700 results about "Green electricity" patented technology

The invention relates to the technical field of data center interaction, and particularly discloses an information interaction method and system for computing power and electric power collaborative scheduling of a data center, which integrates multi-source heterogeneous data and constructs a space-time correlation model to realize accurate prediction and dynamic modeling of resource demands of the data center. The real-time learning ability of the DRL and the discrete decision advantage of the MIP are utilized to realize multi-objective synchronous optimization, the energy efficiency and economy of the data center are significantly improved, the multi-objective optimization can realize "computing power-electric power-heating power" coupling, the block chain technology ensures that data cannot be tampered and is transparent and credible, the security and auditing performance of the system are improved, and the system performance is improved. Carbon footprint tracking, green power authentication and PUE index dynamic energy efficiency optimization are supported, green power use is directly stimulated, the carbon footprint and operation cost is reduced, thermal data is used for cooling control, power data is used for task scheduling, resource conflicts are avoided, the limitation of traditional single resource scheduling is broken through, and the response speed is increased.

The invention provides an elastic computing power scheduling method and device based on space-time prediction and a medium, and belongs to the technical field of cloud computing and edge computing. The method comprises the following steps: acquiring a historical load, a satellite cloud picture, meteorological data, a burst flow mark, an energy price and a green electricity proportion in real time; constructing a fusion prediction model which comprises a multi-modal feature coding module, a space-time attention fusion module and a prediction output layer; a multi-objective optimization function is constructed, cost, delay and resource fluctuation are minimized, and green rewards are maximized; solving a task allocation variable and a resource adjustment amount by adopting mixed integer programming; the actual green electricity utilization rate is monitored, and the green reward coefficient is dynamically adjusted to correct prediction and scheduling deviations. According to the method, the computing power demand fluctuation of different areas in the future time period can be predicted in advance, so that resource allocation is dynamically adjusted, resource idleness and waste are reduced, and the utilization rate of computing power resources is remarkably improved.

The invention relates to the field of electricity transaction, in particular to an electricity transaction intelligent algorithm platform based on block chain consensus. The method comprises the following steps: acquiring a real-time green electricity encrypted data set, uploading the real-time green electricity encrypted data set to a block chain node, and generating an unactivated digital green certificate data set; based on the non-activated digital green certificate data set, generating a grid-connected verification instruction, updating the non-activated digital green certificate data set to a tradable state, and generating a tradable digital green certificate data set; obtaining a power grid dynamic parameter set, and generating a green certificate time pricing data set through a space-time right dynamic pricing strategy based on the power grid dynamic parameter set and the tradable digital green certificate data set; and according to the green certificate time pricing data set, the digital green certificate is cancelled after verification in the power utilization process of the user, and a green power consumption traceability report is output. In the green electricity transaction process, fair and orderly development of the green electricity transaction market is promoted.

The invention discloses an energy storage-photovoltaic-electrolytic aluminum system dynamic optimization method suitable for multiple scenes, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting and preprocessing energy storage-photovoltaic-electrolytic aluminum system data, obtaining photovoltaic, load and electricity price characteristics, constructing an equipment mathematical model, and carrying out the optimization of the equipment mathematical model; the method comprises the steps of predicting a state of charge, battery cycle aging cost, photovoltaic output power, load baseline power and an adjustable flexible interval, constructing a system state prediction model, an economic dispatching optimization model and a self-adaptive control model, outputting a power prediction report, a day-ahead optimal economic dispatching plan and an energy storage power control instruction, and carrying out energy storage control. The energy storage power control instruction is subjected to multi-scene logic judgment and then is transmitted to the energy storage converter to be executed, feedback is obtained, and visualization is carried out through the integrated digital board. Through intelligent prediction and adaptive control, collaborative optimization of economy, reliability and green power consumption efficiency is realized, and the comprehensive performance of the energy storage-photovoltaic-electrolytic aluminum system under variable working conditions is improved.

The invention discloses a carbon right-green certificate collaborative transaction method based on a block chain, and belongs to the field of block chain technology and environment management. The method specifically comprises the following steps: 1) deploying an intelligent contract based on an Ethereum platform, and integrating power transaction, carbon quota delivery and green certificate issuing process chains to realize automatic execution of transaction rules and non-tampering of data; (2) a Carbon Token excitation mechanism is innovatively designed, a DPoS-CT consensus algorithm considering the right of the Carbon Token is proposed, and a market subject is excited to continuously reduce emission; 3) a dynamic reputation model and a reward and punishment mechanism are used to quantify subject performance behaviors and distribute on-chain rights and interests; and 4) resetting the transaction cycle every 24 hours, and optimizing quota allocation and consensus parameters based on historical data. According to the mechanism provided by the invention, renewable energy transaction can be effectively stimulated, and after the block chain carbon pass system is introduced, the quota completion rate, the green electricity utilization rate, the carbon emission, the carbon right-green certificate transaction volume and the economic benefit are optimized to a certain extent.

The invention belongs to the technical field of high-energy-consumption industrial energy conservation and carbon reduction, and particularly relates to an electrolysis process waste heat gradient utilization and green electricity energy storage coupling system and a regulation and control method thereof. An electrolysis process waste heat gradient utilization and green electricity energy storage coupling system comprises a waste heat recovery module used for recovering and gradient utilization of electrolysis high-temperature and medium-low-temperature waste heat for power generation or heat supply; the green electricity direct supply module comprises a photovoltaic unit, a hybrid energy storage unit and an electricity-heat conversion unit and is used for preferential direct supply, fluctuation absorption and storage of green electricity; and the multi-energy coupling regulation and control module is in communication connection with each module and a power grid, and dynamically optimizes and coordinates energy distribution among waste heat power, green power, energy storage and the power grid based on a preset algorithm.

The invention belongs to the technical field of intelligent heat supply, and provides a green power consumption heat storage and release matching control method and system based on a water heat storage boiler, and the method comprises the steps: firstly obtaining a heat load prediction sequence and an electric power spot market price prediction sequence of a heat supply system; secondly, regarding the heat supply network as a graph structure composed of nodes and pipelines, and establishing a pipe network dynamic model for describing the hydraulic and thermal characteristics of the heat supply pipe network; and finally, with the purpose of minimizing the comprehensive operation cost of the heat supply system in the whole scheduling period, and with the whole network heat balance, heat source operation, centralized heat storage device operation, heat supply network safety and user comfort as constraint conditions, solving to obtain a source network load storage cooperative control strategy. The purpose of integrated collaborative optimization of the heat source, the energy storage, the pipe network and the load is achieved, a scheduling strategy with better economical efficiency can be generated, and the problems that the absorption capacity is insufficient, and the overall operation economical efficiency of the system is not high are solved.

The embodiment of the invention discloses an intelligent energy consumption regulation and control steel smelting system based on multi-energy complementation, and the system comprises a multi-energy complementation energy supply system which is used for integrating various energy forms, forming a dynamic cooperative energy supply network, and carrying out the energy cooperative operation according to an energy priority strategy and a dynamic matching mechanism; the intelligent regulation and control platform adopts a three-layer framework of'perception-decision-execution 'to coordinate energy flow and process operation between the multi-energy complementary energy supply system and the multi-energy coupling process equipment; the multi-energy coupling process equipment comprises hydrogen-based direct reduction ironmaking equipment, and hydrogen is used for replacing traditional coke to serve as a reducing agent for ironmaking; comprehensive energy supply monitoring and optimization can be carried out on the steel smelting process, various renewable energy sources are combined, and the problems that a single renewable energy source is intermittent, depends on a power grid and the like are effectively solved; meanwhile, the multi-energy complementary hydrogen storage process is coupled with the hydrogen metallurgy process, and the multi-energy cooperation problems of whole-process green power supply, waste heat utilization and the like are solved.

The invention discloses a universal method and system for dynamically defining a carbon footprint of a product and a related device, and relates to the field of intelligent accounting of the carbon footprint of the product, and the method comprises the steps: obtaining a bill of material of the product, carrying out the disassembly, obtaining a function unit, and associating with a corresponding production process link; the carbon emission of each production process link is automatically calculated; calculating the carbon emission of the dynamic data; describing the unstructured text, and identifying recessive carbon emission; acquiring regional real-time green electricity proportion data, and calculating the green electricity emission reduction of the product after correction; and based on the carbon emission of each production process link, the carbon emission of the dynamic data, the hidden carbon emission, the green electricity emission reduction and the carbon emission of the waste recovery stage, calculating the life cycle carbon footprint of the product, then carrying out balance verification, generating a three-level structured report, and carrying out uplink storage. According to the method, the integrity of the accounting boundary can be improved, the emission source coverage rate and the recognition efficiency are improved, and the accuracy and timeliness of the product carbon footprint accounting result are improved.

The invention discloses an intelligent electric energy meter data acquisition device, method and equipment for electric carbon metering and a storage medium, and relates to the technical field of electric power systems and carbon emission management, and the device comprises a carbon emission hardware calculation circuit module which verifies frozen data and regional-level electric carbon factors and line loss power of a superior node, and generates carbon emission data; a first non-volatile processor storing carbon emission data; and the second nonvolatile processor and the first nonvolatile processor are backups for each other and store electric quantity freezing data. The device expands a carbon emission metering function at low cost through an idle RS485 port of an existing intelligent electric energy meter, generates a dynamic electric carbon factor through local calculation, accurately calculates carbon emission data, and improves the accuracy of the carbon emission data. The data accuracy, real-time performance and reliability are improved, the centralized calculation burden is relieved, the system modification cost is reduced, large-scale popularization and application are supported, and low-carbon transformation and green power management are effectively promoted.

The invention relates to the technical field of building power system energy saving optimization, and discloses a central air conditioner green power consumption optimization control system based on carbon footprint tracing. The system comprises a carbon footprint traceability data acquisition unit, a green power consumption priority evaluation unit, an energy scheduling optimization unit, a real-time load matching unit and a carbon footprint verification unit. The carbon footprint traceability data acquisition unit acquires related data; the green power consumption priority evaluation unit generates a consumption strategy sequence based on a dynamic game theory model; the energy scheduling optimization unit adopts a specific algorithm to construct an optimization model; the real-time load matching unit adjusts a matching relationship by using deep reinforcement learning; the carbon footprint verification unit verifies the carbon footprint based on a zero-knowledge proof algorithm. According to the method, optimal control over green power consumption of the central air conditioner is achieved, the energy utilization efficiency is improved, carbon emission is reduced, and carbon footprints are accurately managed.

The invention discloses an energy system and method for coupling calcium-based thermochemical heat storage and carbon capture. The energy system comprises a calcination unit, a carbon dioxide pretreatment storage unit, a calcium oxide heat exchange storage unit, a carbonation reaction unit and a heat utilization unit which are arranged based on a boiler. The calcining unit takes green valley electricity as energy to drive calcining reaction to realize heat storage, and generated CaO is processed by the calcium oxide heat exchange storage unit and then is stored for use in a heat release stage; generated COs are stored for standby application after passing through the carbon dioxide pretreatment storage unit; in the heat release stage, CaO and the desulfurized flue gas are subjected to heat release carbonation reaction in the carbonation reaction unit, and heat is released; caCO3 generated by the carbonation reaction can be used for the next heat storage stage; and the heat released by the carbonation reaction is conveyed to the heat utilization unit for heat exchange. Through coupling of calcium-based thermochemical heat storage and carbon capture, green electricity and off-peak electricity are fully utilized, and efficient storage and gradient utilization of energy are achieved.

A gas and steam power generation afterburning type heat-electricity decoupling system and a working mechanism thereof, characterized in that the system comprises a gas turbine power generation system, a power transmission and transformation system, a molten salt heat storage system and a steam power generation system; the gas turbine power generation system is connected with the molten salt heat storage system through a flue gas pipeline; the molten salt heat storage system is connected with the power transmission and transformation system through a cable; the gas turbine power generation system is connected with the power transmission and transformation system through a cable; the molten salt heat storage system is connected with the steam power generation system through a main steam pipeline and a backwater pipeline; and the steam power generation system is connected with the power transmission and transformation system through a cable. The system can realize complete heat-electricity decoupling in a green energy storage system, realize the functions of self-supply of external power, storage of green electricity or heat supply, increase the accommodation capacity of a power grid to green electricity, and enhance the stability, flexibility and safety of a new power system.

The invention discloses a wind, light and green power coupled pumping unit well group operation collaborative optimization method. The method comprises the following steps: acquiring field data of a multi-energy micro-grid pumping unit well group; establishing a wind and light output uncertainty model, an energy storage battery model, a pumping unit power model and bidirectional AC / DC converter equipment; based on each model, establishing a multi-objective optimization scheduling model for the intermittent pumping operation of the multi-energy micro-grid pumping unit well group; an NSGA-II method is adopted to solve the established multi-objective optimization scheduling model, and a Pareto optimal scheduling scheme is obtained; and carrying out comprehensive evaluation and decision analysis based on an entropy weight TOPSIS method, and selecting an optimal scheme from a Pareto optimal scheduling scheme set obtained by NSGA-II. According to the multi-energy micro-grid rod-pumped well group off-peak interval pumping operation multi-objective optimization method based on NSGA-II and entropy weight TOPSIS, the power utilization requirement of the rod-pumped well group can be met, the working system of rod-pumped well group interval pumping operation is optimized, the economical efficiency and stability of the operation of a rod-pumped well interval pumping system under a wind-solar storage micro-grid are ensured, and the green power consumption capability of the system is enhanced.

The invention relates to the technical field of charging scheduling, in particular to an expressway unmanned heavy truck intelligent charging system which comprises a data acquisition and preprocessing system and a vehicle pile network cloud integrated cooperative operation and green electricity source tracing carbon emission reduction module. Driving and charging strategies are optimized in combination with algorithms such as reinforcement learning, the energy consumption of the unmanned heavy truck can be effectively reduced, the transportation resource utilization rate is improved, and it is guaranteed that transportation tasks are efficiently completed in a time window; green power full-link traceability is realized based on the block chain technology, and the charging time is dynamically adjusted in combination with peak and valley electricity prices, so that the authenticity of use of green power is ensured, low-cost green power can be preferentially selected, and the charging and overall operation cost is remarkably reduced; the carbon emission in the charging and operation process is accurately quantified, the emission reduction benefit of a traditional heavy truck is scientifically evaluated, meanwhile, the method is in butt joint with a green logistics authentication system, support is provided for obtaining authoritative green authentication for transportation services, and low-carbon transformation of the industry is assisted.

The invention relates to an energy management method and system for a green electricity hydrogen production and hydrogenation all-in-one machine, and relates to the technical field of hydrogen energy application, the system comprises an upper layer control system and a communication interface module, collaborative optimization scheduling is executed in the day-ahead stage, the rolling stage and the real-time stage; the communication interface module supports IEC104, private UDP and other industrial protocols, and realizes real-time data interaction with a green electricity prediction system and a hydrogen production DCS system. The method comprises the following steps: making an electrolytic cell start-stop and power plan in a day-ahead stage; periodically correcting prediction and demand errors in a rolling stage; the power of the electrolytic cell is dynamically adjusted to fluctuate along with green electricity in the real-time stage. According to the invention, cross-section and multi-time scale coordination control is realized, and the green power consumption rate, the system operation economy and the hydrogen energy supply reliability are improved.

The invention provides a green electricity consumption accounting optimization method and device based on a carbon emission factor and a medium, and belongs to the technical field of carbon emission optimization based on green electricity accounting. The method comprises the following steps: establishing a corresponding relation table between a power purchasing form and a tracing tool according to multi-source data to execute power-proof collaborative tracing; performing inter-region green attribute replacement through a calculation engine, constructing a region green power consumption accounting model, and calculating the total region green power consumption; and calculating a residual power carbon emission factor based on the accounting result, and generating a cross-regional green power consumption report so as to realize carbon emission optimization. According to the method, a green electricity consumption source is accurately positioned through an evidence-electricity collaborative traceability mechanism, the problem of cross-regional repeated calculation is reduced, the calculation reliability is remarkably improved based on a carbon emission factor generated by an accurate accounting result, and thus the path and the cost for achieving a carbon reduction target are optimized.

The invention discloses a device for preparing a molten carbon fertilizer through solid waste microwave pyrolysis and waste heat recovery, and belongs to the field of solid waste treatment and recycling. The device is composed of a pretreatment unit, a microwave pyrolysis unit, a gas-solid separation unit, a gas phase treatment unit, a solid phase treatment unit and a waste heat circulation unit, solid waste enters a microwave rotary kiln after being dried and crushed, a magnetron generates microwave anaerobic pyrolysis under photovoltaic power supply, and a high-temperature gas-solid product is generated; the cyclone separator is used for separating flue gas from solid carbon, the flue gas is condensed to obtain pyrolytic oil and gas, and waste heat of medium-temperature flue gas is recovered through the heat accumulating type heat exchanger for drying; cooling the solid carbon through a heat pipe heat exchanger, recovering waste heat and liquefying urea, and mixing the solid carbon and the urea in a stirring melting pool to prepare a molten carbon fertilizer; and the waste heat backflow fan sends the carbon waste heat back to the drying chamber. The system is driven by green electricity, gas and solid phase waste heat is recovered through double paths, solid waste efficient pyrolysis-product high value-energy self-circulation is achieved, and energy consumption and carbon emission are reduced.

The invention discloses a reinforcement learning-consistency theory combined wind-solar-hydrogen storage micro-grid regulation and control method, and belongs to the technical field of new energy and micro-grid regulation and control. The method comprises the following steps: firstly, establishing a micro-grid model covering electrochemistry, thermodynamics and mass transfer processes, wherein the micro-grid model comprises wind-solar power generation equipment, an electrolytic cell and the like; designing an optimization regulation and control model by taking operation cost minimization and hydrogen production maximization as targets and combining with equipment safety constraints; the power of the electrolytic cell is dynamically distributed through an improved consistency algorithm based on gradient compensation, the state of a hydrogen storage tank is balanced, and a layered regulation and control framework is formed by adopting a deep reinforcement learning algorithm with improved TD3 to cooperate with multiple devices. According to the invention, the new energy consumption rate and the system reliability and economy are improved, and intelligent regulation and control support is provided for a green electricity hydrogen production project.

The invention relates to the technical field of power-carbon collaboration, and discloses a block chain-based power-carbon collaboration data management and intelligent optimization method, system, device and medium, and the method comprises the steps: collecting power-carbon multi-source data in real time, and carrying out the data preprocessing; establishing a correlation model between electricity and carbon data so as to quantify a correlation relationship between unit power generation and carbon emission, and obtaining carbon emission reduction data; a double-objective optimization function is adopted to construct an electricity-carbon collaborative decision-making large model, and an optimal unit output scheme is output through deep learning; and generating a block chain hash identifier for each green electricity transaction, performing contract automatic verification, and if a performance risk is monitored, triggering early warning and sending an optimization suggestion. According to the invention, by constructing a technical system based on power-carbon collaborative data management and intelligent optimization, accurate conduction of carbon emission technical constraints, safety interaction of cross-system data, credible tracing of environmental rights and interests and dynamic prevention and control of risks are realized, and fusion and efficient collaborative operation of a power system and carbon emission management are promoted.

The invention provides a green electricity hydrogen production system optimization method and system considering multi-tank service life balance, and belongs to the technical field of new energy and electrochemistry, the method comprises the steps of obtaining market data, power grid data and downstream hydrogen load demand data in a preset scheduling period, and obtaining initial state data of a plurality of electrolytic tanks deployed in a hydrogen production station; constructing a mixed integer quadratic programming optimization model with the goal of maximizing the comprehensive benefit of the system; the model is solved, and the optimal scheduling strategy of the start-stop state and the operation power of each electrolytic cell in each time period in the scheduling period is obtained; and converting the optimal scheduling strategy into a control instruction, and issuing the control instruction to a controller of a hydrogen production station to complete collaborative optimization of the green electricity hydrogen production system. The method aims at solving the problems that in an existing green electricity hydrogen production system scheduling method, the calculation cost of a nonlinear model is high, the nonlinear model is difficult to apply to an actual large-scale system, and short-term market income and long-term equipment life cannot be effectively coordinated and balanced.

The invention provides a green-electricity-driven oil field drilling and production equipment control method, system and equipment and a storage medium, and the method comprises the steps: carrying out the rolling prediction of the load power of a drilling machine in a future time window based on the mapping correlation characteristics between a load power demand and a stratum structure and the working condition information in an oil field drilling and production process; the transient peak value of the load power of the super capacitor of the drilling machine in the next operation period is obtained; when the transient peak value is greater than a preset compensation threshold value, performing constraint scheduling on the transient load power of the super capacitor by using a power optimization model to obtain a confidence compensation amount of the transient load power in the super capacitor; and when the drilling machine is in an oil field drilling and production transient working condition, an emergency regulation factor of the drilling machine in the transient working condition is determined through the confidence compensation amount and the rated power of the drilling machine, and then power distribution of the super capacitor and diesel power generation in the next control period of the drilling machine is subjected to confidence regulation based on the emergency regulation factor. Based on the scheme, balanced green power distribution under the transient working condition of the drilling machine can be realized.

The invention belongs to the technical field of new energy power generation and chemical production, and provides a green power chemical system consumption capability evaluation method and related device, and the method comprises the steps: collecting new energy power scene data, and generating a time sequence scene of wind power and photovoltaic output based on a long and short term sequence generative adversarial network; constructing an operation constraint model of the energy storage power station based on a time sequence scene of wind power and photovoltaic output; on the basis of the operation constraint model of the energy storage power station, constructing an operation constraint model of the hydrogen energy storage system; an operation constraint model of an energy storage power station and an operation constraint model of a hydrogen energy storage system are integrated, new energy consumption maximization and grid power demand minimization are taken as optimization targets, a green power chemical system consumption capability evaluation model considering new energy uncertainty is constructed, the model is solved by adopting a mathematical programming solver, and evaluation indexes are output. According to the method, the uncertainty of wind and light output is quantified through new energy scene generation, and the reliability of evaluation is improved.

The invention belongs to the technical field of energy planning, and discloses a coal mining area green electricity direct connection planning method and system, and the method comprises the steps: integrating geographic space information, obtaining a three-dimensional geographic space information database, carrying out the processing through a land recycling evaluation model, obtaining a thermodynamic diagram, and carrying out the quantitative analysis of the thermodynamic diagram, and obtaining the power generation amount. Processing and fusing by using a space-time convolutional neural network and a distributed intelligent sensing model to generate a meteorological database; analyzing by using a wind power and photovoltaic space-time output model to obtain a photovoltaic combined sample; processing by using the load prediction model to obtain a load prediction curve set; a source-load time sequence mismatch quantitative model is input for analysis, and an energy storage collaborative configuration scheme is obtained; inputting a multi-target line optimization model for analysis to obtain a direct connection line topological structure diagram; and performing data-driven optimization decision optimization to obtain a source storage load collaborative optimization model, and performing coal mining area green electricity direct connection planning through the source storage load collaborative optimization model. The green electricity is obtained through direct connection, and the cost is reduced.

The invention discloses a cross-regional green electricity spot-medium and long term linkage pricing method based on reinforcement learning, and relates to the technical field of electricity market scheduling, and the method comprises the steps: firstly constructing a state bus on a transaction platform, and converging spot price, contract price, power transmission margin, green certificate price and load prediction to generate a transaction state vector; a node quotation interval is output by adopting sharing-private security reinforcement learning, and a credibility score is recorded; when the node credibility is lower than a threshold value, calculating a credibility coefficient by using synchronous spectrum characteristics and a price difference tail form, and locking a suspicious subject through graph convolution; performing contract remapping and price-limited auction on the suspicious subjects, and rearranging and clearing the order to correct the price sequence; and finally, carrying out discount settlement on the corrected price and the contract reference, and carrying out double-layer training and static period write-back according to a priority playback sample, thereby realizing high collaboration of price signal credibility, power transmission safety and green power consumption.

The invention discloses a paper mill load green power consumption potential evaluation method fusing ProbSparse and FEDformer, and belongs to the technical field of power system regulation and control, and the method comprises the steps: constructing an up-regulation capability evaluation model and a down-regulation capability evaluation model, and obtaining a maximum up-regulation capability and a maximum down-regulation capability; based on time-frequency domain decoupling modeling, predicting a future green power factor sequence; and based on the maximum up-regulation capacity, the maximum down-regulation capacity and the future green power factor sequence, a load green power consumption potential evaluation model is constructed, and according to the ProbSparse and FEDformer fused paper mill load green power consumption potential evaluation method, a ProbSparse-based paper mill load operation feature extraction method, the maximum up-regulation capacity, the maximum down-regulation capacity and the future green power factor sequence are extracted, and the maximum up-regulation capacity, the maximum down-regulation capacity and the future green power factor sequence are calculated. Improving the global perception capability of long-sequence load data by using a sparse attention mechanism, and accurately extracting the running rhythm, state change and key load mutation characteristics of each device; and on the basis of a green power factor prediction model of FEDformer, time sequence decomposition and frequency domain modeling capabilities are fused, and high-precision prediction of a future green power ratio factor is realized.

The invention provides a green electricity substance metabolism accounting and evaluation method in a regional power system. The method comprises the steps that S101, a system framework of green electricity substance metabolism accounting of the whole life cycle of the regional power system is built; s102, collecting material flow parameters of the green electricity in each link in the system framework; s103, calculating the flow and stock of green electricity resources in the regional power system based on the system framework and the material flow parameters; and S104, evaluating metabolism of green electricity substances in the system framework according to the flow and stock of green electricity resources in the regional power system, and outputting an evaluation result. The method can realize quantitative relation analysis and utilization level evaluation of the green power resource material flow of the regional power system, can further refine the spatio-temporal evolution law of the regional green power in the whole life cycle based on the flow and stock accounting result and the evaluation index, identifies the utilization efficiency of the green power resource, and improves the utilization efficiency of the green power system. The method provides an important basis for establishing refined green power resource management and power system optimization, and has high industrial application value.

The invention relates to a power grid hour-level green electricity source tracing method and system based on space-time matching. The method comprises the following steps: firstly, collecting power generation data of a power generation end containing a power generation equipment identifier, power generation quantity and a power generation timestamp and power utilization data of a power utilization end containing a user identifier, power utilization quantity and a power utilization timestamp, and preprocessing and integrating into a power generation record and a power utilization record; for each to-be-traced power utilization record, dynamically delimiting a time window according to the power utilization timestamp; all power generation records in the window are inquired and aggregated to calculate the total power generation amount; then, according to the proportion of the generating capacity of each power generation device in the total generating capacity, green electric quantity distributed to the electricity utilization record is calculated, and a distribution relation is recorded to generate a traceability result containing a user identifier, a power generation device identifier, the distributed electric quantity, a time window and matching confidence; and finally, generating an ESG report and an electronic certificate based on the traceability result. Compared with the prior art, the method has the advantages of high traceability precision, high automation degree, high reliability and the like.

The invention discloses a balance unit quotation decision-making method considering uncertainty, and relates to the technical field of power systems, and the method comprises the steps: describing the output of renewable energy sources based on a scene method, carrying out the new energy modeling of the uncertainty of the output of the renewable energy sources, outputting a green power distribution proportion, and collecting market data and the data of a balance unit; building a balance unit quotation decision model based on new energy modeling and input balance unit data; a combined clearing model of the spot market and the green electricity market is constructed through new energy modeling and input market data and balance unit data; and optimizing the balance unit quotation decision model and the joint clearing model, and outputting an optimal quotation strategy of the balance unit. The method has the beneficial effects that the uncertainty of renewable energy sources can be effectively dealt with, the operation risk of the balance unit is reduced, and the operation stability of the system is improved.

The invention relates to the technical field of carbon neutralization, and discloses a critical electricity price calculation method based on green electricity energizing tempering coupling methanol preparation, and the method comprises the steps: building an ironmaking byproduct gas database and defining green hydrogen preparation parameters based on different ironmaking processes, and building a gas purification cost model based on the ironmaking byproduct gas database and the green hydrogen preparation parameters; constructing a ton methanol complete cost model of a tempering coupling methanol preparation path X; a reference process HCTM is selected, C (X) is made to be equal to C (HCTM), and an analytic expression of the critical electricity price is solved; the actual electricity price is compared with the critical electricity price, if the actual electricity price is compared with the critical electricity price, it is judged that the path X has economic advantages relative to the HCTM under the current year T and the carbon tax condition, and if not, it is judged that the path X does not have economic advantages relative to the HCTM. The investment risk of a green methanol project can be remarkably reduced.