907 results about "Energy resources" patented technology

The invention discloses a virtual power plant collaborative optimization operation method based on distributed energy, and the method achieves the real-time monitoring of the operation state of a distributed energy resource through the precise modeling of the distributed energy resource, and predicts the change of the power load demand. A multi-objective optimization model and an advanced optimization algorithm, such as a genetic algorithm or a particle swarm algorithm, are utilized, so that the virtual power plant can dynamically adjust energy supply and load demands, and economic benefit maximization, energy efficiency optimization and carbon emission minimization are realized. The method also considers the energy efficiency and economic benefits of the virtual power plant, and improves the comprehensive benefits of the system through comprehensive analysis and optimization. Through effective interaction with the power grid, the virtual power plant can provide peak regulation, frequency modulation and standby services, a demand response mechanism is implemented, and operation of the power grid is further optimized. The method has the beneficial effects of improving the energy utilization efficiency, enhancing the stability of a power grid, optimizing economic benefits, promoting environmental protection, improving the flexibility and adaptability of the system and supporting policies and market mechanisms.

An Al -based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure, coordination, and orchestration with distributed systems to support important use cases for a range of enterprises. An Al -based energy edge platform may include a graph neural network including a set of nodes respectively representing at least one distributed energy resource (DER) and a set of edges respectively interconnecting the set of nodes, wherein each edge represents at least one energy - related feature among at least two nodes of the set of nodes. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may employ intelligent provisioning, data aggregation, and analytics to leverage energy market connection, communication, and transaction enablement platforms.

The invention discloses a wind field wind resource fine simulation method, system and device based on WRF-CFD medium and micro-scale coupling and a storage medium, and belongs to the technical field of wind energy resource evaluation.The method comprises the steps that mesoscale meteorological analysis data is collected and calculated through a mesoscale WRF numerical simulation technology; performing fitting processing to obtain a fitting function between the wind speed component and the height; extracting wind speed contour information at a micro-scale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determining a boundary condition of a micro-scale CFD model; performing directional calculation and correction on the wind speed of each sector by using a virtual anemometer tower; performing grid division on the wind power plant area to generate a refined grid corresponding to the wind power plant area; and driving numerical simulation of the micro-scale CFD model, and carrying out wind power plant regional range stroke resource evaluation. According to the method, the complex characteristics of the wind field can be comprehensively reflected, the wind resource evaluation precision and the calculation efficiency are remarkably improved, and the resource utilization is optimized.

The invention discloses a cross-regional virtual power plant cooperative scheduling method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring real-time characteristic data such as space-time positions, output / demand prediction and the like of distributed energy resources and loads, and determining dynamic weights of characteristic dimensions based on a global optimization target and data of a current scheduling period; generating a dynamic resource cluster division instruction containing a member list and a coordination constraint condition according to the dynamic weight and the real-time data, and sending an initial cross-regional coordination scheduling instruction containing a net exchange power target value and the like and a compensation price signal to each dynamic resource cluster local agent; after aggregation response boundary information returned by the local agent is received, an instruction and a signal are updated, a target collaborative scheduling instruction is obtained and finally sent to each dynamic resource cluster for execution, and effective control over cross-regional virtual power plant resources is achieved. According to the method, the problem that the adaptability of the cross-regional virtual power plant collaborative scheduling instruction and the actual resource capacity is insufficient can be relieved.

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

The invention discloses a power grid real-time optimization scheduling system and method based on digital twinning, and relates to the technical field of power grid scheduling. A sensor is deployed to collect environmental parameters of key nodes in real time, a dynamic environmental condition coefficient is constructed, a power grid state is analyzed in combination with frequency stability and a relative strength index, and a multi-model fusion prediction mechanism is established, so that space-time two-dimensional accurate prediction of load and power generation is realized. A multi-objective optimization model is adopted to take'maximization of new energy consumption + minimization of scheduling cost 'as a core objective, a genetic algorithm is introduced to solve an optimal scheduling scheme, and a feasible solution is screened in combination with forward simulation of a digital twin model. Through abnormal early warning triggering, environment correlation analysis and model iterative optimization, a scheduling strategy is dynamically adjusted, and the power supply efficiency and the emergency response capability in an extreme scene are improved. According to the method, multi-source heterogeneous data are effectively fused, and real-time sensing of a power grid operation state, collaborative optimization of multiple energy resources and adaptive iteration of a scheduling model are realized.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention discloses a virtual power plant system design method based on multilevel federated learning, and relates to the technical field of power system planning, and the design method comprises the steps: designing and constructing a virtual power plant system architecture comprising an energy resource module, a local controller, a regional coordinator, a central coordinator and a regional block chain network. Intelligent prediction and optimal control of distributed energy resources are realized by using a deep learning framework and a model training algorithm, the intelligent level of the system is improved, automatic management of energy transaction and execution of an intelligent contract are realized by using a block chain technology, transparency and security of the transaction are ensured, and multi-level federated learning is adopted, so that the efficiency of the system is improved. According to the invention, distributed cooperation of data distributed processing and model training is realized, risks of data leakage and privacy invasion are effectively avoided, cooperative work of a local controller and a regional coordinator is cooperated, intelligent management and optimal scheduling of distributed energy resources are realized, and manual intervention and management cost are reduced.

The invention discloses a wind energy resource prediction method, system and device and a storage medium, and relates to the field of renewable energy sources, and the method comprises the steps: obtaining the three-dimensional wind field observation data of a target region; performing space-time alignment processing operation on the three-dimensional wind field observation data to generate a space-time sequence; performing multi-source data fusion on the space-time sequence, and constructing a multi-dimensional wind field matrix; establishing a wind energy potential prediction model based on the multi-dimensional wind field matrix; and predicting to-be-predicted wind energy data based on the wind energy potential prediction model to obtain a wind energy resource prediction result. And efficient, accurate and reliable resource evaluation is provided for wind energy development of various terrain areas by constructing a machine learning model.

The invention relates to the technical field of carbon footprint evaluation, and discloses a carbon footprint optimization method and system based on LCA-GLSS coupling. The carbon footprint optimization method comprises the following steps: calculating carbon footprints of all stages of a product in a full life cycle, carbon footprint contribution ratios corresponding to materials, energy sources, resources and wastes involved in all process units in the full life cycle, and carbon footprint sensitivities corresponding to the materials, the energy sources, the resources and the wastes; a high-carbon emission link found in the carbon footprint evaluation process is optimized, waste and variation in the carbon emission process are recognized and eliminated, a carbon footprint prediction model is established, the carbon emission trend in the future is predicted and simulated, and the prediction accuracy is improved through model optimization and parameter adjustment; the evaluation result is comprehensively analyzed and explained, main contribution factors and key links of the carbon footprint are identified, and emission reduction measures and optimization suggestions are formulated. The method has both quantitative analysis and process improvement, and can effectively improve the efficiency and accuracy of carbon footprint management.

The invention relates to the technical field of virtual power plant scheduling, and discloses a distributed collaborative optimization scheduling method for a virtual power plant. The method includes collecting an operating state data set of a target virtual power plant. And performing distributed collaborative model construction processing on the operation state data set to generate collaborative scheduling features covering power distribution balance degree, constraint matching closeness and interactive response sensitivity. And calling a pre-trained optimization scheduling model to carry out multi-target collaborative optimization processing on the collaborative scheduling features to obtain an optimization scheduling result and a key collaborative region identifier. And based on the association relationship between the load demand fluctuation sequence and the equipment adjustment capability, performing operation environment compensation correction processing on the optimization scheduling result, and generating a corrected result. And generating a virtual power plant scheduling strategy set including a power transfer path adjustment scheme and an energy storage equipment configuration processing scheme according to the key cooperative region identifier. According to the method, distributed energy resources are effectively integrated through multi-dimensional collaborative optimization and dynamic correction.

The invention provides a method for reducing power transmission and distribution loss of a power grid, which belongs to the technical field of power transmission and distribution and comprises the steps of collecting and analyzing historical power consumption data in a region, extracting power time characteristics, acquiring historical weather data related to power consumption requirements and extracting power weather characteristics. Establishing a load prediction model based on the electric quantity time characteristics and the electric quantity weather characteristics; identifying distributed energy resources in the area, classifying and positioning the distributed energy resources, generating a resource distribution heat map, establishing an energy database, and developing a virtual power plant; the virtual power plant outputs a power grid electric energy transmission and distribution strategy according to a predicted load curve output by the load prediction model, performs defect analysis on the power grid electric energy transmission and distribution strategy, deploys an energy storage system and formulates an energy storage scheduling strategy; a final scheduling strategy is obtained by combining an energy storage scheduling strategy and a power grid electric energy transmission and distribution strategy, so that the stability and flexibility of power supply are improved, the utilization rate of renewable energy sources is improved, resource allocation is optimized, and diversified requirements are met.

The floating type offshore wind turbine based on the tuned mass damper comprises a floating type foundation and a wind generating set, the wind generating set comprises a stand column, and the tuned mass damper is arranged in the stand column; the floating foundation comprises a buoy and a mounting platform, a base plate is slidably connected into the buoy, one end of the base plate is connected with the buoy through a damper spring, and the other end of the base plate is connected with the mounting platform through a supporting column. A submersible pump is arranged on the surface, close to the buoy, of the mounting platform, communicates with seawater through a first pipeline and communicates with the buoy through a second pipeline. And the buoys are distributed in a circumferential array along the mounting platform. The wind-resistant strength is improved, the cost is low, resetting is fast, the resetting effect is good, and offshore wind energy resources can be better utilized; the tuned mass damper is installed, vibration is reduced through compression and reset of the damping spring, and safety is improved; an air speed sensor is arranged, offshore air speed and air volume can be monitored, and the controller can conveniently control whether seawater is extracted or not.

The invention discloses a big data driven dynamic resource scheduling method for an IPv6 edge computing node, and relates to the technical field of edge computing, and the scheduling method comprises the following specific steps: S100, resource data collection and integration: deploying monitoring equipment at the IPv6 edge computing node, collecting computing, network, storage and energy resource data, and transmitting the data to a server; through multi-dimensional resource data acquisition and storage, comprehensive monitoring and quantitative integration of calculation, network, storage and energy resource data are realized, the resource scheduling is more accurate and efficient through the comprehensive data acquisition mode, and through construction of the energy consumption performance model, the performance value can be accurately calculated, and the energy consumption performance is improved. And the model is trained and optimized by using big data, so that the balance of energy consumption and performance in the resource scheduling process is realized, the resource utilization efficiency is improved, the energy consumption cost is reduced, and powerful support is provided for green and sustainable development of edge computing nodes.

The uncertainty of availability and risk associated with the cost of power procurement hinders the consumer from using new sources of supply / storage devices or taking part in the competitive power markets or procuring power from green energy resources. Present disclosure provides a method and a system for optimal scheduling of power sources for meeting electricity demand of enterprise. In particular, the system performs portfolio optimization formulation for cost, risk and carbon emission minimization for the enterprise. The portfolio optimization formulation simulates market scenario and guides in providing effective power purchase strategy for enterprise along with real-time adjustments. In particular, the level of risk to be considered along with cost of power procurement and carbon footprint determine portfolio allocation for each time block in portfolio optimization formulation. Thus, enterprise may choose most appropriate terms of contract, best installation size of renewable resources or batteries, and supply sources.

The invention relates to the technical field of wind energy resource evaluation, in particular to a method and a system for calculating a hydrodynamics wind field numerical value based on high-precision landform, and the method comprises the steps: generating a three-dimensional unstructured grid for CFD wind field simulation based on high-precision landform data; analyzing an atmospheric boundary layer structure, and establishing a turbulence calculation model; according to the turbulence calculation model, the influence of atmospheric stability on the mountain flowing wind acceleration factor is researched, and a mountain flowing model is developed; performing CFD wind field simulation according to the three-dimensional unstructured grid and the mountain flow model, and correcting parameters of the mountain flow model; a refined grid is made through terrain elevation data; and according to the refined grid, driving the corrected mountain flow model to carry out numerical simulation, and carrying out wind power plant wind resource evaluation. Through the method, the complex characteristics of the wind field can be reflected more comprehensively, the precision and the calculation efficiency of wind resource evaluation are remarkably improved, and the efficiency and the accuracy of numerical simulation of the complex terrain wind field are improved.

The invention discloses a source network load storage oriented regional collaborative scheduling method and system, and relates to the technical field related to power resource scheduling, and the method comprises the steps: traversing target region source network load storage to carry out real-time data collection, and constructing a source network load storage multi-dimensional data set to carry out multi-scale layered optimization; operation constraint conditions are set, limitation is carried out in combination with a power grid topological structure, a cooperative scheduling instruction set is determined, cooperative control feedback is carried out, and regional power scheduling feedback parameters are generated; and multi-channel dynamic correction is carried out, and a cooperative scheduling optimization strategy is generated to carry out energy balance cooperative scheduling on the source network load storage in the target area. The technical problems that in the prior art, intermittent fluctuation of new energy is difficult to consume, the collaboration of all links of source network load storage is poor, the dispatching flexibility of a power system is insufficient, and the resource configuration efficiency is low are solved. The technical effects of optimizing regional energy resource allocation and improving the clean energy consumption level, the power grid operation stability and the source grid load storage cooperative response capability are achieved.

The invention discloses a product process index prediction method, device and equipment for solid waste resource extraction and a storage medium, and the prediction method comprises the steps: setting process control data of solid waste resource treatment in solid waste resource data as first preset data; the step of preprocessing the solid waste resource data in the preset data database comprises the substeps of updating first preset data; various data of the solid waste resource data are used as feature data, and a prediction model with the purpose of predicting product process indexes is established; the method comprises the following steps: performing multi-dimensional analysis on raw material data of solid waste resources, process route data of solid waste resource treatment, process control data of solid waste resource treatment and product process index data, and preprocessing the solid waste resource data; the relevance between each data of the solid waste resource data and the product process indexes is determined, and a high-accuracy prediction model is established, so that efficient extraction of material resources and energy resources of solid wastes is realized.

The invention belongs to the field of catalyst preparation, and provides a preparation method for rapidly synthesizing a self-supporting high-entropy alloy difunctional electrocatalyst through Joule heat. The prepared FeCuRu-coated Ni1Co1-TPA is benefited from rich active sites, and the high conductivity of effective electron transfer and transmission is achieved. The catalyst shows excellent HER and OER catalytic performance under an alkaline condition, and the current density of 500mA cm <-2 > can be realized by only needing overpotentials of 120mV and 350mV, which is superior to those of a standard noble metal catalyst Pt / C and RuO2. In addition, when the composite material is used as a bifunctional electrocatalyst for electrolyzing water, the composite material shows excellent catalytic activity for electrolyzing water. The method has important significance for realizing the social renewable development target and developing clean and sustainable energy resources to replace fossil fuel.

The embodiment of the invention relates to a multi-energy complementary coordinated control system and method based on multi-element energy storage, electronic equipment and a storage medium. And various data support optimization decisions of multiple stations are obtained through data fusion. And a decision generation scheme is optimized, the operation state and benefit analysis are displayed, and precise regulation and control of the station are realized. And configuring a management algorithm for the model, and setting boundary conditions and objective functions. The internal coordination supplementary logic plays a role in the aspects of active power adjustment, power grid frequency fluctuation and synchronous phase modulation. Through the means, the acceptance degree of the power grid to renewable energy sources is improved, so that the multi-energy complementary system can better respond to a power grid dispatching instruction, and energy resource optimization management is realized. And meanwhile, the stability and reliability of the power grid are enhanced, the economic benefit of the system is improved, and a powerful guarantee is provided for efficient operation of the multi-energy complementary coordination control system.

The disclosure relates generally to methods and systems for real-time voltage stabilization of electrical distribution networks with non-linear power flows. Existing real-time voltage control techniques are neither performance nor stability guarantees. The present disclosure proposes an online robust control algorithm, which operates without knowing an exact information of the line-parameters and resolves the voltage stability problem. In the proposed method a load data, a distributed energy resources (DER) data, and a network data of an electrical distribution network is obtained, to obtain a voltage profile at each time-step of the electrical distribution network. Next, line-parameters of the electrical distribution network are predicted using an on-line convex optimization technique and a Gauss-Seidel technique. Then, a stable control signal for each bus that stabilizes a voltage of the electrical distribution network is determined to utilize the stable voltage for the voltage stabilizing of the electrical distribution network in real-time.

The invention discloses an electricity meter metering error correction method and system based on time sequence anomaly detection and an electricity meter, relates to the technical field of electricity meter metering error correction, and provides an intelligent anomaly detection method integrating multi-source data preprocessing, online learning and federation cooperation for a power system in which distributed energy and diversified loads coexist. The method comprises the following steps of: 1, dynamically filtering and caching original data by adopting a suspicious degree function, and outputting high-quality input; secondly, online learning is carried out through multi-algorithm fusion and expert knowledge constraint, and accurate judgment of traditional and emerging modes is achieved; 3, realizing multi-region cooperative gain based on difference parameter sharing and global aggregation; and 4, deploying a lightweight model at the edge end by using feedback evaluation and model distillation, thereby improving the real-time performance and reliability. In conclusion, the false alarm rate and the missing report rate can be remarkably reduced, various changes of load behaviors are effectively adapted, the operation and maintenance efficiency of a power grid is improved, and meanwhile data privacy safety and collaboration are guaranteed.

The invention provides a wind power data determination method and device based on geographical constraint attention. The wind power data determination method comprises the steps of obtaining topographic data, surface vegetation coverage data and wind power data; for the wind power data, combining the topographic data and the surface vegetation data, performing feature fusion through an adaptive attention mechanism to obtain a fusion feature containing multi-source information; performing shallow feature extraction, deep feature extraction and image up-sampling on the fusion features in sequence, wherein the deep feature extraction realizes multi-scale feature extraction and geographic constraint fusion through a multi-stage residual fusion module RFSTB, a wind field frequency domain decoupling module CFB and a geographic perception global attention module GAB; verifying the effectiveness of the model by using a peak signal-to-noise ratio (PSNR) and a structural similarity index (SSIM); and performing super-resolution reconstruction on the wind power data based on the trained model to generate wind power data with predetermined precision. The method provides support for fine evaluation and efficient development of wind energy resources in complex terrain areas.

The invention provides a multi-modal communication edge aggregation gateway system for a virtual power plant, and the system comprises a smart energy unit which serves as edge gateway equipment and is used for carrying out the bidirectional communication with a user branch load, a distributed energy source and a flexible load; the measurement monitoring unit is arranged on a user branch load loop or a user side equipment end and is used for acquiring power utilization data of a branch line and an equipment operation condition; a multi-mode communication module; and the self-adaptive protocol switching unit is used for dynamically selecting communication protocols and realizing switching among the protocols according to the communication environment parameters, the equipment types and the data transmission requirements. The invention thoroughly solves the problem that the traditional communication system cannot adapt to different application scenes and equipment type communication requirements by adopting a single communication protocol, realizes the goal of dynamically adjusting the communication strategy according to the network environment change, and effectively avoids the phenomenon that the communication quality is reduced or even interrupted.

A method and system for managing demand and electricity supply are disclosed. A distributed energy resource optimizer (DERO) of the system may determine demand for electricity associated with a plurality of premises connected to an electrical grid where the plurality of premises includes one or more distributed generation systems, determine available electricity supply for the plurality of premises, and compare the demand with the available electricity supply. In response to determining that the demand is higher than the available electricity supply, the DERO may cause one or more storage devices associated with the one or more distributed generation systems to supply electricity to the plurality of premises. In response to determining that the demand is lower than the available electricity supply, the DERO may cause the one or more storage devices to store excess electricity supply.

A meter socket adapter (MSA) for connecting a battery energy storage system (BESS) to a household microgrid through a meter combo load center. The MSA includes: a housing matching an electricity meter and a meter socket in the meter combo load center; a plurality of power conductors that connect among the electricity meter, the meter socket, and BESS; a meter interconnection device (MID) switch for connecting a utility grid or one or more distribution energy resource (DER) devices to supply power to household loads in the household microgrid; a MID driving circuit for driving the MID switch to close or open; and a thermal management circuit couple with a plurality of temperature sensors disposed at each high current connection point to prevent overheat.

The invention relates to the field of power, and provides a distributed resource evaluation and aggregation method and device for a virtual power plant and electronic equipment, and the method comprises the steps: dynamically calculating the adjustment capability score and the close degree of each distributed resource according to the adjustment capability index of each distributed resource of the virtual power plant; based on the adjustment capability score and the close degree of each distributed resource, selecting a distributed resource meeting a threshold condition from each distributed resource to participate in aggregation scheduling; and optimizing the runnable boundary of each distributed resource participating in aggregation scheduling based on the power of each distributed resource participating in aggregation scheduling and a pre-established double-layer optimization model. According to the method, efficient aggregation and operation boundary optimization of distributed energy resources by the virtual power plant can be realized through a dynamic scoring mechanism and a hierarchical optimization architecture, and the method has the advantages of high efficiency, flexibility and accuracy.

The invention provides a virtual power plant resource partitioning method based on a multi-layer perceptron. The method comprises the following steps: acquiring geographical location information of each resource in a virtual power plant; adjusting a density-based clustering algorithm according to the geographical location information of each resource in the virtual power plant, and performing clustering analysis on the geographical location information of each resource by using the adjusted density-based clustering algorithm to obtain a plurality of first clustering results; counting the number of geographical location information contained in each first clustering result, adjusting the first clustering results according to the number to obtain a plurality of second clustering results, and taking a range formed by each second clustering result as a partition; and inputting the geographical location information of each resource into a pre-trained multi-layer perceptron to obtain an importance degree score of each resource. According to the method provided by the invention, each energy resource can be partitioned and effectively regulated and controlled.

A method of configuring a microgrid (100) including: receiving configuration inputs (202) from an operator, the configuration inputs (202) including an intended bus structure, a plurality of energy resources data, and energy resources priorities; classifying assets and asset groups based on the plurality of energy resources data; and generating a microgrid (100) configuration based on the one or more configuration inputs (202), the microgrid (100) configuration providing for automatic operation of the microgrid (100).

A smart water heating system is disclosed that uses advanced monitoring and control techniques to optimize energy usage and grid stability. Various features include real-time state of charge estimation for water heaters, a "digital twin" representation of each unit, predictive load modelling, and adaptive control algorithms. The system can operate individual heaters as distributed energy resources, or aggregate them into virtual power plants. It employs strategies such as load shifting to align with renewable energy availability, and cooperative multi-agent control to prevent demand spikes. The technology aims to improve energy efficiency, reduce costs, integrate renewable sources, and enhance grid stability while maintaining user comfort. Additional features include user analytics, comparative consumption reporting, and solutions for low-voltage environments using supplementary battery storage.