169 results about "Renewable energy system" patented technology

Provided are a method, a system, a storage medium, and an electronic device for selecting a combination of equipment in an elementary energy full chain that realizes the conversion and interaction of various types of energy and improves the flexibility of the energy system. [Solution] The method obtains basic technical and economic index parameters of equipment in the hydrogen energy full chain, classifies the basic technical and economic index parameters based on the performance differences of different types of equipment in the hydrogen energy full chain, performs a comparative analysis of each type of equipment in the hydrogen energy full chain according to the classification, and based on the results of the comparative analysis, comprehensively considers the performance difference indexes and operating characteristics of the equipment, establishes constraints for the classification indexes corresponding to each equipment, determines one of the classification indexes as part of the target function, finds the optimal results under different renewable energy penetration rates, and obtains a rule for selecting equipment combinations in the hydrogen energy full chain in a high-proportion renewable energy system.

The invention relates to a dynamic network reconstruction strategy of a digital electric power energy storage system in the technical field of electric power energy storage. The strategy comprises the following steps: S1, building an energy storage system; the system consists of an intelligent layer, a control layer and a data layer, s2, modeling an energy storage network; learning an optimal control strategy of the energy storage network by adopting a graph capsule neural network; s3, dynamic network reconstruction; generating a fault emergency response model based on a Markov decision process; by systematically optimizing a topological structure and an optimal control strategy of the energy storage network, rapid isolation of a fault area and dynamic adjustment and reconfiguration of working modes of energy storage equipment and load equipment are realized, the energy storage network is ensured to still operate stably in a fault situation, and the energy storage efficiency is improved. The flexibility and elasticity (fault tolerance) of the energy storage system in a dynamic environment are improved; by reducing the energy loss in the transmission process, balancing the network load and preventing local faults, the technical problems existing in the prior art are solved, and the sustainable development of a novel renewable energy system is promoted.

A renewable power system with a twin-configurable architecture is described. The system includes a renewable energy source (RES), an energy storage system (ESS), and at least one controllable load (CL) (e.g., AI training / datacenter). The system can serve as a baseload or semi-baseload plant for CL(s) and / or as a peaker or semi-peaker plant for an electric grid, or vice-versa, and optionally in parallel, can also provide ancillary services to the electric grid and / or to the CL(s). In certain embodiments, e.g. solar PV RES(es), the system can have capacity factors of at least about 60% and up to 100%, higher asset utilization, better economics for the RES-ESS, improved system performance, and lower energy costs as compared with known systems without a CL(s). By making load a variable, and integral part of the system, sophisticated resource allocation strategies, including AI algorithms, can be developed not previously possible with known systems lacking a CL(s).

The invention discloses a renewable energy system operation scene generation method. The method comprises the following steps: acquiring historical data information of a target renewable energy system; establishing a diffusion process model for the operation scene of the target renewable energy system; de-noising processing is carried out based on Gaussian noise; modeling a conditional diffusion model; constructing a neural network for a conditional diffusion model; adopting a large model to guide and generate conditional information, and training the established conditional diffusion model to obtain a trained conditional diffusion model; and generating a renewable energy system operation scene by adopting the trained conditional diffusion model. The invention also discloses a system for realizing the renewable energy system operation scene generation method. According to the method, the large language model is used as condition guidance, and the diffusion model is used for directly generating the deterministic operation scene from any Gaussian noise, so that the method not only can realize the generation of the operation scene of the renewable energy system, but also is high in reliability, good in accuracy and high in efficiency.

The invention discloses a source network load storage real-time scheduling method for MADRL-GAN collaborative optimization, and the method comprises the steps: firstly constructing a carbon pollution collaborative optimization model, and converting the model into a solvable convex problem through function linearization and mixed integer conversion; further mapping the model into a multi-agent reinforcement learning model, dividing agents according to an electrical coupling degree, and designing a state and action space containing a dual-Critic reward mechanism; thirdly, learning system uncertainty distribution by using a generative adversarial network, generating diversified scenes to train a reinforcement learning model, and obtaining a preliminary strategy; and finally, correcting the strategy through the generative adversarial network, generating a final scheduling action through a strategy mixing mechanism, and realizing distributed real-time optimal scheduling. According to the method, the problems of multi-target collaboration, high-uncertainty scheduling and real-time distributed decision making in a high-proportion renewable energy system are effectively solved.

The invention provides an energy storage capacity optimal configuration method based on multiple time scales, which solves the coordination problem of power dispatching supply and demand balance and energy storage system investment decision under multiple time scales through a designed multi-target double-layer optimization model, and optimizes and synthesizes the stability and economy of a renewable energy system. According to the method, the problem of renewable energy fluctuation under long and short time scales is considered firstly, an inner-layer model with minimum power fluctuation under the short time scale is constructed by taking the minimum real-time scheduling error as a target function, and supply and demand stability is ensured in hour-level short time scale scheduling. In day-level medium and long term time scale scheduling, an outer layer regulation and control model under a medium and long term time scale is constructed by taking intra-day scheduling power balance and minimum economic cost as objective functions, and renewable energy fluctuation stabilization and energy storage system cost optimization under multiple time scales are ensured.

The invention discloses a multi-energy power system optimization scheduling method based on improved robust optimization, and relates to the technical field of power system scheduling. Minimizing the total operation cost of the power system as a total objective function; establishing constraint conditions for the total target, wherein the constraint conditions comprise thermal power generating unit constraint, renewable energy system constraint and energy storage system constraint; establishing a robust optimization scheduling model according to the total objective function and each constraint condition, and processing robust optimization by adopting a Benders decomposition method to obtain a decision variable meeting the objective function; and adjusting a scheduling plan according to the decision variable to realize optimal scheduling of the power system. Introducing a constraint condition of multi-energy overall balance; an adjustment factor is introduced to control the balance between robustness and economy, the uncertainty influence of wind power photovoltaic fluctuation is dynamically controlled through an uncertainty influence factor, Benders are introduced, a large-scale problem is decomposed into a main problem and a sub-problem, the calculation complexity is reduced while efficient solving is achieved, and the method is suitable for large-scale real-time scheduling.

The invention discloses a photovoltaic module early-stage fault early-warning method based on electric-thermal characteristic joint analysis, and belongs to the field of photovoltaic module early-stage fault early-warning, and the method comprises the steps: segmenting an irregular high-temperature or low-temperature region according to a temperature distribution image through employing a data segmentation algorithm, and extracting the boundary contour and area quantification characteristics of the abnormal region; performing joint feature vector construction by using the abnormal region boundary contour and area quantization feature and the current and voltage fluctuation amplitude in the electrical signal time sequence to obtain the comprehensive fault diagnosis vector; according to the preliminary fault classification label, extracting a temperature electrical corresponding association mode of a similar fault case from a historical fault diagnosis database, and obtaining an enhanced fault corresponding relation model; and generating a system risk assessment report by adopting the final fault diagnosis classification result, triggering a corresponding alarm mechanism, and judging the overall risk level of the renewable energy system.

The invention relates to the technical field of nuclear energy comprehensive utilization, and discloses an energy system with nuclear energy as base load and a control method.The system comprises a nuclear power secondary loop system, a renewable energy system, an energy storage system, a steam supply system, a seawater desalination system, a hydrogen production system, a heating system and a refrigeration system; the heat of the reactor core enters the steam generator to generate main steam; the photovoltaic module and the wind turbine generator set carry out renewable energy power generation; a storage battery is used for storing renewable energy electric power, and a molten salt heat storage system is used for storing heat energy; the steam supply heat exchanger is used for heating fresh water produced by the seawater desalination system to obtain superheated steam; the low-temperature multi-effect evaporation system and the reverse osmosis membrane method seawater desalination system generate fresh water; the electrolytic bath generates hydrogen and oxygen; the heating system and the refrigerating system are used for refrigerating in summer and heating in winter, and nuclear energy, wind energy, solar energy and the like are coupled and utilized in a stepped mode.

The invention discloses a state of health (SOH) prediction method for a super-capacitor system, and the method comprises the steps: obtaining a plurality of pieces of discharge segment data according to the historical charge and discharge data of the super-capacitor system under a real operation condition; identifying equivalent parameters of the data of each discharge segment through a resistance-capacitance equivalent model to obtain equivalent capacitance corresponding to each discharge segment; carrying out capacitance value calibration according to the equivalent capacitance corresponding to each discharge segment so as to obtain capacitance value labels of different historical preset time periods; constructing an SOH track smooth curve according to the capacitance value labels of different historical preset time periods; performing prediction model training according to the SOH trajectory smooth curve to obtain an SOH prediction model; and predicting the health state of the super-capacitor system by using the SOH prediction model. The method can accurately predict the health state of the supercapacitor system in the electrified traffic system carrying tool and the renewable energy system in actual operation, and improves the operation safety and economic benefits of the electrified traffic system carrying tool and the renewable energy system.

A method of adjusting energy delivered by a renewable energy system to an electrical grid and an energy-consuming load is disclosed. The method may comprise determining a production schedule of a renewable energy system (RES) and establishing a prioritization order between the electrical grid and the energy-consuming load. The method may further comprise setting a power receiving threshold for the electrical grid or the energy-consuming load based on the prioritization order, and adjusting the energy delivered by the RES by controlling a first amount of energy delivered to the electrical grid and a second amount of energy delivered to the energy-consuming load. A variability schedule of at least one of the first amount of energy or the second amount of energy can be amplified or dampened over a time period relative to the natural variability energy production schedule of the RES.

A load frequency controller for the integration of a thermal power generator with renewable energy generators includes first and second power systems situated in separate geographic regions, interconnected through an inter-area tie-line. Each power system incorporates a 3DOF-FOPIDN controller, programmed with a salp swarm algorithm to adjust the controller gain parameters. A model predictive controller (MPC) slaved to the 3DOF-FOPIDN controller generates droop correction values then transmitted to the energy generators. The power deviations from the resources, the tie-line power deviations and a load center power deviation are combined to generate a combined power deviation signal. A frequency generator converts the combined power deviation signal to a frequency deviation value for each area. Feedback loops channel the frequency deviation value to bias factor generator in each area. An ACE signal is generated, which is utilized by the 3DOF-FOPIDN-MPC controller to modulate frequency and maintain grid stability.

The invention relates to a normal-conducting high-speed maglev traffic energy fusion system which comprises a high-speed maglev traction power supply system, a renewable energy power generation system, a composite energy storage system, an energy management system, a cloud computing platform system and a maglev train. Compared with the prior art, efficient fusion of a renewable energy source system and a high-speed maglev traffic system is achieved, and the high-speed maglev traffic system has the advantages of being efficient and safe in high-speed maglev traffic, energy-saving and low-carbon in operation and the like.

The invention discloses an operation state prediction method and device of a building new energy system, and the method comprises the steps: obtaining various monitoring variables of a target building, inputting the various monitoring variables of the target building into a trained prediction model, and obtaining the prediction results of the various monitoring variables of the target building in a set prediction time range; based on the prediction result, evaluating the operation state of the target building: firstly calculating a basic threshold value, and then determining a dynamic threshold value according to the basic threshold value, an equipment aging factor and a seasonal factor; the prediction result is compared with a dynamic threshold value, and if the prediction result is smaller than or equal to the dynamic threshold value, the operation state of the building new energy system is a normal state; and if the prediction result is greater than the dynamic threshold value, indicating that the operation state of the building new energy system deviates from the normal interval and the building new energy system has an abnormal phenomenon.

The invention discloses a distributed renewable energy system based on methanol energy storage and a configuration optimization method. The system comprises a photovoltaic unit, a battery, a P2M module, a methanol storage tank, an M2P module, an ORC module and a power grid power supply. The photovoltaic unit converts solar energy into electric energy and directly supplies power to users. According to the configuration optimization method, the minimum leveling cost of kilowatt-hour electricity, the minimum carbon emission factor of kilowatt-hour electricity and the minimum electricity abandoning rate are taken as optimization targets, and the installed capacity of a photovoltaic storage tank, a battery storage tank and a methanol storage tank is taken as decision variables; performing configuration optimization solution on the system to obtain an optimal configuration scheme of each device; according to the system, methanol rectification and hydrogen purification processes are not needed, the system flow is simplified, and the system investment is reduced; according to the method, the system flow can be simplified, the consumption of renewable energy sources is improved, and the operation cost is reduced.

The invention discloses a method and a system for evaluating internal dynamic complementary characteristics of a multi-energy complementary power system. The method comprises the following steps: S1, acquiring an actual output time sequence of each unit; defining a fluctuation energy unit total output sequence, an adjustment energy unit total output sequence and a system total output sequence; s2, basic quantitative indexes of the random fluctuation dimension, the climbing trend dimension and the energy time shift dimension are calculated respectively; s3, calculating a random fluctuation compensation index, a climbing compensation index and an energy time shift index; s4, a comprehensive evaluation index MIRCI is calculated; s5, calculating an adjustment capability utilization factor RUF of each adjustment unit; and S6, based on MIRCI and RUF, performing complementary effect diagnosis. According to the method, the overall stabilizing effect of the adjusting resources on the fluctuating energy is effectively quantified, diagnosis of the complementary effect and limiting factor positioning are achieved, and the absorption capacity and the operation efficiency of the high-proportion renewable energy system are remarkably improved.

An FFO-DADRCNN-based energy management method for a renewable energy system and an energy storage system belongs to the technical field of renewable energy systems and energy storage systems, and comprises the steps of constructing an energy storage system ESS cost model including ESS capital cost and ESS operation cost; constructing a wind generating set model, a solar generating set model, a micro-grid load model and a frequency response load model; and setting voltage and current input factors of a wind turbine generator and a photovoltaic system, and carrying out optimal distribution of micro-grid energy by adopting an FFO algorithm. According to the method, the neural network architecture DADRCNN is adopted to predict the future energy demand load of the electric vehicle, various innovative technologies are combined, the operation management level of the micro-grid is improved, the RESs are allowed to operate independently of an EMS reference power supply, the system achieves higher flexibility and autonomy in the aspect of power generation, and therefore efficiency is improved.

A renewable energy production system includes a plurality of solar panels and a torque tube. The torque tube has an exterior shape defined by an arcuate surface and a flat surface connected to the arcuate surface. The plurality of solar panels are coupled to the flat surface. A plurality of support columns is used to support the torque tube.

An intelligent catalyst discovery and electrolysis optimization system for the production of green hydrogen, consisting of: • a schematic water electrolysis unit configured to simulate the electrolytic splitting of water into hydrogen and oxygen by modeling the hydrogen evolution reaction (HER) at a cathode and the oxygen evolution reaction (OER) at an anode and enabling analysis of catalyst effectiveness in reducing energy input; • a catalyst structure analysis unit configured to visualise and simulate atomic and molecular structures of candidate catalyst materials, including base metals, transition metal-based nanostructures and two-dimensional materials, and to evaluate charge distribution, surface morphology and active site behaviour; • a computational modeling and simulation unit using density functional theory (DFT) for quantum simulations to evaluate the thermodynamic stability, electronic structure, reaction energetics and adsorption energy of potential catalysts; • a machine learning optimization module trained on simulated and experimental data, using supervised and unsupervised learning models to determine optimal catalyst descriptors and predict catalytic activity, stability, and durability; • an experimental validation and test interface comprising electrochemical test environments including three-electrode cells and electrolyzers for performance benchmarking under realistic electrolysis conditions and capable of logging overvoltage, efficiency and degradation behavior; • an energy integration unit configured to interface with renewable energy systems, including photovoltaic systems, to dynamically power the electrolysis process and manage energy consumption through a power management subsystem; • a data processing and control interface to manage cross-system communication between simulation, experimental, and machine learning modules, enabling real-time parameter tuning and visualization of system diagnostics; and • a secure storage and logging unit adapted for storing simulation data, machine learning training sets and experimental results, with features for offline retrieval and integration with cloud platforms for collaborative research.

The invention discloses an intelligent optimal scheduling method and system for a power system in an energy transmission limited environment, and the method comprises the steps: constructing a power supply party-power utilization party energy management model, and enabling the optimal scheduling target of the model to be a minimum power supply party energy management target function and a minimum power utilization party energy management target function; and converting the optimal scheduling target into a shared reward function maximization form of a multi-agent system, and solving the distributed energy management model based on a multi-agent reinforcement learning method to obtain a scheduling scheme of the mobile energy storage vehicle. According to the design, on the basis of meeting the load demand of the power consumer, a power supplier energy management objective function is constructed by taking the minimum transportation cost and the renewable energy source discarding amount as the target, and the power consumer energy management objective function is constructed by taking the minimum controllable load amount for maintaining supply and demand balance removal as the target; therefore, the effect of maximizing the wind and light utilization rate is achieved on the basis of ensuring the independent, safe and stable operation of the renewable energy system in the energy transmission limited area.

The invention discloses a water-wind-light hybrid energy system optimization capacity configuration method and equipment considering resource space-time heterogeneity, and relates to the field of hybrid renewable energy system planning and optimization. According to the method, fine modeling is carried out on inherent high-dimensional space-time heterogeneity and related planning uncertainty of water, wind and light resources, meanwhile, high-fidelity water and electricity simulation is integrated, and therefore optimal configuration of the whole system is achieved. According to the method, firstly, a high-dimensional problem is simplified through a strategy of clustering first and then decomposition; and diversified climate scenes are generated in combination with a conditional variation auto-encoder and Latin hypercube sampling, and rare events are covered to enhance robustness. Results show that through fine consideration of spatial-temporal heterogeneity and uncertainty, remarkable and quantifiable economic and reliable double benefits can be brought.

The present application belongs to the technical field related to electrical engineering, and discloses a method for optimizing the dispatch of an electric power system taking into account the electric-thermal coupling of a carbon capture power plant and the improvement of its operational flexibility. The electric power system includes a carbon capture thermal power system, a renewable energy system, an electric boiler, and a heat storage device. The carbon capture thermal power system has a low-pressure cylinder zero-output transformation structure, a parallel steam extraction structure formed by a small back-pressure steam turbine and a pressure reducing valve. The optimization dispatch method includes: constructing an optimization dispatch model and solving it, outputting the optimal decision for dispatch, and the optimization dispatch model includes an objective function and constraints. The constraints include electric power conservation constraints, steam conservation constraints, and electric-thermal coupling constraints. The electric-thermal coupling constraints indicate that the total power of carbon capture thermal power and the steam consumption of carbon capture desorption are in the electric-thermal feasible domain. The above dispatch method can accurately reflect the carbon capture electric-thermal coupling relationship to improve the feasibility of dispatch. The two structural designs of auxiliary equipment and carbon capture thermal power can improve the flexibility of power system dispatch.

The present disclosure provides systems and methods for managing electrical energy generated by a renewable microgrid. One or more processors of a system may store priorities for one or more consumer loads; forecast an amount of available electrical energy for a time period; allocate a first electrical energy amount to a first consumer load to a first energy limit for the time period; determine more electrical energy is forecast to be available from the RES or the ESS for the time period; identify a second consumer load of the one or more consumer loads; allocate a second electrical energy amount to the second consumer load for the time period; and direct electrical energy from the RES or the ESS to the first and second consumer loads according to the allocations.

The invention relates to the development of low-cost functionalised copper (II) hydroxide (f-Cu(OH)2) electrodes for energy storage technologies and electrochemical devices. By means of surface functionalisation using high-energy X-rays, the energy storage capacity of the material is increased and an environmentally friendly production process is provided. The electrodes produced by this method can be used in applications such as supercapacitors, portable electronic devices, and renewable energy systems.

The invention discloses a renewable energy coupling hydrogen production control method and system based on a virtual synchronous machine. The control method comprises the steps that a virtual synchronous machine controller obtains renewable energy power generation system parameters; setting a given value of a virtual synchronous machine controller; establishing a power distribution strategy of the ALK hydrogen production system, the PEM hydrogen production system and the electric energy storage device BMS system according to the output active power condition of the renewable energy power generation system; constructing a virtual synchronous machine model of the renewable energy coupling hydrogen production system according to the obtained parameters, and associating the current condition of the coupling hydrogen production system according to the model; output parameters of the virtual synchronous machine model are sent to a grid-side converter, and a switching signal required by a switching tube is generated; and distributing the given output of the power distribution strategy to the corresponding system through the grid-side converter. The problems that an existing system is not high in integration degree, electric power splicing of a renewable energy system and a hydrogen production system is singly considered, and the renewable energy fluctuation stabilizing capacity is limited are solved.

The invention discloses a virtual power plant collaborative optimization scheduling method and system based on multi-time scale prediction, and relates to the technical field of virtual power plant collaborative optimization scheduling, and the method comprises the steps: obtaining multi-dimensional operation data of a virtual power plant scheduling object; the scheduling object comprises a distributed renewable energy system, an energy storage system and a controllable load system; performing medium and long term and short term multi-time scale prediction according to the acquired data; performing preliminary scheduling planning on the basis of medium and long term prediction results, and generating a short-term optimization scheduling plan in a rolling manner by taking the preliminary planning result as a constraint and utilizing a short-term rolling optimization model introducing a reinforcement learning algorithm to continuously adjust the weight of an objective function of the optimization model according to the short-term prediction data; and taking the short-term optimization scheduling plan as a reference, performing calculation by using a real-time control model based on measured data, and issuing a real-time control instruction to each scheduling object. According to the invention, adaptive optimization scheduling of the virtual power plant can be realized, and comprehensive benefits are guaranteed.

The invention discloses a new energy station and distributed pumped storage power station coordinated optimization method and system, and the method comprises the steps: firstly constructing an output power time sequence model of wind power generation and photovoltaic power generation, and predicting the output power at different moments; secondly, the fluctuation rate of the new energy station yard is calculated according to the output power, and the wind and light capacity ratio is optimized to reduce fluctuation; a distributed pumped storage power station output model is constructed based on hydrological conditions; and finally, combining the output model of the pumped storage power station with the optimized wind and light capacity ratio, constructing a multi-objective optimization function, and outputting the optimal capacity ratio of the new energy station yard to the pumped storage power station. According to the method, the wind and light output time sequence characteristics of the new energy station and the operation constraint of the distributed pumped storage power station are combined, a multi-objective optimization function with the total cost minimization and new energy consumption maximization as objectives is constructed, new energy and energy storage collaborative optimization is achieved through calculation, decision support is provided for planning of a high-proportion renewable energy system, and the method is suitable for large-scale popularization and application. And investment and decision risks are reduced.

The application relates to a kind of digital power energy storage system dynamic network reconfiguration methods in the technical field of power energy storage, S1. Build energy storage system;It is composed of intelligent layer, control layer and data layer;S2. Energy storage network modeling;Adopt graph capsule neural network to learn the optimal control strategy of energy storage network;S3. Dynamic network reconfiguration;Based on Markov decision process, generate fault emergency response model;By systematic optimization of the topological structure and optimal control strategy of energy storage network, the quick isolation of fault area is realized, and the dynamic adjustment and reconfiguration of the working mode of energy storage equipment and load equipment are realized, to ensure that the energy storage network can still operate stably under fault situation, improve the flexibility and elasticity (fault tolerance) of energy storage system in dynamic environment;By reducing the energy loss in the transmission process, balancing network load and preventing local failure, the technical problems existing in the prior art are solved, and the sustainable development of new renewable energy system is promoted.

The application belongs to the field of green building and building construction technology, and specifically discloses a green low-carbon building civil engineering integrated efficient construction building method. In view of the problems of design construction disconnection, multi-specialty cross conflict, high carbon emission and serious resource waste in traditional building civil engineering construction, the application constructs a 'cooperative design-factory prefabrication-assembly construction-low-carbon operation and maintenance' integrated system, and the method comprises the following steps: multi-specialty cooperative deepening design and carbon emission calculation based on BIM; factory prefabrication of low-carbon concrete components and integration of thermal insulation structure exterior wall; on-site assembly type construction and dry method connection; renewable energy system and photovoltaic building integrated synchronous installation; and whole life cycle carbon footprint monitoring and closed loop optimization. The application realizes the synchronous construction of the envelope structure and the main structure by driving the deep integration of industrialized production and assembly type construction through digital design, so that the construction period can be shortened, the whole life cycle carbon emission of the building can be reduced, and the civil engineering integrated green construction is realized.

Disclosed is a battery aging assessment method based on multi-source and multi-scale high-dimensional state space modeling in the field of energy storage in renewable power systems. The method includes: acquiring a time series of each discharge process within a preset number of discharge cycles of a sample battery; determining a first state transition path and a second state transition path based on discharge parameters corresponding to the time series; establishing a benchmark working-state transition path; calculating multiple sample distances between the second state transition path and the benchmark working-state transition path; training a battery aging assessment model using the sample distances as input and corresponding target state-of-health values as output; calculating a target distance between a state transition path of a to-be-predicted target battery and the benchmark working-state transition path.