817 results about "Green electricity" patented technology

The invention relates to the technical field of new energy power systems, and discloses an energy storage coordination system and control method based on meteorological prediction and multi-energy complementation, and the system comprises a power generation end module which carries out the dynamic adjustment according to a power adjustment instruction of a power generation end; the meteorological prediction and data acquisition module is used for acquiring meteorological data in a future time period in real time and sending the meteorological data to the intelligent control and scheduling platform; the energy storage end module sends data including lithium battery SOC and hydrogen storage tank pressure to the intelligent control and scheduling platform and performs charging and discharging according to an energy storage end charging and discharging priority strategy; and the intelligent control and scheduling platform receives the electricity price data of the weather prediction and data acquisition module, the energy storage end module and the power grid in real time, generates a power generation end power regulation instruction and an energy storage end charging and discharging priority strategy, and meanwhile, improves the income and maximizes the hydrogen energy use proportion through electricity price peak-valley arbitrage and green electricity transaction premium. The power grid stability and the energy utilization rate are greatly improved, the energy storage life is prolonged, and the investment payback period is shortened.

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 a regional carbon emission responsibility determination method and device based on a carbon emission factor, terminal equipment and a storage medium. The method comprises the following steps: acquiring the electric power type, the called-in electric power total amount, the called-out electric power total amount, a first carbon emission factor, the net consumption data of fossil fuel, fuel characteristic parameters, the fuel type number, the transaction electric quantity, the local power generation amount and the non-green power utilization electric quantity of a target area; calculating the total direct carbon emission according to the net consumption data, the fuel characteristic parameters and the fuel type quantity; calculating the total indirect carbon emission according to the electric power type, the total called-in electric power, the total called-out electric power and the first carbon emission factor; calculating a regional power average carbon emission factor of the target region according to the transaction power quantity, the local power generation quantity, the total direct carbon emission quantity and the total indirect carbon emission quantity; and according to the regional power average carbon emission factor and the non-green power consumption quantity, the carbon emission responsibility of the target region is checked. According to the invention, accurate accounting of electric power carbon emission is realized.

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 provides a wind-solar hydrogen production and ammonia synthesis system and a configuration method thereof, electronic equipment and a storage medium. The wind and light hydrogen production and ammonia synthesis system comprises a wind and light hydrogen production subsystem, an ammonia synthesis subsystem and a coordination controller which are relatively independent, the ammonia synthesis subsystem comprises a hydrogen storage tank, the hydrogen storage tank is connected with the wind and light hydrogen production subsystem, and the configuration method of the wind and light hydrogen production and ammonia synthesis system can be executed by the coordination controller. The configuration method of the wind-solar hydrogen production ammonia synthesis system comprises the following steps: firstly, determining capacity configuration parameters of a wind-solar hydrogen production subsystem by taking the minimum leveling hydrogen production cost as a target; and determining the capacity configuration parameters of the ammonia synthesis subsystem based on the capacity configuration parameters of the wind-solar hydrogen production subsystem by taking the maximization of the wind-solar absorption rate, the minimization of the total cost of the system and the minimization of the operation cost of the system as targets. The wind and light hydrogen production subsystem and the ammonia synthesis subsystem are subjected to two-stage decoupling type system configuration, and stable operation of the ammonia synthesis process is ensured while efficient utilization of green electricity is achieved.

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 invention discloses a photovoltaic output prediction-based green electricity hydrogen production power adaptive regulation and control system and a photovoltaic output prediction-based green electricity hydrogen production power adaptive regulation and control method, relates to the technical field of green electricity hydrogen production, and solves the technical problems of high light abandoning rate, unstable equipment operation and difficulty in maximizing green electricity absorption efficiency on the premise of guaranteeing equipment safety caused by real-time matching of photovoltaic output. According to the method, historical photovoltaic data and weather forecast data are fused, the sun position and theoretical irradiance are calculated, short-term photovoltaic power generation output power is predicted, an advance is provided for hydrogen production power adjustment, power deviation is calculated through a PID controller, the hydrogen production power is dynamically adjusted, and second-level response is achieved. Meanwhile, an equipment constraint upper limit is introduced, when the total power exceeds a safety threshold value, the adjusting power is automatically corrected, overload is avoided, then secondary adjusting signals are combined, the adjusting power is reversely deduced directly through the safety threshold value, signal processing links are reduced, and the adjusting efficiency is improved.

The invention provides a renewable energy driven iron-based zero-carbon-emission energy storage power generation system based on iron-oxygen circulation, and belongs to the technical field of renewable energy utilization and power generation, and the renewable energy driven iron-based zero-carbon-emission energy storage power generation system comprises a renewable energy consumption module, an alkaline water electrolysis hydrogen production module, a hydrogen reduction reaction module, a combustion energy supply module and a steam Rankine cycle module; the renewable energy consumption module is used for acquiring and converting renewable energy into electric energy; the alkaline water electrolysis hydrogen production module is used for producing hydrogen by using electric energy; the hydrogen reduction reaction module utilizes hydrogen to carry out reduction reaction to store energy; and the steam Rankine cycle module is used for converting energy released by combustion into electric energy. Based on iron-oxygen circulation and through cooperative operation of the renewable energy consumption module, the alkaline water electrolysis hydrogen production module, the hydrogen reduction reaction module, the combustion energy supply module and the steam Rankine cycle module, efficient consumption of renewable energy, effective storage and conversion of energy and zero-carbon-emission green electricity production are achieved.

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 energy storage type virtual power plant energy distribution method considering green power transaction and new energy consumption, and the method comprises the steps: building an uncertainty model of each random variable under a green power transaction condition through a scene random differential method through generating a classification scene data set of frequency modulation demands and wind and light output original data under a green power transaction environment; generating a frequency modulation demand and a wind and light output scene with time sequence correlation; with the optimal benefit of the energy storage type virtual power plant as a target function, establishing an energy management two-stage stochastic optimization model of the energy storage type virtual power plant participating in the electric energy-frequency modulation combined market, and setting energy management model constraint conditions of the energy storage type virtual power plant participating in the electric energy-frequency modulation combined market; and solving the energy management model to obtain an optimization scheme that the energy storage type virtual power plant participates in power market profit. According to the method, the specific flexible adjustment characteristic of the energy storage type virtual power plant in a novel power system can be exerted, the scheduling plan performability is improved, the income mode of the virtual power plant is enriched, and the income level of the independent virtual power plant participating in the green electricity transaction market is effectively improved.

The invention provides a plasma heating hydrogen-rich direct reduction ironmaking system and method. The system comprises a plasma shaft furnace, a heat exchanger, a washer, a desulfurization device, a dehydration device and a pressurization device, the plasma shaft furnace comprises a feeding area, a reduction area, a blanking area and a plasma heating area from top to bottom; the plasma heating area is provided with a hydrogen plasma torch, a thermal reduction gas circulation outlet and a hydrogen inlet; and the hydrogen plasma torch is used for heating and ionizing hydrogen entering from the hydrogen inlet. According to the system and method, the green electricity hydrogen plasma torch is adopted for heating and ionizing hydrogen, then sponge iron is heated and reduced to produce molten iron, compared with a traditional hydrogen heating mode, the heating speed is high, the temperature is high, carbon-containing fuel is not combusted, safety and hydrogen embrittlement are achieved, compared with a traditional shaft furnace for producing sponge iron, low-quality ore can be adopted for producing molten iron, and the production cost is low. The control is convenient.

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 intelligent monitoring, in particular to a carbon emission factor correction method and device based on dynamic energy component analysis, and the method comprises the steps: obtaining fire coal calorific value data and power grid green power proportion data, and constructing a multi-dimensional feature tensor in combination with time, space and energy dimension information; a physical constraint change model is constructed according to the multi-dimensional feature tensor, the physical constraint change model comprises an encoder and a decoder, a theoretical carbon emission factor and a dual-drive loss value are calculated according to the encoder and the decoder, and the deviation degree between prediction and the theoretical carbon emission factor is obtained; the method comprises the steps of determining the number of sampling times of Monte Carlo Dropout according to the deviation degree, obtaining a prediction result, calculating a carbon emission factor uncertainty interval, determining an attention weight matrix according to the region or characteristics of the carbon emission factor uncertainty interval, tracing a key influence factor, correcting a model, and achieving accurate carbon emission factor prediction. Therefore, the problems that in the prior art, carbon emission factors are static, hysteresis and low in interpretability are solved.

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 green electric power intelligent operation and maintenance system and method for a low-carbon power station, and belongs to the technical field of low-carbon operation and maintenance of a transformer substation, and the method specifically comprises the steps: constructing a transformer substation capacitor region digital twin model through three-dimensional laser scanning, deploying a sensor to collect operation data in real time, and transmitting the operation data to a centralized monitoring center after edge calculation preprocessing; extracting data features by using a deep learning algorithm, analyzing and predicting a fault probability and residual life by combining with a time sequence, linking a prediction result with a transformer substation capacitor area digital twinborn model, and marking a fault risk area on a three-dimensional interface; a panoramic visual interface is developed based on the three-dimensional model, a dynamic three-level threshold value is generated in real time, a reinforcement learning algorithm is started to generate an optimal capacitor switching strategy when early warning is triggered, and switching operation is executed in combination with security constraints; the operation safety of the power system is improved, and the operation and maintenance cost and carbon emission are reduced.

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 electricity markets, in particular to a green electricity transaction tracing method and system, and the method comprises the following steps: collecting original green electricity transaction information generated after the transaction of a green electricity producer is completed, and carrying out the preprocessing of the original green electricity transaction information, and obtaining the standardized green electricity transaction information; verifying the standardized green electricity transaction information by using an intelligent contract, and calculating a decision factor value of each node in the block chain network by using a node decision model after the verification is passed; based on the decision factor value, using a diffusion storage strategy to perform uplink storage on the verified standardized green electricity transaction information; and retrieving each block in the block chain according to the query information provided by the producer, thereby obtaining the corresponding traceability information and feeding back the traceability information to the producer. According to the method, the security of the consensus node is improved by using the node judgment model and the diffusion storage strategy, the transaction processing speed and throughput are improved, and the credibility and speed of transaction tracing are further improved.

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.