705 results about "Light storage" patented technology

The invention discloses a protocol conversion and protocol self-learning method and system for transformer area optical storage and charging cooperation, and the method comprises the steps: constructing a digital twinborn body of a transformer area optical storage and charging system, carrying out the parallel operation of a protocol agent and a protocol agent in a virtual environment, and achieving the cooperative training of the protocol agent and the protocol agent through a hierarchical reinforcement learning architecture, the protocol agent is responsible for learning a dynamic priority scheduling and compression strategy for heterogeneous protocol messages such as Modbus, CAN and IEC 104, the protocol agent is responsible for learning a power balance and voltage stability control strategy based on photovoltaic output, energy storage SOC and charging load, and the two agents realize cross-domain collaborative optimization through a reward function mutual coupling mechanism. And finally, safely deploying the collaborative strategy obtained by training to the edge control equipment of the physical transformer area. According to the method, the problems of disjunction of protocol conversion and cooperative control, protocol strategy solidification, insufficient cross-domain cooperation and the like in the prior art are solved, and the operation efficiency and the self-adaptive capability of the transformer area optical storage and charging system are improved.

The invention relates to the technical field of intelligent power grids, in particular to an optical storage agricultural micro-grid operation optimization method and system, and the method comprises the steps: constructing an optical storage agricultural greenhouse micro-grid, and constructing a robust optimization scheduling model under the photovoltaic output uncertainty to carry out robust optimization scheduling; based on the scheduling scheme of robust optimization scheduling, constructing a micro-grid load model for the light storage agricultural greenhouse; on the basis of the load demand in the micro-grid load model, a cost-benefit optimization model under two operation modes of autonomous investment construction and energy performance contract is constructed, and the cost-benefit is calculated on the basis of the cost-benefit optimization model under the actual operation mode, so that the energy storage capacity configuration and the operation strategy are subjected to collaborative optimization. And constructing an energy storage system capacity configuration and operation optimization model, and finally determining an energy storage configuration scheme and a multi-time scale scheduling strategy. According to the invention, the problems of photovoltaic output uncertainty, single investment operation mode and insufficient economical efficiency optimization of the existing system are effectively solved.

The invention relates to a multi-data center elastic scheduling method, which comprises the following steps of: establishing a data center polymer (DCA) to integrate the space-time regulation potential of a dispersed data center, establishing a comprehensive model covering dynamic migration of a working load, power consumption regulation and control of a DVFS (Distributed Valve File System) and collaboration of an optical storage system, and adopting a double-layer elastic scheduling architecture, energy storage charging and discharging, photovoltaic output and other hyperopia actions are optimized to improve long-term decision robustness; and the lower layer solves short view actions such as server scheduling and load distribution in real time based on mixed integer linear programming to realize instant cost optimization, so that power-computing power collaborative scheduling optimization of the data center is realized. According to the method, flexible scheduling of time delay sensitive type and time delay tolerant type workloads is combined, the cooperative effect of an energy storage system and photovoltaic power generation is utilized, the power use efficiency and economical efficiency of the data center are optimized, and dynamic participation of multiple data centers in the power market is achieved.

The invention relates to the technical field of energy management, and provides a park light storage and charging energy distribution control method and system, and the method comprises the steps: transmitting an instruction to an energy storage system in an emergency, enabling the energy storage system to release an electric energy pulse to a micro-grid at the maximum available power within a preset time, and enabling the micro-grid to be in the maximum available power; providing time buffer for the power reduction process of the load units in the park; after the power reduction instruction is executed, identifying response characteristics of each load unit in the park to the power reduction instruction, and grading each load unit according to the response characteristics to obtain grading information; during the time buffering period, according to the grading information of the load units, a grading decreasing load reduction plan is formulated and executed, and a reduction instruction is issued in a peak shifting manner, so that the overall load is smoothly reduced; and continuously monitoring the power quality of the micro-grid, and finely adjusting the output of the energy storage system according to the power quality of the micro-grid. The method has the effect of improving the stability and reliability of light storage and charging in the park.

The invention belongs to the technical field of energy management, and particularly relates to a light storage direct current flexible load self-evolution cooperative scheduling method, system and equipment, and the method comprises the steps: constructing a parameterized energy utility curve, quantifying the comprehensive utility of flexible load response in energy efficiency, comfort and equipment loss, and calculating the unit power marginal utility as the flexibility; establishing a multi-target collaborative scheduling model considering the time-varying carbon intensity, the electricity price and the utility curve, and solving by adopting a model predictive control and reinforcement learning mixed strategy; static and dynamic data are fused to construct a knowledge graph, and flexibility is predicted and cross-scene migration is realized through a sequence diagram neural network; and cooperatively optimizing a knowledge graph prediction result and a scheduling instruction through a Lagrangian relaxation method to form a self-evolution closed-loop control system. According to the method, flexible load refined modeling, carbon perception economic optimization scheduling and system adaptive learning are realized.

The invention discloses a method and system for improving the energy utilization rate by utilizing light storage resource prediction, and the method comprises the steps: collecting irradiance temperature historical output data and load consumption feature data through a distributed sensor and an edge gateway, processing time sequence features through a sequence prediction model, and integrating multi-dimensional input through the fusion of an integrated learning model; a future prediction result of photovoltaic output and load demand is obtained; further acquiring real-time parameters of the charge state and the charge and discharge efficiency of the energy storage equipment, constructing an energy storage full life cycle state evaluation model to calculate dynamic indexes, and determining an available capacity boundary; and if the boundary exceeds a preset threshold value, peak-valley period characteristics are extracted, and an optimal strategy sequence including photovoltaic peak charging and load peak discharging is generated by adopting an intelligent optimization algorithm in combination with power grid peak-valley electricity prices and carbon emission constraints. According to the invention, the photovoltaic consumption rate is obviously improved, the operation cost is reduced, the carbon emission requirement is met, and intelligent and efficient management of the optical storage system is realized.

The invention discloses a security event traceability and response management platform suitable for a wind and light storage station. The platform comprises a station edge layer and a cloud middle platform layer. The station edge layer comprises an acquisition and isolation unit, a time synchronization and evidence shaping unit and a credible evidence storage and equipment snapshot unit; the evidence storage module is used for underlying data acquisition, time synchronization, evidence shaping and credible evidence storage to form original event data and an encrypted evidence chain; the cloud middle platform layer comprises a cross-domain causal atlas and root cause analysis engine, a control physical consistency verification model, a hierarchical response and energy storage security state control unit, a drill and strategy verification unit and a redisk and adaptive optimization unit; the method is used for cross-domain causal analysis, control consistency verification, hierarchical response decision and strategy redisk optimization. Unified acquisition, credible recording and cross-domain traceability of security events are realized, event sources and propagation paths can be accurately identified in a complex operation environment, and closed-loop control of security response is supported.

The invention discloses a solar photovoltaic power generation and energy storage integrated communication machine room and an energy management and multi-mode refrigeration control method thereof, and belongs to the field of solar photovoltaic power generation green low-carbon communication machine rooms. The communication machine room takes an optical storage all-in-one machine and a unified direct-current bus as a core, photovoltaic power is connected to the direct-current bus through DC / DC, stored energy is connected to the direct-current bus through bidirectional DC / DC, commercial power is connected to the direct-current bus through AC / DC, a bus voltage stabilizing module is configured, and photovoltaic power supply priority, residual power charging and gap energy complementation are achieved. The energy management method comprises the steps of collecting power and a charge state to generate a state quantity, distributing the power according to priorities and issuing a control instruction. According to the refrigeration control method, the refrigeration mode or the mixed mode of the heat pipe and the compressor is switched according to the outdoor temperature threshold value and the refrigeration requirement, the three-way valve, the compressor and the fan are cooperatively controlled, and hysteretic and minimum retention time constraints are adopted to restrain frequent switching. According to the scheme, the renewable energy proportion and power supply continuity are improved, refrigeration energy consumption is reduced, and temperature control of the machine room is kept stable.

The invention discloses an optical storage and charging dynamic power distribution control method and system based on AI prediction, and the method comprises the steps: collecting photovoltaic, energy storage, charging, power grid and environment multi-dimensional operation data through a sensor network, and obtaining a historical and real-time data set through preprocessing; training an AI joint prediction model by using the historical data set, and outputting a photovoltaic output and charging load demand prediction result; inputting system operation constraint condition parameters to delimit an optimization boundary; solving a power instruction reference value of each power module through a multi-objective optimization algorithm in combination with a prediction result and a constraint condition to realize global optimal distribution; and correcting the reference value based on the hardware operation parameters and the deviation between the predicted value and the real-time value, issuing an instruction, monitoring operation, and triggering fault protection. The method effectively solves the problems that a traditional system is rigid in power dispatching, low in photovoltaic consumption rate, large in energy storage life loss, remarkable in power grid impact, insufficient in prediction precision, high in energy transmission loss and weak in multi-target coordination.

The invention discloses a multi-target collaborative optimization scheduling control method and system for an optical storage and charging micro-grid, and aims to solve the multi-target conflict problem of economy, environmental protection and stability in the operation of the micro-grid by constructing a micro-grid model containing photovoltaic, energy storage, load and a power grid. A multi-objective function set for economic cost minimization, carbon emission minimization and supply and demand balance optimization is constructed, and an adaptive multi-population genetic algorithm and a Pareto optimization algorithm are innovatively proposed to collaboratively iteratively solve a Pareto optimal solution set. The search efficiency is improved through an adaptive variation rate mechanism and a multi-population strategy, and the problem of multi-target conflict is effectively solved. The method is suitable for optimal scheduling of the independent micro-grid containing photovoltaic power generation, an energy storage system and a charging load, and the energy utilization efficiency and the system stability are improved.

The invention discloses a household light storage and charging countercurrent prevention and grid-connected and off-grid intelligent switching system based on a closed-loop algorithm. The system comprises the following units: a data acquisition unit, a prediction model construction unit, a dynamic mode adaptive adjustment unit, a risk calculation unit and a grid-connected and off-grid control unit. The data acquisition unit is used for acquiring multi-source real-time operation data of the household optical storage and charging system; and the prediction model construction unit is used for constructing an energy demand prediction model and a photovoltaic generating capacity prediction model based on an intelligent algorithm and outputting a prediction result. According to the household optical storage and charging countercurrent prevention and grid-connected and off-grid intelligent switching system based on the closed-loop algorithm, the closed-loop algorithm is adopted, the operation mode of the system can be intelligently judged through real-time prediction and dynamic mode adjustment, self-adaptive adjustment is achieved, efficient operation of the optical storage and charging system is ensured, and the service life of the system is prolonged. And energy waste caused by excessive dependence on a fixed rule of the system can be avoided.

The invention discloses a light storage energy scheduling optimization recommendation method based on artificial intelligence, and relates to the technical field of energy management. According to the method, power generation side state parameters, energy storage side working data, load side demand characteristics and power grid side economic signals are collected in real time, and a comprehensive energy data set is formed through space-time alignment and standardized fusion; analyzing the data set by adopting a time-space two-dimensional prediction model, and outputting the power generation capacity, the power generation fluctuation ratio, the load and the risk probability distribution in a future time period; the predicted value is input into an intelligent decision engine, and an optimal scheduling strategy is deduced and generated in a virtual simulation environment through a strategy optimization algorithm; the strategy is converted into a control instruction through a northbound interface to be issued and executed, and execution deviation is collected to generate a verification report; strategy effect data is fed back to the cloud incremental learning platform, model parameters are updated online through distributed collaborative learning, and continuous evolution and scene self-adaptive optimization of the scheduling strategy are achieved. The method has the effect of improving the economic benefit of light storage energy scheduling.

The invention relates to the technical field of energy internet and intelligent micro-grids, and discloses a dynamic energy-carbon coupling modeling and collaborative optimization method for a wind and light storage and charging system, which comprises the following steps of: a, acquiring operation data of the wind and light storage and charging system; b, calculating to obtain the equipment-level dynamic real-time carbon potential of the output port of the energy conversion equipment; step c, updating the virtual total carbon load by using the state data of the energy storage unit and the real-time carbon potential; step d, constructing and solving a rolling horizon optimization problem to obtain an optimal control strategy covering the prediction horizon; and step e, issuing and executing a control instruction, and repeating the step a to the step e. According to the method, the physical working condition and the real efficiency of the equipment are dynamically mapped, the carbon emission intensity and the equipment operation state are associated in real time, and then static emission factors are replaced, so that the influence of the equipment on the carbon flow is accurately and dynamically reflected, and the precision and the real-time performance of microscopic carbon accounting are improved.

The invention relates to the technical field of electric digital data processing and energy storage system safety control, in particular to an optical storage and charging integrated station energy storage system. Comprising a data acquisition module which is used for acquiring a state feature vector of an energy storage system, planned scheduling power of an upper-layer energy management system, an external power grid interference index, and a real-time safety state and an economic value index of a battery management system in real time; the risk assessment module is used for generating a predictive risk score; the decision threshold module is used for calculating a dynamic intervention threshold; the arbitration decision module is used for generating a negative sign; the instruction correction module is used for determining a derating factor in response to the override mark and generating a correction scheduling instruction; or when the negative sign is not generated, outputting the planned scheduling power as a corrected scheduling instruction. According to the invention, early warning and active prevention of the potential thermal runaway risk are realized, and the intrinsic safety level and the operation reliability of the system are obviously improved.

The invention belongs to the technical field of photovoltaic power stations, and particularly relates to an energy scheduling and peak-valley arbitrage collaborative optimization method and system for a photovoltaic power station, and the method employs a two-stage optimization architecture: in a day-ahead planning stage, building a distributed robust optimization model based on prediction data, and generating a reference scheduling plan; in the real-time scheduling stage, different control instructions are generated by calculating the photovoltaic output deviation ratio and the income deviation value and combining the state of the storage battery. And when the system is in a power shortage state, switching to a power supply reliability guaranteeing mode, otherwise, executing a peak-valley arbitrage strategy through the random dynamic programming model. According to the method, the deviation between the expected income and the actual income is compared in real time, the photovoltaic output fluctuation and the battery state are combined to switch the operation mode, and the dynamic balance path breaks through the limitation that a traditional scheduling scheme is too conservative or too dangerous, so that the system can cope with uncertainty and keep the optimal economical efficiency at the same time.

The invention discloses a load control method, device and equipment and a medium, and the method comprises the steps: obtaining the active power of a grid-connected point at the grid-connected point, and determining the photovoltaic residual power of an optical storage system based on the active power of the grid-connected point, and enabling the direction of the power flowing into a power grid to be a positive direction; based on the photovoltaic residual power, the preset priorities of the n intelligent loads and the rated power of each intelligent load, starting operation or stopping operation of the n intelligent loads is controlled according to a preset rule, i is a positive integer and represents the rated power of the i intelligent load, and the preset rule comprises the steps that when the photovoltaic residual power is larger than 0, the intelligent load with the high preset priority is started preferentially, and when the photovoltaic residual power is larger than 0, the intelligent load with the high preset priority is started preferentially; and when the photovoltaic residual power is less than 0, the low-preset-priority intelligent load is stopped preferentially. According to the scheme provided by the invention, a control scheme for the intelligent load in the optical storage system is provided, so that the effects of remarkably improving the self-generation and self-use rate of the optical storage system and maximizing the benefits of photovoltaic power generation are achieved.

The invention discloses a multi-type resource participation power grid frequency modulation method based on a consistency algorithm, and the method comprises the steps: firstly constructing an improved frequency response model fusing distributed optical storage equipment, hydrogen storage and energy storage equipment and a DC flexible load, identifying system parameters, solving a multi-resource comprehensive adjustment coefficient, constructing a directed graph model, employing a grouping consistency algorithm, and obtaining a multi-type resource participation power grid frequency modulation model; the optimal adjustment coefficient of each resource is solved through distributed optimization of an augmented Lagrange function and a consistency index, and primary frequency modulation of a multi-type resource collaborative response system is driven. According to the multi-type resource participation power grid frequency modulation method based on the consistency algorithm, by driving a multi-type resource collaborative response system to perform primary frequency modulation, collaborative regulation and control of light storage, hydrogen storage and flexible load are realized, economy and operation constraints are considered through distributed optimization, and communication complexity is reduced through a clustering iteration mechanism; and the frequency stability of the high-proportion new energy power grid is obviously improved.

The invention relates to the technical field of power systems, and discloses a self-adaptive equalization control method, system and device for the state of charge of energy storage units of an optical storage direct current micro-grid and a medium, and the method comprises the steps: building a system model comprising a photovoltaic power generation assembly and a plurality of parallel energy storage units, constructing a droop control coefficient adjustment mechanism associated with the state of charge of each energy storage unit, and realizing dynamic nonlinear adjustment of the control coefficient along with the state of charge; meanwhile, a bus voltage secondary compensation mechanism based on the average value of the output voltage of the energy storage unit and a current balance adjustment mechanism based on the virtual voltage drop difference are constructed; and overlapping the two types of compensation quantities to a voltage control instruction of each energy storage unit, and synchronously realizing the voltage stabilization of the direct-current bus and the rapid equalization of the charge states of the energy storage units. Manual intervention is not needed, uneven power distribution caused by line impedance difference or operation condition change is effectively restrained, the system reliability can be improved, and the energy storage service life can be prolonged.

The invention discloses a wind and light storage cooperative control system based on source load matching. The system aligns a source load storage network and communication information and carries out credible evaluation; wind, light and load are predicted, and uncertainty levels are given; calculating a matching degree and a short plate hot spot, and combining network side hard constraints such as voltage, power flow and reverse transmission to obtain an acceptable boundary; when the matching is qualified and the complementary energy is still available, identifying the complementary energy / energy shortage and the reason, quickly checking the transferable margin and loss by the edge, generating a routing plan containing retention time and action budget, and scheduling the stored energy and the flexible load in a rolling manner; meanwhile, the risk budget and standby are written back according to feasibility, prediction uncertainty and communication quality, gray release and automatic rollback are achieved in cooperation with degradation and playback scoring, and consumption and traceability are improved.

The invention discloses an energy operation management method and device of a light storage system and a medium. The method comprises the steps of obtaining operation data of the optical storage system and performing feature extraction; according to the extracted feature data, an improved multi-strategy fusion GTO algorithm is adopted to construct a prediction model; and constructing a multi-objective function, and solving the constructed multi-objective function by adopting an improved multi-strategy fusion GTO algorithm to obtain a charging and discharging strategy of the optical storage system and a scheduling strategy of the flexible energy consumption equipment. The device and the medium are both used for implementing the method. The method has the advantages that starting from two aspects of macroscopic system optimization and microcosmic battery mechanism, low efficiency and safety problems caused by lack of battery operation mechanism analysis in a traditional method are effectively avoided, meanwhile, actual demands and electricity price changes of users are fully considered, an operation management strategy of the optical storage system is formulated, and the operation management efficiency of the optical storage system is improved. And the dual goals of minimizing the operation cost and maximizing the service life of the battery are achieved.

The invention relates to the technical field of power system stability constraint optimization and computer algorithms, and provides an energy storage fault ride-through optimization method considering optical storage access weak power grid stability constraint, which comprises the following steps: collecting power grid operation data to generate a dynamic enhancement data set; constructing and correcting a digital twinborn body on line; generating a guide vector field and initializing an optimization framework; executing hybrid iteration to generate a candidate strategy; an optimization process is fed back through simulation evaluation; verifying a decision output control instruction after convergence; and deploying an instruction and realizing online updating. According to the method, the characteristics of the power grid are accurately modeled by using the digital twin, optimization search is guided in combination with the guide vector field, the non-convex optimization problem caused by stability constraints is effectively processed through a hybrid iteration strategy, and the voltage and frequency recovery capability in the fault ride-through process is improved.

The invention discloses an optical storage charging-UPQC multi-port cycle calendar life cost collaborative energy management method, and belongs to the technical field of electric energy quality management and optical energy storage collaborative control of a power distribution and utilization system. The invention aims to provide a light storage and charging-UPQC multi-port cycle calendar life cost collaborative energy management method which introduces a photovoltaic flexible power regulation and'cycle + calendar 'combined life modeling mechanism and embeds energy storage life cost into a model predictive control (MPC) framework on the premise that UPQC undertakes power quality compensation. According to the invention, a model predictive control (MPC) framework is adopted to carry out cooperative energy management on photovoltaic flexible output, energy storage power distribution and charging load limit, and when the SOC approaches a boundary, photovoltaic load reduction and power change rate limitation are automatically triggered. According to the method, energy storage deep circulation and excessive loss can be actively inhibited while the energy balance of the direct-current bus is maintained, and the comprehensive targets of electric energy quality treatment, energy stabilization and energy storage life protection are achieved.

The invention relates to a water-light-storage integrated micro-grid system and a control method thereof. The system comprises a distributed hydroelectric power supply, a distributed photovoltaic power supply, an energy storage system, a medium-voltage AC-DC flexible interconnection device, a micro-grid energy management system and a load. The medium-voltage AC / DC flexible interconnection device is connected in series between a grid-connected point of the micro-grid and a superior power distribution network, an AC port of the medium-voltage AC / DC flexible interconnection device is used for realizing flexible power flow mutual aid between the micro-grid and the superior power distribution network, and a DC port of the medium-voltage AC / DC flexible interconnection device is used for being connected with an energy storage system; and the micro-grid energy management system is in communication connection with the distributed hydroelectric power supply, the distributed photovoltaic power supply, the energy storage system and the medium-voltage AC / DC flexible interconnection device, and is used for collecting system operation data, executing an optimization control strategy and realizing autonomous operation of the micro-grid and cooperative control of the micro-grid and a superior power distribution network. The system can effectively cope with seasonal load peaks, greatly improve the local consumption rate of distributed water and light resources, and enhance the power supply reliability and operation flexibility of a local power grid.

The utility model relates to the technical field of bird repelling, in particular to a light storage intelligent bird repelling device based on sound-gas pulses. The light storage intelligent bird repelling device based on the sound-gas pulse comprises a machine box, and a controller, a sensor assembly and a magazine are arranged in the machine box; the controller is electrically connected with the magazine and the sensor assembly. An infrared sensor and a millimeter wave radar sensor are arranged on the sensor assembly; a plurality of bird repelling bullets capable of being launched by the controller are arranged in the bullet bin, bird repelling is carried out in the mode that airflow is jetted forwards and explosion sound is made, bird repelling intelligence is high, the effective time is long, the effect is fast, and cost is low.

The invention discloses a panoramic situation awareness and early warning method, system, equipment and medium for real-time interaction between an optical storage and charging micro-grid and a power distribution network, and belongs to the technical field of situation awareness and early warning, and the method comprises the steps: building a data collection network covering the optical storage and charging micro-grid and the power distribution network, obtaining electrical quantity, environmental quantity and equipment state information, carrying out the preprocessing of the data, and carrying out the early warning of the real-time interaction of the optical storage and charging micro-grid and the power distribution network; pre-processed multi-source data is obtained; inputting the multi-source preprocessed data into an improved Kalman filtering algorithm to obtain fused state data; constructing a panoramic situation vector of the optical storage and charging microgrid and the power distribution network based on the fused state data, and obtaining a situation assessment result; inputting the situation assessment result into a hybrid prediction model composed of a long short-term memory network and a Bayesian network to generate a risk assessment result; constructing a multi-objective optimization model according to a risk assessment result, and outputting an intervention control signal; and performing operation monitoring and feedback updating based on the intervention control signal. According to the invention, the accuracy and real-time performance of situation identification are improved.

The invention discloses a reliable peak power supply capability design and comprehensive benefit evaluation method for a wind and light storage power station, and the method comprises the steps: constructing a multi-source data collection and intelligent preprocessing system, integrating power grid load data, meteorological observation data, satellite remote sensing data and policy documents, and forming a standardized database; carrying out load characteristic deep analysis and wind and light resource refined evaluation on the basis, generating a wind and light combined output scene through simulation, establishing a peak power supply capacity model considering probability reliability, and optimizing and determining a wind and light storage capacity configuration scheme under 95% confidence probability; a full-life-cycle benefit evaluation model is established, and the construction cost, the operation and maintenance cost, the power generation income and the peak special income are calculated; and finally, through financial feasibility analysis and dynamic optimization feedback, a technical economy comprehensive optimal scheme is formed. According to the method, the problems that the peak capacity is not matched with the power grid demand and the benefit evaluation dimension is single at present are solved, and accurate planning design and high-benefit operation of the wind and light storage power station are realized.

The invention discloses a wide-temperature-range perovskite optical storage material and a preparation method and application thereof, the chemical general formula of the wide-temperature-range perovskite optical storage material is CsCdCl3: xSb < 3 + >, yMn < + >, Mn < + > is Zr < 4 + >, Sn < 2 + >, Hf < 4 + > or Gd < 3 + >, x is more than 0 and less than 0.06, and y is more than 0 and less than 0.02. The perovskite crystal can generate free carriers under excitation of X-rays or 365-nanometer ultraviolet light, the carriers can be stored in a crystal defect trap, and the storage temperature of the carriers covers an ultra-wide temperature range from 100 K to 350 K, so that wide-temperature-range X-ray imaging, information storage and anti-counterfeiting application can be realized.

The invention discloses a multi-target collaborative energy management method and system for a light storage integrated power station. The method comprises the following steps: acquiring real-time operation data of the light storage integrated power station in a current detection period; based on the real-time operation data, calculating an operation efficiency evaluation value, an energy storage device life attenuation evaluation value and a power grid supporting capability evaluation value of the light storage integrated power station; based on the real-time calculation result, generating a multi-target collaborative optimization instruction by adopting a dynamic priority strategy; and decomposing the multi-target collaborative optimization instruction to obtain a photovoltaic inverter output instruction, an energy storage device charging and discharging power instruction and a grid-connected power instruction, and controlling the operation of the light storage integrated power station according to the photovoltaic inverter output instruction, the energy storage device charging and discharging power instruction and the grid-connected power instruction. Self-adaptive switching of multi-target weights is realized by constructing a dynamic priority strategy, and the problems of aggravation of equipment loss, lagging of power grid support and the like caused by single-target optimization are effectively solved.