16 results about "Loss minimization" patented technology

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 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 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.

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 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 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 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 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 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 invention relates to a multi-target production parameter optimization method and system based on deep learning, and the method comprises the steps: obtaining production parameters of a production field product, and carrying out the preprocessing of the production parameters; predicting the preprocessed production parameters by using a pre-trained deep learning model in combination with a preset product quality index to obtain a predicted value of the product quality index; constructing a multi-objective optimization problem based on the predicted value of the product quality index, wherein the multi-objective optimization problem comprises an objective function taking product quality maximization, production efficiency maximization, energy consumption minimization and equipment loss minimization as objectives and constraint conditions constructed based on production parameters; solving a multi-objective optimization problem by using an improved multi-objective particle swarm algorithm to obtain a Pareto optimal solution set, the Pareto optimal solution set comprising one or more optimal production parameter groups; and selecting an optimal production parameter group from the Pareto optimal solution set based on an actual production condition.

The invention discloses a low-voltage distribution network adaptive voltage reactive power optimization method and equipment based on local measurement, which are suitable for at least one photovoltaic power generation system. Each system operates independently according to a local electrical measurement signal of a grid-connected point. The method comprises the following steps: measuring grid-connected point voltage and upstream line current in real time; according to the grid-connected point voltage and a preset Volt-Var curve, an initial reactive power output reference value is determined, and the curve defines a mapping relation between the two; constructing a local objective function related to the photovoltaic power generation system, wherein the value of the local objective function is positively correlated with the current square value of the upstream line; applying a periodic disturbance signal by using an extreme value search algorithm and taking a Volt-Var curve adjustable parameter as a control variable, observing a local objective function response, and generating an adjustment amount; and updating a control variable according to the adjustment amount, adjusting a curve, and adjusting actual reactive power output, so that a local objective function value approaches a minimum value. The invention aims to design a distributed control method which can adaptively coordinate voltage regulation and network loss minimization targets and is suitable for a dynamic operation environment of a low-voltage power distribution network on the premise that an accurate network model and complex communication are not needed.

The invention relates to the technical field of flywheel energy storage systems, and discloses a flywheel energy storage system-based computing power load stabilization and energy-saving control system, which comprises a load prediction module, an energy storage scheduling module, a dynamic regulation module, an energy optimization module, an optional threshold setting module, a state monitoring module, a cooperative control module and the like. The load prediction module analyzes a historical rule and establishes a prediction model by collecting computing power demand data; the energy storage scheduling module generates a flywheel energy storage device charging and discharging instruction according to the model; the dynamic adjusting module generates an adjusting strategy by combining the energy storage response parameter and the flywheel rotating speed state; the energy optimization module dynamically adjusts the real-time power output of the flywheel according to the power supply state (voltage fluctuation, time-of-use electricity price and the like) of a power grid. According to the system, accurate calculation power load prediction, dynamic energy storage strategy optimization, flywheel loss minimization and reasonable energy distribution are realized through multi-module cooperation, load fluctuation can be effectively stabilized, energy consumption cost is reduced, and the system is suitable for calculation power center load management and energy saving scenes.

The invention relates to an optimal configuration method and device for an energy storage power station, computer equipment, a storage medium and a program product. The method comprises the following steps: constructing a target function by taking power distribution network loss minimization of a target city power distribution network and economic benefit maximization of an energy storage power station as targets, the constraint conditions of the target function comprise the power constraint of the energy storage power station, the capacity constraint of the energy storage power station, the charging and discharging efficiency constraint of the energy storage power station, the voltage constraint of the target urban power distribution network and the power balance constraint of the target urban power distribution network; and solving the target function to obtain a globally optimal solution, evaluating the globally optimal solution to obtain an evaluation result, and taking the globally optimal solution as the optimal configuration of the energy storage power station under the condition that the evaluation result is passing. By adopting the method, the optimal configuration of the energy storage power station comprising the network construction type energy storage converter can be realized.

The invention discloses an offshore power transmission networking planning method considering the maximum power of an offshore wind power cluster, and the method comprises the steps: constructing an integrated model of the offshore wind power cluster and a power transmission network, and taking the maximization of the power output of the offshore wind power as a primary target; and meanwhile, auxiliary targets such as power transmission loss minimization, converter station loss minimization and power flow safety margin improvement are introduced to form a multi-target optimization function. The model constraints comprise a wind turbine generator output constraint, a line thermal stability constraint, a power flow distribution constraint, a node voltage constraint, a power balance constraint and a converter station capacity and control constraint. An executable control instruction is generated through optimization calculation, and the control instruction is issued to the offshore switchgear or the flexible DC converter station by the remote control center, so that rapid and reliable power transmission network planning is realized. According to the method, the offshore wind power output uncertainty, the marine environment and the equipment parameter change are comprehensively considered, the maximum power output of the offshore wind power cluster can be achieved in the complex and uncertain environment, and the operation safety and economical efficiency of the system are improved.