17502 results about "Charge and discharge" patented technology

The invention discloses an optical storage charging and discharging integrated power station vehicle network interaction method considering demand side response, and aims to improve the bidirectional interaction capability of a power station and a power grid and realize dynamic matching between the demand side response of the power grid and user charging and discharging behaviors. A high-precision photovoltaic output prediction model, an energy storage system SOC dynamic model and an electric vehicle charging load probability distribution model are constructed, and a joint output feature library is formed. A dynamic charging and discharging priority division method is put forward, and the charging and discharging power distribution weight is dynamically adjusted in combination with the vehicle state, the user electricity price sensitivity and the power grid load curve. And designing a composite demand side response excitation mechanism combining time-of-use electricity price and capacity compensation, and guiding the user to participate in peak regulation and valley filling of the power grid. An edge computing and cloud cooperative control framework is adopted, and a dynamic security check module based on model predictive control is adopted, so that potential out-of-limit risks are evaluated in advance, and the security of a power station and a power grid is guaranteed. And efficient operation of the light storage charging and discharging integrated power station and efficient utilization of new energy are realized.

The invention provides an optical storage charging and discharging station aggregation control and optimization method based on a virtual power plant, and aims to solve the problems of multi-target collaborative optimization, dynamic resource response and uncertainty robustness. By introducing a Markov decision process and an adaptive clustering algorithm, the system can dynamically aggregate photovoltaic, energy storage and charging pile resources according to equipment characteristics, and power dispatching is optimized. A multi-objective optimization model is adopted, economical, technical and environmental objectives are combined, a dynamic weight factor is introduced, and optimal scheduling is generated in combination with a fuzzy decision theory. And real-time compensation is carried out by adopting a rolling time domain control framework and deep reinforcement learning, so that the scheduling precision and the response speed are improved. The edge computing and cloud collaboration mechanism reduces the communication load through a lightweight federated learning model, and improves the scheduling response efficiency. According to the invention, the scheduling efficiency of the optical storage charging station can be obviously improved, the operation cost is reduced, the system stability is improved, and the system has good adaptability and expandability.

The invention discloses an optical storage and charging integrated micro-grid energy management system, which relates to the related technical field of micro-grids and comprises an electric energy collection layer, a digital twinborn layer, a collaborative optimization layer, an application execution layer, a photovoltaic power generation unit, an energy storage unit, a charging load unit and a distributed measurement and control terminal. The invention further discloses an energy management method of the photovoltaic, storage and charging integrated micro-grid. The energy management method comprises the steps of data acquisition, digital twin modeling, multi-target strategy generation and optimization, strategy evaluation and screening, strategy issuing and execution and closed-loop feedback and dynamic correction. According to the invention, through short-term prediction of a digital twinborn layer and real-time generation of a charging and discharging strategy by a collaborative optimization layer, fluctuating renewable energy sources are preferentially consumed, the light abandoning rate is reduced, energy storage charging is automatically triggered in an illumination peak period, and power overflow is avoided; based on the simulation result of the digital twinborn model, the power distribution of energy storage and load is dynamically adjusted, so that the photovoltaic utilization rate is improved, and the dependence on a traditional power grid is reduced.

The invention provides an optical storage and charging cooperative control method and system based on multi-energy complementation. The method comprises the following sub-steps: respectively establishing a photovoltaic power generation model, an energy storage system model and a charging load model; acquiring historical illumination information, charging information and electricity price information, constructing a prediction model based on a neural network, and predicting and outputting illumination intensity, charging load demand power and electricity price in a future time period; constructing a target optimization function by taking the annual net cost and the power deviation rate as optimization targets; according to the method, the cooperative optimization control of the photovoltaic power generation, energy storage and charging system is realized, the annual net cost is reduced, the power deviation is reduced, the energy storage and charging cooperative control strategy is established, the target optimization function is solved by adopting the improved whale optimization algorithm, and the charging and discharging power sequence is generated according to the optimal solution obtained by solving and is input to the system for execution. And the operation efficiency and reliability of the whole system are improved.

The invention discloses an energy management and safety protection cooperation method for a liquid cooling industrial and commercial energy storage system, and particularly relates to the technical field of energy storage system management. A battery electrochemical model, a heat distribution diagram, temperature gradient data and electrical parameters are used as input, and a battery temperature change trend curve is output; a liquid cooling control strategy is set according to the prediction result; fusing the temperature gradient abnormal parameters, the temperature trend risk and the multi-modal environment data abnormal parameters, starting a fire risk assessment model, predicting the fire probability and position, calculating a fire risk coefficient, generating a fire risk report and setting safety protection measures; a multi-objective optimization mathematical model is constructed based on the energy efficiency ratio, the full life cycle income and the battery health degree, energy storage operation data and power grid requirements are combined, a Pareto optimal solution set is generated by adopting a non-dominated sorting genetic algorithm, and a charging and discharging strategy and liquid cooling parameters are optimized; the liquid cooling pipeline layout is optimized through reinforcement learning, and the problem that the battery temperature cannot be effectively managed is solved.

The invention provides an electric vehicle battery health state monitoring and service life estimation method and system, and the method comprises the steps: S1, obtaining a real-time data flow of a battery during the operation of a vehicle, extracting the original records of charging and discharging depth, temperature change, internal resistance parameters and capacity data from the real-time data flow, a dynamic monitoring sample is calculated through voltage, current and temperature values collected by a sensor; s3, calculating a capacity attenuation rule through the time sequence feature set, analyzing the decrease amplitude of the capacity after each charge-discharge cycle by adopting an exponential attenuation model, obtaining a long-term operation trend from historical data, and obtaining a dynamic curve of capacity attenuation; and S8, if the confidence interval of the final estimation probability distribution is greater than a preset threshold value, recollecting data with higher frequency for the dynamic monitoring sample, and performing iterative optimization on the life estimation model through the updated sample to obtain an adjusted life detection result.

The invention relates to an electric energy comprehensive management control method for high and low load working conditions, and belongs to the field of energy supply system control. The method comprises the following steps: acquiring current operation information of an energy supply system, including a working condition mode, a target power demand and a battery state of charge (SOC) value, and further judging a working condition type and calculating a total power demand value of the system; on the basis, a power distribution scheme of a power generation unit and a battery is judged according to an SOC value and a power demand: under a normal working condition, the balance of power generation and battery charging and discharging is realized by setting an SOC threshold interval, and under an emergency working condition, the maximum rate discharging of the battery is dynamically controlled or the charging current is limited to cope with load fluctuation. According to the system, the bus voltage and the SOC are maintained in a safe range through a closed-loop regulation mode, so that the stability and the response capability of the energy supply system are improved.

The invention relates to the technical field of operation prediction, in particular to an optical storage flexible direct current operation prediction method based on deep learning, and the method achieves the precise evaluation of photovoltaic power fluctuation through the analysis of historical operation data and the combination of a power state conversion matrix, guarantees that the energy storage charging and discharging rate can be matched with the load demand distribution, and improves the prediction precision. Power scheduling is optimized, topology analysis and power flow path identification are carried out, topology weight calculation is carried out by using a graph convolutional network, power flow can be dynamically adjusted, the adaptability of an operation mode is improved, the influence of power fluctuation on system stability is reduced, power matching is carried out by adopting an adversarial generative network, photovoltaic output is optimized, and the system stability is improved. The energy storage release path is more reasonable, the power loss is reduced, the overall power distribution efficiency is improved, and the load compensation strategy is optimized through short-period fluctuation detection and long-period trend fitting. And variable correction is performed based on error calculation and anomaly detection, so that the reliability of regulation and control response is improved.

The invention discloses a self-generating and self-using distributed photovoltaic power station power anti-countercurrent control method and system, and relates to the technical field of photovoltaic anti-countercurrent control, and the method comprises the steps: obtaining the real-time load power of a grid-connected point of a power grid and the output power of a photovoltaic inverter, and calculating the net load power deviation; when the net load power deviation is smaller than a preset countercurrent risk threshold value, it is judged that a countercurrent risk exists, and an anti-countercurrent adjusting instruction is generated; in response to the anti-countercurrent regulation instruction, dynamically correcting the maximum output power limit value of the photovoltaic inverter, and synchronously activating the charge and discharge compensation mode of the energy storage system; an anti-countercurrent control coefficient is calculated, and the power reduction proportion of the photovoltaic inverter and the compensation power of the energy storage system are synchronously adjusted based on the anti-countercurrent control coefficient, so that the power flow of the grid-connected point of the power grid is kept to be zero or forward flow; according to the method, dynamic and accurate cooperation of photovoltaic reduction and energy storage compensation power in time and magnitude is realized, and the efficiency of a self-generating and self-using distributed photovoltaic power station is improved.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention provides an intelligent optimization method and system for a revenue mode of a commercial energy storage power station, and relates to the technical field of energy storage power stations. Establishing a multi-dimensional life loss coefficient matrix by analyzing the health state data of the energy storage equipment; a candidate charging and discharging scheduling strategy is generated based on a deep reinforcement learning model, and a double-constraint optimization charging and discharging strategy with predicted income and life loss as a reward function is adopted; and finally generating a control instruction to realize optimized operation of the energy storage power station. According to the method, equipment life loss and economic benefits are comprehensively considered, and long-term benefit maximization of the energy storage power station is realized.

The invention relates to the technical field of lithium battery management, in particular to a lithium battery pack dynamic balancing method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: predicting battery cell state parameters of each battery cell based on a preset long-short-term memory neural network model to obtain a capacity attenuation trend of each battery cell; building a health state evaluation index based on the capacity attenuation trend; based on the health state evaluation indexes, performing health grade classification on each battery cell by adopting a preset clustering algorithm; obtaining the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell, and determining the balance priority of each battery cell based on the health grade category division result, the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell; and on the basis of the equalization priority, a target dynamic equalization adjustment strategy is generated, and each battery cell is adjusted according to the target dynamic equalization adjustment strategy, so that the energy scheduling accuracy of the lithium battery pack is improved.

The invention discloses a solid-state battery performance testing method and system based on data analysis, and relates to the field of battery performance testing, and the method comprises the steps: building a multi-physics field coupled digital twinborn model, collecting a strain signal and temperature field distribution of a failure region according to a three-dimensional physics field state map of the failure region, and when abnormally abruptly changed, carrying out the testing of the performance of a solid-state battery. Triggering multi-stage early warning, matching physical failure evidences with a historical database, extracting an optimal test parameter combination through a meta-learning framework, dynamically adjusting a charging and discharging strategy and monitoring frequency, generating a real-time data stream, and updating parameters of a multi-physics field coupled digital twin model through a back propagation algorithm. According to the method, the test parameters are dynamically optimized through the meta-learning framework, the performance of the solid-state battery is monitored and predicted, the safety and the reliability of the solid-state battery are improved, and a charging and discharging strategy is optimized to prolong the service life of the battery.

The invention discloses an optical storage and charging integrated micro-grid energy management system, relates to the technical field of micro-grid energy management, and is used for solving the problems of low energy scheduling efficiency and insufficient stability in an optical storage and charging system. The system comprises a power generation monitoring module, an energy storage management module, a charging load control module and an energy coordination module. The photovoltaic power generation state and environmental parameters are monitored in real time through a multi-type sensor network, and the running state of the photovoltaic module is judged; based on battery monitoring data and photovoltaic output characteristics, a differential energy storage management strategy is formulated; the charging power is dynamically distributed in combination with the energy state to realize charging and discharging intelligent regulation and control; by converging multi-source information, source storage and load interaction is coordinated to cope with different power states; accurate monitoring, intelligent scheduling and efficient cooperation of the photovoltaic, storage and charging integrated micro-grid are realized, and the energy utilization efficiency and the operation stability are remarkably improved.

The invention provides an anti-islanding protection method based on cloud edge collaboration, and the method comprises the steps: obtaining historical fault data, carrying out the data processing of the historical fault data, obtaining a fault mode discrimination result, judging whether a micro-grid has an islanding operation risk or not through combining the topological structure information of a power grid, real-time operation data, and load demand prediction data, and obtaining an islanding protection result. If so, acquiring power output fluctuation data; the output power of the power supply and the load demand power are monitored in real time, if the output fluctuation of the power supply exceeds a voltage protection constant value or the load demand power exceeds a frequency protection constant value, the edge computing gateway starts a local anti-islanding control strategy, and meanwhile, the charging and discharging power of the power distribution network is adjusted; in the anti-islanding control strategy execution process, the edge computing gateway continuously collects real-time operation state data of the micro-grid, the power supply duration, the electric energy quality and the standby capacity of the micro-grid under islanding operation are evaluated according to the operation state data, and the multi-target optimization model and the operation constraint condition of the micro-grid are dynamically adjusted.

The invention discloses a dynamic control method of a lithium battery module intelligent equalization management system, and relates to the field of lithium battery module equalization, and the method comprises the steps: constructing a hierarchical data fusion platform, and generating a battery state data set containing SOC, SOH and an inconsistency evaluation value; a credibility reasoning algorithm is adopted to calculate a single body imbalance credibility factor value to divide an equalization stage, and equalization current is dynamically adjusted; a charge and discharge curve health factor is extracted through an LAB health factor algorithm, SOH is updated online in combination with extended Kalman filtering, and an SOH attenuation rate is fed back to the battery model; in-module equalization is carried out through a bidirectional energy transfer circuit, inter-module equalization is carried out through a ring bus topology, and layered topology linkage control is formed. The method has the advantages that the consistency and the heat management efficiency of the battery pack are improved through the hybrid equalization strategy and the layered topology linkage control, the service life of the battery pack is prolonged, and the overall performance and the safety of the electric vehicle are improved.

The invention discloses a game reinforcement learning-based reactive power dispatching method for an electric vehicle to participate in a power distribution network, and the method comprises the steps: collecting node voltage, line current, electric vehicle charging and discharging states and topological information, and constructing graph attention embedding; generating an initial reactive power regulation action in the multi-agent game reinforcement learning framework, and shaping and updating a strategy gradient through double rewards; the improved particle swarm is initialized according to the updating strategy gradient, and a self-adaptive inertia coefficient is set; iteratively searching according to the node voltage sensitivity, the line reactive margin and the expected convergence step number to obtain an optimized particle swarm; the particle speed is mapped to a main strategy network through online cooperative training, the inertia coefficient is synchronously adjusted, a cooperative optimization strategy is output, a reactive power dispatching instruction is generated, and the algorithm is updated in a closed-loop mode according to real-time feedback. According to the invention, rapid and cooperative reactive dynamic scheduling of an electric vehicle group is realized, and the voltage stability and the electric energy quality are remarkably improved.

The invention relates to the technical field of battery intelligent monitoring, and discloses an intelligent monitoring system and method for a new energy automobile battery. The method comprises the following steps: acquiring a target battery operation data set of a new energy automobile battery, and extracting time domain differential characteristics and frequency domain energy loss characteristics in a battery charging and discharging process to obtain a multi-dimensional state characteristic set; performing electrochemical characteristic analysis on the new energy automobile battery based on the multi-dimensional state feature set to obtain a micro degradation state judgment result; and dynamically adjusting the parameter configuration of a hybrid Kalman filter according to the micro degradation state judgment result, and generating a battery state-of-charge estimation value. According to the method, the problems of insufficient estimation precision and accumulative errors in a traditional method are solved, and the use safety and reliability of the battery are improved.

The invention belongs to the technical field of power systems, and particularly relates to an audit data early warning method and system, and the method comprises the steps: collecting the node voltage and current of a power supply network and the charging and discharging efficiency data of an energy storage system in real time through a distributed sensor network, and generating an original data flow; carrying out localized cleaning and standardized format conversion through an edge computing node; generating a dynamic risk assessment result and a scheduling scheme based on fuzzy logic reasoning and digital twinborn simulation; and analyzing and predicting deviation through meta-learning, and realizing closed-loop optimization by adopting a model distillation technology. The system comprises a distributed sensor network, an edge computing node, a multi-source data fusion module and the like. According to the method, the anomaly detection real-time performance, the risk assessment accuracy and the energy storage scheduling economy of the power system are improved.

The invention discloses a charging pile system management method based on dynamic load balancing, and particularly relates to the technical field of charging pile management, and the method comprises the steps: collecting time domain scheduling data of a charging pile side, a power grid side and a distributed renewable energy output device side, carrying out the fusion filtering, and obtaining a station-level state vector; generating a station-level prediction sequence of the charging pile in the next control period based on the station-level state vector and a rolling prediction model; an inner-layer controller reads the station-level state vector and the station-level prediction sequence, establishes a power prediction optimization model and solves the model, and outputs an expected station-level power trajectory and station-level power redundancy; a result output by the inner-layer controller is received, and a station-level power envelope curve and an energy storage charging and discharging set value are comprehensively solved and transmitted; the slope upper limit of the station-level power envelope curve is contracted based on the oscillation amplitude, the new slope upper limit is substituted into the next solution, the subsequent power change rate is limited, and the problem of unstable power exchange between the charging station and the superior power grid is effectively solved.

The invention relates to a source load storage control method for sustainable and stable output of new energy output power of an active power grid, and belongs to the technical field of new energy power systems. The method mainly comprises the following steps: based on ARIMA model prediction and load elastic response, guiding a user to optimize power consumption through real-time state perception, power prediction and time-of-use electricity price; a reference value is set and dynamically adjusted through multi-source data fusion, historical data and a scheduling instruction are integrated, and an output value of the source-storage combined system is corrected and output through an ARIMA model; a collaborative optimization mechanism is constructed, and load prediction, energy storage life and power grid interaction economy are optimized through multiple objective functions. Dynamic monitoring is realized through generalized power modeling and bidirectional flow discrimination, precision is improved by combining ARIMA prediction and rolling correction, and abandoned power is reduced. The load side actively participates in the electricity market, and the smooth curve relieves peak pressure; the intelligent charging and discharging strategy prolongs the energy storage life, optimizes economical efficiency and environmental protection, and provides a feasible technical path for a high-proportion new energy power grid.

The invention discloses a lithium battery charge state estimation method based on a Bayes-TLCO optimization deep neural network, and belongs to the technical field of battery state monitoring. The method comprises the following steps: firstly, preprocessing a lithium battery charging and discharging data set; then, constructing a deep neural network model comprising a convolutional neural network, a long-short-term memory network and a multi-head attention mechanism, dynamically optimizing hyper-parameters of the model by using a Bayesian optimization-assisted termite life cycle optimization algorithm, introducing Bayesian optimization during iteration stagnation in a TLCO algorithm iteration process, and finally obtaining a termite life cycle optimization model; fitting historical data through a Gaussian process to construct a search empirical model, generating high-value sampling points, and accelerating model hyper-parameter convergence to a globally optimal solution; and finally, estimating the state of charge of the lithium battery. The method breaks through the limitation of a single algorithm, achieves the high-precision estimation of the state of charge of the lithium battery under a complex working condition, effectively improves the model training efficiency, is suitable for electric vehicles, energy storage systems and other scenes, and provides a key technical support for the intelligent upgrading of battery management.

The invention relates to an intelligent charging method and system based on light and wind complementation and intelligent energy storage. According to the method, dynamic optimized charging is realized through multi-source data collaborative decision, and the method comprises the following steps: firstly, collecting real-time data such as light-wind power generation, energy storage SOC, charging load and commercial power supply, analyzing a wind-solar power generation complementary relationship and calculating a real-time complementary coefficient; a dynamic weight distribution scheme of light wind and commercial power is generated in combination with the commercial power state, and an energy storage charging and discharging instruction is generated by fusing peak and valley electricity prices and energy storage efficiency optimization; and based on the adjusted energy storage output power, comprehensively considering the charging demand and the vehicle battery state, and dynamically distributing the power of the charging pile through the charging priority. By adopting the method, energy complementation, economic scheduling and charging demand management can be combined, the utilization rate of renewable energy sources is effectively improved, and the charging cost is reduced.

The invention discloses a charge and discharge controllable system and method for a decommissioned battery, and relates to the technical field of intelligent charge control. By collecting the capacity fading rate, the internal resistance value, the cycle index and the environment temperature data of the decommissioned battery in real time, a health degree parameter is calculated by adopting a nonlinear coupling algorithm; and the future health degree evolution trend is predicted in combination with the LSTM neural network. And dynamically generating a grading label according to a preset scene threshold matrix, and matching the charging demand thermodynamic diagram with the battery grading label through a dynamic scheduling algorithm to realize intelligent distribution of charging and discharging power. And introducing a photovoltaic-battery-power grid cooperative power supply model, predicting and dynamically adjusting the power supply proportion based on the environment temperature and the photovoltaic output, and deploying to a target scene. And through a dynamic health degree evaluation and scene adaptive matching mechanism, the utilization rate of the retired battery is improved, the deployment cost of charging facilities is reduced, and the power supply reliability under multiple scenes is remarkably improved.

Disclosed in the present invention are a method and system for full-range participation of energy storage charging and discharging in power grid frequency regulation. The method comprises: collecting operating power and frequency data of a power grid, so as to construct a full-range droop coefficient; setting constraints for an upper limit value and a lower limit value of a frequency dead band; obtaining an actual output power instruction value by means of a constraint function constructed on the basis of a theoretical power instruction value and a charging / generated power rated value, and controlling the power grid to operate on the basis of the actual output power instruction value; setting a hysteresis range on a power instruction of an energy storage system, and when a power demand of the power grid exceeds an upper limit threshold value of the hysteresis range, switching the energy storage system from a charging mode to a discharging mode; and when the power demand of the power grid is lower than a lower limit threshold value of the hysteresis range, switching the energy storage system from the discharging mode to the charging mode. In the present invention, when the frequency of the power grid is increased, generated power is reduced until power is absorbed from the power grid, and when the frequency of the power grid is reduced, the absorption of power from the power grid is reduced until electrical power is delivered to the power grid, thus achieving full-range participation of energy storage in power grid frequency regulation control, and satisfying regulation requirements of power grids.

The invention relates to the technical field of new energy supply management, in particular to a charging pile background management system and method. The method comprises the steps of obtaining photovoltaic output, an energy storage state and charging load data, completing time alignment and normalization processing, and constructing a unified data input structure; configuring a photovoltaic priority strategy and energy storage scheduling parameters based on the structure, constructing and solving a multi-energy power supply optimization model, and generating a power supply scheduling output result; combining the electricity price sequence to identify peak and valley time periods, generating an arbitrage label, and further generating an energy storage charging and discharging control strategy and an energy storage regulation and control instruction structure; issuing a control instruction, collecting execution data, and generating an energy consumption deviation feedback structure; and finally, realizing adaptive closed-loop correction of the scheduling model based on a feedback structure. According to the invention, the photovoltaic utilization rate can be improved, peak-valley arbitrage and energy storage cooperative control is realized, the power supply cost of the charging station is significantly reduced, and the system scheduling precision and operation stability are enhanced.

The invention relates to the technical field of electrical engineering, and provides an electric energy storage system optimization scheduling method and system based on reinforcement learning, so as to solve the problems that it is difficult to effectively evaluate and control the battery aging risk in the frequent charging and discharging process and it is difficult to meet the real-time scheduling requirement in the edge side low computing power environment. The method comprises the steps of obtaining battery charging state and health state data in an electric energy storage system and power grid load and power grid electricity price data of an industrial and commercial park, and generating a state input data set; using the reinforcement learning agent to obtain a charging and discharging scheduling instruction; performing power distribution on the electric energy storage system, the multi-port converter power interface and the park load to generate multi-source cooperative power flow data; in combination with historical operation data, a reinforcement learning agent is utilized to optimize the charging and discharging scheduling instruction; and performing optimal scheduling of the electric power energy storage system according to the optimized instruction. According to the invention, efficient, intelligent and adaptive optimization scheduling of the power energy storage system in the industrial and commercial park is realized.

The invention provides a charging and discharging control strategy optimization method and system of V2G applied to a virtual power plant. The method comprises the steps of constructing a collaborative optimization strategy of comprehensive income data based on electricity price change prediction and peak regulation demand prediction; based on the user behavior mode and the user historical charging behavior data, constructing a collaborative optimization strategy matched with the user demand; constructing a collaborative optimization strategy of battery health degree management based on the health degree quantitative model of the battery; and based on a collaborative optimization strategy of comprehensive income data, user demand matching and battery health degree management, a multi-target dynamic optimization model is constructed, and charging and discharging control strategy optimization of the V2G application in the virtual power plant is realized. According to the method, full-process cooperation of strategy customization-dynamic invitation-user response is realized under the guidance of the virtual power plant, multi-mode charging and discharging control strategy combination is realized, load aggregation is carried out by effectively utilizing the distributed energy storage characteristic of the electric vehicle group, and the power grid regulation capability is maximized.

The invention relates to the technical field of new energy power systems, and discloses an energy storage coordination system and control method based on meteorological prediction and multi-energy complementation, and the system comprises a power generation end module which carries out the dynamic adjustment according to a power adjustment instruction of a power generation end; the meteorological prediction and data acquisition module is used for acquiring meteorological data in a future time period in real time and sending the meteorological data to the intelligent control and scheduling platform; the energy storage end module sends data including lithium battery SOC and hydrogen storage tank pressure to the intelligent control and scheduling platform and performs charging and discharging according to an energy storage end charging and discharging priority strategy; and the intelligent control and scheduling platform receives the electricity price data of the weather prediction and data acquisition module, the energy storage end module and the power grid in real time, generates a power generation end power regulation instruction and an energy storage end charging and discharging priority strategy, and meanwhile, improves the income and maximizes the hydrogen energy use proportion through electricity price peak-valley arbitrage and green electricity transaction premium. The power grid stability and the energy utilization rate are greatly improved, the energy storage life is prolonged, and the investment payback period is shortened.

The invention relates to the technical field of data center energy management, and discloses a flywheel energy storage system control system for data center computing power load energy recovery, which comprises an energy storage unit, a load monitoring unit, a flywheel control unit and an energy distribution unit. The energy storage unit is provided with a plurality of groups of flywheel energy storage devices capable of charging / discharging; the load monitoring unit monitors load data by covering key nodes of computing power equipment through a power sensor; the flywheel control unit collects flywheel rotating speed and power grid frequency fluctuation data; and the energy distribution unit decides charging and discharging based on load data, establishes a load and frequency coordinated alternating control strategy, and dynamically adjusts and predicts the running state of the flywheel. The system adopts time sequence synchronous control, the energy distribution unit comprises multiple modules, the alternate controller supports dual-mode switching and parameter optimization, and the flywheel unit adopts an annular redundancy or star-shaped concentrated structure. The system realizes efficient recovery of computing power load energy and flywheel energy storage optimization scheduling, and improves the energy utilization rate of a data center and the stability of a power grid.