model of the washed coal blending structure of the mixed coal; solving the multi-target stochastic optimization model of the washed coal blending structure of the mixed coal; and obtaining an optimization result of the washing coal blending structure of the mixed coal. According to the method, the non-dominated sorting genetic algorithm with the elitist strategy is adopted for multi-objective optimization, and by combining non-dominated sorting, the elitist strategy and congestion degree calculation, the multi-objective optimization problem in the field of coal blending structure optimization can be effectively solved, the global search capability and convergence precision of the algorithm are improved, and the coal blending structure optimization of mixed coal entering washing is realized.

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.

A shared energy storage multi-mode optimal configuration method based on master-slave game pricing belongs to the technical field of configuration under shared energy storage tracking new energy output, and comprises the following steps: firstly, constructing a regional power grid system containing shared energy storage, and designing two sharing modes of electric quantity sharing and capacity sharing; secondly, in order to reduce the influence of the wind and light output uncertainty on the configuration model, a large number of scenes are generated by adopting a stochastic optimization method, and the required scenes are obtained through reduction; and then, based on a master-slave game pricing strategy, establishing a random optimization configuration model which takes a shared energy storage operator as a master, takes a new energy station as a slave and maximizes the net profit of each main body, and solving an optimal energy storage service price and optimal configuration through the game of both parties. According to the invention, benefits of both parties can be increased, the energy storage configuration amount can be reduced, and uncertainty of new energy output can be well dealt with; and due to the two sharing modes, the electric quantity sharing is more economical, and the capacity sharing investment return period is shorter. The method has the advantages of scientificity, reasonability, high applicability, good effect and the like.

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.