1513 results about "Energy technology" patented technology

The present invention belongs to the technical field of building integrated energy. Disclosed is a virtual energy storage-considered double-layer optimization scheduling method for a building integrated energy system, the method comprising: constructing an energy hub-based low-carbon building integrated energy system containing wind-solar energy storage and energy conversion devices; comprehensively analyzing characteristics of loads of the system to improve the demand response capability thereof; further providing a double-layer optimization model containing an upper-layer energy operator pricing layer and a lower-layer building user optimization layer, building virtual energy storage and building user comfort indicators being considered in said model to improve the system scheduling flexibility so as to construct an overall user satisfaction indicator; and finally, solving the double-layer optimization model to optimize device contributes, demand responses and electricity purchasing and selling plans of the building integrated energy system, so as to obtain an optimal scheduling policy. The present invention can finely regulate and control various loads of the building integrated energy system, thus improving the energy utilization efficiency, alleviating the power supply pressure of the system, and achieving the purposes of energy conservation and emission reduction of buildings.

The invention relates to the technical field of new energy, in particular to a fan blade composite deicing system and method.The fan blade composite deicing system comprises a sensing layer, a decision-making layer, an execution layer and a feedback layer, and the decision-making layer comprises a graph neural network ice type recognition module, a CFD-icing coupling simulation module, a health management module and an energy consumption scheduling algorithm unit; the execution layer comprises a control unit and an execution unit, the control unit comprises a dynamic partition heating controller and a self-adaptive vibration controller, and the execution unit comprises an electric heating system and a vibration deicing system; compared with the prior art that a single deicing mode is usually adopted, and the adaptability to different ice type physical characteristics is poor, the scheme adopts a differential execution strategy, and partition electric heating control is carried out on different chord length areas of the blade based on the ice type recognition result; and high-frequency piezoelectric standing wave vibration is adopted for glaze ice, low-frequency eccentric wheel vibration is adopted for glaze ice, and the beneficial effects that the deicing efficiency is remarkably improved, invalid energy consumption is reduced, and damage to blades is reduced are achieved.

The invention relates to the technical field of new energy, and particularly discloses a new energy short-term intelligent optimization scheduling method under high-proportion new energy grid connection, and the method comprises the steps: data collection and preprocessing, multi-source data fusion prediction, hierarchical supply and demand balance, distributed collaborative optimization, and real-time regulation and feedback. According to the scheme, a multi-time-scale hierarchical supply and demand balance mechanism is adopted, fine management of different scheduling targets is achieved, the supply and demand relation is coordinated in combination with a dynamic weight adjustment mechanism, key load requirements are met preferentially, economy and stability are considered, and meanwhile the real-time performance and adaptability of scheduling decisions are improved; a distributed collaborative optimization structure is designed, the adaptability and robustness of a scheduling system to operation environment changes are effectively enhanced, a global scheduling problem is decomposed into a plurality of regional sub-problems, parallel solution and asynchronous update are achieved, the scheduling efficiency and the computing resource utilization rate are improved, and the requirements for real-time performance and high concurrency of large-scale new energy grid-connected scheduling are met.

The invention relates to the technical field of energy, in particular to a dynamic self-adaptive control method and system for a gas-steam combined cycle unit, and the method comprises the steps: constructing a multi-modal model library comprising a plurality of operation modes; operating parameters and environment parameters of the unit are collected in real time, and multi-source data fusion and system state evaluation are carried out; carrying out multi-time scale prediction on the load demand, fuel characteristics and environmental parameters of the unit based on a deep learning model; dynamically selecting model precision according to a prediction result, and implementing a multi-mode collaborative optimization decision to generate a control instruction; and executing the control instruction and monitoring an execution effect, and carrying out adaptive adjustment on model parameters and strategies according to feedback. According to the method, the unit can maintain efficient and stable operation under the scenes of power grid peak regulation, rapid load fluctuation, complex environment change and the like, meanwhile, pollutant emission is reduced, and the service life of key equipment is prolonged.

The invention provides a new energy station digital intelligent operation system, a control method, equipment and a medium, and relates to the technical field of new energy. According to the system, a marketization electricity price prediction result and a new energy output prediction result are output through an electricity price prediction module and a new energy output prediction module respectively, and an energy storage optimization scheduling module and a planning layout decision module work cooperatively. And combining the marketization electricity price prediction result and the new energy output prediction result to realize operation strategy optimization and capacity configuration decision of the new energy station, and solving and outputting a capacity configuration scheme corresponding to the new energy device and the energy storage device. According to the technical scheme, the multi-module collaborative new energy station digital intelligent operation system is constructed, so that full-link closed-loop optimization from prediction to scheduling to planning is realized, the adaptability of the system to power market fluctuation can be remarkably enhanced, and the operation income of the new energy station is ensured.

The invention provides a resource intelligent bus and task parallel hydrogen energy data scheduling optimization method and system, relates to the technical field of energy, and aims to solve the problems of resource heterogeneity, lack of real-time performance and dynamic adjustment, insufficient energy efficiency optimization, poor adaptability to abnormal working conditions and the like in the prior art. According to the method, a resource intelligent bus architecture is constructed, and the resource intelligent bus architecture comprises a resource virtualization encapsulation layer, a resource state sensing layer and a global prediction and scheduling layer; in the resource state sensing layer, performing multi-granularity task decomposition on hydrogen energy business requirements, and establishing an inter-task dependency relationship model by adopting a directed acyclic graph; in the global prediction and scheduling layer, a multi-objective optimization model is constructed based on a dependency relationship between resource states and tasks, and resources and tasks are dynamically matched according to an optimal solution of the model; and dynamically scheduling the resources according to a matching result of the resources and the tasks. The problems in the prior art are solved, and the hydrogen energy data scheduling optimization performance is improved.

The invention relates to the field of new energy technology and power electronics, and discloses a photovoltaic diesel generator energy storage hybrid off-grid and grid-connected household energy storage system, which comprises a data acquisition and state monitoring module for acquiring photovoltaic, diesel generator, energy storage, load and commercial power states in real time and generating system state parameters; the central control and decision module generates a control strategy containing an operation mode and an energy scheduling instruction; the energy flow and conversion execution module executes energy conversion and flow; the seamless switching control module realizes non-impact switching between modes; the self-adaptive safety protection module dynamically adjusts protection parameters; and the emergency response and recovery module executes black start and active recovery in an emergency state. According to the invention, through the unified central control and decision-making module, photovoltaic, diesel generator, energy storage and commercial power scheduling logics are integrated to form a collaborative energy management core, and the system can carry out global optimization on energy flow in grid-connected and off-grid modes according to a preset priority principle.

The invention relates to the technical field of new energy, and discloses a battery and a battery pack. A pole; the laminated battery cell comprises a battery cell body and a tab extending from one end of the battery cell body along the first direction; the pole lugs are electrically connected with the pole columns; the tabs comprise a positive tab and a negative tab, and the positive tab and the negative tab are both located at the same end of the battery cell body in the first direction; the positive electrode lug and the negative electrode lug are arranged at an interval along a second direction, and the second direction is perpendicular to the first direction; the spacing distance between the positive pole lug and the negative pole lug along the second direction is a1, the width of the laminated battery cell along the second direction is a2, a = a1 / a2, the length of the battery cell body along the first direction is b, and b.a is more than or equal to 8mm and less than or equal to 350mm. According to the battery provided by the invention, the over-large b.a easily causes the increase of the over-current demand of the battery cell and the reduction of the area of the welding region. And too small battery energy density is low.

The invention provides a modified lithium-rich manganese-based positive electrode material, a preparation method thereof and a lithium ion battery, and relates to the technical field of new energy. According to the modified lithium-rich manganese-based positive electrode material disclosed by the invention, multi-element point position doping is carried out on a base material to enhance the structural stability of a polycrystalline material, inhibit transition metal ion migration and interlayer dislocation, improve the internal lattice strength and manufacture a stable base material core; the heterostructure layer can greatly improve the surface structure phase change and transition metal dissolution of the material surface under high voltage, the stability of the surface of the matrix material is improved, and the rate capability of the material can be improved by the in-situ generated three-dimensional ion channel; the coating layer can effectively reduce side reaction of a solid-liquid interface, improve dissolution of metal ions on the surface and improve the cycle performance of the material; in a word, the internal structure and the surface of the material are effectively modified mainly through three means of doping, interface structure design and coating, the multiplying power of the material can be greatly improved, and the long-term service life of the material can be greatly prolonged.

The invention relates to the technical field of new energy, and discloses a hybrid energy system and a task scheduling optimization method and device thereof, and a storage medium. For the hybrid energy system with wide distribution and strong heterogeneity, edge gateway devices arranged in different regions are used for carrying out multi-source data acquisition on all energy devices in the regions; a dynamic environment state space is constructed according to the collected multi-source data, task scheduling is carried out by utilizing a locally deployed task scheduling model, tasks are executed in cooperation with other surrounding edge gateway devices, and the local task scheduling model is dynamically optimized and updated according to a task execution result; according to the method, resources of each edge gateway device can be reasonably utilized, resource allocation can be automatically realized, cloud centralized scheduling is not needed, task time delay is greatly reduced, the reliability of task execution is improved, accurate matching of tasks and resources is guaranteed, and by dynamically optimizing a local task scheduling model, the task scheduling efficiency is improved. And the task scheduling efficiency and accuracy are further improved.

The invention relates to the technical field of new energy, and discloses an independent energy supply system suitable for a high-altitude area and a control method thereof, electric power is generated through photovoltaic power generation, part of the electric power is stored in a storage battery, and the residual electric power is used for electrolyzing water to prepare hydrogen and oxygen, and the hydrogen and the oxygen are respectively stored. The oxygen content in the air is increased by mixing the oxygen, the oxygen and the hydrogen jointly enter the fuel cell for chemical reaction, and stable electric power and hot water are output. In order to ensure the stability of the system, a low-temperature air source heat pump is configured to serve as standby heat supply equipment, and accurate adjustment and storage of electric power are achieved through cooperative work of a load tracking module and an efficient storage battery. According to the system, photovoltaic power generation, hydrogen energy storage and fuel cell technologies are combined, continuous and reliable power and heat combined supply can be achieved in high-altitude areas lack of power grid support, and an efficient and environment-friendly solution is provided for energy supply in remote areas.

The invention discloses a photovoltaic power generation prediction method and device based on digital twinning and a medium, and relates to the technical field of new energy, and the method comprises the steps: constructing a three-dimensional geometric model of a photovoltaic power station; acquiring electrical parameters and environmental data of each component in the photovoltaic power station, and inputting the electrical parameters and environmental data into the three-dimensional geometric model according to the three-dimensional coordinates corresponding to the components to form a digital twinborn model; outputting a future environment data sequence in a preset time period based on the environment data through a trained generative model, and inputting the future environment data sequence into a digital twinborn model; performing power generation simulation through a digital twinborn model to obtain a power prediction value in a preset time period; and adjusting model parameters of the digital twinborn model according to the power predicted value and the actual power. And in combination with a high-resolution future environment sequence output by the generative model, power generation simulation of the digital twin model is fully fused with physical position characteristics and meteorological evolution laws.

The invention relates to the technical field of hybrid energy, and particularly provides a self-adaptive power distribution multi-source parallel mobile power supply system which comprises a multi-source input interface, a multi-channel input adjusting module, an energy storage module, an energy storage bidirectional adjusting module, a self-adaptive power distribution module and a power output interface. The method is characterized in that an MPPT algorithm is independently executed for each heterogeneous power supply through a multi-channel input adjusting module, and energy extraction is maximized; based on load detection and a multi-stage feedback mechanism, dynamic self-adaptive power distribution is realized; a multi-channel input end is adjusted into a unified standard bus voltage, and an output end dynamically adjusts the voltage according to a load, so that the energy conversion efficiency is improved while the use convenience is ensured; and the standard lithium battery units spliced without tools and the modular circuit design are adopted, so that the flexible combination and disassembly of the system are realized. According to the invention, the complexity of the hybrid energy system is greatly reduced, the system stability is improved, and efficient and lightweight hybrid energy mobile power supply is realized.

The invention provides an energy coordination control system and scheduling method for multi-energy complementary new energy storage, and belongs to the technical field of new energy storage and energy technology management. The data acquisition and processing module is connected with a current and voltage sensor, a power transmitter, a meteorological station sensor and a battery management system sensor, and is used for acquiring voltage, current, power, state of charge and environmental data of photovoltaic power, wind power, energy storage and load through the sensors; and sliding window filtering and wavelet de-noising processing are carried out through the embedded processor. The method has the beneficial effects that the uncertainty of wind and light output is effectively processed by establishing a layered distributed optimization architecture and adopting a method of combining multi-objective optimization and robust optimization, and the consumption rate of renewable energy sources can be remarkably improved, the net load fluctuation can be reduced and the energy consumption can be reduced on the premise of meeting various system operation constraints. Therefore, dependence on conventional fossil energy is reduced, and the economic operation level and the environmental protection benefit of the whole energy system are improved.

The invention provides an intelligent evaluation and gradient recovery method for an ex-service power battery of a hybrid architecture, and belongs to the technical field of energy, the method is used for realizing accurate evaluation and graded recovery decision of the health state of the ex-service power battery of an electric vehicle, the framework extracts battery degradation characteristics through a CNN network, and captures time sequence dynamic characteristics in combination with a GRU network; and performing hyper-parameter optimization by adopting a WOA algorithm, and finally constructing a multi-stage recovery strategy according to an SOH evaluation result, so as to formulate differentiated recovery paths such as regeneration utilization, echelon utilization or harmless treatment for the retired batteries with different health grades.

The invention discloses a fusion power generation system, a power generation control method, a storage medium and a controller, and relates to the technical field of fusion energy. The fusion power generation system comprises a steam generator set, a plurality of Tokamak devices and a plurality of thermal conversion units, the Tokamak devices and the thermal conversion units are in one-to-one correspondence, each thermal conversion unit is provided with a first channel and a second channel, and the Tokamak devices and the first channels of the corresponding thermal conversion units form a cladding cooling loop. The steam generator set and a plurality of second channels connected in sequence form a power generation loop; wherein the plurality of Tokamak devices are configured to operate in an alternating or complementary manner; the thermal conversion units are configured to receive heat from the corresponding cladding cooling loops and convert working media in the power generation loops into steam through the heat so as to drive the steam generator set to generate power. According to the system, the plurality of Tokamak devices operate in an alternating or complementary mode, so that heat loss can be reduced, the power generation efficiency is improved, and the plasma control difficulty is reduced.

The invention relates to the technical field of intelligent traffic and new energy, and discloses a multi-mode-based intelligent driving vehicle energy supply configuration method, which comprises the following steps: acquiring a real-time energy consumption baseline, real-time sliding window energy consumption and an environmental influence factor corresponding to each intelligent driving vehicle in an unmanned taxi team, according to the real-time energy consumption base line, the real-time sliding window energy consumption and the environment influence factors, comprehensive energy consumption predicted values of all the intelligent driving vehicles are determined; according to the comprehensive energy consumption predicted value of each intelligent driving vehicle, primary scheduling strategy decision making is carried out on each intelligent driving vehicle; constructing a target function according to a preset multi-target optimization condition, and solving the target function based on the preliminary scheduling strategy of each intelligent driving vehicle in the unmanned taxi team to realize optimization of the preliminary scheduling strategy to obtain a target scheduling strategy, the multi-objective optimization condition comprises an operation cost optimization condition, a vehicle availability rate optimization condition and a power grid load optimization condition; and performing energy supply strategy configuration on each intelligent driving vehicle based on the target scheduling strategy.

The invention belongs to the technical field of new energy, and particularly relates to a fault voltage ride-through optimization method and system for a network construction type energy storage converter. Carrying out positive and negative sequence separation and Clark-Park coordinate transformation on the three-phase power grid voltage and the inversion current, calculating active power and reactive power of the network-forming type energy storage converter, and carrying out power loop control to obtain three-phase reference voltage and angular velocity; performing voltage loop control, and dynamically adjusting a positive sequence active current and a positive sequence reactive current according to whether a low-voltage-crossing indication signal exists or not; performing positive-sequence current loop control and negative-sequence current loop control, and then performing inverse Clark-park conversion to obtain positive-sequence three-phase reference voltage and negative-sequence three-phase reference voltage; and superposing the positive-sequence three-phase reference voltage and the negative-sequence three-phase reference voltage for PWM (Pulse Width Modulation). According to the invention, when the power grid voltage is unbalanced due to a fault, the oscillation of the direct current side voltage and power caused by the negative sequence component is eliminated, and the power angle stability is improved and the fault current is suppressed by dynamically adjusting the related reference value during the fault voltage ride-through period.

The invention discloses a topology simulation analysis method and system for a low-voltage power distribution area, and belongs to the technical field of power systems and energy, and the method comprises the steps: extracting a multi-scale feature matrix through wavelet transform processing by using real-time electrical data and distributed power output data of the low-voltage power distribution area, and obtaining a feature vector after dimension reduction; combining the dimensionality-reduced feature vector with the topological structure of the low-voltage distribution area, and generating a dynamic topological relation graph through graph convolutional network processing; calculating to obtain a dynamic impedance matrix through adaptive Kalman filtering processing; analyzing and processing through the fault tree, and determining a fault propagation path and a key fault node; and through Markov decision process modeling, a state space, an action space and a reward function are obtained, and an optimized topological structure and an optimized control strategy are generated. The method has the beneficial effect that the accuracy, timeliness and practicability of topology simulation analysis of the low-voltage distribution area are remarkably improved.

The invention relates to the technical field of energy, in particular to a solar coupling hydrocarbon fuel reforming low-carbon oil displacement system which comprises a solar collecting lens, a gasification cavity, a reaction cavity, a solar heat collector, a condensation cavity, a hydrogen separation assembly and a catalyst. The solar collecting lens is arranged on one side of the solar heat collector; the gasification cavity and the reaction cavity are arranged in the solar heat collector; a hydrogen separation assembly and a catalyst are arranged in the reaction cavity, the gasification cavity is used for gasifying input excessive liquid water and hydrocarbon fuel to obtain gaseous hydrocarbon fuel and excessive water vapor, the gaseous hydrocarbon fuel and the excessive water vapor are subjected to a water vapor reforming reaction under the action of the catalyst, and generated hydrogen is discharged through the hydrogen separation assembly; the remaining carbon dioxide and water vapor are conveyed into a gas injection well to conduct oil displacement on an oil field; the condensation cavity is connected with the mining well. The efficient and environment-friendly device based on hydrocarbon fuel wet reforming directly heats and displaces oil by recovering hydrogen and residual reaction products, so that the energy utilization rate is increased, and carbon dioxide is recovered.

The invention relates to the technical field of ocean new energy technology utilization, and discloses an oscillating water column type wave energy power generation system with an energy gathering structure at the bottom, which comprises a jacket, an air chamber, a turbine power generation system, a radar altimeter, an infrared camera, an airflow velocity sensor, a control host and a bottom energy gathering assembly, the bottom energy-gathering assembly is installed at the lower end of the air chamber and provided with a transmission mechanism, reflection and diversion of seawater at the bottom of the air chamber are achieved through the arranged horizontal bottom plate and the two symmetrically-arranged energy-gathering plates, energy gathering beyond the width of the air chamber is completed, and opening and closing of the energy-gathering plates are synchronously controlled through the transmission mechanism. And the amount of seawater flowing into the air chamber from the water inlet is changed. According to the wave energy power generation system, the oscillation intensity of the water column in the air chamber can be adjusted according to changes of sea surface waves, the speed of the generated air flow can be matched with the optimal working frequency of the turbine power generation system, the power generation efficiency of the device is guaranteed, and the oscillation water column type wave energy power generation device can be protected and prevented from being impacted by sea waves.

The invention provides a lithium manganese iron phosphate precursor and a preparation method thereof, lithium manganese iron phosphate, a lithium ion battery and electric equipment, and relates to the technical field of new energy. Primary particles of the lithium manganese iron phosphate precursor provided by the invention are thick-sheet-shaped, the side surfaces of the primary particles are porous, the large-size primary particles and the porous lithium manganese iron phosphate precursor have many defects, are easy to grind and have excellent processability, Li < + > is easier to react with manganese iron phosphate in the sintering process to generate lithium manganese iron phosphate with high sphericity degree, the compaction density is high, and the lithium manganese iron phosphate precursor has good performance. No other impure phase is generated, and the capacity and the rate capability are good. According to the preparation method of the lithium manganese iron phosphate precursor provided by the invention, the ammonium manganese iron phosphate hydrate with large-size primary particles can be generated by adding excessive phosphorus sources into the mixed metal salt solution. Furthermore, pyrophosphorylation is generated through high-temperature calcination, a large number of air holes are generated, and finally the porous lithium iron manganese phosphate precursor is generated.

The invention provides a digital twin modeling method and device for a wind turbine generator control system, and relates to the technical field of new energy, and the method comprises the steps: constructing a multi-physical field mechanism model of the wind turbine generator control system according to obtained aerodynamic characteristic parameters, mechanical characteristic parameters and electrical characteristic parameters; performing comparative analysis based on the multi-physics field mechanism model and the preprocessed operation data to generate deviation data; if the deviation data is greater than a preset first threshold value, performing deviation prediction through a pre-trained deviation prediction model based on the preprocessed operation data, and generating a deviation correction value; and correcting a simulation result of the multi-physics field mechanism model by using the deviation correction value to form a data-mechanism dual-driven digital twinborn model. According to the digital twinning modeling method and device for the wind turbine generator control system, the output precision and environmental adaptability of the wind turbine generator digital twinning system under various working conditions are effectively improved, and the fitting ability and continuous stability of the model to the actual operation state are enhanced.

The invention relates to the technical field of building energy, in particular to a hybrid energy storage and photovoltaic day-ahead real-time collaborative optimization control method and system, and the method comprises the following steps: constructing a building energy system model; in the day-ahead scheduling stage, an optimization objective function is constructed based on photovoltaic output prediction, load demand prediction and time-of-use electricity price information; in a real-time control stage, monitoring actual photovoltaic output and actual load demand, and calculating a deviation value with a day-ahead predicted value; the equivalent heat storage capacity of building heat quality is evaluated in real time through a 3R2C heat dynamic model, a heat energy storage dispatching instruction is corrected in combination with an indoor temperature constraint condition, and load following control over building passive heat energy storage is achieved. According to the day-ahead real-time collaborative optimization control method and system for hybrid energy storage and photovoltaic, optimization collaborative control of a hybrid energy storage system is achieved, the adaptive capacity to photovoltaic power generation uncertainty is improved, and passive heat energy storage of a building is effectively utilized.

The invention belongs to the technical field of data mining and recommendation, and discloses an energy science and technology news recommendation method based on deentanglement representation learning, which comprises the following steps: firstly, obtaining a user behavior data set, and constructing a user feature sequence and global energy science and technology news co-occurrence graph data; secondly, constructing a prediction model DISNER which comprises an SMPRedict model, a CMPRedict model, a dual deentanglement module and an interest stability adaptive fusion module, and then training the DISNER model by utilizing the constructed data to obtain a weight; finally, the weight is loaded back to the model, user behavior data is input, prediction of the probability that the user clicks the candidate energy science and technology news is achieved, and the problems that in an existing energy science and technology news recommendation system, recommendation content is single, user interest modeling is insufficient, and semantic and collaborative information integration is unbalanced are solved.

The present application provides a negative electrode material and a preparation method thereof, a negative electrode sheet and a sodium ion battery, which relate to the field of new energy technology. The negative electrode material provided in the present application includes a core and a soft carbon coating layer coated on the surface of the core, the core includes hard carbon and carbon dots distributed inside and on the surface of the hard carbon, and closed pores are distributed inside the hard carbon. The soft carbon coating modifies the surface of the material, reduces the material specific surface area, and improves the first efficiency of the hard carbon material; the carbon dots are cross-linked with the coal-based precursor to inhibit the graphitization of the precursor material during high-temperature carbonization, increase the degree of disorder, help to form closed pores, and improve the sodium storage performance of the hard carbon material. The negative electrode material of the present application uses coal as a precursor material, mixes asphalt and carbon dots to form a composite precursor as an anode, an inert electrode as a cathode, and a mixed acid solution as an electrolyte. The composite precursor is electrolytically modified by an electrochemical method, and then separated, washed, and dried to obtain a modified composite precursor, which is finally obtained by sintering.

The invention relates to the technical field of new energy, in particular to a new energy large-base economic optimization scheduling method and system considering multi-energy complementation. The method comprises the following steps: acquiring and processing operation data of each station in the new energy large base; constructing a multi-energy complementary model based on the processed operation data; on the premise that the scheduling target value is fully responded, the operation cost is minimized as the target, and an economical objective function is constructed; setting operation constraint conditions of the new energy large base; solving the economic objective function based on the operation constraint condition and an optimal scheduling algorithm to obtain an optimal scheduling scheme; and making an operation control strategy of each station based on the optimal scheduling scheme. According to the method, collaborative optimization of the new energy large base in multiple time scales and multiple space dimensions is realized, the new energy consumption rate and the system operation economy are remarkably improved, and a key technical support is provided for constructing a novel power system.

The invention discloses a lithium battery health state and service life prediction method based on HA-BiLSTM, and belongs to the technical field of new energy, and the method comprises the following steps: S1, data preprocessing and loading; s2, calculating the SOH (state of health) of the battery and extracting features; s3, constructing a mixed attention bidirectional LSTM model; s4, generating time sequence data; s5, multiple times of training and optimal model selection are carried out; step S6, predicting a future SOH sequence; according to the method, the physical characteristics of the battery and the advantages of a deep learning algorithm can be effectively fused, an intelligent prediction model with the dynamic feature screening capability is constructed, high-precision prediction of SOH and RUL is achieved, and meanwhile technical support is provided for intelligent operation and maintenance of a new energy automobile battery management system BMS and an energy storage system.

The invention discloses a wind turbine generator life extension dynamic evaluation method and system based on multi-source data fusion, and belongs to the technical field of new energy. According to the method, multi-modal detection is carried out on key parts such as blades, a gearbox, a generator and a tower by collecting multi-source data such as basic parameters, historical operation data, environmental data and design files of a wind turbine generator, and residual life evaluation is carried out based on material performance, operation data and numerical simulation. And performing economic evaluation by combining cost-benefit analysis and investment payback period analysis, and finally comprehensively giving a life extension dynamic evaluation conclusion. The method solves the problems of data island, insufficient dynamic prediction capability, lack of economic analysis, low detection efficiency and the like in a traditional evaluation method, provides a scientific and comprehensive basis for the decision-making of the life extension of the wind turbine generator, and has remarkable economic benefits and engineering application values.

The invention relates to an electro-hydrogen conversion new energy base capacity planning configuration optimization method, belongs to the technical field of new energy, and solves the technical problems of seasonal imbalance, excessive conservative traditional robust optimization, imperfect evaluation system and insufficient multi-energy flow coupling optimization in existing new energy base capacity configuration. Comprising the steps of obtaining historical data in a new energy base scheduling period; generating a typical scene set based on the historical operation data, constructing a confidence interval of the wind and light output uncertainty of the new energy base, and obtaining a confidence robustness gamma value; constructing an electricity-hydrogen collaborative capacity optimization model with the goal of maximizing the comprehensive efficiency of the new energy base power system; converting the electricity-hydrogen collaborative capacity optimization model into a robust optimization model based on the confidence interval and the confidence robustness gamma value of the wind-solar output uncertainty; and solving the robust optimization model by using a CPLEX solver to obtain a new energy base capacity planning configuration scheme. And reliable low-carbon operation of the new energy base is realized.