32 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.

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 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 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 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 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 application belongs to the technical field of waste incineration, more particularly to an incineration heat graded utilization method based on energy quality matching. The application breaks through the limitation of fixed pressure grade, establishes accurate mapping of steam quality and energy utilization scene based on real-time thermodynamic calculation of real-time steam parameters and real-time dynamic grading decision. Secondly, a steam demand dynamic response model is constructed through an LSTM load prediction module to identify the supply-demand gap of steam of different qualities in advance and form a priority allocation matrix. Finally, a multi-level optimization system with the goal of maximizing economic benefits and minimizing heat loss is constructed by combining a fuzzy PID control engine and a three-level path switching mechanism, so as to realize optimal dynamic matching of steam parameters and energy utilization equipment.

The invention relates to an optimal power flow control method and system for a power distribution network containing a hybrid power flow controller, and the method comprises the steps: firstly, considering a dynamic uncertainty risk, and constructing a three-phase four-wire system power distribution network optimal power flow model containing the hybrid power flow controller by taking the control cost and network loss minimization of the power distribution network as targets; and solving the optimal power flow model of the three-phase four-wire power distribution network to obtain an optimal power flow control strategy of the power distribution network containing the hybrid power flow controller. According to the method, the distributed power supply and the hybrid power flow controller are considered, the three-phase four-wire system power distribution network optimal power flow model is constructed by taking the control cost and the network loss of the power distribution network as the target, and the safe operation and the electric energy quality of the power grid are effectively guaranteed through local real-time adjustment; and the economical efficiency and the reliability when the high-proportion distributed energy is accessed to the power distribution system are improved.

The invention discloses a three-phase balanced power supply energy-saving control method and system, and the method comprises the steps: building a three-phase four-wire system soft switching topology model, collecting the real-time operation parameters of a power supply system, and transmitting the real-time operation parameters to a complex domain convex optimization decision platform; extracting parameter characteristics in the platform, determining key parameters, and establishing a mapping relation between the key parameters and the three-phase unbalance degree and the energy consumption loss; inputting the mapping relation into a multi-target symmetric positive semidefinite programming algorithm, constructing constraint conditions by taking three-phase unbalance degree and energy loss minimization as targets, and solving to obtain an optimal conduction time sequence and a duty ratio of a controllable switch element and optimal parameters of a filtering component; the optimal parameters are fed back to the topology model, and the working state of the assembly is adjusted to achieve control. The system comprises six units which work cooperatively. The system can reduce energy consumption, accurately control three-phase balance, adapt to complex working conditions, improve the operation efficiency and stability of a power supply system, and meet the dual requirements of an electric power system for energy conservation and balance.

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 application discloses a kind of aircraft energy system night distributed scheduling control method, comprising the following steps: S1, the demand of actual near space aircraft night cruising to energy system is established Energy system single bus DC microgrid system model and establishes near space aircraft energy system operation loss minimization problem;S2, based on multiple update recursive least square algorithm DC / DC converter loss model parameter identification;S3, the optimal working point of system under different working conditions is obtained by finite time distributed scheduling algorithm;S4, according to the optimal working point of system, based on multi-agent PI consistency algorithm Design distributed control algorithm, realize current distribution and bus voltage regulation.The application can realize the distributed optimization control for near space aircraft energy system, while taking into account the operation loss of aircraft energy system, the bus voltage regulation and current distribution of aircraft energy system are realized.

The invention discloses a power distribution network consumption capability improving method and system based on multi-type resources, and belongs to the technical field of intelligent power grids, and the method comprises the steps: constructing a distributed source load output model, obtaining the real-time operation data of a power distribution network, determining the adjustable power and rolling adjustment time window of each single device, and obtaining the power distribution network consumption capability. A distributed power supply, a flexible load and a capacitor bank are taken as regulation and control objects, peak valley difference stabilization, cost minimization and network loss minimization are taken as targets, power balance and safe operation are taken as constraints, and a multi-type flexible resource collaborative optimization scheduling model is constructed; and the regulation and control model is simplified through linear processing, and a commercial solver is used for rapid solving, so that regulation and control instructions of all the objects are obtained. And issuing the regulation and control instruction to various types of adjustable and controllable equipment in the power distribution network, indicating to execute operation and updating the state of the equipment. According to the invention, by optimizing the collaborative scheduling of flexible resources, the consumption capability of the power distribution network is improved, so that the power distribution network can efficiently cope with the increased power consumption demand and quickly respond to the changing load.

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 present invention relates to a complex electrochemical cell (complex membrane electrode assembly (MEA)) that integrates one or more individual electrochemical cells made of ceramic and metal materials and connected to each other (for example, in parallel and / or series arrangements), so that electrical losses are minimised and strategies for controlling the cell which makes the operation more flexible are allowed, maintaining maximum energy efficiency and adaptability to external requirements of the operation.