44 results about "Loss minimization" patented technology

The invention discloses an industrial park virtual power plant reactive voltage control method of multi-target multi-agent deep reinforcement learning considering energy storage life, and belongs to the technical field of virtual power plant reactive voltage control methods based on industrial data acquisition. The problem that in the prior art, a traditional industrial park virtual power plant reactive voltage control method is difficult to consider park network loss optimization and energy storage life protection at the same time is solved. According to the method, a multi-objective optimization model including network loss minimization, node voltage deviation minimization and energy storage battery service life maximization is constructed, a virtual power plant is divided through a distributed multi-agent framework, each agent trains multiple strategies in parallel through a multi-objective multi-agent deep reinforcement learning algorithm based on local observation, the Pareto front is approached, and the energy storage battery service life is maximized. High-efficiency control is realized, and each intelligent agent independently generates an energy storage reactive power regulation and active power charging and discharging instruction. The reliability of the energy storage equipment is improved, and the method can be applied to an industrial park active power distribution network scene with high renewable energy source permeation.

The invention relates to the technical field of industrial big data, in particular to a transformer consumption reduction intelligent control method and system based on a power generation end, and the method comprises the steps: building a source-load space-time correlated industrial big data set, determining an operation reference, and synchronously monitoring real-time electrical parameters; load component decoupling is executed, a basic load component is stripped, and a power fluctuation deviation parameter is extracted; reconstructing equivalent control power of a power generation end based on a nonlinear fluctuation compensation strategy; and constructing a power grid energy efficiency function to execute loss minimization optimization, determining a power supply voltage setting value and driving on-load voltage regulation. Through multi-dimensional data fusion and non-linear load reconstruction, the problem that a traditional method is difficult to deal with random fluctuation of the load is solved, collaborative optimization of transformer loss and line transmission loss is achieved, and operation energy consumption of a power supply loop of a power generation end is effectively reduced.

The invention provides a method for suppressing internal misfire caused by turn-to-turn short circuit of a dry-type air-core reactor, and belongs to the technical field of dry-type air-core reactors. A turn-to-turn short circuit fault detection device combining differential protection and infrared thermal image detection is established to form a three-dimensional turn-to-turn short circuit feature vector, a four-dimensional internal misfire minimum ignition vector is obtained, and a fire development prediction model is established based on an Arrhenius equation. A double-layer game model for minimizing the upper-layer fire loss and minimizing the lower-layer fire extinguishing cost is constructed to optimize five-dimensional suppression vector parameters, and coordinated actions of suppression equipment such as power-off protection, forced ventilation, temperature suppression and inert gas release are controlled in real time through a suppression vector parameter adjustment function. The technical problem of fire suppression measure starting lag or excessive response caused by difficulty in accurately predicting the opportunity and degree of development of the turn-to-turn short circuit fault of the dry-type air-core reactor into the internal fire disaster is solved.

The invention relates to the technical field of power systems, and discloses a digital power grid area operation optimization method, system, device, medium and product, and the method comprises the steps: obtaining the operation data of a plurality of preset monitoring nodes in a power grid area, and carrying out the deep mining of the data; and performing clustering analysis on the operation characteristic data, dividing the power grid region into a plurality of sub-regions, and inputting the operation data corresponding to each sub-region into a digital twinborn model of the power grid region for real-time simulation. Based on real-time simulation data, a plurality of operation scenes are generated, electric energy loss minimization, load imbalance minimization, new energy consumption maximization, electric power cost minimization and power grid reliability maximization of a power grid area are taken as multi-optimization targets, an optimal operation scene is determined through the targets, and then an optimal operation strategy of the power grid area is formulated. Operation optimization can be rapidly carried out, and the stability of power grid operation is effectively improved.

A triple-active bridge (TAB) converter and methods to optimize the same is disclosed. The TAB converter includes a high-frequency three-winding transformer and three active full bridges, each coupled to the high-frequency transformer through a different passive network. The converter also includes a centralized controller providing gating pulses to semiconductor switches within the active full bridges, a power pulsating buffer (PPB) configured to reduce capacitor requirements on the DC port, and an unfolder stage to convert rectified AC voltage to a pure sine wave. The centralized controller may optimize performance by varying at least one of a switching frequency, a duty cycle of bridge voltages, and a phase shift between bridge voltages. The PPB may include a buck active power decoupler with a half-bridge, inductor, and capacitors, and also a primary full bridge and a power transfer impedance block.

The invention discloses a power distribution network cluster voltage regulation and control method and device based on automatic reinforcement learning, and belongs to the technical field of power distribution network regulation and control, and the method comprises the steps: taking network loss minimization and voltage stabilization as core targets, and designing a multi-region cooperative control problem; an independent agent is set for an inverter cluster in each area of the power distribution network, and a multi-area cooperative control problem is modeled as a multi-agent Markov decision process; a layered automatic reinforcement learning framework is introduced into a multi-agent Markov decision process, the outer layer autonomously optimizes PPO hyper-parameters of each agent through a TPE algorithm, and the inner layer optimizes AC network parameters of each agent through a centralized training and distributed execution mechanism; and carrying out power distribution network cluster voltage regulation and control according to each optimized intelligent agent. According to the method, manual experience deviation is eliminated through automatic parameter adjustment, the global optimization target and the local dynamic response advantages are combined, and the voltage quality and the operation economy of the complex power distribution network are remarkably improved.

The invention discloses a distributed voltage control method and device under transmission and distribution cooperation and a medium, and the method comprises the following steps: S1, obtaining the basic parameter information of a transmission and distribution system; s2, establishing a distributed voltage reactive power control model with minimum power transmission network loss and power distribution network loss; s3, decomposing the reactive voltage control model of the power transmission and distribution system into control sub-models about a power transmission network and a power distribution network; and S4, performing decomposition by using the step S3 to obtain a control sub-model, constructing a solving framework for solving based on the parameter information in the step S1, and finally obtaining a distributed voltage control strategy. According to the method, the power transmission and distribution system network loss minimization is set as an optimization target, so that the power generation cost can be reduced and the new energy consumption can be improved while the system voltage safety is ensured. Compared with a centralized method, the distributed solving method for optimizing the network loss in the whole power transmission and distribution system ensures safe operation of the system and reduces the calculation complexity at the same time.

The invention provides a multi-zone-area ordered charging scheduling method and system based on a simulated annealing genetic algorithm, and the method comprises the steps: generating an initial population through employing a real number coding mode, and enabling a population individual to represent a charging scheduling scheme; constructing a fitness function in the genetic algorithm by taking load balancing and network loss minimization as optimization targets; selecting individuals by adopting a roulette selection and elitism strategy, and reserving a plurality of individuals with the highest fitness to enter the next generation by adopting a self-adaptive crossover mutation operation; taking the optimal individual of the current population as an initial solution, and executing simulated annealing operation; after optimization of a preset number of iterations, an optimal charging scheduling scheme is output, and the load rate, the voltage and the charging pile state of each transformer area are monitored in real time; and when abnormality is detected, charging power distribution is dynamically adjusted, and the load of the overload area is transferred to the low-load area. Through fusion of simulated annealing and a genetic algorithm, multi-zone-area charging scheduling is optimized, load balancing and network loss minimization are realized, and the stability and dynamic adaptability of a power grid are improved.

The invention discloses a model prediction control system and method for minimizing loss of a magnetic flux adjustable motor. The system comprises a magnetic flux adjustable motor module, a three-phase inverter module, a coordinate change module, an active current calculation module, a current prediction module, a value function minimization module, a positive and negative pulse detection module, a double-bridge arm inverter circuit module and a rotating speed loop regulator module. The method comprises the following steps: constructing a magnetic flux adjustable motor mathematical model considering iron loss; obtaining a current prediction model considering iron loss based on the magnetic flux adjustable motor loss model; from the perspective of d-axis and q-axis active current, constructing a current constraint relation under the condition of loss minimization; and constructing a value function. According to the invention, the efficiency optimization of the system is improved; the problems of large calculation burden, increased solving error and the like caused by high-order equation solving are avoided; the efficiency optimization of the whole motor driving system is realized; the method is high in response speed, strong in anti-interference capability, small in torque ripple and strong in current harmonic suppression.

The invention relates to the technical field of power distribution network optimization control, in particular to a power distribution network distributed resource coordination optimization method, system and device based on voltage stability constraint and a storage medium. The method comprises the following steps: firstly, acquiring a power distribution network topological structure and distributed resource characteristic parameters, constructing a voltage sensitivity analysis model, calculating voltage sensitivity indexes of nodes, determining voltage weak nodes and constructing a voltage stability evaluation model; then, a voltage stability margin quantitative model is established in combination with distributed resource operation characteristics, and a multi-target coordination optimization model including voltage deviation minimization, voltage stability margin maximization, distributed resource access capacity maximization and network loss minimization is constructed; solving the optimization model by adopting an improved particle swarm algorithm to obtain a distributed resource optimal configuration scheme; and finally, establishing a dynamic access control strategy to realize adaptive capacity adjustment and reactive power coordination control of distributed resources. The problem of voltage stability in a high-permeability distributed resource access scene is solved.

The application discloses a kind of three-phase balanced power supply energy-saving control method and system, method includes: building three-phase four-wire soft switching topological model, and real-time operating parameter of power supply system is collected and transmitted to complex domain convex optimization decision platform;Parameter characteristics are extracted in the platform, determine key parameters and establish its mapping relationship with three-phase unbalance degree, energy loss;Mapping relationship is input into multi-objective symmetric semi-positive programming algorithm, with three-phase unbalance degree and energy loss minimization as target to construct constraint condition, and the optimal conduction timing of controllable switching element, duty ratio and filter component optimal parameter are solved;Optimal parameter is fed back to topological model, and the working state of component is adjusted to realize control.System contains six units, and each unit works cooperatively.The application can reduce energy consumption, accurately control three-phase balance, adapt to complex working conditions, improve the operating efficiency and stability of power supply system, meet the dual needs of energy saving and balance of power system.

The invention belongs to the technical field of waste incineration, and particularly relates to an incineration heat grading utilization method based on energy and quality matching. According to the method, a real-time dynamic grading decision is made based on thermodynamic calculation of real-time steam parameters, the limitation of a fixed pressure grade is broken through, and accurate mapping of steam quality and an energy consumption scene is established; secondly, a steam demand dynamic response model is constructed through an LSTM load prediction module, supply and demand gaps of steam of different qualities are recognized in advance, and a priority distribution matrix is formed; finally, a fuzzy PID control engine and a three-level path switching mechanism are combined, a multi-level optimization system with the economic benefit maximization and the diverse loss minimization as targets is constructed, and optimal dynamic matching of steam parameters and energy consumption equipment is achieved.

The invention belongs to the technical field of power transmission network toughness improvement, and particularly relates to a power transmission network ice disaster toughness optimization method based on remote sensing, which comprises the steps of S1, adopting an ice disaster scene generation process to generate a scene set reflecting an actual evolution rule of an ice disaster in a region; s2, constructing a source-network-load multi-dimensional system toughness evaluation index system; s3, constructing a two-stage robust toughness improvement planning model of the power transmission network; S4, solving the two-stage robust planning model in the S3, and outputting an optimal energy storage configuration scheme and an emergency maintenance scheduling strategy; and S5, deploying the energy storage configuration scheme output in the step S4 in actual operation of the power grid, and dynamically adjusting energy storage charging and discharging and unit output according to the emergency maintenance scheduling strategy when an ice disaster occurs, thereby realizing toughness improvement and economic loss minimization of the power transmission network. According to the method, the whole-process toughness improvement and the total cost minimization of the power transmission network under the slow-development ice disaster can be realized.

An object is to obtain a power conversion device that can prevent ripple from increasing owing to change in switching frequency. A power conversion device includes: semiconductor switching elements connected between a DC voltage source and an output side, and connected in series to each other; a reactor; a control unit which controls a switching frequency of each semiconductor switching element; and a voltage detector, a voltage detector, and a current detector which respectively detect a voltage value of an input voltage, a voltage value of an output voltage, and a current value of an inductor current. Changing of the switching frequency and detection of the voltage value of the input voltage, the voltage value of the output voltage, and the current value of the inductor current are each performed at a timing that allows synchronization with a carrier.

The application belongs to the technical field of power grid optimization control and artificial intelligence, and particularly relates to a multi-agent power grid voltage optimization control method and device based on a large language model, an electronic device and a readable storage medium, which comprises the following steps: using prompt word engineering technology, inputting power grid environment information, task target optimization model and device characteristics as context information into a large language model to generate time sequence running data sets covering different working conditions; constructing a multi-agent system based on a distributed partially observable Markov decision process mechanism and training the same by using a TD3 (double-delay deep deterministic policy gradient) algorithm; and deploying the multi-agent system in a power grid control system, and each regional agent executes voltage regulation actions according to real-time collected local state information. The application can realize safe stability and network loss minimization of power grid voltage without relying on accurate system mechanism models / equations, and has good scalability and engineering application prospects.

The invention discloses a mobile energy storage peak load shifting scheduling control method and device, a terminal and a medium, and the method comprises the steps: collecting historical output scene data according to renewable resources in a target power distribution network, and constructing a source load prediction model containing a power generation power probability density distribution function in combination with output characteristics; the output simulation parameters output by the source load prediction model are substituted into a preset double-layer structure mobile energy storage optimization scheduling model, and an upper optimization sub-model aims to maximize the net revenue of mobile energy storage configuration and is solved by a heuristic optimization algorithm; and the lower-layer optimization sub-model aims at realizing power distribution network loss minimization, solving by an adaptive wave search algorithm, and obtaining an optimal mobile energy storage scheduling strategy through collaborative optimization, so that efficient solving is realized, the efficiency bottleneck of a traditional algorithm in complex problem solving is broken through, the peak-valley difference of the power distribution network is reduced, and the peak clipping and valley filling effect is improved.

The invention discloses a multi-constraint multi-target power distribution network optimization control decision-making method based on state feedback. The method comprises the following steps: firstly, establishing a multi-objective function and constraint conditions of a reactive voltage control problem for a power distribution network; the reactive voltage control problem is modeled as a Markov decision process De-POMDP; and solving a reactive voltage control problem by utilizing AT-MADRL, and searching an optimal joint determination strategy. According to the method, the self-service optimization control decision network of the power distribution network is constructed, the dual goals of voltage control and loss minimization are achieved by monitoring the running state of the power distribution network in real time and dynamically adjusting the reactive power output of the PV and the SVC, and it is ensured that the decision process can be adjusted according to the real-time state of the power distribution network. According to the method, a state feedback mechanism and a multi-constraint multi-target optimization control method are combined, a decision network model for distribution network autonomous optimization control is achieved, the method can adapt to changes of the operation state of a power grid, and efficient and stable operation of the power grid is ensured.

The invention discloses a method for minimizing the scheduling weighted total error work loss of a serial batch processor, and belongs to the field of production plan scheduling theories and optimizing.The method comprises the steps that the prepreg cutting process is described as the scheduling problem of the serial batch processor, and an optimization model for minimizing the weighted total error work loss on the serial batch processor is established; and solving the optimization problem of the minimum weighted total work loss on the serial batch processing machine according to the work condition to obtain a work processing scheme, so as to improve the prepreg utilization rate and the production scheduling level in aviation composite material manufacturing while the optimization model can meet the production requirement by accurately arranging the work batch and the processing sequence, and improve the production efficiency. The problems that a traditional production scheduling method is low in processing efficiency, insufficient in prepreg utilization rate and the like are solved.

The invention provides a power distribution network node load matching digital modeling method based on an ultrafast charging pile, and the method comprises the steps: firstly collecting the node operation data of a power distribution network and the charging data of the ultrafast charging pile, eliminating abnormal values through a 3 sigma criterion, and constructing a high-quality data set through min-max standardization; calculating an instantaneous power fluctuation coefficient beta (t) to quantify the load fluctuation intensity, dividing the load fluctuation intensity into a stationary section, an ascending section and a descending section, and constructing a space-time coupling model to predict a node load in combination with a space weight factor and an LSTM and Attention mechanism; taking network loss minimization and voltage offset minimization as targets, optimizing load distribution under the constraint of charging pile power and node voltage by using a particle swarm optimization algorithm with an adaptive inertia weight, and adjusting a correction coefficient alpha according to beta (t) to correct a prediction deviation; and finally, outputting an optimal scheme, generating an ultrafast charging pile power adjustment instruction, and uploading the instruction to a monitoring platform to realize dynamic matching of the node load of the power distribution network.

The invention belongs to the technical field of electric vehicle charging scheduling, and discloses an electric vehicle charging scheduling method and system, and the method comprises the steps: constructing an electric vehicle charging load model which comprises a residential slow charging load model and a public fast charging load model; optimizing a residence slow-charging load model and a public fast-charging load model to obtain an optimal scheduling scheme by taking a constraint condition in a second-order cone form and an optimization objective function with minimum network loss as an objective; according to the method, an electric vehicle is charged based on an optimal scheduling scheme, constraint conditions comprise branch power balance, branch voltage balance, branch power constraint, node power constraint, node voltage constraint, transmission power constraint and electric vehicle charging number constraint, a mixed integer nonlinear model is converted into a convex programming model, and compared with a traditional heuristic algorithm, the method has the advantages that the optimal scheduling scheme is obtained; the solution efficiency and the solution precision of the optimal scheduling scheme are improved, the electric vehicle is charged based on the optimal scheduling scheme, and safe operation of a power grid is ensured.

The invention discloses a strategy determination method and device of a power grid system and electronic equipment. The method comprises the steps that power grid state data corresponding to a target power grid system at the current moment are acquired, and the target power grid system comprises target equipment; a target function corresponding to the target power grid system and a target constraint corresponding to the target function are called, the target function takes total network loss minimization as a target, the target constraint comprises a voltage qualification rate constraint, and the target function comprises voltage functions and network loss data items corresponding to the multiple feeder lines respectively; the corresponding voltage function is a function taking the network loss minimum value of the corresponding feeder line as a target; and according to the power grid state data, under the target constraint, solving the target function to obtain an execution strategy corresponding to the target power grid system. According to the method and the device, the technical problem of poor system adaptability and economical efficiency easily caused when control is carried out according to the determined power grid system execution strategy in related technologies is solved.

The invention discloses an energy storage frequency modulation system control method and device based on digital twinning, and a storage medium. The method comprises the following steps: acquiring historical operation data of a fire storage combined frequency modulation system; according to the historical operation data, constructing a thermal power generation unit AGC response prediction model, an energy storage battery life evaluation model and a frequency modulation economy evaluation model; collecting system operation data in real time and correcting model parameters of the thermal power storage combined frequency modulation digital twinborn model on line; taking frequency modulation income maximization and battery loss minimization as targets, and utilizing a reinforcement learning algorithm to generate dynamic optimization control parameters of the energy storage frequency modulation system; and controlling the energy storage frequency modulation system according to the dynamic optimization control parameters, and continuously correcting the digital twin model by using the controlled operation data. Through the digital twinning technology and the reinforcement learning algorithm, online adaptive optimization of the control strategy is realized, and the operation efficiency and economical efficiency of the fire-storage combined frequency modulation system are improved.

The invention discloses a winding overlapping calculation method of a stator winding circuit board, which comprises the following steps of: decomposing a circuit board winding into a plurality of geometric units, defining a wire path, a wire width and an interlayer distance of each layer, then introducing a three-dimensional coordinate system, and marking a spatial position and a current direction of each unit; solving a Maxwell equation set by adopting a finite element method, and obtaining magnetic field intensity distribution of the overlapping region; calculating eddy-current loss and copper loss according to magnetic field distribution, and predicting a temperature field by combining a heat conduction equation; iteratively adjusting the width and the angle of the overlapping region by taking loss minimization and heat dissipation uniformization as targets; an overlapping coefficient # imgabs0 # is defined, wherein Aoverlapping is the overlapping area, and Atotal is the total lead area; and determining an optimal overlapping parameter according to a constraint condition that K belongs to [0.2, 0.5] and a temperature rise threshold value delta T is less than or equal to 15 DEG C. Through quantitative analysis of electromagnetic field and thermal field distribution, motor efficiency and reliability are improved, and the method is suitable for winding optimization design of precision motors such as permanent magnet motors and stepping motors.

The invention discloses an offshore wind turbine group operation and maintenance resource regulation and control method and system, and the method comprises the steps: S1, dynamically recognizing a wind turbine group needing to be maintained in an operation and maintenance period, and forming a to-be-operated and to-be-maintained wind turbine group set; s2, a fuzzy C-means clustering algorithm is adopted to divide the fan group set to be operated and maintained into k clusters, and k dynamic operation and maintenance centers are generated; s3, a multi-target offshore wind power operation and maintenance child-mother ship path planning mathematical model with the purposes of operation and maintenance cost minimization, operation and maintenance time minimization and fan generating capacity loss minimization is constructed, and constraint conditions are added; and S4, forming a plurality of different operation and maintenance schemes by changing the value of the clustering number k, and solving the mathematical model based on mixed integer linear programming to obtain an optimal operation and maintenance center number and an operation and maintenance sub-mother ship path. The method can significantly reduce the total operation and maintenance cost, shorten the operation and maintenance response time, reduce the power generation loss, and improve the operation and maintenance window utilization efficiency and the resource scheduling flexibility.

The invention discloses an AC / DC power distribution network protection method based on a dynamic threshold value and related equipment. The method comprises the following steps: acquiring real-time operation data and historical operation data of an AC / DC power distribution network in real time; when detecting that the AC / DC power distribution network has a state change or receives a fault signal, performing probabilistic load flow calculation according to the real-time operation data, and determining fault current probability distribution information; a preset neural network is called to be combined with the historical operation data, and electrical parameter change trend information is predicted; constructing a dynamic threshold value optimization model by taking protection expectation loss minimization as a target and combining fault current probability distribution information and electrical parameter variation trend information; and iteratively solving the dynamic threshold optimization model to obtain a dynamic protection constant value of the AC / DC power distribution network, and issuing the dynamic protection constant value to a protection device in the AC / DC power distribution network. The method combines load flow calculation with change trend prediction of a neural network, quickly and accurately calculates a protection constant value, and effectively realizes fault isolation under different operation conditions.

The application discloses a kind of smart grid multi-objective operation optimization method and system of network attack defense, the number of security defense components of this method will defend network attack, generator active output vector, generator voltage vector, transformer decomposition ratio vector, reactive power generation vector, transformer tapping number and reactive power unit number as optimization variable, the load demand loss minimization of smart grid, the procurement and operation cost minimization of security defense components, the total fuel cost minimization of power generation is respectively as three optimization targets, realizes the multi-objective adaptive operation optimization process of smart grid of network attack defense.This application can efficiently realize the high safety and reliability and economic operation of the compromise optimization of multiple performance indicators of smart grid, with higher power supply reliability, higher security defense and lower operating cost.

The application belongs to the technical field of photovoltaic power generation, and particularly relates to a power optimizer escape circuit applied to a group string type photovoltaic system, which comprises: a fault detection unit for monitoring the working state of the power optimizer and outputting a control signal; a channel switching unit responding to the control signal output by the fault detection unit; a main power channel containing a power regulation module, used for transmitting and regulating the output power of a photovoltaic module when the power optimizer is normally working; and an escape channel used for transmitting the output power of the photovoltaic module when the power optimizer is faulty. The application constructs an intelligent switching channel through double MOSFET switches, seamlessly switches to a passive bypass path when the optimizer is faulty, guarantees the continuous power generation of the module, completely avoids the high-voltage electric shock risk through the hardware-level direct current channel, automatically restores the operation of the main power channel after the fault is eliminated, does not need manual intervention, and finally realizes the minimization of power generation loss, the maximization of system safety, and the fundamental reduction of operation and maintenance cost.

The invention relates to the technical field of motor control, in particular to a controllable loss minimization control method of a multi-stage starter generator under a controllable excitation condition, which comprises the following steps: obtaining controllable loss of a main motor; obtaining controllable loss of the main exciter; obtaining the controllable loss of the rotary integer current converter; obtaining controllable loss of the multi-stage starter generator; and obtaining a target stator shaft magnetization current of the main motor and a target excitation voltage of the main exciter when the controllable loss of the multi-stage starter generator is minimum, and controlling the multi-stage starter generator based on the target stator shaft magnetization current and the target excitation voltage of the main exciter. The controllable loss of the multi-stage motor in the starting process of the aviation main engine is reduced, then the thermal load of the multi-stage motor is reduced, meanwhile, heat accumulation in the starting process is reduced, a starting power generation system is helped to complete the task of starting the aviation main engine, and the continuous starting capacity is improved.

The invention belongs to the technical field of power distribution network optimization operation, and particularly relates to a power grid energy storage configuration and operation collaborative optimization method and system based on moment difference analysis. Then, a power grid energy storage configuration and operation collaborative optimization model is constructed, an upper layer is obtained by taking annual comprehensive cost minimization of energy storage as a target and taking rated capacity and power of energy storage as decision variables, and a lower layer is obtained by taking energy storage operation income maximization and operation cost and network loss minimization as targets and taking rated capacity and power of energy storage as decision variables; an energy storage charging and discharging power strategy is used as a decision variable to construct and obtain, and the model constraint comprises an energy storage charging and discharging power feasible region constraint and a network loss optimal energy storage power constraint; and solving the model based on a moment difference analysis result to obtain an optimal scheme. According to the method, collaborative optimization of energy storage site selection, capacity power configuration and time sequence charging and discharging strategies is realized, so that the problems of power flow return, voltage out-of-limit and network loss increase of the power distribution network are solved.

The aero-engine particle separator parameterization design method comprises the following specific steps that firstly, air parameters of dust-containing airflow are determined, and meanwhile material parameters of sand dust are determined; 2, designing initial geometric parameters of the particle separator; 3, a parameter optimization range is given, an optimization algorithm is utilized, the total loss delta P serves as an optimization target, loss minimization serves as an optimization direction, new delta P is compared with original loss, and a new geometric parameter combination with the minimum total loss is obtained; and 4, modeling by using a new geometric parameter combination, checking calculation by using three-dimensional simulation, carrying out iteration with the step 3 according to the simulation result satisfaction degree, and otherwise, finishing the design. According to the method, the parameterization design method is adopted, the main geometric parameters and the performance parameters of the separator are associated, the loss model is formed, the model is optimized and solved, the geometric parameter combination with the minimum loss is obtained, the experience dependence degree of the prior art can be relieved, and the design efficiency is greatly improved.