7 results about "Low-carbon power" patented technology

A process for process for generating power in an ammonia-fueled gas turbine power plant includes flowing an ammonia feed stream to an ammonia cracker reactor in geographical proximity to an ammonia-fueled gas turbine power plant to crack ammonia feed stream under cracking conditions, thereby producing a product effluent comprising ammonia and hydrogen, flowing the product effluent comprising ammonia and hydrogen to an ammonia-fueled gas turbine of an ammonia-fueled gas turbine power plant, and combusting the product effluent comprising ammonia and hydrogen to drive the ammonia-fueled gas turbine. An exhaust gas stream generated from the ammonia-fueled gas turbine is recycled back to supply heat to the ammonia cracker reactor.

Aiming at the problems of low solving efficiency and the like in the online operation optimization of the multi-energy coupling energy supply system, the invention provides a knowledge rule-strict constraint collaborative two-stage offline operation optimization method based on a long / short time domain-coarse / fine time granularity coupling concept, a high-quality offline operation strategy data set is obtained, and the optimization efficiency of the online operation of the multi-energy coupling energy supply system is improved. The data basis required by time sequence prediction model training is met; based on an off-line operation strategy data set and a deep neural network, mining a mapping relation between input characteristics such as wind and light resources and the like and an equipment start-stop state, and constructing an equipment start-stop state prediction model; an initial solution is output by using the start-stop state prediction model, the complexity of an online operation optimization problem is reduced, the solving efficiency is improved, and finally a rule guidance-strict constraint collaborative multi-energy coupling system online operation optimization strategy is established; the optimization precision is considered while the timeliness is ensured, a real-time scheduling decision can be made for wind and light fluctuation and load change, and the operation level of the multi-energy coupling low-carbon power generation system is effectively improved.

The invention discloses an energy-saving and carbon-reducing optimization control method and system for a power plant. The system comprises a multi-medium fuel gas regulation and control module, a fuel dynamic adaptation module, a fuel gas supply optimization module, a low-carbon power generation module, a waste heat recycling module, a whole-plant fuel gas balance module, a fuel gas collection and supply module, an energy storage peak regulation module and a fuel gas safety diffusion module. The method comprises the following steps: acquiring output and consumption data of blast furnace gas, converter gas and coke oven gas of a target industrial plant area, judging a fuel gas balance state, dynamically optimizing a fuel gas consumption path and replacing high-calorific-value fuel gas when the fuel gas balance state is unbalanced, determining a fuel gas supply scheme of a generator set, regulating and controlling operation parameters, and adjusting a fuel ratio and a fuel consumption. According to the invention, surplus industrial fuel gas can be effectively consumed, fuel gas diffusion and energy waste are reduced, the effective components of the fuel gas are fully utilized to improve the energy utilization efficiency, the solid fuel consumption and the CO emission are reduced, and the win-win situation of energy conservation, carbon reduction and economic benefit of a power plant is realized.

The invention provides a gas turbine methanol compound cycle power generation system. The gas turbine methanol compound cycle power generation system comprises a liquid methanol storage tank, a pump unit, a liquid methanol gasifier unit, a gaseous methanol cracker unit, an expansion machine power generation unit and a gas turbine power generation unit, exhaust smoke of the gas turbine sequentially passes through the gaseous methanol cracker unit and the liquid methanol gasifier unit to be subjected to heat exchange and cooling and then enters the exhaust system. Liquid methanol is discharged from a liquid methanol storage tank, sequentially passes through a booster pump, a liquid methanol gasifier unit and a gaseous methanol cracker unit and then is converted into mixed gas of methanol, hydrogen and carbon monoxide, and the mixed gas is expanded by an expansion machine power generation unit to do work and then enters a combustion chamber of a gas turbine to be mixed with air for combustion. The system has the advantages that the methanol cracking hydrogen production technology, the expansion machine technology and the gas turbine cycle are integrated, and efficient low-carbon power generation is achieved.

The application discloses a load response type coal-fired boiler multi-coal-ammonia collaborative blending optimization method and system, and belongs to the technical field of coal-fired boiler combustion optimization and low-carbon power generation. The method generates an initial blending scheme by establishing a coal quality database and a compatibility matrix and adopting a multi-objective optimization algorithm; in combination with real-time load rate data of a unit, the blending ratio is dynamically adjusted through a three-section load interval division model; based on a calorific value compensation model, the ammonia blending amount is calculated to realize calorific value gap compensation, and finally an ammonia-coal collaborative blending scheme is generated and a cost evaluation report is formed. The system is correspondingly configured with data acquisition, scheme generation and optimization, ammonia blending and cost evaluation modules. The application realizes the technical goal of improving the utilization rate of low-quality coal while reducing carbon emissions under variable load conditions of a coal-fired unit.

The present application relates to an anti-aging compound insulating oil and oil-paper insulation system and its preparation method and application, belong to transformer insulation technical field. The present application provides a kind of compound insulating oil, the compound insulating oil contains electrical insulating oil and compound additive;The electrical insulating oil includes at least one of mineral insulating oil, synthetic insulating oil and natural ester insulating oil, and the compound additive contains at least two of carbonic acid dioctyl ester, carbonic acid dinonyl ester and carbonic acid didecyl ester.The present application improves the aging insulating paper polymerization degree, reduces the water content and acid value of aging insulating oil, improves the anti-aging performance of oil-paper insulation system, prolongs its service life, and provides continuous and efficient protection for oil-paper insulation in actual long-term operation, in line with the operation concept of green and low carbon power grid.

The invention belongs to the technical field of low-carbon comprehensive energy power generation, and relates to a multi-energy coupling low-carbon power generation system configuration optimization method oriented to a northwest wind and light base, which constructs a unified multi-energy flow system model covering wind and light, coal power, a hydrogen energy chain, a chemical energy storage chain and carbon capture, and proposes a sequential operation optimization process based on operation feasible region constraint. According to the flow, two scenes of power surplus and power shortage are distinguished according to the balance condition of the real-time output of renewable energy and the system load, and power storage energy, heat storage energy, hydrogen / ammonia storage energy and chemical chain storage energy are sequentially adjusted for flexible adjustment and energy transfer; and constructing a configuration optimization model which takes the leveling cost per kilowatt-hour and the carbon emission per kilowatt-hour as objective functions, and solving the model through a multi-objective optimization algorithm to obtain the Pareto boundary of the optimal capacity configuration of the system equipment. The problems that in the prior art, renewable energy fluctuation is not considered sufficiently, multi-target optimization coordination is difficult, and large-scale optimization solving efficiency is low are solved.