CN115423330A - Hydrogen production capacity planning method for utilizing electrolyzed water to produce hydrogen and absorb abandoned wind power - Google Patents

Hydrogen production capacity planning method for utilizing electrolyzed water to produce hydrogen and absorb abandoned wind power Download PDF

Info

Publication number
CN115423330A
CN115423330A CN202211102621.1A CN202211102621A CN115423330A CN 115423330 A CN115423330 A CN 115423330A CN 202211102621 A CN202211102621 A CN 202211102621A CN 115423330 A CN115423330 A CN 115423330A
Authority
CN
China
Prior art keywords
wind power
hydrogen production
hydrogen
load
representing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202211102621.1A
Other languages
Chinese (zh)
Inventor
梅建春
赵盛杰
钱君霞
闫安心
宋鹏程
马鸿娟
黄雪正
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangsu Keneng Electric Power Engineering Consulting Co ltd
Original Assignee
Jiangsu Keneng Electric Power Engineering Consulting Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiangsu Keneng Electric Power Engineering Consulting Co ltd filed Critical Jiangsu Keneng Electric Power Engineering Consulting Co ltd
Priority to CN202211102621.1A priority Critical patent/CN115423330A/en
Publication of CN115423330A publication Critical patent/CN115423330A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2474—Sequence data queries, e.g. querying versioned data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P20/00—Technologies relating to chemical industry
    • Y02P20/10—Process efficiency
    • Y02P20/133—Renewable energy sources, e.g. sunlight

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Development Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Data Mining & Analysis (AREA)
  • Health & Medical Sciences (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Operations Research (AREA)
  • Educational Administration (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Quality & Reliability (AREA)
  • Public Health (AREA)
  • Artificial Intelligence (AREA)
  • Fuzzy Systems (AREA)
  • Primary Health Care (AREA)
  • Databases & Information Systems (AREA)
  • Water Supply & Treatment (AREA)
  • Probability & Statistics with Applications (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Power Engineering (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)

Abstract

The invention relates to the technical field of new energy, in particular to a hydrogen production capacity planning method for producing hydrogen by using electrolyzed water, absorbing and eliminating wind power, which comprises the following steps: step 1: utilizing the annual load predicted value and the typical daily load curve, introducing a load adjustment coefficient, and simulating to generate a daily load curve; step 2: calculating a wind power plant simulation time sequence output curve according to historical wind power output simulation of a planning region, and calculating annual abandoned wind electric quantity of the wind power plant by combining a wind power absorption space obtained through a daily load curve and the wind power plant simulation time sequence output curve; and step 3: and establishing an objective function with the maximum hydrogen production benefit as a target, establishing constraint conditions, and solving the established water electrolysis hydrogen production absorption and wind abandonment electric quantity model by utilizing a particle swarm algorithm to obtain a hydrogen production capacity configuration scheme. The method effectively improves the accuracy of planning the hydrogen production capacity of the electrolyzed water.

Description

Hydrogen production capacity planning method for utilizing electrolyzed water to produce hydrogen and absorb abandoned wind power
Technical Field
The invention relates to the technical field of new energy, in particular to a hydrogen production capacity planning method for producing hydrogen by using electrolyzed water, absorbing and eliminating wind-abandoned electric quantity.
Background
In recent years, wind power generation is developed rapidly, but a wind abandon phenomenon still exists, wind abandon of a wind power plant not only reduces the utilization rate of renewable energy, but also reduces the profit of the wind power plant, the development of wind power is restricted to a certain extent, and a solution is urgently needed to be found. In fact, hydrogen energy has a wide application prospect as an energy form. Influenced by price factors, the main source of global hydrogen energy is natural gas hydrogen production, and the water electrolysis hydrogen production amount is less than 0.1 percent. The hydrogen production by water electrolysis is greatly influenced by electricity price, the reduction of the electricity price cost is one of important means for popularizing and developing the hydrogen production by water electrolysis, and the electricity cost is obviously reduced by utilizing the abandoned wind to generate the hydrogen.
The existing planning research is mostly analyzed based on statistical data, the actual time-sharing electricity utilization condition of hydrogen production by water electrolysis is concerned a little, the actual time-sharing electricity utilization condition is ignored, and the planning accuracy is low.
Disclosure of Invention
The invention aims to provide a hydrogen production capacity planning method for consuming abandoned wind electricity by hydrogen production through water electrolysis, which effectively improves the accuracy of hydrogen production capacity planning through water electrolysis.
In order to solve the technical problems, the technical scheme of the invention is as follows: a hydrogen production capacity planning method for utilizing electrolyzed water to produce hydrogen and absorb abandoned wind power comprises the following steps:
step 1: utilizing the annual load predicted value and the typical daily load curve, introducing a load adjustment coefficient, and simulating to generate a daily load curve;
step 2: calculating a wind power plant simulation time sequence output curve according to historical wind power output simulation of a planning region, and calculating annual abandoned wind electric quantity of the wind power plant by combining a wind power absorption space obtained through a daily load curve and the wind power plant simulation time sequence output curve;
and 3, step 3: and establishing an objective function with the maximum hydrogen production benefit as a target, establishing constraint conditions, and solving the established water electrolysis hydrogen production absorption and wind abandonment electric quantity model by utilizing a particle swarm algorithm to obtain a hydrogen production capacity configuration scheme.
Further, step 1 comprises:
step 101: establishing a constraint model of the load adjustment coefficient, and calculating to obtain the load adjustment coefficient;
step 102: assuming that 2 heavy load days, 3 medium load days and 2 light load days are random in each week, calculating a daily load curve according to an actual calendar:
Figure BDA0003841240790000011
Figure BDA0003841240790000012
Figure BDA0003841240790000013
in the formula:
Figure BDA0003841240790000021
and
Figure BDA0003841240790000022
respectively representing daily load curves, kW, of m-month large load days, medium load days and small load days; α represents a load adjustment coefficient; p Y Representing the annual load predicted value, kW;
Figure BDA0003841240790000023
the load per unit value of m months is expressed and is known by a load curve of the past year;
Figure BDA0003841240790000024
and (3) representing a typical daily load per unit value curve of m months on an hour scale.
Further, in step 101, the constraint model of the load adjustment coefficient:
α min ＜α＜α max
Figure BDA0003841240790000025
in the formula: alpha is alpha min And alpha max Respectively representing the upper limit and the lower limit of the load adjustment coefficient; w Y Indicating annual power usage, kWh.
Further, the step 2 specifically includes:
step 201: calculating a per unit value curve of historical wind power output by planning the historical wind power output of the region and corresponding wind power installed capacity;
step 202: calculating a wind power plant simulation time sequence output curve according to the proposed wind power plant installed capacity and the historical wind power output per unit value curve;
Figure BDA0003841240790000026
in the formula:
Figure BDA0003841240790000027
representing a simulation time sequence output curve, kW, of the wind power plant; c W Representing the installed capacity, kW, of the wind power plant to be built;
Figure BDA0003841240790000028
representing a per unit value curve of historical wind power output;
step 203: calculating the difference value between the daily load curve and the known power grid minimum output curve to obtain a wind power absorption space;
step 204: and calculating the annual abandoned air quantity of the difference between the wind power plant simulation time sequence output curve and the wind power absorption space.
Further, the step 3 specifically includes:
step 301: considering initial construction cost, later operation and maintenance cost, water consumption cost and hydrogen and oxygen selling income of a hydrogen production plant, establishing an objective function with the maximum hydrogen production benefit as a target:
Figure BDA0003841240790000029
in the formula: f represents the hydrogen production efficiency;
Figure BDA00038412407900000210
and
Figure BDA00038412407900000211
respectively representing the market selling prices of hydrogen and oxygen; c P Represents the investment cost of a unit volume hydrogen plant; c O Representing the operation and maintenance cost; c S Represents the cost of water; beta represents the unit hydrogen production water consumption;
Figure BDA00038412407900000212
and
Figure BDA00038412407900000213
respectively representing the hydrogen production yield and the oxygen production yield in the nth year, wherein the oxygen production yield is half of the hydrogen production yield;
Figure BDA00038412407900000214
representing hydrogen plant capacity; n represents the operating life; i.e. i s Representing the average social discount rate, taking 8 percent;
step 302: establishing a constraint model of hydrogen production capacity by electrolyzing water:
Figure BDA00038412407900000215
in the formula:
Figure BDA0003841240790000031
and
Figure BDA0003841240790000032
the lower limit and the upper limit of the capacity of the water electrolysis hydrogen production equipment are respectively;
step 303: establishing a hydrogen yield constraint model:
in a certain period of time, the abandoned wind power is less than the power consumption of the hydrogen production equipment in full-load operation, then:
Figure BDA0003841240790000033
otherwise:
Figure BDA0003841240790000034
in the formula:
Figure BDA0003841240790000035
denotes hydrogen volume, nm 3 ；Q ab Representing the wind abandoning amount, kWh, obtained in the step 2; t represents the running time, h; eta represents the standard cubic hydrogen power consumption, nm 3 /kWh；
Step 304: the model forms an electrolytic water hydrogen production absorption and wind abandonment electric quantity model, and the electrolytic water hydrogen production absorption and wind abandonment electric quantity model is solved through a particle swarm algorithm to obtain the capacity of electrolytic hydrogen production equipment.
Further, in the step 304, the specific step of solving through a particle swarm algorithm includes:
step 3041: taking the capacity of the water electrolysis hydrogen production equipment as particles, randomly generating a certain population quantity of particle swarms, and setting population initial parameter values;
step 3042: calculating by using the initial parameters set in the step 3041 and taking the hydrogen production income as a fitness value, and storing the optimal fitness value and the corresponding particles as optimal positions of the particles;
step 3043: judging whether the iteration times are reached, if not, turning to the step 3044, otherwise, turning to the step 3046;
step 3044: the particle position and velocity are updated using the following equation:
Figure BDA0003841240790000036
Figure BDA0003841240790000037
in the formula: k represents the number of iterations;
Figure BDA0003841240790000038
represents the velocity of the ith particle at the kth time;
Figure BDA0003841240790000039
indicates the position of the ith particle at the kth time;
Figure BDA00038412407900000310
represents the velocity of the ith particle at the k +1 th time, i.e., the updated velocity;
Figure BDA00038412407900000311
represents the position of the ith particle at the k +1 th time, i.e. the updated position;
Figure BDA00038412407900000312
representing the historical optimal position of the ith particle after being updated to the kth time;
Figure BDA00038412407900000313
representing the historical optimal position of the particle swarm after the updating to the kth time; ω represents an inertial weight, which represents a coefficient for maintaining the original velocity; c. C 1 A weight coefficient representing an individual learning factor, i.e., an optimal value of the particle tracking history; c. C 2 A weight coefficient representing a social learning factor, namely an optimal value of a particle tracking group; ξ and η represent randomly generated [0,1]A random number of intervals;
step 3045: calculating and comparing the current fitness value with the historical optimal fitness value, if the current fitness value is more optimal, saving the current fitness value as the optimal fitness value, and after saving the optimal position of the corresponding particle, turning to the step 3043, otherwise, directly turning to the step 3043; step 3046: and outputting the optimal position and the optimal fitness value of the particle swarm, and finishing the calculation.
The invention has the following beneficial effects:
the method comprises the steps of generating a wind power output curve with hours as a time scale based on historical wind power output data of the planning region in the past year in a simulation mode, obtaining wind curtailment power according to a power grid absorption space, optimizing the wind curtailment power based on detailed wind curtailment power with optimal economy as a target, and solving the hydrogen production capacity configuration of electrolyzed water by adopting a particle swarm algorithm; the planning effect is better, and the economic effect is more excellent.
Drawings
FIG. 1 is a flow chart for planning and solving the hydrogen production capacity by electrolyzing water according to the present invention;
FIG. 2 is a flow chart of a particle swarm algorithm of the present invention;
FIG. 3 is a schematic diagram of a particle swarm algorithm calculation iteration process.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1, the present invention is a hydrogen production capacity planning method for producing hydrogen by electrolyzing water to absorb wind-abandoned electricity, which includes: step 1: utilizing the annual load predicted value and the typical daily load curve, introducing a load adjustment coefficient, and simulating to generate a daily load curve; the method comprises the following steps:
step 101: establishing a constraint model of the load adjustment coefficient, and calculating to obtain the load adjustment coefficient;
constraint model of load adjustment coefficient:
α min ＜α＜α max
Figure BDA0003841240790000041
in the formula: alpha is alpha min And alpha max Respectively representing the upper limit and the lower limit of the load adjustment coefficient; w is a group of Y Representing annual power usage, kWh;
step 102: assuming that 2 heavy load days, 3 medium load days and 2 light load days are random in each week, calculating a daily load curve according to an actual calendar:
Figure BDA0003841240790000042
Figure BDA0003841240790000043
Figure BDA0003841240790000044
in the formula:
Figure BDA0003841240790000045
and
Figure BDA0003841240790000046
respectively representing daily load curves, kW, of m-month large load days, medium load days and small load days; α represents a load adjustment coefficient; p Y Representing the annual load predicted value, kW;
Figure BDA0003841240790000047
the load per unit value of m months is expressed and is known by a load curve of the past year;
Figure BDA0003841240790000048
represents a typical daily load per unit value curve of m months on an hour time scale.
Step 2: calculating a wind power plant simulation time sequence output curve according to historical wind power output simulation of a planning region, and calculating annual abandoned wind electric quantity of the wind power plant by combining a wind power absorption space obtained through a daily load curve and the wind power plant simulation time sequence output curve; the step 2 specifically comprises the following steps:
step 201: from the planning perspective, the prediction of the wind power plant solar output curve is difficult, and the simulation is performed by using historical wind power output data; calculating a per unit value curve of historical wind power output through the historical wind power output of the planned area and the corresponding wind power installed capacity;
step 202: calculating a wind power plant simulation time sequence output curve according to the proposed wind power plant installed capacity and the historical wind power output per unit value curve;
Figure BDA0003841240790000051
in the formula:
Figure BDA0003841240790000052
representing a simulation time sequence output curve, kW, of the wind power plant; c W Representing the installed capacity, kW, of the wind power plant to be built;
Figure BDA0003841240790000053
representing a per unit value curve of historical wind power output;
step 203: calculating the difference value between the daily load curve and the known power grid minimum output curve to obtain a wind power absorption space; the minimum output curve of the power grid is a power grid parameter and is a known quantity;
step 204: and calculating the annual abandoned air quantity of the difference between the wind power plant simulation time sequence output curve and the wind power absorption space.
And step 3: establishing an objective function with the maximum hydrogen production benefit as a target, establishing constraint conditions, and solving the established water electrolysis hydrogen production absorption and wind abandonment electric quantity model by utilizing a particle swarm algorithm to obtain a hydrogen production capacity configuration scheme; the step 3 specifically comprises the following steps:
step 301: considering initial construction cost, later operation and maintenance cost, water consumption cost and hydrogen and oxygen selling income of a hydrogen production plant, establishing an objective function with the maximum hydrogen production benefit as a target:
Figure BDA0003841240790000054
in the formula: f represents the hydrogen production efficiency;
Figure BDA0003841240790000055
and
Figure BDA0003841240790000056
respectively representing the market selling prices of hydrogen and oxygen; c P Represents the investment cost of a unit volume hydrogen plant; c O Representing the operation and maintenance cost; c S Represents the cost of water; beta represents the unit hydrogen production water consumption;
Figure BDA0003841240790000057
and
Figure BDA0003841240790000058
respectively representing the hydrogen production yield and the oxygen production yield in the nth year, wherein the oxygen production yield is half of the hydrogen production yield;
Figure BDA0003841240790000059
representing hydrogen plant capacity; n represents the operating life; i all right angle s Representing the average social discount rate, taking 8 percent;
step 302: establishing a constraint model of the hydrogen production capacity by electrolyzing water, wherein the hydrogen production capacity by electrolyzing water is constrained by the upper limit and the lower limit of the capacity, and the constraint model is shown as the following formula:
Figure BDA00038412407900000510
in the formula:
Figure BDA00038412407900000511
and
Figure BDA00038412407900000512
the lower limit and the upper limit of the capacity of the water electrolysis hydrogen production equipment are respectively;
when the planning model is calculated, the hydrogen yield is not limited by the capacity of the hydrogen production equipment, but also limited by the wind abandoning power which can be used by the hydrogen production equipment every day, namely, the small value of the consumed power and the wind abandoning power of the hydrogen production equipment is calculated within a certain period.
Step 303: establishing a hydrogen yield constraint model:
in a certain period of time, the abandoned wind power is less than the power consumption of the hydrogen production equipment in full-load operation, then:
Figure BDA0003841240790000061
otherwise:
Figure BDA0003841240790000062
in the formula:
Figure BDA0003841240790000063
denotes hydrogen volume, nm 3 ；Q ab Representing the wind abandoning amount, kWh, obtained in the step 2; t represents run time, h; eta represents the standard cubic hydrogen power consumption, nm 3 /kWh；
Step 304: establishing an electrolytic water hydrogen production absorption and abandonment wind power model according to the objective function, the constraint model of the electrolytic water hydrogen production capacity and the hydrogen yield constraint model, wherein the established planning model is a nonlinear planning model and a non-convex nonlinear model, and solving is performed through a particle swarm algorithm to obtain the capacity of electrolytic hydrogen production equipment; the water electrolysis hydrogen production capacity planning solving process is shown in figure 1, and the particle swarm algorithm solving process is shown in figure 2; in step 304, the specific steps of solving by the particle swarm algorithm include:
step 3041: taking the capacity of the water electrolysis hydrogen production equipment as particles, randomly generating a particle swarm with a certain population quantity, and setting initial parameter values such as the position, the quantity, the speed of the population, the lower limit and the upper limit of the water electrolysis hydrogen production capacity, the iteration times and the like;
step 3042: calculating by using the initial parameters set in the step 3041 and taking the hydrogen production income as a fitness value, and storing the optimal fitness value and the corresponding particles as optimal positions of the particles;
step 3043: judging whether the iteration times are reached, if not, turning to a step 3044, otherwise, turning to a step 3046;
step 3044: the particle position and velocity are updated using the following equation:
Figure BDA0003841240790000064
Figure BDA0003841240790000065
in the formula: k represents the number of iterations;
Figure BDA0003841240790000066
represents the velocity of the ith particle at the kth time;
Figure BDA0003841240790000067
indicates the position of the ith particle at the kth time;
Figure BDA0003841240790000068
represents the velocity of the ith particle at the k +1 th time, i.e., the updated velocity;
Figure BDA0003841240790000069
represents the position of the ith particle at the k +1 th time, i.e. the updated position;
Figure BDA00038412407900000610
representing the historical optimal position of the ith particle after being updated to the kth time;
Figure BDA00038412407900000611
representing the historical optimal position of the particle swarm after the updating to the kth time; ω represents an inertial weight and represents a coefficient for maintaining the original velocity; c. C 1 A weight coefficient representing an individual learning factor, i.e., an optimal value of the particle tracking history; c. C 2 A weight coefficient representing a social learning factor, namely an optimal value of a particle tracking group; ξ and η represent randomly generated [0,1 []A random number of intervals;
step 3045: calculating and comparing the current fitness value with the historical optimal fitness value, if the current fitness value is more optimal, saving the current fitness value as the optimal fitness value, and after saving the optimal position of the corresponding particle, turning to the step 3043, otherwise, directly turning to the step 3043;
step 3046: and outputting the optimal position and the optimal fitness value of the particle swarm, and finishing the calculation.
In order to verify the effectiveness of the method, the performance of the method is tested in the established hydrogen production system of the wind power plant, and the following experiments are specifically carried out: simulation is carried out through a modified IEEE14 node power system and 2021 year wind power data in Belgium, and the installed capacity of a grid-connected wind power plant is assumed to be 100MW.
With the time scale of 1h and the aim of maximizing the benefit, when the calculated hydrogen production capacity is configured to be 16.8529MW, the maximum benefit during the operation is 2500.4 ten thousand yuan. The iterative process of the particle swarm algorithm is shown in fig. 3.
The simulated total abandoned wind power is 3865MWh, and the consumed abandoned wind power of the hydrogen production equipment configured by the method is 2335.7MWh, so that the hydrogen production capacity configured by the method is smaller than the abandoned wind scale, and a certain amount of abandoned wind still exists.
The method comprises the steps of obtaining a wind power output per unit value curve based on historical wind power output data of the past year in a planning area, simulating and generating a newly-built wind power plant wind power output curve with hour as a time scale by using the per unit value curve, and obtaining the abandoned wind power quantity by combining with the wind power consumption space of a power grid. And optimally establishing an objective function with economical efficiency, establishing a constraint model for related variables, and solving the established model through a particle swarm algorithm to obtain the configuration of the hydrogen production capacity of the electrolyzed water. And finally, simulating through the modified IEEE14 node power system and 2021 year wind power data in Belgium, and verifying the effectiveness of the proposed planning method.
The hydrogen production capacity planning method for utilizing the electrolyzed water to produce hydrogen and absorb the electric quantity of the abandoned wind has the following advantages: compared with the traditional planning method for utilizing the average utilization hours of wind power, the method provided by the invention not only contains the annual average characteristic of the abandoned wind data, but also fully considers the time characteristic of the data, so that the capacity configuration is more accurate, and the total benefit is higher.
The parts not involved in the present invention are the same as or implemented using the prior art.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (6)

1. A hydrogen production capacity planning method for utilizing electrolyzed water to produce hydrogen and absorb abandoned wind power is characterized in that: comprises that
Step 1: utilizing the annual load predicted value and the typical daily load curve, introducing a load adjustment coefficient, and simulating to generate a daily load curve;
step 2: calculating a wind power plant simulation time sequence output curve according to historical wind power output simulation of a planning region, and calculating annual abandoned wind electric quantity of the wind power plant by combining a wind power absorption space obtained through a daily load curve and the wind power plant simulation time sequence output curve;
and step 3: and establishing a target function with the maximum hydrogen production benefit as a target, establishing constraint conditions, and solving the established water electrolysis hydrogen production absorption wind abandonment power model by utilizing a particle swarm algorithm to obtain a hydrogen production capacity configuration scheme.
2. The method for planning the hydrogen production capacity by utilizing the electrolyzed water for hydrogen production and wind power consumption abandonment according to claim 1, which is characterized in that: the step 1 comprises the following steps:
step 101: establishing a constraint model of the load adjustment coefficient, and calculating to obtain the load adjustment coefficient;
step 102: assuming that 2 heavy load days, 3 medium load days and 2 light load days are random in each week, calculating a daily load curve according to an actual calendar:
Figure FDA0003841240780000011
Figure FDA0003841240780000012
Figure FDA0003841240780000013
in the formula:
Figure FDA0003841240780000014
and
Figure FDA0003841240780000015
respectively representing daily load curves, kW, of m-month large load days, medium load days and small load days; α represents a load adjustment coefficient; p Y Representing the annual load predicted value, kW;
Figure FDA0003841240780000016
the load per unit value of m months is expressed and is known by a load curve of the past year;
Figure FDA0003841240780000017
and (3) representing a typical daily load per unit value curve of m months on an hour scale.
3. The method for planning the hydrogen production capacity by utilizing the electrolyzed water to produce hydrogen and absorb the wind power consumption according to claim 2, which is characterized in that: in step 101, a constraint model of the load adjustment coefficient:
α min ＜α＜α max
Figure FDA0003841240780000018
in the formula: alpha is alpha min And alpha max Respectively representing the upper limit and the lower limit of the load adjustment coefficient; w Y Indicating annual power usage, kWh.
4. The method for planning the hydrogen production capacity by utilizing the electrolyzed water for hydrogen production and wind power consumption abandonment according to claim 1, which is characterized in that: the step 2 specifically comprises:
step 201: calculating a per unit value curve of historical wind power output through the historical wind power output of the planned area and the corresponding wind power installed capacity;
step 202: calculating a wind power plant simulation time sequence output curve according to the proposed wind power plant installed capacity and the historical wind power output per unit value curve;
Figure FDA0003841240780000019
in the formula:
Figure FDA0003841240780000021
representing a simulation time sequence output curve, kW, of the wind power plant; c W Representing the installed capacity, kW, of the wind power plant to be built;
Figure FDA0003841240780000022
representing a per unit value curve of historical wind power output;
step 203: calculating the difference value between the daily load curve and the known power grid minimum output curve to obtain a wind power absorption space;
step 204: and calculating the annual abandoned air quantity of the difference between the wind power plant simulation time sequence output curve and the wind power absorption space.
5. The method for planning the hydrogen production capacity by utilizing the electrolyzed water for hydrogen production and wind power consumption abandonment according to claim 1, which is characterized in that: the step 3 specifically includes:
step 301: considering initial construction cost, later operation and maintenance cost, water consumption cost and hydrogen and oxygen selling income of a hydrogen production plant, establishing an objective function with the maximum hydrogen production benefit as a target:
Figure FDA0003841240780000023
in the formula: f represents the hydrogen production efficiency; c H2 And C O2 Respectively representing the market selling prices of hydrogen and oxygen; c P Represents the investment cost of a unit volume hydrogen plant; c O Representing the operation and maintenance cost; c S Represents the cost of water; beta represents the unit hydrogen production water consumption;
Figure FDA0003841240780000024
and
Figure FDA0003841240780000025
respectively representing the hydrogen production yield and the oxygen production yield in the nth year, wherein the oxygen production yield is half of the hydrogen production yield;
Figure FDA0003841240780000026
representing hydrogen plant capacity; n represents the operating life; i.e. i s Representing the average social discount rate, taking 8 percent;
step 302: establishing a constraint model of hydrogen production capacity by electrolyzing water:
Figure FDA0003841240780000027
in the formula:
Figure FDA0003841240780000028
and
Figure FDA0003841240780000029
the lower limit and the upper limit of the capacity of the water electrolysis hydrogen production equipment are respectively;
step 303: establishing a hydrogen yield constraint model:
in a certain period of time, the abandoned wind power is less than the power consumption of the hydrogen production equipment in full-load operation, then:
Figure FDA00038412407800000210
otherwise:
Figure FDA00038412407800000211
in the formula:
Figure FDA00038412407800000212
denotes hydrogen volume, nm 3 ；Q ab Representing the wind abandoning amount, kWh, obtained in the step 2; t represents the running time, h; eta represents the standard cubic hydrogen power consumption, nm 3 /kWh；
Step 304: the model forms an electrolytic water hydrogen production absorption and wind abandonment electric quantity model, and the electrolytic water hydrogen production absorption and wind abandonment electric quantity model is solved through a particle swarm algorithm to obtain the capacity of electrolytic hydrogen production equipment.
6. The method for planning the hydrogen production capacity by utilizing the electrolyzed water to produce hydrogen and absorb the wind power consumption according to claim 5, which is characterized in that: in step 304, the specific steps of solving by the particle swarm algorithm include:
step 3041: taking the capacity of the water electrolysis hydrogen production equipment as particles, randomly generating a certain population quantity of particle swarms, and setting population initial parameter values;
step 3042: calculating by using the initial parameters set in the step 3041 and taking the hydrogen production income as a fitness value, and storing the optimal fitness value and the corresponding particles as optimal positions of the particles;
step 3043: judging whether the iteration times are reached, if not, turning to the step 3044, otherwise, turning to the step 3046;
step 3044: the particle position and velocity are updated using the following equation:
Figure FDA0003841240780000031
Figure FDA0003841240780000032
in the formula: k represents the number of iterations;
Figure FDA0003841240780000033
represents the velocity of the ith particle at the kth time;
Figure FDA0003841240780000034
represents the position of the ith particle at the kth time;
Figure FDA0003841240780000035
represents the velocity of the ith particle at the k +1 th time, i.e., the updated velocity;
Figure FDA0003841240780000036
represents the position of the ith particle at the k +1 th time, i.e. the updated position;
Figure FDA0003841240780000037
representing the historical optimal position of the ith particle after being updated to the kth time;
Figure FDA0003841240780000038
representing the historical optimal position of the particle swarm after the updating to the kth time; ω represents an inertial weight, which represents a coefficient for maintaining the original velocity; c. C 1 A weight coefficient representing an individual learning factor, i.e., an optimal value of the particle tracking history; c. C 2 A weight coefficient representing a social learning factor, namely an optimal value of a particle tracking group; ξ and η represent randomly generated [0,1]A random number of intervals;
step 3045: calculating and comparing the current fitness value with the historical optimal fitness value, if the current fitness value is more optimal, saving the current fitness value as the optimal fitness value, and after saving the optimal position of the corresponding particle, turning to the step 3043, otherwise, directly turning to the step 3043;
step 3046: and outputting the optimal position and the optimal fitness value of the particle swarm, and finishing the calculation.
CN202211102621.1A 2022-09-09 2022-09-09 Hydrogen production capacity planning method for utilizing electrolyzed water to produce hydrogen and absorb abandoned wind power Pending CN115423330A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211102621.1A CN115423330A (en) 2022-09-09 2022-09-09 Hydrogen production capacity planning method for utilizing electrolyzed water to produce hydrogen and absorb abandoned wind power

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211102621.1A CN115423330A (en) 2022-09-09 2022-09-09 Hydrogen production capacity planning method for utilizing electrolyzed water to produce hydrogen and absorb abandoned wind power

Publications (1)

Publication Number Publication Date
CN115423330A true CN115423330A (en) 2022-12-02

Family

ID=84202415

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211102621.1A Pending CN115423330A (en) 2022-09-09 2022-09-09 Hydrogen production capacity planning method for utilizing electrolyzed water to produce hydrogen and absorb abandoned wind power

Country Status (1)

Country Link
CN (1) CN115423330A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117075498A (en) * 2023-10-16 2023-11-17 三峡科技有限责任公司 Water electrolysis hydrogen production energy consumption monitoring and bionic optimizing system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117075498A (en) * 2023-10-16 2023-11-17 三峡科技有限责任公司 Water electrolysis hydrogen production energy consumption monitoring and bionic optimizing system

Similar Documents

Publication Publication Date Title
Xu et al. Data-driven configuration optimization of an off-grid wind/PV/hydrogen system based on modified NSGA-II and CRITIC-TOPSIS
CN102623989B (en) Method for optimization and configuration of intermittent distributed generation (DG)
CN113822496B (en) Multi-unit thermal power plant heat supply mode and parameter online optimizing method
CN109103926A (en) Photovoltaic power generation based on more Radiation Characteristics year meteorology scenes receives capacity calculation method
CN109103929B (en) Power distribution network economic optimization scheduling method based on improved dynamic kriging model
CN112084652B (en) Capacity configuration calculation method and system for wind-solar-energy-storage complementary power generation system
CN105552965B (en) Opportunity constraint planning-based distributed energy optimization configuration method
CN104158203A (en) Micro-grid power supply capacity optimization configuration method
Xu et al. Look-ahead risk-constrained scheduling for an energy hub integrated with renewable energy
CN113779792B (en) Affine-based comprehensive energy system optimal configuration method
CN115600858A (en) Wind-solar energy storage hydrogen production system economical optimization scheduling method considering wind abandoning and light abandoning punishment
CN115471014A (en) Power grid enterprise electricity carbon consumption prediction method based on deep neural network
Jia et al. Optimal operation of cascade hydro-wind-photovoltaic complementary generation system with vibration avoidance strategy
CN112072643A (en) Light-storage system online scheduling method based on depth certainty gradient strategy
CN114400698A (en) Optimal carbon reduction operation method for high-proportion clean energy power grid power supply
CN116402210A (en) Multi-objective optimization method, system, equipment and medium for comprehensive energy system
CN115423330A (en) Hydrogen production capacity planning method for utilizing electrolyzed water to produce hydrogen and absorb abandoned wind power
Wen et al. ELCC-based capacity value estimation of combined wind-storage system using IPSO algorithm
Ma et al. Double recovery strategy of carbon for coal-to-power based on a multi-energy system with tradable green certificates
Lopes et al. Metaheuristic methods applied to the pumps and turbines configuration design of water pumped storage systems
CN115759604B (en) Comprehensive energy system optimal scheduling method
CN113937767B (en) Micro-grid capacity optimization configuration method based on improved whale algorithm
CN111126675A (en) Multi-energy complementary microgrid system optimization method
CN116468215A (en) Comprehensive energy system scheduling method and device considering uncertainty of source load
CN113450186A (en) Bidding strategy applicable to monthly centralized market with participation of coupled system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination