CN-121998386-A - Hydrogen energy park scheduling method based on improved cloud drift optimization algorithm

CN121998386ACN 121998386 ACN121998386 ACN 121998386ACN-121998386-A

Abstract

The invention discloses a hydrogen energy park dispatching method based on an improved cloud drift optimization algorithm, which relates to the technical field of intelligent development and dispatching of comprehensive energy systems and comprises the following steps: and constructing an objective function by taking the lowest economic cost and the lowest carbon treatment cost of the park comprehensive energy system as targets, setting wind-light power supply constraint, energy storage equipment constraint, power balance constraint, carbon sealing capacity limit, carbon trapping constraint, gas turbine output and climbing constraint, gas boiler constraint, fuel cell constraint and electric conversion constraint, solving by using an improved cloud drift optimization algorithm, and scheduling the hydrogen energy park. The invention breaks the bottleneck of insufficient regulation capability of a single energy network.

Inventors

ZHU WENQIANG
CHI XINYU
HU YIWEI
PI YU

Assignees

武汉纺织大学

Dates

Publication Date: 20260508
Application Date: 20260409

Claims (10)

1. A hydrogen energy park scheduling method based on an improved cloud drift optimization algorithm comprises a wind power generation, photoelectricity, electricity-to-gas conversion, carbon capture, a gas turbine, a gas boiler, a hydrogen fuel cell and energy storage equipment of three energy sources, wherein the energy storage equipment comprises a storage battery, a heat storage tank and a hydrogen storage tank; the wind power, photoelectricity, electricity-to-gas conversion, storage battery and hydrogen storage tank equipment jointly form a hydrogen adding station in the park and provide hydrogen energy for the park hydrogen power automobile; the method is characterized by comprising the following steps: And constructing an objective function by taking the lowest economic cost and the lowest carbon treatment cost of the park comprehensive energy system as targets, setting wind-light power supply constraint, energy storage equipment constraint, power balance constraint, carbon sealing capacity limit, carbon trapping constraint, gas turbine output and climbing constraint, gas boiler constraint, fuel cell constraint and electric conversion constraint, solving by using an improved cloud drift optimization algorithm, and scheduling the hydrogen energy park.
2. The hydrogen energy park scheduling method based on the improved cloud drift optimization algorithm as set forth in claim 1, wherein the method comprises the following steps: in constructing an objective function, the objective function is expressed as: ; in the formula, 、、、 Respectively carbon transaction, carbon sealing cost, natural gas purchasing cost and waste wind/waste light cost, wherein, Carbon trade cost: ; in the formula, Indicating the total carbon dioxide emissions; Represents a carbon quota; representing a carbon trade price; wherein the total carbon dioxide emissions The calculation formula is as follows: ; in the formula, Representing the carbon emissions of the gas turbine at time t; Representing the carbon emission of the gas boiler at the time t; The sealing cost with carbon is as follows: ; Wherein: To seal single-bit quality Cost of (2); representing the power of the carbon capture device at time t; The annual natural gas purchase cost is as follows: ; Wherein: Is the price of natural gas; Respectively representing the natural gas consumption of the gas turbine and the gas boiler at the time t; The wind and light discarding punishment cost is as follows: ; Wherein: The wind discarding price and the light discarding price at the time t are respectively; The wind power and the photovoltaic power are respectively predicted to obtain force values; the actual wind power generation amount and the photovoltaic power generation amount at the time t are respectively.
3. The hydrogen energy park scheduling method based on the improved cloud drift optimization algorithm as set forth in claim 2, wherein the method comprises the following steps: When constraint conditions are set, the wind-solar power supply constraint consists of wind power constraint and photoelectric constraint, wherein the wind power constraint is as follows: ; The photoelectric constraint is as follows: ; Wherein: The amount of abandoned wind and abandoned light at time t are respectively shown; Respectively representing wind power and photovoltaic predicted output at the time t; respectively representing wind power and photovoltaic power supply power at the time t.
4. The hydrogen energy park scheduling method based on the improved cloud drift optimization algorithm as set forth in claim 3, wherein the method comprises the following steps of: when a constraint condition is set, the power balance constraint represents: ; Wherein: representing the consumption and the output electric power of the storage battery at the time t and the electric load demand of the system; Representing the heat power absorbed and output by the heat storage tank at the time t and the heat load demand of the system; representing the volume of natural gas purchased by the system at time t; representing the volume of natural gas output from the methane reactor at time t; the natural gas volume consumed by the gas turbine and the gas boiler at the time t is represented; Indicating the hydrogen power absorbed and released by the hydrogen storage tank at time t and the hydrogen load demand of the system.
5. The hydrogen energy park scheduling method based on the improved cloud drift optimization algorithm as set forth in claim 4, wherein the method comprises the following steps: when the constraint condition is set, the carbon sequestration capacity limitation represents: ; Wherein: Respectively at time t Is (are) stored and system Maximum amount of sealing; the carbon capture constraint represents: Running energy consumption of CCS Cannot exceed the maximum operating power : ; The gas turbine output and climbing constraint represent: ; Wherein: total output of the gas turbine; Respectively representing the output electric power and the thermal power of the gas turbine at the time t; Upper and lower limits for the electrical output of the gas turbine; Upper and lower limits for gas turbine heat output; Climbing for gas turbine upper and lower limits of force; the gas boiler constraint represents: ; in the formula, Is the heat of the gas boiler upper and lower limits of force; climbing for gas boiler upper and lower limits of force; The fuel cell constraint represents: ; in the formula, An upper force limit for the fuel cell; the electrical conversion gas constraint represents: ; In the formula, the operation energy consumption of P2G To be within the range of ; Respectively represent the upper limit and the lower limit of the climbing force of P2H.
6. The hydrogen energy park scheduling method based on the improved cloud drift optimization algorithm as set forth in claim 5, wherein the method comprises the following steps: When the improved cloud drift optimization algorithm is obtained, the improved cloud drift optimization algorithm is obtained by improving the cloud drift optimization algorithm through setting a self-adaptive parameter control mechanism, a weight updating mechanism, a position updating strategy, a diversity maintaining mechanism and convergence monitoring and strengthening search based on the cloud drift optimization algorithm CDO.
7. The hydrogen energy park scheduling method based on the improved cloud drift optimization algorithm as set forth in claim 6, wherein the method comprises the following steps: when the self-adaptive parameter control mechanism is set, the self-adaptive parameter control mechanism is set through nonlinear parameter attenuation and dynamic balance adjustment, so that the algorithm model achieves the optimal balance between global search and local refinement.
8. The hydrogen energy park scheduling method based on the improved cloud drift optimization algorithm as set forth in claim 7, wherein the method comprises the following steps: When a weight updating mechanism is set, differential search strategy distribution is realized according to individual performance differences, a hierarchical processing strategy is adopted to divide a population into elite individuals, intermediate individuals and laggard individuals, namely, greater weight is given to the elite individuals with the top 30 percent of rank to enhance development capability, convergence to an optimal solution is accelerated, a balance strategy is adopted to the intermediate individuals with the middle 40 percent of rank, deterministic updating and random disturbance are considered, the weight is reduced to the laggard individuals with the bottom 30 percent of rank, exploratory search is encouraged to maintain population diversity, and the basic weight wbase dynamically changes along with time to realize strategy self-adaption adjustment.
9. The hydrogen energy park scheduling method based on the improved cloud drift optimization algorithm as set forth in claim 8, wherein the method comprises the following steps: When setting the position updating strategy, the position updating strategy is set by covering the multisource information fusion and time attenuation mechanism, so that more comprehensive searching coverage of the algorithm model is realized.
10. The hydrogen energy park scheduling method based on the improved cloud drift optimization algorithm as set forth in claim 9, wherein the method comprises the following steps: When the diversity maintaining mechanism is set, the diversity maintaining mechanism is realized by taking active maintenance of population diversity and early maturity convergence prevention as a core and through dynamic subgroup division, subgroup information communication, diversity monitoring and active restarting mechanisms, the method is used for automatically determining the number of subgroups according to population distribution to realize a self-adaptive population structure, periodically exchanging information among different subgroups to promote excellent mode propagation and recombination, calculating the quantitative exploration state of population diversity indexes in real time, automatically restarting part of individuals to reinject diversity when the diversity is lower than a threshold value, and realizing search space distributed coverage through the subgroup mechanism at the same time, thereby improving global search capability.

Description

Hydrogen energy park scheduling method based on improved cloud drift optimization algorithm Technical Field The invention relates to the technical field of intelligent development and scheduling of comprehensive energy systems, in particular to a hydrogen energy park scheduling method based on an improved cloud drift optimization algorithm. Background Currently, in integrated energy systems, the hydrogen energy utilization technology path often extends around a core function, and the deep and diversified coupling of hydrogen energy as a multi-energy carrier and the system cannot be realized. The main technical form comprises single energy storage type utilization, which is the most mainstream single utilization mode at present. The system is provided with an electrolytic tank, a hydrogen storage tank and a fuel cell, and has the core function of taking hydrogen energy as a large-scale electric power storage medium. The method comprises the steps of utilizing renewable energy sources such as wind power, photovoltaic power and the like which are surplus in a park to electrolyze water to produce hydrogen, converting electric energy into hydrogen energy to store the hydrogen energy, and generating electricity through a hydrogen fuel cell and feeding the electricity back to a power grid when the electric power is in shortage. In this process, the hydrogen energy plays a major role as a "giant battery". The mode focuses the utilization scene of hydrogen energy in the traffic field. The energy system of the park provides hydrogenation service for the fuel cell buses, logistics vehicles or passenger vehicles in the park. The hydrogen production facility and the hydrogen station form a relatively independent infrastructure, hydrogen energy is narrowed from an energy carrier into a traffic fuel, and interaction with other energy forms such as electricity, heat, cold and the like in a park is weak. Single industrial feedstock/fuel utilization hydrogen energy may be used singly as feedstock or high temperature heat source for industrial processes in industrial parks such as chemical, metallurgical and the like. For example, as a raw material for synthesis gas in chemical production or as a reducing agent in iron and steel plants instead of coke. Although this achieves deep decarbonization, the hydrogen energy system is usually decoupled from the power and heating network of the campus, failing to exert its synergistic value of balancing the grid, multi-energy co-generation. The coupling of carbon treatment technology and electric, thermal and gas multi-energy flows mainly depends on the prior art mode of 'sub-link adaptation', and no deep cooperative integrated architecture is formed yet. In terms of power coupling, carbon trapping equipment (such as an amine absorption trapping device) is mostly designed to operate under a constant load, and the fluctuation of renewable energy source power generation is dealt with by connecting a power grid standby power supply or an energy storage system, for example, part of power plants directly apply electric energy generated by photovoltaic and wind power to an electrolytic tank for hydrogen production, and then are combinedThe method mainly utilizes industrial waste heat or waste steam of a power plant to provide heat energy for a solvent regeneration process of carbon capture in thermal coupling, such as a coal-fired power plant introduces waste heat of flue gas at the tail part of a boiler into an amine liquid regeneration tower to reduce the consumption of an additional heat source, and in the field of fuel gas coupling, the method indirectly correlates carbon treatment through a natural gas pipeline hydrogen adding technology to prepare green hydrogen and carry out the methodThe trapped electric conversion gas products are mixed into a natural gas pipe network, so that the simple connection of the energy carrier and the carbon flow is realized. In terms of solving, heuristic algorithms are widely applied to park comprehensive energy scheduling by virtue of the adaptability of the heuristic algorithms to complex problems. The Genetic Algorithm (GA) simulates a biological evolution process, and iteratively optimizes the population through operations such as selection, crossing, variation and the like, so that the method is suitable for a multi-target scheduling scene. For example, in a park scheduling of hydrogen-containing energy utilization, GA can take "economy-carbon emission reduction-equipment loss" as a multi-objective function, take the output of an electrolytic cell, the hydrogen charging and discharging amount of a hydrogen storage tank and the like as chromosome gene fragments, and screen out the pareto optimal solution set through multi-generation evolution. The Ant Colony Optimization (ACO) algorithm is based on an ant foraging pheromone mechanism, and is suitable for scheduling problems including path optimization, such as cooperative transmission scheduling of a