CN114049004B - Method, system and device for randomly planning capacity of electric hydrogen energy station - Google Patents
Method, system and device for randomly planning capacity of electric hydrogen energy station Download PDFInfo
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- 239000001257 hydrogen Substances 0.000 title claims abstract description 185
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 185
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 151
- 238000000034 method Methods 0.000 title claims abstract description 37
- 230000002441 reversible effect Effects 0.000 claims abstract description 108
- 239000007787 solid Substances 0.000 claims abstract description 104
- 238000003860 storage Methods 0.000 claims abstract description 38
- 230000015556 catabolic process Effects 0.000 claims abstract description 17
- 238000006731 degradation reaction Methods 0.000 claims abstract description 17
- 238000006243 chemical reaction Methods 0.000 claims abstract description 15
- 230000007246 mechanism Effects 0.000 claims abstract description 9
- 238000005286 illumination Methods 0.000 claims description 17
- 238000010248 power generation Methods 0.000 claims description 15
- 239000011159 matrix material Substances 0.000 claims description 13
- 238000012937 correction Methods 0.000 claims description 12
- 230000005611 electricity Effects 0.000 claims description 10
- 230000009194 climbing Effects 0.000 claims description 9
- 238000000354 decomposition reaction Methods 0.000 claims description 9
- 238000005070 sampling Methods 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 5
- 239000002245 particle Substances 0.000 claims description 4
- 230000001105 regulatory effect Effects 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 abstract description 4
- 239000000446 fuel Substances 0.000 description 9
- 238000005868 electrolysis reaction Methods 0.000 description 6
- 238000012423 maintenance Methods 0.000 description 6
- 230000007812 deficiency Effects 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 4
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 4
- 238000009826 distribution Methods 0.000 description 3
- 239000007789 gas Substances 0.000 description 3
- 238000005984 hydrogenation reaction Methods 0.000 description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
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- 230000005540 biological transmission Effects 0.000 description 2
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- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 150000002431 hydrogen Chemical class 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 239000001301 oxygen Substances 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000011234 economic evaluation Methods 0.000 description 1
- 239000003792 electrolyte Substances 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 125000004435 hydrogen atom Chemical group [H]* 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 238000012946 outsourcing Methods 0.000 description 1
- 230000033116 oxidation-reduction process Effects 0.000 description 1
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- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The application discloses a method, a system and a device for randomly planning capacity of an electric hydrogen energy station, and belongs to the technical field of power planning. A method, a system and a device for randomly planning the capacity of an electric hydrogen energy station comprise the following steps: constructing a probability model based on energy supply conditions of wind power output, photovoltaic output, electric load and hydrogen energy load; generating a random scene through a probability model, and correcting the scene by adopting an autocorrelation coefficient method; compared with the prior art, the electric hydrogen energy station capacity random planning method fully considers the uncertainty of an electric hydrogen energy system and the degradation mechanism of the reversible solid oxide battery, plans the reversible solid oxide battery and the hydrogen storage capacity of the electric hydrogen energy station by minimizing the comprehensive cost of the system, is beneficial to helping the investors of electric hydrogen conversion facilities to know the investment economy of the reversible solid oxide battery and the hydrogen storage and reasonably plan the investment economy.
Description
Technical Field
The invention relates to the technical field of power planning, in particular to a method, a system and a device for randomly planning the capacity of an electric hydrogen energy station.
Background
The electric hydrogen conversion (P2H) technology can convert electric energy into storable hydrogen or methane, so that the flexibility of the system is greatly improved, and the electric hydrogen conversion (P2H) technology is a great interest in solving the problem of new energy consumption and is more and more concerned and researched. The reversible solid oxide battery (Reversible Solid Oxide Battery, RSOC) is an electric hydrogen conversion device capable of realizing electric hydrogen bidirectional conversion, and can realize flexible interaction of heterogeneous energy sources in an electric hydrogen energy system.
The existing electric conversion device planning research considers that the alkaline electrolysis and the proton exchange membrane electrolysis are used for producing hydrogen, and the hydrogen is unidirectional electric hydrogen energy flow, so that the flexibility of an electric hydrogen energy system is limited to a certain extent. Although partial researches also consider that a gas turbine is used for generating electricity by hydrogen or methane, namely, hydrogen flows to an energy source of electricity, the gas turbine and hydrogen production equipment are respectively provided to complete the bidirectional conversion of the electricity and the hydrogen, the configuration cost and the land requirement are higher, and the problems of information transmission and energy transmission lag existing in the respective configurations among different main bodies are considered, so that the economy and the flexibility of interaction of an electricity and hydrogen energy system are influenced; therefore, a method for randomly planning the capacity of the electric hydrogen energy station is provided.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method, a system and a device for randomly planning the capacity of an electric hydrogen energy station.
The aim of the invention can be achieved by the following technical scheme:
A method for randomly planning the capacity of an electric hydrogen energy station comprises the following steps:
constructing a probability model based on the energy supply condition of the electric hydrogen energy station; generating a random scene through a probability model, and correcting the scene by adopting an autocorrelation coefficient method;
combining the comprehensive cost of the electric hydrogen energy system, the correction scene and the capacity random planning constraint condition to construct a capacity planning model of the reversible solid oxide battery and the hydrogen storage library; solving a capacity planning model by adopting a particle swarm algorithm to obtain initial capacity configuration of the reversible solid oxide battery and the hydrogen storage library;
And constructing a life model of the reversible solid oxide battery based on the operation characteristics and the degradation mechanism of the reversible solid oxide battery, and correcting the initial capacity configuration of the reversible solid oxide battery according to the life model of the reversible solid oxide battery.
Further, the construction of the life model of the reversible solid oxide cell based on the operation characteristics and the degradation mechanism of the reversible solid oxide cell comprises the following steps:
the life model of the reversible solid oxide cell is built in combination with the operation time of the SOEC and SOFC modes of the reversible solid oxide cell: n f＝nf,max-(λ1+λ2+λ3)nf,max;
Wherein: n f is the actual life of the reversible solid oxide cell, lambda 1、λ2、λ3 is the life-span loss factor of the redox, microstructure damage, thermodynamic damage of the reversible solid oxide cell, and n f,max is the ideal life of the reversible solid oxide cell.
Further, the redox life damage coefficient lambda 1 and the microstructure damage life damage coefficient lambda 2 are both related to the operation duration of SOFCs and SOECs;
The oxidation-reduction life-span damage coefficient lambda 1 has the expression of lambda 1＝μ·Ton; wherein mu is a constant, and T on is the working time;
The microstructure damage life-time break coefficient lambda 2 is expressed as: Wherein T SOEC is SOEC mode operation time of the reversible solid oxide cell, and T SOFC is SOFC mode operation time of the reversible solid oxide cell; τ 1、τ2 is constant; t 0 is the upper limit of the microstructure damage degradation reversibility time.
Further, the probability model comprises a reversible solid oxide cell energy conversion model, a wind power output probability model, a photovoltaic output probability model and an electric hydrogen load probability model;
The reversible solid oxide cell energy conversion model is as follows:
The wind power output probability model is as follows:
vs,t≥0
Wherein: a s,t and b s,t are the scale and shape parameters, respectively, at time t of s-season; σ s,t represents the standard deviation of wind speed at time t in s season; v s,t represents the actual wind speed at time t in s season; the average value of wind speed at the time t of s season is represented; c p is expressed as a performance parameter of the wind turbine; ρ is expressed as the air density; a WT is expressed as the projection of the swept area of the blade area and the vertical plane of the wind speed; v is denoted wind speed; /(I) Expressed as rated power of the wind turbine generator; v r is the rated wind speed, and V f is the cut-out wind speed; v c is denoted as cut-in wind speed;
The photovoltaic output probability model is as follows:
Wherein l s,t represents the per unit value of the illumination intensity at the time t of the s season; phi s,t Is a shape parameter with positive values at the time t of s season; mu s,t and/>Respectively representing the per unit value and standard deviation of the illumination intensity mean value at the time t of the s season; p pv,cap represents the rated capacity of the photovoltaic; g r represents the rated illumination intensity; b represents a reference value of illumination intensity;
The electric hydrogen load probability model is as follows:
Wherein: the load size of the mth load at the time t of the s season is shown; /(I) And/>The mean and standard deviation of the mth load at time t of s-season are shown respectively.
Further, the generating a random scene through the probability model and correcting the scene by adopting an autocorrelation coefficient method comprises the following steps:
calculating a time sequence autocorrelation coefficient matrix S ref of real scenes of the output of each monsoon power, the output of the photovoltaic power, the power load and the hydrogen energy load;
Randomly sampling the probability model to obtain an initial uncertainty scene, and calculating a time sequence autocorrelation matrix S ran of wind power output, photovoltaic output, power load and hydrogen energy load in the uncertainty scene.
Cholesky decomposition was performed on S ran: s ran＝Dran·(Dran)T
F′ran＝(Dran)-1·Fran
Wherein: d ran is a lower triangular matrix obtained by Cholesky decomposition of S ran; f' ran is a sequence of wind power output, photovoltaic output, electric load and hydrogen energy load after eliminating time sequence autocorrelation generated by random sampling;
performing Cholesky decomposition on a time sequence autocorrelation coefficient matrix S ref of a real scene to obtain D ref, and overlapping the D ref with F' ran to obtain a time sequence autocorrelation wind speed sequence
Further, the comprehensive cost of the electro-hydrogen energy system comprises the cost of an electro-hydrogen energy station, the power generation cost of the electro-hydrogen system, the wind and light discarding cost of the electro-hydrogen system and the load shedding cost of the electro-hydrogen system;
the expression function of the comprehensive cost of the electric hydrogen energy system is as follows: c total＝CEH+CGE+Ccur+Cdel;
Wherein: c EH is the cost of an electro-hydrogen energy station, C GE is the power generation cost of the electro-hydrogen system, C cur is the wind and light discarding cost of the electro-hydrogen system, and C del is the load shedding cost of the electro-hydrogen system.
In another aspect, the present invention also provides a system for randomly planning the capacity of an electric hydrogen energy station, where the system includes:
the energy supply module is used for constructing a probability model of energy supply conditions of wind power output, photovoltaic output, electric load and hydrogen energy load;
The scene generating module is used for randomly extracting the scene of the energy supply module and carrying out time sequence correction on the scene by using an autocorrelation coefficient method;
the initial capacity determining module is used for calculating initial capacity configuration of the reversible solid oxide battery and the hydrogen storage library by combining comprehensive cost of the electric hydrogen energy system, a corrected scene and a capacity random planning constraint condition;
and the correction module is used for correcting the initial capacity configuration of the reversible solid oxide battery by combining the life model of the reversible solid oxide battery and the correction scene.
In a third aspect, the present invention provides a storage medium having stored therein a plurality of programs for loading and executing by a processor to implement the electrical hydrogen energy station capacity stochastic programming method of any one of the above.
In a fourth aspect, the present invention provides a regulating device, comprising a processor, adapted to execute programs, which are loaded and executed by the processor to implement the method for random planning of capacity of an electrical hydrogen energy station according to any one of the above.
The invention has the beneficial effects that:
The invention fully considers the uncertainty of the electric hydrogen energy system and the degradation mechanism of the reversible solid oxide battery, and plans the capacities of the reversible solid oxide battery and the hydrogen storage in the electric hydrogen energy station by minimizing the comprehensive cost of the system, thereby being beneficial to helping the investors of the electric hydrogen conversion facilities to know the investment economy of the reversible solid oxide battery and the hydrogen storage in a refined way and reasonably plan the investment economy.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of an overall flow of capacity planning according to the present application;
FIG. 2 is a schematic diagram of an exemplary electrical hydrogen energy system architecture according to the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
A method for randomly planning the capacity of an electric hydrogen energy station comprises the following steps:
1. An electro-hydrogen integrated energy station model is built in an electro-hydrogen energy system, and a probability model is built for four uncertain factors of wind power output, photovoltaic power output, power load and hydrogen energy load in the electro-hydrogen energy system:
the electric-hydrogen integrated energy station mainly comprises a reversible solid oxide battery, a charging pile, a hydrogenation machine and a hydrogen storage library, and can provide charging and exchanging service for an Electric Vehicle (EV) and hydrogenation service for a hydrogen fuel vehicle (HFCV). Meanwhile, the high-proportion renewable energy power grid can participate in system demand response.
The method comprises the steps of constructing a probability model for four uncertain factors of a reversible solid oxide battery, wind power output, photovoltaic output, electric load and hydrogen energy load in an electric hydrogen energy system:
The energy conversion model of the reversible solid oxide cell is:
Wherein: For the power consumption of the ith reversible solid oxide cell in electrolysis mode,/> Generating power for the ith reversible solid oxide cell in fuel cell mode; /(I)The hydrogen production rate of the ith reversible solid oxide cell in SOEC mode and the hydrogen consumption rate of the ith reversible solid oxide cell in SOEC mode are respectively; η SOEC、ηSOFC is the electrolysis efficiency and the power generation efficiency of the fuel cell respectively; h h is the high heating value of hydrogen.
Wind power output probability model: wind speeds are typically subject to Weibull distributions, the probability density function of which can be expressed by:
vs,t≥0
Wherein: a s,t and b s,t are the scale and shape parameters, respectively, at time t of s-season; σ s,t represents the standard deviation of wind speed at time t in s season; v s,t represents the actual wind speed at time t in s season; the mean value of the wind speed at time t in s season is shown.
The wind power generation output is mainly influenced by wind speed, and the expression is as follows:
Wherein: c p is expressed as a performance parameter of the wind turbine; ρ is expressed as the air density; a WT is expressed as the projection of the swept area of the blade area and the vertical plane of the wind speed; v is denoted wind speed; Expressed as rated power of the wind turbine generator; v r is the rated wind speed, and V f is the cut-out wind speed; v c is denoted as cut-in wind speed.
Photovoltaic output probability model: the illumination intensity obeys the beta distribution, and the probability density function thereof can be represented by the following formula:
Wherein l s,t represents the per unit value of the illumination intensity at the time t of the s season; phi s,t Is a shape parameter with positive values at the time t of s season; mu s,t and/>The average value per unit value and standard deviation of the illumination intensity at the time t of s season are respectively shown.
The photovoltaic output can be expressed as:
wherein: p pv,cap represents the rated capacity of the photovoltaic; g r represents the rated illumination intensity; b represents the illumination intensity reference value.
Probability model of electrical hydrogen loading: the electric and hydrogen loads obey normal distribution, and the probability density function satisfies the following formula:
Wherein: the load size of the mth load at the time t of the s season is shown; /(I) And/>The mean and standard deviation of the mth load at time t of s-season are shown respectively.
2. Generating a random scene of uncertain factors by using a probability model, and carrying out time sequence correction on the scene by using an autocorrelation coefficient method:
Because the scene obtained by directly sampling by using the probability model does not reflect the actual time sequence factor, the scene can cause a certain degree of scene distortion, and a certain error can be caused to the planning result, so that the random scene which is more in line with the actual situation can be generated by adopting the autocorrelation coefficient matrix to carry out random scene correction, and the accuracy of the planning result is improved.
The method for carrying out scene time sequence correction on the sampling scene by using the autocorrelation coefficient method comprises the following steps:
Calculating a reference time sequence autocorrelation coefficient matrix S ref of real scenes of the power output, the photovoltaic output, the power load and the hydrogen energy load of each quarter wind;
Random sampling is carried out through probability models of wind power output, photovoltaic output, power load and hydrogen energy load, an initial uncertainty scene is obtained, and a time sequence autocorrelation matrix S ran of different factors in each random scene is calculated.
The timing autocorrelation effect due to random sampling can be eliminated by Cholesky decomposition of S ran, as shown in the following equation:
Sran＝Dran·(Dran)T
F′ran＝(Dran)-1·Fran
Wherein: d ran is a lower triangular matrix obtained by Cholesky decomposition of S ran; f' ran is a sequence of wind power output, photovoltaic output, electric load and hydrogen energy load after eliminating time sequence autocorrelation generated by random sampling.
Similarly, the sequential autocorrelation coefficient matrix S ref of the real scene is subjected to Cholesky decomposition to obtain D ref, and the D ref is superimposed on F 'ran to obtain a sequential autocorrelation wind speed sequence F' ran conforming to the real situation, as shown in the following formula:
F″ran＝(Dref)-1·F′ran
3. The method comprises the steps of constructing a capacity planning model of a reversible solid oxide battery and a hydrogen storage library of an electric hydrogen integrated energy station by combining comprehensive cost and related constraint of an electric hydrogen energy system, solving the nonlinear mixed integer planning problem by using a particle swarm algorithm, and generating capacity configuration of the reversible solid oxide battery and the hydrogen storage library:
Constructing an objective function of capacity random programming, namely the comprehensive cost of the electro-hydrogen energy system, and aiming at minimizing the comprehensive cost of the system so as to avoid the too little investment and redundant construction of equipment; the objective function expression is as follows:
Ctotal＝CEH+CGE+Ccur+Cdel
Wherein: c EH is the cost of an electro-hydrogen energy station, C GE is the power generation cost of the electro-hydrogen system, C cur is the wind and light discarding cost of the electro-hydrogen system, and C del is the load shedding cost of the electro-hydrogen system.
Initial investment cost annual value of electricity-hydrogen energy station cost C EH from reversible solid oxide batteryAnnual operating cost/>, of reversible solid oxide cellsChu Qingku initial investment cost/>Cost/>, of hydrogen load reductionThe composition is as follows:
a. annual investment cost of reversible solid oxide cell
The planning problem often uses an equal annual fee method to consider the time value of funds, thereby performing economic evaluation on the planning project. Assuming that the reversible solid oxide battery is built in the initial stage of the project, the total investment cost C RSOC,inv of the reversible solid oxide battery is converted into the equal-annual investment cost according to the expected operation life of the reversible solid oxide battery by adopting an equal-annual gold methodThe following formula is shown:
wherein: r is the discount rate; n R is the life expectancy of the reversible solid oxide cell in years; i is a reversible solid oxide cell device collection; The initial investment cost sum for a reversible solid oxide cell for an electrical hydrogen energy station i, which is not simply a linear relationship with capacity, can generally be fitted by:
Wherein: Representing construction fixed costs; /(I) Is a cost coefficient, and is constant; s 0 is the reversible solid oxide cell reference capacity.
B. annual operation and maintenance cost of reversible solid oxide battery
Cost of operation and maintenance in ROSC yearThe method comprises the steps of subtracting electricity price arbitrage benefit from the sum of annual electricity consumption cost, annual hydrogen consumption cost and annual maintenance cost, wherein the electricity consumption cost refers to SOEC outsourcing electric energy cost, the hydrogen consumption cost refers to SOEC hydrogen consumption energy cost, and the annual maintenance cost takes a constant, and the formula is as follows:
wherein: I. t represents an electric hydrogen energy station set and a time set respectively; the right four terms of the equation are SOEC annual electricity consumption cost, SOFC annual hydrogen consumption cost, annual maintenance cost and electric market arbitrage income in sequence; The ROSC hydrogen production rate and the t-period power price of the electric hydrogen energy station i are respectively; /(I) The ROSC power generation power and the self-provided hydrogen unit cost of the electric hydrogen energy station i in the t period are respectively; /(I)Annual maintenance costs for the electrical hydrogen energy station i.
C. Cost of hydrogen energy storage
The cost of hydrogen energy storage is also considered by an equal annual value method, and the following formula is adopted:
Wherein: Investment cost annual value for the gas storage; /(I) Initial investment cost sum of the hydrogen storage library of the electric hydrogen energy station i; n S is the life expectancy of the hydrogen reservoir.
The total high pressure gaseous hydrogen storage cost C Sto,total is shown as follows:
Wherein: c b,s is the cost of the standard capacity hydrogen storage warehouse, and the unit is yuan/kg; s b,s is the reference capacity of the hydrogen storage library; s i,s is the hydrogen storage capacity of the electric hydrogen energy station i; p b,s is the reference operating pressure of the hydrogen reservoir; p i,s is the hydrogen storage pressure set by the electricity hydrogen energy station i according to the actual demand.
D. Cost of hydrogen deficiency punishment
When the hydrogen storage capacity of the electric hydrogen energy station is smaller, the situation that the hydrogen supply is insufficient or the SOFC cannot be started due to the lack of hydrogen is easy to generate when the hydrogen load is larger, so that the hydrogen deficiency punishment cost is introduced as a component part of a planning objective function, and the unreasonable low configuration of the hydrogen storage capacity is avoided. The annual hydrogen deficiency cost can be represented by the following formula:
Wherein: The hydrogen deficiency of the electric hydrogen energy station i at t is expressed in kg; /(I) The unit is yuan/kg for hydrogen deficiency penalty coefficient.
The electricity-hydrogen system power generation cost C GE comprises three items of traditional unit power generation cost, wind turbine generator power generation cost and photoelectric turbine generator power generation cost, and the following formula is shown:
Wherein: p g,t、Pwt,t、Ppv,t is the generated energy of the traditional generator set g, the wind turbine set wt and the photovoltaic set pv in the period t respectively; c g、cwt、cpv is the power generation cost of the traditional unit g, the wind turbine wt and the photovoltaic unit pv respectively.
Wind and light discarding cost of the electro-hydrogen system:
Wherein: respectively reducing the wind power generation and the photovoltaic power generation of the period t; and c pv、cwt is the punishment price of wind abandon and light abandon.
Load shedding cost of the electro-hydrogen system:
Wherein: c del is the load-shedding cost, The amount of electrical load for the t period is reduced.
An electric energy supply and demand balance constraint model of an electric hydrogen energy system is constructed, and the expression is as follows:
wherein: the 1 st to 4 th items on the left side of the equation are respectively the generated power of a traditional unit, a wind turbine unit, a photoelectric unit and a reversible solid oxide battery at the time t, and the electrolytic load of the 2 nd to 4 th reversible solid oxide battery at the time t, the charging load of an electric automobile of an electric hydrogen energy station, the loads of other power users and the reduction amount of the power load on the right side of the equation.
The capacity random planning constraint conditions comprise electric energy supply and demand balance constraint, hydrogen energy supply and demand balance constraint, traditional unit power and climbing constraint, wind-light unit power and climbing constraint, reversible solid oxide battery power and climbing constraint, and hydrogen storage capacity and storage rate constraint;
constructing a hydrogen energy supply and demand balance constraint model of an electric hydrogen energy system, wherein the expression is as follows:
Wherein: hydrogen energy short shortage of electric hydrogen energy station i, hydrogen fuel automobile hydrogenation load, SOFC power generation hydrogen consumption rate,/>, respectively The hydrogen storage variable quantity of the hydrogen storage warehouse is an electric hydrogen energy station. HS i,t+1、HSi,t is the hydrogen storage amount of the hydrogen storage pool of the electric hydrogen energy station i at t+1 and t.
The method comprises the steps of constructing a traditional unit of an electric hydrogen energy system, a wind-solar unit power and climbing constraint model, wherein the expression is as follows: p Ω,min≤PΩ(t)≤PΩ,max
Wherein: omega represents a traditional unit and a wind-light unit set, and various generator sets have power limiting constraint and climbing constraint.
And constructing a power and climbing constraint model of the reversible solid oxide battery, wherein the expression is as follows:
χSOEC(t)+χSOFC(t)≤1
wherein: χ SOEC(t)、χSOFC (t) is a 0-1 variable representing the mode of operation of the ROSC during time t; The minimum and maximum hydrogen production rates of the ith reversible solid oxide cell; /(I) The minimum and maximum power of the i-th reversible solid oxide battery; u SOEC、DSOEC is the maximum speed of the SOEC up-slope and down-slope; u SOFC、DSOFC is the maximum rate of the SOFC uphill and downhill respectively.
And constructing a capacity and storage rate constraint model of the hydrogen storage library, wherein the expression is as follows:
wherein: s HS,min、SHS,max is the lower limit of the hydrogen storage capacity and the upper limit of the hydrogen storage capacity, and ψ min、ψmax is the minimum and maximum values of the hydrogen storage charging and discharging rate respectively.
And solving the formula by using a particle swarm algorithm to generate the initial capacity configuration of the reversible solid oxide battery and the hydrogen storage library.
4. Constructing a life model of the reversible solid oxide battery, and correcting an initial capacity configuration scheme of the reversible solid oxide battery by combining annual operation data of comprehensive cost, correction scene and capacity random planning constraint conditions of an electro-hydrogen energy system:
Constructing a life model of the reversible solid oxide battery:
the current development of the reversible solid oxide fuel cell is greatly limited by service life, so that a life model of the reversible solid oxide fuel cell is constructed, the operation characteristics of the reversible solid oxide fuel cell are considered in planning, the economy in the life cycle of the reversible solid oxide fuel cell is further improved, and the planning economy is improved.
During continuous electrolytic operation at high current densities, reversible solid oxide cells present an extreme degradation mechanism, severe microstructural damage near the oxygen electrode/electrolyte interface due to internal oxygen pressure increases. However, the damage is reversible, i.e. the degradation of the cell can be alleviated by periodic cycling of both the fuel cell and the electrolysis modes, and the reversible cycling can even completely inhibit the degradation of the microstructure due to the above reasons under certain conditions.
However, the SOFC mode repair SOEC mode suffers from cell degradation problems, which have a time limit beyond which degradation is irreversible, i.e. a time limit for continuous electrolysis.
The life model of the reversible solid oxide cell is mainly related to the running time of two modes, SOEC and SOFC, and the expression is:
nf＝nf,max-(λ1+λ2+λ3)nf,max；
Wherein: n f is the actual life of the reversible solid oxide cell, lambda 1、λ2、λ3 is the life-span loss factor of the redox, microstructure damage, thermodynamic damage of the reversible solid oxide cell, and n f,max is the ideal life of the reversible solid oxide cell.
The redox life loss coefficient lambda 1 is mainly related to the operation duration of SOFC and SOEC, and the expression is:
Lambda 1＝μ·Ton; wherein: mu is a constant; t on is the working time.
The microstructure damage life-span damage coefficient lambda 2 is related to the operation time of the SOFC and the SOEC, namely T SOEC、TSOFC, the SOEC generates microstructure damage, the SOFC has a repair effect on the microstructure damage, and the expression is as follows:
Wherein: τ 1、τ2 is constant; t 0 is the upper limit of the microstructure damage degradation reversibility time.
Lambada 3 is the thermodynamic damage coefficient, where the reversible solid oxide cell thermal control model is considered to be relatively stable, so it is considered to be a constant, and thus is a simplified treatment.
Modification of initial capacity configuration scheme for reversible solid oxide cells
The obtained initial capacity configuration scheme is operated throughout the year of the system to obtain the optimal annual operation scheme of the reversible solid oxide battery, so that the annual operation data and the life model of the reversible solid oxide battery are combined to correct the expected life, and the process is further carried out until the expected life is stabilized within a certain range. Therefore, the capacity planning method not only considers the uncertainty of supply and demand of electric hydrogen energy and related factors, but also builds a life model by combining the battery degradation mechanism of the reversible solid oxide battery, and considers the life model in a planning algorithm, thereby being beneficial to planning in a targeted manner according to the operation characteristics of the life model and improving the rationality and accuracy of planning.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.
Claims (7)
1. The method for randomly planning the capacity of the electric hydrogen energy station is characterized by comprising the following steps of:
constructing a probability model based on the energy supply condition of the electric hydrogen energy station; generating a random scene through a probability model, and correcting the scene by adopting an autocorrelation coefficient method;
combining the comprehensive cost of the electric hydrogen energy system, the correction scene and the capacity random planning constraint condition to construct a capacity planning model of the reversible solid oxide battery and the hydrogen storage library; solving a capacity planning model by adopting a particle swarm algorithm to obtain initial capacity configuration of the reversible solid oxide battery and the hydrogen storage library;
Constructing a life model of the reversible solid oxide battery based on the operation characteristics and degradation mechanism of the reversible solid oxide battery, and correcting the initial capacity configuration of the reversible solid oxide battery according to the life model of the reversible solid oxide battery;
The construction of the life model of the reversible solid oxide battery based on the operation characteristics and the degradation mechanism of the reversible solid oxide battery comprises the following steps:
the life model of the reversible solid oxide cell is built in combination with the operation time of the SOEC and SOFC modes of the reversible solid oxide cell: ；
Wherein: For the practical life of reversible solid oxide cells,/> 、/>、/>Life-time loss factor for redox, microstructural damage, thermodynamic damage of reversible solid oxide cell,/>Is an ideal life for reversible solid oxide cells;
The redox life-span loss coefficient And microstructure damage life-fold coefficient/>Both are related to the operating time of SOFC and SOEC;
Redox life loss coefficient The expression is/>; In/>Is constant,/>The working time is the working time;
microstructure damage life-span breakage coefficient The expression is: /(I); In/>SOEC mode operation duration for reversible solid oxide cell,/>SOFC mode operation duration for reversible solid oxide cells; /(I)、/>Are all constants; /(I)An upper limit for the microstructure damage degradation reversible time;
the probability model comprises a reversible solid oxide cell energy conversion model, a wind power output probability model, a photovoltaic output probability model and an electric hydrogen load probability model;
The reversible solid oxide cell energy conversion model is as follows: ；
The wind power output probability model is as follows:
Wherein: and/> The scale parameter and the shape parameter at the time t of the s season are respectively; /(I)The standard deviation of wind speed at the time t of s season is shown; /(I)The actual wind speed at the time t of the s season is shown; /(I)The average value of wind speed at the time t of s season is represented; /(I)Expressed as wind turbine performance parameters; /(I)Expressed as air density; /(I)Expressed as the projection of the swept area of the blade area to the vertical plane of the wind speed; /(I)Expressed as wind speed; /(I)Expressed as rated power of the wind turbine generator; /(I)Expressed as rated wind speed,/>Expressed as cut-out wind speed; /(I)Expressed as cut-in wind speed;
The photovoltaic output probability model is as follows:
Wherein: A per unit value of the illumination intensity at the time t of s season; /(I) And/>Is a shape parameter with positive values at the time t of s season; -And/>Respectively representing the per unit value and standard deviation of the illumination intensity mean value at the time t of the s season; Representing the rated capacity of the photovoltaic; /(I) Indicating the rated illumination intensity; /(I)Representing the illumination intensity reference value;
The electric hydrogen load probability model is as follows:
Wherein: The load size of the mth load at the time t of the s season is shown; /(I) And/>The mean and standard deviation of the mth load at time t of s-season are shown respectively.
2. The method for random planning of electric hydrogen energy station capacity according to claim 1, wherein the generating a random scene by a probability model and correcting the scene by an autocorrelation coefficient method comprises the following steps:
calculating a time sequence autocorrelation coefficient matrix of real scenes of the output of each monsoon power, the output of the photovoltaic power, the power load and the hydrogen energy load ；
Randomly sampling the probability model to obtain an initial uncertainty scene, and calculating a time sequence autocorrelation matrix of wind power output, photovoltaic output, power load and hydrogen energy load in the uncertainty scene；
For a pair ofCholesky decomposition was performed: /(I)
Wherein: For passing/> Performing Cholesky decomposition to obtain a lower triangular matrix; /(I)The sequence of wind power output, photovoltaic output, power load and hydrogen energy load after the time sequence autocorrelation generated by random sampling is eliminated;
Time sequence autocorrelation coefficient matrix for real scene Cholesky decomposition to give/>Will be superimposed to/>Acquiring a time sequence autocorrelation wind speed sequence/>：/>。
3. The method for randomly planning the capacity of an electro-hydrogen energy station according to claim 1, wherein the comprehensive cost of the electro-hydrogen energy system comprises the cost of the electro-hydrogen energy station, the cost of power generation of the electro-hydrogen system, the cost of wind and light discarding of the electro-hydrogen system and the cost of load shedding of the electro-hydrogen system;
the expression function of the comprehensive cost of the electric hydrogen energy system is as follows: ；
Wherein: for electricity hydrogen energy station cost,/> Generating cost for electro-hydrogen system,/>The wind and light discarding cost for the electro-hydrogen system,Load shedding costs for electro-hydrogen systems.
4. The method according to claim 1, wherein the capacity stochastic programming constraint conditions comprise an electric energy supply and demand balance constraint, a hydrogen energy supply and demand balance constraint, a traditional unit power and climbing constraint, a wind-solar unit power and climbing constraint, a reversible solid oxide battery power and climbing constraint, and a hydrogen storage capacity and storage rate constraint.
5. An electrical hydrogen energy station capacity stochastic programming system, the system comprising:
the energy supply module is used for constructing a probability model of energy supply conditions of wind power output, photovoltaic output, electric load and hydrogen energy load;
The scene generating module is used for randomly extracting the scene of the energy supply module and carrying out time sequence correction on the scene by using an autocorrelation coefficient method;
the initial capacity determining module is used for calculating initial capacity configuration of the reversible solid oxide battery and the hydrogen storage library by combining comprehensive cost of the electric hydrogen energy system, a corrected scene and a capacity random planning constraint condition;
the correction module is used for correcting the initial capacity configuration of the reversible solid oxide battery by combining the service life model of the reversible solid oxide battery and the correction scene;
The construction of the life model of the reversible solid oxide battery based on the operation characteristics and the degradation mechanism of the reversible solid oxide battery comprises the following steps:
the life model of the reversible solid oxide cell is built in combination with the operation time of the SOEC and SOFC modes of the reversible solid oxide cell: ；
Wherein: For the practical life of reversible solid oxide cells,/> 、/>、/>Life-time loss factor for redox, microstructural damage, thermodynamic damage of reversible solid oxide cell,/>Is an ideal life for reversible solid oxide cells;
The redox life-span loss coefficient And microstructure damage life-fold coefficient/>Both are related to the operating time of SOFC and SOEC;
Redox life loss coefficient The expression is/>; In/>Is constant,/>The working time is the working time;
microstructure damage life-span breakage coefficient The expression is: /(I); In/>SOEC mode operation duration for reversible solid oxide cell,/>SOFC mode operation duration for reversible solid oxide cells; /(I)、/>Are all constants; /(I)An upper limit for the microstructure damage degradation reversible time;
the probability model comprises a reversible solid oxide cell energy conversion model, a wind power output probability model, a photovoltaic output probability model and an electric hydrogen load probability model;
The reversible solid oxide cell energy conversion model is as follows: ；
The wind power output probability model is as follows:
Wherein: and/> The scale parameter and the shape parameter at the time t of the s season are respectively; /(I)The standard deviation of wind speed at the time t of s season is shown; /(I)The actual wind speed at the time t of the s season is shown; /(I)The average value of wind speed at the time t of s season is represented; /(I)Expressed as wind turbine performance parameters; /(I)Expressed as air density; /(I)Expressed as the projection of the swept area of the blade area to the vertical plane of the wind speed; /(I)Expressed as wind speed; /(I)Expressed as rated power of the wind turbine generator; /(I)Expressed as rated wind speed,/>Expressed as cut-out wind speed; /(I)Expressed as cut-in wind speed;
The photovoltaic output probability model is as follows:
Wherein: A per unit value of the illumination intensity at the time t of s season; /(I) And/>Is a shape parameter with positive values at the time t of s season; -And/>Respectively representing the per unit value and standard deviation of the illumination intensity mean value at the time t of the s season; Representing the rated capacity of the photovoltaic; /(I) Indicating the rated illumination intensity; /(I)Representing the illumination intensity reference value;
The electric hydrogen load probability model is as follows:
Wherein: The load size of the mth load at the time t of the s season is shown; /(I) And/>The mean and standard deviation of the mth load at time t of s-season are shown respectively.
6. A storage medium having stored therein a plurality of programs for loading and execution by a processor to implement the electrical hydrogen energy station capacity stochastic programming method of any one of claims 1-4.
7. A regulating device comprising a processor adapted to execute respective programs, characterized in that said programs are loaded and executed by the processor to implement the method for stochastic planning of electrical hydrogen energy station capacity according to any one of claims 1-4.
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