69011results about "Batteries circuit arrangements" patented technology

The invention relates to the technical field of battery management, and discloses a battery energy balanced distribution and optimization method, device and equipment and a storage medium. The method comprises the following steps: performing multi-source parameter acquisition and feature decoupling on each single battery in a target battery pack to obtain a single battery state data set, and performing multi-band excitation scanning and impedance spectrum fitting to obtain internal features of the battery and decompose an attenuation trend to generate equivalent circuit model parameters; performing state mapping and capacity calculation based on the model parameters to obtain a performance evaluation result of the battery pack, and constructing an energy transfer network and identifying unbalanced propagation characteristics through performance difference analysis and consistency calculation; and temperature coupling calculation and compensation matrix reconstruction are carried out based on propagation characteristics, multi-path parallel balance control is realized, and an optimization result of the battery pack is obtained through voltage deviation calculation and polarization compensation. According to the method, the multi-dimensional characteristic parameters and the dynamic change characteristics are combined, and accurate control over battery pack equalization is achieved.

The present invention provides an energy storage synchronous coordinated management method and system based on virtual synchronization technology. The method comprises: selecting an appropriate energy storage device according to the requirements of a power grid and collecting related parameters; building a dynamic model of a virtual rotor, and setting rotational inertia of the rotor and torque parameters; building an adaptation layer; configuring a line monitoring apparatus, carrying out a safety operation when the fault occurs in the adaptation layer, or carrying out dynamic control and predictive planning in the absence of faults. The present invention utilizes intelligence to reduce reliance on experience, and enables dynamic optimization to improve the automation level of energy storage control.

The invention discloses a hybrid energy storage system optimization scheduling method based on AI intelligent regulation and control, and relates to the technical field of hybrid energy storage, and the method comprises the following steps: collecting real-time data, and carrying out the cross verification of data consistency through a multi-source data fusion technology; and identifying data abnormity caused by sensor faults, communication delay and environmental interference by combining adaptive threshold detection with a statistical analysis method, and eliminating abnormal data. According to the method, energy storage scheduling is optimized through data cleaning, time sequence prediction and reinforcement learning, and the intelligent management and dynamic adaptive capacity is improved. Multi-source data fusion and anomaly detection are adopted to ensure data accuracy, energy consumption is predicted by means of LSTM and Transform, and an energy storage strategy is optimized in advance. By combining reinforcement learning and system dynamic adjustment charging and discharging, the photovoltaic consumption rate is improved, the electricity purchasing cost is reduced, the SOC is intelligently controlled to be 40%-80%, and the service life of the battery is prolonged. Meanwhile, the system stability is improved through anomaly detection and correction, and the maintenance cost is reduced.

The invention discloses a day-ahead and intra-day economic dispatching method for a wind and light storage system, which relates to the technical field of wind and light storage and comprises the following steps of: establishing a dynamic aging model based on historical operation data of a battery, fitting actual attenuation characteristics of the battery through machine learning, acquiring the health state of the battery in different operation states and the change rule of the battery along with time, and calculating the economic dispatching result. Therefore, the basis of the dynamic adjustment strategy is formed. Energy storage charging and discharging are optimized through health factors, the system monitors parameters such as SOH and SOC in real time, tasks are dynamically adjusted, battery aging is reduced, stability is improved, the SOC strategy is optimized in combination with deep reinforcement learning, the service life is prolonged, and the maintenance cost is reduced. Rolling optimization scheduling and experience playback are adopted, a system can dynamically adjust a strategy, the wind and light absorption rate is improved, power grid balance is optimized, thermal power dependence is reduced, scheduling cost is reduced, extreme weather adaptability is enhanced, and green and low-carbon development of electric power is promoted.

The invention discloses a battery charging monitoring and adjusting system based on multiple areas, and relates to the technical field of battery management. According to the system, electrochemical impedance spectroscopy, infrared thermal imaging and electric heating parameters are synchronously collected, a physical connection and heat conduction topological relation of a battery pack is constructed in combination with a graph neural network, and monomer aging distribution and a cross-regional heat diffusion path are accurately recognized. Based on multi-region data aggregation, battery health protection, temperature equalization and a power grid demand target are fused, and charging current constraint and equalization priority parameters are dynamically generated. And through a fuzzy control algorithm, an active energy transfer mode and a passive risk isolation mode are intelligently switched, the energy distribution efficiency is optimized, fault spreading is blocked, the risk level of the system is evaluated in real time, a hierarchical regulation and control instruction is triggered, and the weight is dynamically adjusted and optimized through a feedback mechanism. The problems of extensive monitoring, strategy stiffness and safety response lag of a multi-area battery system are solved, the charging safety and economy in a complex scene are remarkably improved, and the service life of the system is remarkably prolonged.

The invention relates to a control method and system for a photovoltaic energy storage system, and the method comprises the steps: obtaining the real-time irradiance data of the photovoltaic energy storage system, carrying out the power generation prediction of an irradiance region, and obtaining a dynamic power generation prediction value; acquiring charge state parameters of a photovoltaic energy storage system and aggregated power load demand data of a target power utilization area, and performing energy storage scheduling analysis in combination with the dynamic power generation power prediction value to obtain a reference energy storage scheduling strategy; acquiring energy consumption equipment parameters of the target power utilization area, and performing equipment scheduling control with the reference energy storage scheduling strategy to obtain an equipment-level control instruction set; and performing grid-connected mode and off-grid mode collaborative analysis on the dynamic generation power prediction value, the reference energy storage scheduling strategy and the equipment-level control instruction set to obtain an energy storage global control strategy. According to the invention, multi-level collaborative optimization can be realized, and the economical efficiency and sustainability of the system are improved while stable power supply is guaranteed.

The invention discloses an energy storage and power grid coordination control system based on photovoltaic preferential energy supply, and relates to the technical field of power system dispatching automation, and the method comprises the steps: collecting the output power of a photovoltaic module, the charge state value of an energy storage unit and a load power demand in real time; dynamically calculating a photovoltaic and energy storage power distribution weight based on a preset photovoltaic priority energy supply strategy, and determining target output power of a photovoltaic module and an energy storage unit; when photovoltaic and energy storage cannot meet load requirements, power grid access control is automatically judged and triggered, so that continuity and stability of power supply are guaranteed. The problems that in the prior art, in the power dispatching process of an optical storage integrated system, dynamic reflection of the photovoltaic priority energy supply principle is lacked, the power distribution mode is not flexible, the power grid access response lags behind, and self-adaptive adjustment of dynamically adjusting the output current according to the real-time operation state is lacked are solved.

A battery pack includes a battery housing; a battery module disposed in the battery housing, where the battery module includes multiple battery cells, and at least one of the battery cells is a solid-state battery; and a control circuit disposed in the battery housing and configured to use the battery module to supply the electric power to the power tool. The energy W of the battery pack and the volume V1 of the battery pack satisfy the following: when the energy W is greater than or equal to 200 Wh, the volume V1 is less than or equal to 400 cm3; or when the energy W is greater than or equal to 300 Wh, the volume V1 is less than or equal to 800 cm3; or when the energy W is greater than or equal to 700 Wh, the volume V1 is less than or equal to 2500 cm3.

The invention relates to the technical field of mobile energy storage vehicles, and discloses an AI-based mobile energy storage vehicle running state monitoring and analysis system which comprises a main controller, an AI monitoring coprocessor and a state analysis coprocessor. The main controller calls a monitoring analysis instruction and sends the monitoring analysis instruction to the state analysis coprocessor; an AI monitoring coprocessor dispatches a state analysis instruction and processes multi-modal data; and the state analysis coprocessor analyzes the parameters to generate a reference monitoring signal, cooperatively monitors the battery temperature, the charging and discharging efficiency and the load fluctuation in real time, and generates an abnormal correction signal and a state control instruction in combination with an analysis optimization mode. The system realizes accurate analysis of multi-source data and dynamic control of the energy storage unit through data preprocessing, feature dimension reduction and deep neural network prediction, improves monitoring accuracy, dynamic adaptability and intelligent level, and ensures safe and efficient operation of the mobile energy storage vehicle.

The invention discloses a light storage and charging energy allocation system based on data driving and a control method, and relates to the technical field of light storage and charging energy allocation, and the method comprises the following steps: carrying out the real-time collection of the real-time power generation amount of a photovoltaic power generation system, the current power state of an energy storage battery, and the power consumption data of a load; and cleaning, normalization processing and time serialization are carried out on the collected data, so that the accuracy and continuity of the input data are ensured. According to the invention, by monitoring the power consumption data of the load in real time, combining with the long-term energy prediction model and dynamically adjusting the energy storage and discharge strategy, excessive discharge in a non-peak period is avoided, sufficient energy storage in a peak period is ensured, the power grid dependence is reduced, and the power consumption cost is reduced. Through load sudden increase detection and an adaptive scheduling algorithm, the system can quickly cope with sudden load changes, preferentially guarantee power supply of key loads, flexibly adjust a discharge mode, improve endurance time and system stability, and maximize green energy utilization efficiency.

Provided in the present invention are an energy-storage synchronous and coordinated management method and system based on a virtual synchronization technique. The method comprises: selecting a suitable energy storage device on the basis of grid requirements, and collecting parameters of the energy storage device; on the basis of the parameters of the energy storage device, establishing a dynamic model of a virtual rotator, and setting a rotator rotational inertia and a torque parameter; establishing an adaption layer, so as to realize the interaction between the virtual rotator and a physical energy storage device; providing a line monitoring apparatus in a grid so as to collect real-time data of the energy storage device and detect, in real time, whether a fault occurs, and the adaption layer executing a safety operation when a fault occurs, and performing dynamic control and prediction planning when no fault occurs, so as to solve the problem of the uneven distribution of resources; and the adaption layer determining whether grid nodes and the real-time data of the energy storage device are normal, and if the grid nodes and the real-time data of the energy storage device are abnormal, performing detection and setting again, and if the grid nodes and the real-time data of the energy storage device are normal, saving a log and ending the process. In the present invention, an intelligent means is used to replace a conventional experiential control method, such that the dependence on experience is reduced, and dynamic optimization can be performed to increase the level of automation of energy storage control.

Systems, methods, and articles for a portable power case are disclosed. The portable power case is comprised of at least one battery and at least one PCB. The portable power case has at least one USB port and at least two access ports, at least two leads, or at least one access port and at least one lead. The portable power case is operable to supply power to an amplifier, a radio, a wearable battery, a mobile phone, and a tablet. The portable power case is operable to be charged using solar panels, vehicle batteries, AC adapters, non-rechargeable batteries, and generators. The portable power case provides for modularity that allows the user to disassemble and selectively remove the batteries installed within the portable power case housing.

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 relates to the technical field of electric energy storage systems, in particular to an energy storage power station operation monitoring management system and method. The method comprises the following steps: obtaining voltage data of each energy storage node in a standing state, and carrying out periodic voltage sampling within a preset standing time, thereby obtaining voltage springback time sequence data; performing temperature correction processing on the voltage springback time sequence data so as to obtain standardized voltage springback characteristic data; acquiring charge state data of each energy storage node before and after operation; calculating a charge-discharge depth change value of each node according to the charge state data; and constructing a node health state evaluation model according to the standardized voltage rebound characteristic data and the charge and discharge depth change value, and obtaining node aging differentiation parameters. According to the method, the node charging and discharging power allocation model is constructed based on the node aging differentiation parameters, differentiation management of energy storage nodes in different states is achieved, and the problem that the aging degree between the nodes is further expanded in a traditional method is solved.

To enable accurate estimation of a degree of deterioration of a secondary battery with a metal lithium layer on an anode.SOLUTION: A battery deterioration estimation device comprises a current measurement part, a voltage measurement part, a calculation part, and an estimation part. The current measurement part measures a discharge current of a secondary battery at predetermined timing immediately after the start of discharge of the secondary battery. The voltage measurement part measures a drop voltage that is a difference between a voltage of the secondary battery before the start of discharge and a voltage of the secondary battery at the predetermined timing. The calculation part calculates an internal impedance of the secondary battery at the predetermined timing from the measured discharge current and the measured drop voltage. The estimation part estimates a degree of deterioration of the secondary battery from the calculated internal impedance.SELECTED DRAWING: Figure 1

The invention discloses a multi-user energy storage power station charging and discharging control method and system based on electricity price, and the method comprises the steps: arranging a monitoring terminal at energy storage equipment, and collecting the load data of each energy storage equipment; carrying out data preprocessing on the collected load data; constructing a cascade neural network prediction model, training the model by using the preprocessed load data, and generating a regional total load demand prediction value in a future time period; designing a deep reinforcement learning model DRL by using the regional total load demand predicted value to obtain a preliminary power adjustment amount; designing a target function considering the battery aging cost by using the initial power adjustment amount, and determining an initial value range of a coefficient in the target function of the optimization model according to a constraint condition; and a multi-objective optimization problem is considered, a Pareto optimal solution set is obtained based on the initial value range, and a final power distribution scheme is determined. The method can accurately predict the load, optimize the charging and discharging strategy, reduce the power consumption and operation cost, balance the service life of the battery and the economic benefit, and improve the intelligent and efficient level of the energy storage power station.

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 discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The invention discloses a charging pile intelligent control management platform, relates to the technical field of charging pile intelligent control, and can identify the peak and valley periods of the power grid load in advance by collecting and analyzing the power grid load data and the operation state of a charging pile in real time and combining a regression model of a load prediction and demand analysis module. The power output of each charging pile is dynamically adjusted through the charging pile scheduling and power adjusting module, the power grid resource distribution is optimized, the overload phenomenon is avoided, and the stability of power grid operation is improved. By monitoring and feeding back the charging behavior data of the user, the platform can reasonably plan the working state of the charging pile. Therefore, too fast loss of some charging piles due to continuous overload is avoided, resource waste of idle charging piles is reduced, the utilization efficiency of equipment is improved on the whole, and the service life of the charging piles is prolonged. By using the load prediction and demand analysis module, the platform can pre-judge the charging demand peak of the user and optimize the scheduling strategy in advance, thereby shortening the waiting time of the user.

An optical storage system designed to compensate for the water hammer effect in hydropower turbines and its cooperative frequency modulation method are provided. The method involves detecting the water hammer effect and utilizing a pre-established system model integrating a hydropower unit, photovoltaic system, and hybrid energy storage system. Through model predictive control combined with a whale optimization algorithm, the governor parameters of the hydropower unit are optimized. The system then obtains the photovoltaic active power output and the battery's state of charge (SOC) within the hybrid energy storage system. Based on these parameters, a collaborative control strategy for the photovoltaic and hybrid energy storage systems is determined and executed. This strategy enables effective control of the photovoltaic system and / or hybrid energy storage to compensate for reverse power adjustments in the hydropower unit, addressing the water hammer effect and ensuring stable power grid operation.

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 discloses a V2G-based dynamic discharge power optimization control method, and belongs to the field of smart power grid and electric vehicle cooperative control, and the method comprises the following steps: collecting the battery state information of an electric vehicle and the load demand data of a power grid in real time; determining a demand response priority through a multi-objective optimization algorithm based on the battery state information of the electric vehicle and the load demand data of the power grid; calculating a power distribution strategy scheme based on the response priority; when the power distribution strategy scheme does not meet the battery health constraint, a safe discharge power range is adjusted based on a battery state evaluation result; generating a correction parameter based on the adjusted discharge power range and the environment variable; and monitoring the operation state of bidirectional energy flow in real time, and when the operation state of bidirectional energy flow does not achieve the response effect, adaptively adjusting the power distribution strategy based on the correction parameter and updating the execution parameter.

The invention relates to the technical field of ship power systems, and discloses a ship comprehensive power system based on composite energy storage, comprising an energy generation module which comprises a diesel generator, a gas turbine and a renewable energy sub-module and is used for generating electric energy; the energy storage management module comprises a lithium battery, a super capacitor and a flywheel energy storage system and is used for storing and releasing electric energy; the energy distribution and scheduling module is used for controlling energy distribution between the energy generation module and the energy storage management module; the load management module is used for adjusting the load power requirement according to the load priority; and the health management and fault monitoring module is used for monitoring the operation states of the energy storage management module and the system in real time. Through cooperative work of multiple energy storage units and combination with optimization control of the energy distribution and scheduling module, characteristics of different energy storage devices are fully exerted. Therefore, loss in the energy conversion process is remarkably reduced, distribution and utilization of electric energy are optimized, and the overall energy utilization efficiency of the system is improved by about 15%-20%.

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 relates to the technical field of battery management, and discloses an equalizing charging method, device and equipment for a power battery pack. The method comprises the following steps: performing parameter acquisition on a power battery pack, measuring terminal voltage values, temperature values and charge and discharge current values of each single battery, and calculating a state-of-charge value and an internal resistance change rate of each single battery; inputting into a multi-head self-attention network for feature extraction, and generating a state correlation feature matrix; constructing an Actor network and a Critic network based on the state correlation characteristic matrix, and outputting an equalization current value, a constant-current or constant-voltage equalization mode and equalization time through the Actor network; according to the equalization current value, the equalization mode and the equalization time, the DC-DC converter in the main equalization loop is controlled to generate constant current output, or the Buck-Boost converter in the secondary equalization loop is controlled to generate constant voltage output, the equalization efficiency of the power battery pack is guaranteed, meanwhile, energy loss is reduced, and temperature self-adaptive control over the equalization circuit is achieved.

The invention discloses a battery energy storage safety management system based on a BMS, and relates to the technical field of battery energy storage safety management, the system comprises a data acquisition module, a data analysis processing module, an equalization strategy generation module, an equalization execution module and a safety monitoring early warning module; operation parameters of the battery pack and the single batteries are comprehensively acquired through the data acquisition module, the actual operation state of the battery pack is accurately sensed, health factors are calculated by comprehensively considering multiple factors, weight coefficients are dynamically adjusted, the characteristics of the single batteries are comprehensively considered, and the charging and discharging states of the battery pack are calculated according to the health factors, the charging and discharging stages and the environment temperature. The equalization strategy and current are determined through a self-adaptive algorithm, dynamic response to the running state of the battery pack and environment change is achieved, equalization operation is accurately executed through the equalization execution module, accurate equalization control is achieved, and through real-time monitoring of the safety monitoring and early warning module, safe running of the battery in different environments is guaranteed, the performance of the battery is improved, and the service life of the battery is prolonged.

Disclosed in the present application are a container, an energy storage apparatus, an energy storage system and a charging network. The energy storage apparatus comprises containers. There are m containers, where m≥2, and the m containers are arranged in the direction of height of the containers. The size of each container in the direction of length thereof is consistent with the size of a standard container in the direction of length thereof, the size of each container in the direction of width thereof is consistent with the size of a standard container in the direction of width thereof, the size of each container in the direction of height thereof is smaller than the size of a standard container in the direction of height thereof, and the sum of the sizes of m1 adjacent containers among the m containers in the direction of height thereof is equal to the sum of the sizes of n standard containers in the direction of height thereof. The size of m1 containers in the direction of height thereof is equal to the size of n standard containers in the direction of height thereof, such that the space occupied by m1 stacked containers is the same as the space occupied by n standard containers, thereby improving the space utilization rate of container placement, and reducing the container transportation cost, thus reducing the use cost of energy storage apparatuses.

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 a battery state of health (SOH) cooperative control method and system of a dynamic reconfigurable energy storage system, and belongs to the field of battery health. The method comprises the following steps: designing a multi-time-scale SOH real-time identification model based on transient characteristic quantification, and realizing second-level updating of an electric core-level health state map; and by adopting power-SOH dynamic matching double-layer reconstruction control, the health and balance of the battery pack are ensured. In addition, the SOH tracking error is less than 1.5% by utilizing a distributed collaborative observer and topology adaptive state estimation; dual-mode control is realized through model predictive control and grouping dynamic optimization; and finally, topology is optimized by using a deep learning strategy and a life loss entropy objective function, series-parallel mode life equalization is realized, and the combined life cycle of the system is prolonged. According to the invention, the service efficiency of the battery can be effectively improved, the service life of the battery is prolonged, and the reliability and stability of the whole energy storage system are greatly enhanced.

The invention provides an intelligent scheduling control method for an optical storage charging and discharging integrated power station, and the method constructs a multi-time-scale prediction-optimization-correction three-stage control architecture, and achieves the real-time scheduling of the optical storage charging and discharging integrated power station through the coordinated operation in the upper-layer 24-hour rolling optimization, the middle-layer real-time scheduling and the lower-layer equipment control. And multi-target and multi-time-scale coordinated control optimization of maximization of the photovoltaic consumption rate, minimization of the power grid electricity purchase cost and prolonging of the service life of the energy storage system is realized. And a model prediction control algorithm is combined to dynamically adjust photovoltaic power generation output, an energy storage charging and discharging plan and a charging pile scheduling strategy. In addition, an adaptive parameter adjustment mechanism is introduced, and MPC parameters are adjusted online by using a reinforcement learning algorithm so as to cope with dynamic changes of environmental parameters and equipment states. The method can effectively improve the operation efficiency and economy of the optical storage charging and discharging integrated power station, prolongs the service life of an energy storage system, and is suitable for intelligent scheduling control of various optical storage charging and discharging integrated power stations.