70 results about "Renewable energy supply" patented technology

The invention discloses a green data center scheduling optimization method under computing power and electric power coupling, data load types of a data center comprise a transferable load, a reducible load and a rigid load, and the method comprises the following steps: constructing a double-target scheduling optimization model by taking the minimum power consumption cost of the data center and the maximum green power fitting degree as targets; the power consumption cost of the data center is used for representing the sum of expenses generated by the data center for purchasing power from a power grid in order to meet the power required for processing the data load in each time period, and the green power fitting degree is used for evaluating the matching degree between the power consumption demand of the data center and the renewable energy source supply; solving the dual-target scheduling optimization model by using a multi-target dogvessel swarm algorithm to obtain a Pareto frontier; and determining an optimal scheduling scheme based on the Pareto frontier, wherein the optimal scheduling scheme comprises a data center power consumption curve, an energy storage power station charging and discharging curve and a transferable load scheduling strategy. According to the invention, the power consumption cost of the data center can be reduced, and the renewable energy consumption capability can be improved.

The invention discloses a cabinet-level computing power server dynamic temperature regulation and load optimization method, and relates to the technical field of data centers, and the method comprises the following steps: collecting data, respectively building a thermodynamic model and a price prediction model, predicting a thermodynamic state, an electric power spot market price, and a renewable energy source supply condition, outputting and executing an optimal refrigeration system control plan by adopting an MPC algorithm; in combination with electric power spot market price data, cabinet-level computing power server energy consumption data and renewable energy supply data, the environment temperature of the cabinet-level computing power server and refrigeration system output are adjusted in real time through an MPC algorithm, and the future state of the cabinet-level computing power server can be accurately predicted. The MPC algorithm predicts the thermodynamic state of the cabinet-level computing power server and dynamically optimizes the temperature regulation strategy, the real-time performance is good, the temperature and the load can be adjusted in time, the stability and the energy efficiency of the cabinet-level computing power server are ensured, and safe and stable operation of the data center and the minimization of the energy consumption cost are ensured.

The invention discloses a near-zero energy consumption building energy supply method and system based on multi-objective optimization, and relates to the technical field of building energy efficiency and renewable energy application, and the method comprises the steps: collecting energy consumption data in a building, and constructing a multi-objective evaluation system according to the energy consumption data; performing weight optimization on the multi-target evaluation system to obtain an optimal weight coefficient; combining the optimal weight coefficient with a solar photovoltaic system and a ground source heat pump system, and establishing a renewable energy supply strategy model; and outputting an energy supply scheduling instruction of the building in each time period based on the renewable energy energy supply strategy model, and adjusting operation parameters of a solar photovoltaic system and a ground source heat pump system. According to the invention, by adjusting the operation mode of the equipment in real time, the balance between the energy efficiency and the comfort level is ensured, the common problem of energy efficiency waste or insufficient comfort level in the traditional method is avoided, and by means of intelligent scheduling and optimization, the energy consumption of the building can be effectively reduced, and the sustainability of energy utilization of the building is improved.

Disclosed in the present invention a green power tracing method for a high-proportion renewable energy supply park, comprising: building a park network architecture model, and, on the basis of power flow data at any moment t throughout the year, splitting the park network architecture model into a plurality of active radiation-type power distribution lines; calculating the green power component of each renewable energy in each power distribution line in a single power user; accumulating the green power components to obtain green power consumed by the single power user at any time t and green power generation capacity output by a single renewable energy at any moment t; and forming a green power consumption matrix of the single power user throughout the year and a green power generation capacity matrix of the single renewable energy throughout the year. According to the present invention, the green power consumption of power users can be accurately evaluated, thereby providing accurate data support for green power tracing for users within parks.

The invention discloses a dynamic resource allocation system and method for smart travel. The system comprises a multi-source data acquisition module, a data fusion module, a prediction module, a framework construction module, an algorithm optimization module, a feedback module and a resource allocation module. According to the method, environment data, energy consumption data, renewable energy supply data and tourist flow data in a plurality of sub-regions of the tourist attraction are collected, and fusion and deep learning prediction are carried out on the data, so that future energy consumption, energy supply and flow data are obtained. And a reinforcement learning scheduling framework is constructed, and intelligent scheduling of the charging pile, the electric sightseeing vehicle, the lighting equipment and the monitoring equipment is realized. And carrying out global optimization on the resource scheduling scheme by adopting a speed pause particle swarm optimization algorithm so as to output a resource allocation scheme with optimal energy consumption. The scheduling scheme is further optimized based on user feedback data, and the dynamic adaptability of resource configuration is ensured. According to the invention, the energy utilization efficiency in the smart travel scene is improved, and the operation cost is reduced.

The invention discloses a building cluster demand response coordinated multi-objective optimization scheduling method and system, and the method comprises the steps: predicting the energy demand and supply information of a building cluster, and building an energy transaction model of a building unit and the cluster based on renewable energy supply and battery storage electric energy; the building cluster is integrated into a single entity, the energy supply of the single entity is the total energy supply of all buildings, the energy demand is the total energy demand of all buildings, the battery capacity is the total battery capacity of all buildings, and the energy transaction model takes a cost function of the whole cluster as a target function; and optimizing the overall cost function of the building cluster through an iterative demand response algorithm, calculating to obtain an optimal operation parameter, controlling the operation of each building unit according to the optimal operation parameter of the cluster by using a nonlinear programming algorithm, and carrying out multi-target optimization scheduling on the building cluster. Flexible resources in the building cluster can be fully utilized, and efficient utilization and supply and demand balance of energy are achieved.

The invention relates to the technical field of energy management, and discloses a green energy utilization system and method for environment-friendly mixture production. Comprising a renewable energy supply module, an energy storage module, an energy allocation and optimization module, a waste heat recovery module, a carbon capture and conversion module, a fuel characteristic monitoring and optimization module, a waste treatment and regeneration module and a full-process intelligent management module. The renewable energy source supply module is used for obtaining energy from renewable energy sources such as solar energy, wind energy and geothermal energy; and the energy storage module is used for storing redundant renewable energy sources and releasing energy in peak hours. Waste heat is effectively recycled through the high-low temperature waste heat recycling module, and energy consumption is reduced; the carbon capture and conversion module dynamically captures carbon dioxide and converts the carbon dioxide into renewable fuel or industrial raw materials, closed-loop utilization of carbon resources and synergistic effect of waste heat and carbon capture technologies are achieved, energy and carbon resources are recycled to the maximum extent, carbon emission is reduced, meanwhile, recyclable resources are provided, and environmental protection benefits are remarkably improved.

A power converter includes: a power conversion unit configured to convert power input from a bus, output the converted power to a power element, convert power input from the power element, and output the converted power to the bus; a measurement unit configured to measure an electrical characteristic value of power input to and from the power conversion unit; a storage unit configured to store a first reference function and a second reference function; a setting unit configured to set a power conversion characteristic of the power conversion unit based on an electrical characteristic value; and a management unit configured to manage a renewable energy charge amount and manage an amount of power derived from renewable energy supplied from the power element.

This application relates to the field of data processing technology, and discloses a power distribution method and an intelligent power distribution cabinet based on AI artificial intelligence. The method includes: real-time data collection of key nodes of the power distribution system through an intelligent sensor network to obtain the original power distribution system operation data, and performing cleaning and standardization processing on the data to obtain a standardized data set; analyzing and modeling the standardized data set through multi-level hybrid model training to obtain an AI analysis model of the power distribution system; processing and analyzing the real-time collected power distribution data to obtain a load prediction result and a dynamic balance strategy; performing abnormal pattern recognition and multi-path reasoning on the system operation parameters to obtain an equipment health status assessment and maintenance suggestions; based on the equipment health status assessment and maintenance suggestions, performing multi-objective optimization calculation on the renewable energy supply and electricity demand to obtain an intelligent energy scheduling scheme. This application improves the efficiency and accuracy of the power distribution method based on AI artificial intelligence.

The invention relates to the technical field of data processing, and discloses an intelligent computing center energy consumption optimization method and system based on the Internet of Things. The method comprises the steps of collecting storage medium energy consumption parameters and data block access behavior parameters to generate an original data set, calculating a weighted access characteristic value and a cold and hot state transition probability, screening candidate data blocks and calculating an energy consumption benefit evaluation index, matching a renewable energy source supply state to construct a migration task sequence, and obtaining a new energy consumption benefit evaluation index. And executing migration operation and dynamically adjusting the prediction parameters. The problems that in the prior art, data cold and hot state conversion cannot be intelligently predicted, energy consumption benefit quantitative evaluation is lacked, cooperation of data migration and renewable energy source supply is insufficient, and prediction model parameters cannot be adaptively adjusted are solved.

The invention discloses an all-weather negative carbon stable operation methanol preparation system. The system comprises a methanol synthesis device, a hydrogen acquisition device, a carbon dioxide acquisition device, a heat collection device, a power supply device and a methanol storage device. The methanol synthesis device is used for synthesizing methanol and comprises a reaction tank, the hydrogen obtaining device comprises a water electrolysis hydrogen production module, the carbon dioxide obtaining device is used for obtaining carbon dioxide and supplying the carbon dioxide to the reaction tank, and the energy source of the heat collection device is renewable energy and is used for supplying heat to the reaction tank; the energy source of the power supply device is renewable energy and is used for supplying power to the water electrolysis hydrogen production device. The methanol storage device is used for storing methanol generated by the methanol synthesis device and supplying heat to the reaction tank in an auxiliary manner. According to the all-weather negative carbon stable operation methanol preparation system provided by the embodiment of the invention, a methanol production process is green and environment-friendly, so that methanol can be stably produced under the condition of renewable energy source supply fluctuation, and the yield of methanol is increased.

The invention belongs to the technical field of power system control, and provides a source-grid-load interactive renewable energy supply flexibility control method and system, and the method comprises the steps: obtaining a power grid state deviation according to related physical information; generating and broadcasting synthetic pheromones according to the power grid state deviation to obtain a pheromone gradient field; secondly, extracting field features of a position pheromone gradient field where the distributed flexible resources are located; the field features comprise pheromone concentration and pheromone concentration gradient; according to the field feature and the state of the flexible resource, calculating a power regulation amount through a preset response vector graph; and finally, according to the power regulation quantity, controlling the flexible resource to carry out power output regulation. According to the method, the communication and calculation bottlenecks of centralized control can be avoided, and the limitation of global coordination deficiency of distributed control can be overcome, so that rapid, efficient and stable cooperative control of massive distributed flexible resources is realized.

The present invention discloses a system and method for synthesizing ammonia by utilizing renewable energy. The system comprises an ammonia synthesis tower, a waste heat recovery device, a heat exchanger, a cooling device and an ammonia separator. An air inlet pipeline and an air outlet pipeline are connected to the ammonia synthesis tower. A hot circulating gas pipeline is arranged on the air outlet pipeline between the heat exchanger and the ammonia separator. The hot circulating gas pipeline is connected to the air inlet pipeline on the heat exchanger via a circulating gas compressor. A secondary reflux pipeline is arranged at the connection between the hot circulating gas pipeline and the air inlet pipeline to connect the air inlet end of the circulating reflux pipeline. Aiming at the instability of energy supply by renewable energy sources such as wind and light, the present invention controls the ammonia reflux amount by the hot circulating gas pipeline when the total flow rate of hydrogen and nitrogen input into the system differs from the total flow rate of hydrogen and nitrogen at full load, and controls the total gas flow rate of the ammonia synthesis tower by the secondary reflux pipeline, thereby finally ensuring the stability of the overall system of the ammonia synthesis tower under different energy supply conditions.

The disclosure pertains to a plant for the production of ammonia. The ammonia is produced from hydrogen obtained by electrolysis of water. The electrolysis is powered by a renewable source of energy, complemented with power obtained from the plant during periods of low or no availability of the renewable energy. To this end, the plant is configured such that it can be operated in a charge configuration (obtaining and storing power) and a discharge configuration (employing said power).

The present invention discloses an energy supply method and system for nearly zero - energy buildings based on multi - objective optimization, which relates to the technical field of building energy efficiency and renewable energy applications. The method includes collecting energy consumption data in a building and constructing a multi - objective evaluation system according to the energy consumption data; optimizing the weights of the multi - objective evaluation system to obtain the optimal weight coefficients; combining the optimal weight coefficients with a solar photovoltaic system and a ground - source heat pump system to establish a renewable energy supply strategy model; and based on the renewable energy supply strategy model, outputting energy supply scheduling instructions for each time period of the building to adjust the operating parameters of the solar photovoltaic system and the ground - source heat pump system. The present invention ensures the balance between energy efficiency and comfort by real - time adjusting the equipment operation mode, avoiding the problems of common energy efficiency waste or insufficient comfort in traditional methods. Through intelligent scheduling and optimization, it can effectively reduce the energy consumption of buildings and improve the sustainability of their energy utilization.

The invention discloses a renewable energy supply source management system and a method thereof, which are used for supplying power to at least one power generation end by at least one power utilization end through at least one power purchase protocol so as to calculate a plurality of green energy indexes required by management. The system comprises an electric quantity data module; a scene data module; the green power index calculation module is used for calculating various green energy indexes for a specified time interval based on the corresponding electric quantity data, the corresponding scene data and a green energy transfer formula corresponding to the time interval; and the report module is used for presenting various green power indexes, scene data or charts related to the power generation end / protocol / power utilization end and the like.

The application provides a renewable energy supply-distribution-demand-storage system space-time coordination configuration method, relates to the technical field of intelligent energy, and comprises the following steps: acquiring data of multiple regions; calculating time mismatch degrees, space mismatch degrees and space-time coupling mismatch degrees of the regions; decomposing an optimization problem into a long-term planning layer, a medium-term scheduling layer and a short-term real-time layer in a time dimension; constructing multi-objective optimization functions of the time layers; decomposing the optimization problems of the time layers in a space dimension; alternately solving local optimization sub-problems and global coordination problems of the regions by using a distributed iteration method; outputting a configuration result of a current time layer when the algorithm converges; executing a decision of a first time period by each time layer and taking the decision as a constraint condition of a next time layer to obtain power generation installed capacities, power transmission line capacities, energy storage capacities and operation strategies of the regions. The application can improve a renewable energy consumption rate and reduce system operation costs.

The primary energy sources for low / zero carbon intensity (green) hydrogen production, such as sunlight and wind, are inherently intermittent in duration and variable in strength. An integrated, dynamically controlled production process is needed for the optimization of continuous green hydrogen production. The process needs to manage this dynamic state on a frequent basis within a green hydrogen production process to assess impacts from incoming renewable energy through to the outgoing continuous (e.g., 24 hours per day and up to 365 days per year) production of both gaseous and liquid hydrogen. The process needs to manage operational performance and metrics continuously using the production facility configuration, renewable energy supply profiles and operational safety requirements together with the capability to adapt to a variety of external constraints, including weather variation, water supply variation and offtake requirements.

The invention discloses a renewable energy source supply system capable of responding to demands. The renewable energy source supply system comprises a renewable energy source collection device, an intelligent control and monitoring system, an energy storage device, a demand response device, a power grid interaction device and a heat energy conversion and utilization device. The renewable energy source collecting equipment comprises a photovoltaic panel, a wind driven generator, a tidal power generator and an air source heat pump; the intelligent control and monitoring system comprises an energy management system, an intelligent inverter and an Internet of Things sensor. The energy storage equipment comprises electrochemical energy storage equipment, mechanical energy storage equipment and thermal energy storage equipment. According to the renewable energy source supply system capable of responding to the requirements, the wind driven generator is installed, the radar monitoring and noise reduction technology is adopted, the tidal power generator is provided with the ecological flow rate and the fishway, and then the operation noise of the wind driven generator is reduced, so that the influence of the wind driven generator on birds is reduced, and the influence on bird flocks is avoided.

The invention relates to the technical field of energy management, in particular to an automatic energy management strategy optimization method based on a stochastic optimization algorithm, and the method comprises the following steps: S1, collecting data including renewable energy output, traditional energy consumption, prediction demands and environmental parameters; s2, performing cleaning, normalization and feature extraction on the data acquired in the S1; s3, optimizing the energy management strategy; s4, adjusting an energy distribution and utilization plan; s5, predicting future energy demands and renewable energy supply conditions; s6, displaying the implementation effect of the energy management strategy through a user interface; and S7, regularly adjusting parameters of a random optimization algorithm and an energy management strategy according to a prediction result of S5 and implementation feedback of the energy management strategy. According to the invention, by dynamically adjusting and optimizing the energy management strategy, the energy utilization efficiency is improved, the energy cost is reduced, and the environmental influence is reduced.

It is the objective of the present invention to provide a method for negotiating and assigning data processing tasks among a plurality of participating data centers. This objective is achieved according to the present invention a method for negotiating and assigning data processing tasks among a plurality of data centers; said method comprising the steps of: a) providing a framework for a dynamic allocation of the data processing tasks and connecting a plurality of geographically distributed data centers participating via computing nodes to said framework; b) providing with the dynamic task allocation framework a task allocation instance, c) said task allocation instance is receiving bids and / or offers coming from the participating data centers wherein the bids and / or offers for data processing tasks reflect the share of real-time renewable energy supply and the remaining share of non-CO2-neutral energy supply as compared to a defined CO2-budget; and d) said task allocation instance is allocating the data processing tasks in the dependency of the received bids and / or offers to preferred data centers in the sense of applying one or more of the following allocation criteria: i) at least a majority of the participating data centers does not exceed the maximum share of non-renewable electricity allowable under the defined CO2-budget for the respective data center. ii) the data centers with the most advantageous share of renewable energy are loaded with data processing tasks first; iii) the remaining data processing tasks are allocated to the remaining data centers in the priority of the decreasing share of renewable energy generation and / or supply. Thus, the data centers participating via the computing nodes can achieve significant energy cost savings by offloading some or all of their excess workload during periods of high energy prices and leveraging surplus renewable energy supply at distant locations. Additionally, the computing nodes can maximize their self-consumption of renewable energy, thereby reducing reliance on external energy sources, promoting sustainability and observing the existing political directives.

The primary energy sources for low / zero carbon intensity (green) hydrogen production, such as sunlight and wind, are inherently intermittent in duration and variable in strength. An integrated, dynamically controlled production process is needed for the optimization of continuous green hydrogen production. The process needs to manage this dynamic state on a frequent basis within a green hydrogen production process to assess impacts from incoming renewable energy through to the outgoing continuous (e.g., 24 hours per day and up to 365 days per year) production of both gaseous and liquid hydrogen. The process needs to manage operational performance and metrics continuously using the production facility configuration, renewable energy supply profiles and operational safety requirements together with the capability to adapt to a variety of external constraints, including weather variation, water supply variation and offtake requirements.

The application discloses a kind of carbon emission reduction portrait determination method and device for building, it is related to building energy saving and emission reduction technical field.The method comprises: obtaining the target data of target building in historical time period;According to the target data of target building, the maximum installed capacity of target building, energy-saving energy of energy consumption system, renewable energy supply energy;According to the maximum installed capacity of target building, energy-saving energy of energy consumption system, renewable energy supply energy, determine the carbon emission reduction proportion value of target building;According to carbon emission reduction proportion value, determine the carbon emission reduction portrait of target building, wherein, carbon emission reduction portrait is used to represent at least one implementation mode to achieve carbon emission reduction proportion value.The present application solves the technical problem that the existing technology only analyzes carbon dioxide emission reduction of building from single emission reduction means, leading to poor carbon dioxide emission reduction effect for building.

To provide a system capable of improving efficiency of energy consumption and reducing an environmental load.SOLUTION: The system includes means for collecting household or business power consumption data in real time, means for acquiring weather data, means for acquiring renewable energy supply prediction data, means for analyzing a power consumption pattern based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather fluctuations, means for generating a consumption prediction model, means for integrating consumption prediction and supply prediction and simulating a supply-demand balance, means for creating an optimal supply plan, means for notifying a household or business of the supply plan, and means for adjusting a supply amount.SELECTED DRAWING: Figure 1

The utility model relates to a building integrated photovoltaic unit enclosing hasp type heat preservation vertical face structure which comprises a solar photovoltaic system and a heat preservation stress system, and the heat preservation stress system comprises a keel frame, an inner supporting plate, an inorganic heat preservation material layer, a windproof and waterproof air blocking plate and a U-shaped steel support. The inner supporting plate is installed in the keel frame to fix the inorganic heat preservation material layer on the upper portion, the windproof and waterproof air blocking plate and the U-shaped steel support are sequentially installed on the outer side of the inorganic heat preservation material layer, and the solar photovoltaic system is installed on the heat preservation stress system through the U-shaped steel support. A solar photovoltaic system and a heat preservation stress system are combined, and efficient utilization of energy is achieved while building functions are guaranteed. According to the integrated design, the high-performance heat insulation effect is provided, the building can be converted into a localized renewable energy supply point, energy benefits can be maximized, dependence on an external power grid is reduced, and the aim of achieving the zero-energy-consumption or positive-energy-consumption building is assisted.

The present invention provides a gas-fired power generation system and power generation method that utilizes efficient coupling of renewable energy. The gas-fired power generation system comprises: a renewable energy supply unit, a heat storage and exchange unit, a fuel gas supply unit, and a power generation unit. The heat storage and exchange unit stores heat from the renewable energy and exchanges heat with the fuel gas supply unit and / or the power generation unit. The fuel gas supply unit produces and stores fuel gas using renewable energy. The power generation unit comprises a SOFC cell and an induction coil. The SOFC cell receives the fuel gas and reacts to generate electricity. The induction coil is arranged around the SOFC cell and, at least when the SOFC cell is activated, supplies power to the induction coil, causing the SOFC cell to quickly heat up and start. This system can improve energy utilization efficiency and enhance the stability and continuity of the system's external power supply, thereby meeting future demands for efficient utilization of renewable and clean energy.

The invention discloses a battery charging network deployment method using renewable energy. The method comprises the following steps: S1, collecting power grid state data and renewable energy supply data in real time through a sensor network; s2, predicting the power grid load and the renewable energy source supply based on the power grid state data and the renewable energy source supply data by using a long short-term memory network; s3, adopting regression analysis to determine an optimal scheduling plan of the charging demand of the electric vehicle based on a long short-term memory network prediction result; and S4, according to the real-time change of the power grid and the renewable energy source supply, a charging scheduling plan is dynamically updated, and the charging scheduling plan comprises adjustment of charging power and time. According to the invention, through intelligent and automatic charging network management, power grid changes are effectively adapted, energy utilization is optimized, environmental influences are reduced, and a solid technical support is provided for sustainable and intelligent power grid management.

An energy management system and method designed to analyze the energy consumption of a building or cluster of buildings is provided. The system includes a user device and a controller. The controller includes a building energy audit analysis level, an energy demand-supply management level, and a heat and mass balance simulation level. The system integrates energy consumer-type users, energy server-type users, and energy developer-type users on one single platform. The energy management system provides a single platform for the sustainable management of energy data, integrates the users, inputs energy data, analyzes the data, and outputs energy performance and energy efficiency data based on the analyzed data.

The present invention discloses an electric power sharing energy docking method taking into account the expected contribution value deviation and the supply-demand ratio, comprising: each electric power sharing participant calculates the net surplus energy in the current period, determines the role, and calculates the expected contribution value; calculates the average expected contribution value of all electric power sharing participants with the same role as its own in the current period; calculates the expected contribution value deviation in its electric power sharing in the current period; calculates the supply-demand ratio in its electric power sharing in the current period; calculates the renewable energy supply adjustment coefficient and the energy demand adjustment coefficient in the electric power sharing in the next period; each electric power sharing participant calculates the renewable energy supply and energy demand in the electric power sharing in the next period; each electric power sharing participant reports its participation information in the electric power sharing in the next period to complete its energy docking with the electric power sharing platform; the use of the method of the present invention can significantly improve the cumulative contribution value of the electric power sharing participants in electric power sharing and improve the utilization rate of renewable energy.