CN115392631A - Multi-dimensional value evaluation method for electricity-to-hydrogen device based on fuzzy hierarchical analysis - Google Patents
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Abstract
The invention belongs to the field of comprehensive energy systems, and particularly relates to a multidimensional value evaluation method for a hydrogen conversion device based on fuzzy hierarchical analysis, which comprises the following steps: establishing a multidimensional value evaluation index mathematical model taking electricity-to-hydrogen as a newly added flexible resource, constructing a comprehensive evaluation matrix, carrying out fuzzy estimation on risk probability by adopting a weight fuzzy set of factors and comprehensive evaluation matrix operation, and taking an evaluation set corresponding to the largest evaluation index as a final evaluation result of the comprehensive analysis matrix. The effectiveness and the practicability of the system value evaluation method are verified by comparing the system values of the power-to-hydrogen device under different capacities and standard layer targets with different factor sensitivities.
Description
Technical Field
The invention belongs to the field of comprehensive energy systems, and particularly relates to a multidimensional value evaluation method for a hydrogen conversion device based on fuzzy hierarchical analysis.
Background
In the future, the structural form of the power system is clean in power supply, electronic in power grid and diversified in load. However, in the face of problems of inertia reduction, frequency modulation capability reduction, limited absorption and the like of a high-proportion new energy power system, a key technology for improving the flexibility of the system needs to be excavated urgently. Therefore, it is necessary to find a new flexible resource to solve the problems caused by various complicated changes of the system. The hydrogen energy is used as a clean, efficient and sustainable carbon-free energy source, the power and the energy can be optimized separately, time-sharing operation is not needed in the electricity storage and power generation processes, the hydrogen energy is an ideal urban green secondary resource, and with the increase of the proportion of renewable energy sources in an energy structure, clean electric energy is used for electrolyzing water to prepare green hydrogen to replace blue hydrogen or grey hydrogen, so that the hydrogen energy is an important means for realizing energy conservation and emission reduction.
The existing evaluation method has the limitations of the existing evaluation methods such as single value evaluation, multiple value evaluation, standard cost and the like, and the measurement of the value of the electricity-to-hydrogen is limited by the cost of the electricity-to-hydrogen device.
Disclosure of Invention
The invention aims to provide a multi-dimensional value evaluation method of a power-to-hydrogen device based on fuzzy hierarchical analysis, and solve the limitations of the existing evaluation methods such as single value evaluation, multiple value evaluation and standard cost.
The present invention is achieved in such a way that,
a multidimensional value evaluation method for a power-to-hydrogen device based on fuzzy hierarchical analysis comprises the following steps:
establishing a multidimensional value evaluation index mathematical model taking electricity-to-hydrogen as a newly added flexible resource:
max R i ＝λ T λ i ，i＝1,2,...,n
wherein: r is i Is a comprehensive evaluation index; n is the number of decision schemes; lambda is a feature vector between standards; lambda [ alpha ] i Is the feature vector of the ith scheme;
constructing a comprehensive evaluation matrix:
wherein r is ij So that the ith influence factor accounts for the importance degree of the jth comment in the comment set;
carrying out fuzzy estimation on the risk probability by adopting a weight fuzzy set of factors and a comprehensive evaluation matrix operation:
C＝A×R＝(C 1 ,C 2 ,C 3 …C j )
wherein C represents the comprehensive judgment result of the risk probability, C j Representing the importance degree of the result of the comprehensive evaluation accounting for the jth comment in the comment set;
the maximum evaluation index C j The corresponding comment set is used as the final evaluation result of the comprehensive analysis matrix C:
V＝{v i |v i and (C) the maximum element in the step of going to step C.
Further, the comprehensive evaluation index comprises: the system comprises an electricity-to-hydrogen economic value evaluation index, a system level flexibility evaluation index, a novel power system new energy consumption evaluation index and a novel power system electricity-to-hydrogen carbon emission index.
Further, the evaluation index of the economic value of electricity to hydrogen is that the profitability is used as the evaluation index of the economic value of electricity to hydrogen:
in the formula: v y The yield of electricity-to-hydrogen on the y day; rho is the bank daily interest rate;
converting the current value of the system cost into the annual value cost by using the equal-amount series capital recovery coefficients, wherein the expression is
In the formula: c NPC The system current cost; c AC The daily cost; n is the cost reduction days; i is the daily rate of interest;
the calculation formula of the investment cost of the electricity-to-hydrogen system is
C NPC ＝γ h P elc,max (3)
In the formula: c NPC Investment cost of the system for converting electricity into hydrogen, gamma h Investment cost per unit volume, P, of an electric hydrogen conversion plant elc,max Capacity of an electrical to hydrogen plant;
the system level flexibility evaluation index is the proportion of the square sum of upward flexibility and downward flexibility of the system in the t period to the maximum adjustable flexibility of the system;
the new energy consumption evaluation index of the novel power system takes the sum of squares of the air curtailment rate of change of all adjacent time periods of new energy as a dynamic consumption index, and is calculated as follows:
wherein:
P rescur,t ＝[(1+μ)P load.max -U G C G -P cl -kC self )-[(P load,t -L G C G -kC self -P elc,t )-P res,t )]；
P elc,t part of the flexibility requirements for the electricity-to-hydrogen plant, P rescur,t Indicates the new energy margin at time t, L G Being of conventional power supplyMinimum technical output coefficient, P res,t Indicates new energy output, U G Is the maximum technical output coefficient, C, of a conventional power supply G The starting capacity of a conventional power supply; k is the average output coefficient of the self-contained power plant; c self For the starting-up capacity of the self-contained power plant, P cl Is the confidence capacity of renewable energy, the spare coefficient is mu;
the electric-to-hydrogen-carbon emission index of the novel power system is calculated as follows:
in the formula: ρ is a unit of a gradient m As coal fuel CO 2 Coefficient of emission, F m The coal consumption of the thermal power generating unit.
The coal consumption of the coal-electricity hydrogen production under the same condition.
Further, the constraint conditions of the multidimensional value evaluation index mathematical model comprise:
node power balance constraint:
in the formula: p is i Active power injected into the node i;
active power generated by a generator on a node i;
active power generated by the wind farm at node i;
the consumed power for converting the electricity into the hydrogen for the node i;
is the active load power on node i;
unit output restraint:
in the formula:
the minimum output value of the generator on the node i is obtained;
the maximum output value of the generator on the node i is obtained;
the minimum output value of the wind power plant above the node i is obtained;
the maximum output value of the wind farm at node i is:
and (3) electric-to-hydrogen output constraint:
0≤P ELC ≤P ELC，MAX
the unit climbing restraint, thermal power unit and electricity change hydrogen device need satisfy power climbing restraint as follows:
|P elc,t -P elc,t-1 |≤ΔP elc,max
in the formula: delta P elc,max Cap is the maximum power of P2H in the power-on state in unit time interval g Is a systemThe total capacity of the internal fire-electricity generator set,
respectively the down-regulation climbing speed and the up-regulation climbing speed of the thermal power.
Compared with the prior art, the invention has the beneficial effects that:
the invention determines the weight of each factor of the power to hydrogen device in system flexibility, new energy consumption, economy, environmental protection and the like and the membership degree of each index based on a fuzzy analytic hierarchy process, establishes a multidimensional value evaluation index mathematical model based on power to hydrogen (P2H) as a newly added flexible resource, and comprehensively evaluates the power to hydrogen in a novel power system. The fuzzy analytic hierarchy process can change corresponding standard layer targets according to different requirements, and the validity and practicability of the system value evaluation method are verified by comparing the system values of the power-to-hydrogen device under different capacities under the standard layer targets with different factor sensitivities.
Drawings
FIG. 1 is a hierarchical diagram of a power to hydrogen system layout;
FIG. 2 is a load versus wind turbine output curve;
FIG. 3 is a new energy static consumption curve;
FIG. 4 is a graph of power consumed by converting electricity to hydrogen;
FIG. 5 is a graph showing the variation of various indexes;
FIG. 6 is a diagram showing evaluation of various indexes of a 10MW/50MW electrical hydrogen production system;
FIG. 7 is a radar chart of technical economic indicators;
FIG. 8 is a comprehensive evaluation index of the P2H system under different scales and weights;
FIG. 9 is a flow chart of a method employed by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
A multidimensional value evaluation method for a hydrogen electrolysis-to-conversion device based on fuzzy hierarchical analysis comprises the following steps:
establishing a multidimensional value evaluation index mathematical model taking electricity-to-hydrogen as a newly added flexible resource:
max R i ＝λ T λ i ，i＝1,2,...,n
wherein: r i Is a comprehensive evaluation index; n is the number of decision schemes; lambda is a feature vector between standards; lambda i Is the feature vector of the ith scheme;
constructing a comprehensive evaluation matrix:
wherein r is ij So that the ith influence factor accounts for the importance degree of the jth comment in the comment set;
carrying out fuzzy estimation on risk probability by adopting a weight fuzzy set of factors and comprehensive evaluation matrix operation:
C＝A×R＝(C 1 ,C 2 ,C 3 …C j )
wherein C represents the comprehensive judgment result of the risk probability, C j Representing the importance degree of the result of the comprehensive evaluation accounting for the jth comment in the comment set;
the maximum evaluation index C is j The corresponding comment set is used as the final evaluation result of the comprehensive analysis matrix C:
V＝{v i |v i and (C) the maximum element in the step of going to step C.
The comprehensive evaluation index comprises: the system comprises an electricity-to-hydrogen economic value evaluation index, a system level flexibility evaluation index, a novel power system new energy consumption evaluation index and a novel power system electricity-to-hydrogen carbon emission index.
By integrating a series of energy policies, the application value of electricity-to-hydrogen in the aspects of improving the peak regulation capacity of a power system, promoting the local consumption of renewable energy, improving the load response level, constructing an energy internet and the like is preliminarily embodied, the value of electricity-to-hydrogen is not fully embodied due to the fact that the existing market capacity is small, the transaction mechanism is not sound enough, and the system value of electricity-to-hydrogen in various links such as power generation/transmission/transformation/distribution/use and the like is increasingly shown in the future high-proportion renewable energy scene. The invention establishes evaluation indexes from four aspects of economy, environmental protection, new energy consumption and flexibility.
(1) Evaluation index of economic value of electricity-to-hydrogen
For economic evaluation of electricity to hydrogen in a novel power system, the profitability index represents the value and risk of electricity to hydrogen investment. The profitability index is defined as the present value of a unit investment, i.e. the ratio of the present value of the system to the initial investment amount for all prospective future conversions of electricity to hydrogen after the initial investment. For an economically favourable investment, the profitability index should be higher than 1. The profitability is used as an economic index:
in the formula: v y The yield of electricity-to-hydrogen on the y day; ρ is the bank daily interest rate.
The current value of the system cost is converted into the equal annual value cost by using the equal-amount series capital recovery coefficient in consideration of the currency time value, and the expression is
In the formula: c NPC The system current cost; c AC A daily cost; n is the cost reduction days; i is the daily interest rate.
Investment cost of the electricity-to-hydrogen system: is calculated by the formula
C NPC ＝γ h P elc,max (3)
In the formula: c NPC For investment costs of hydrogen plants by electricity, gamma h Investment cost per unit volume, P, of an electric hydrogen conversion plant elc,max Is the capacity of the electrical hydrogen conversion equipment.
(2) System level flexibility assessment indicator
In the operation process of the power system, all schedulable resources can become flexible resources, the flexibility provided by the schedulable resources for the power system is continuously changed, and as time goes by, the unit performs corresponding scheduling operation according to a daily plan, and the output power is continuously changed. The upper limit of the output of the unit and the current difference value are the upward flexibility capacity of the unit, and are called upward flexibility resources of the system; the difference value between the current output of the unit and the standby capacity of the unit is the downward flexibility capacity of the unit, and is called downward flexibility resource of the system. Therefore, each unit has certain flexibility, and the sum of the flexibility capacity of each unit is the total capacity of the flexibility resources of the system.
Because the flexibility has the characteristic of directionality, namely the system has upward flexibility and downward flexibility at the same time, the ratio of the square sum of the upward flexibility and the downward flexibility of the system in the period t to the maximum adjustable flexibility of the system is defined as a system-level flexibility evaluation index, and the following steps are included:
(3) Novel new energy consumption evaluation index of power system
The basic operation criterion of the power system is power electricity quantity balance, namely the power system needs to ensure power supply and demand balance in real time. When a day-ahead power-on mode is arranged in a traditional power system, the power-on capacity required to participate in power balance needs to reach a day-ahead load prediction peak value, and a certain margin is reserved for a load prediction error, as follows:
U G C G +kC self +P cl ≥(1+μ)P load.max (5)
in the formula: u shape G The maximum technical output coefficient of a conventional power supply is usually 1.0 of that of a large conventional thermal power supply; c G The starting capacity of a conventional power supply; k is the average output coefficient of the self-contained power plant; c self Is the starting capacity of the self-contained power plant. P cl The spare factor is μ for the confidence capacity of the renewable energy source. From formula (5)It can be known that, when the power system is arranged in a startup mode, the principle of ensuring the power adequacy of the system is the first one, so the confidence level of the new energy is usually set to be very low, and the system can still ensure the load demand even if the new energy has zero output.
At time t, if the system is required to be able to consume the renewable energy in full, it needs to satisfy:
L G C G +kC self +P res,t ≤P load.t (6)
in the formula: l is G The minimum technical output coefficient of a conventional power supply is usually 0.5 of that of a large thermal power generating unit; p is res,t And representing the new energy output.
If the formula (6) is not satisfied, the conventional power supply cannot provide enough space to absorb the renewable energy source after the output pressure of the conventional power supply reaches the minimum value at the moment, the system power balance can be realized only by reducing the output of the renewable energy source, namely the wind and light abandoning is performed, and the expression of the system power balance at the moment is as follows:
L G C G +kC self +P res,t -P rescur,t ＝P load.t (7)
in the formula P rescur,t The new energy surplus (wind curtailment and light curtailment) at time t is shown.
In actual operation, the equation (5) is usually satisfied in the form of an equation, taking into consideration the power adequacy constraint and the renewable energy consumption condition. On the basis, the new energy margin in each time interval can be obtained by subtracting the formula (7) from the formula (5)
P rescur,t ＝[μP load.max +(P res.t -P cl )+(P load.max -P load.t )]-(U G -L G )C G (8)
Considering electric conversion to hydrogen as a flexibility resource requires adding the electric conversion to hydrogen device flexibility demand part P on the right side elc,t
P rescur,t ＝[(1+μ)P load.max -U G C G -P cl -kC self )-[(P load,t -L G C G -kC self -P elc,t )-P res,t )]
(9)
As mentioned above, in actual operation, formula (5) appears in equation form, so that the system new energy surplus at any time in formula (9) is basically determined by the term 2 on the right side of the equation. Item 2 on the right includes the turndown flexibility resources provided by the system for the new energy consumption and the flexibility resources required by the new energy in the system. If the supply is larger than the demand, the current renewable surplus is negative, and the system still has a space for consuming more renewable energy sources; otherwise, the system is provided with renewable energy surplus caused by insufficient down-regulation flexibility. As can be seen from equation (9), the down-regulation flexibility resource provided by the system for renewable energy consumption depends on the system load level, the unit down-regulation depth, the self-contained power plant output level, and the action condition of the electric-to-hydrogen conversion device. If the self-contained power plant part participates in power regulation, the new energy consumption capacity of the system can be improved; if the electricity-to-hydrogen device in the system participates in operation, the surplus of new energy of the system is also reduced.
The static evaluation index is a flexibility-based air curtailment evaluation index, as shown in formula (9), the air curtailment of the system at the time t is expressed, and in order to clarify the dynamic consumption capability of the new energy of the system, the sum of squares of the air curtailment change rates of all adjacent time periods of the new energy is used as a dynamic consumption index, and the calculation is as follows:
(4) Novel electric power system electricity conversion hydrogen carbon emission index
The full life cycle of an electrical power system generally refers to the entire life of the manufacturing installation, production operation, operational maintenance, and recovery processes. The wind power generation almost has no carbon emission in the production and operation links, and the carbon emission is mainly concentrated in the remaining 3 links; coal-fired power generation is the main power supply source of the inland large power grid, and carbon emission exists in 4 links of the whole life cycle. CO 2 2 The contribution of emissions to carbon emissions exceeds 60%. The carbon emission index herein can be calculated as follows:
in the formula: rho m As coal fuel CO 2 Coefficient of emission, F m The coal consumption of the thermal power generating unit.
The coal consumption of the coal-electricity hydrogen production under the same condition.
Different electricity-to-hydrogen projects are different in the mathematical model due to different stages, the electricity-to-hydrogen project of quasi-commercial operation is taken as the background, multiple technical and economic indexes such as flexibility, new energy consumption, economy and environmental protection of the electricity-to-hydrogen system are comprehensively considered, a comprehensive evaluation index mathematical model is established based on an analytic hierarchy process, and a comprehensive evaluation index value is taken as the basis for planning the electricity-to-hydrogen scheme.
max R i ＝λ T λ i (i＝1,2,...,n) (12)
In the formula: r i Is a comprehensive evaluation index; n is the number of decision schemes; lambda is a characteristic vector between standards; lambda [ alpha ] i Is the feature vector of the ith scheme.
The membership function is characterized by constructing a mathematical expression (15) of the membership function according to the quantitative relationship between the judgment level and the influence factor (the influence factor refers to an evaluation index, and the judgment level is the importance degree of each index). For the influencing factor in a specific state, the degree of membership of the influencing factor to the target at the moment can be obtained through a membership function. Fuzzy statistics are used to determine membership functions.
Before carrying out fuzzy estimation on the risk probability, various settings are firstly made on various judgment results, corresponding sets are established, a comprehensive judgment matrix is established by combining with a membership function, and the comprehensive judgment matrix is expressed in a formula (13).
In the formula: rij so that the ith influencing factor accounts for the importance of the jth comment in the set of comments.
In order to reflect the comprehensive influence of all factors, a weight fuzzy set and a comprehensive evaluation matrix of the factors are adopted to calculate and carry out fuzzy estimation on the risk probability.
C＝A×R＝(C 1 ,C 2 ,C 3 …C P ) (14)
In the formula, C represents a comprehensive judgment result of the risk probability, cj represents the importance degree of the comprehensive judgment result in the jth comment in the comment set, and A is a pairwise comparison matrix established by each index according to the importance degree.
The method adopts a maximum membership method to evaluate and process the results of the wind curtailment rate and the flexibility shortage rate of the power grid. The maximum membership method is to take the evaluation set corresponding to the maximum evaluation index Cj as the final evaluation result of the comprehensive analysis matrix C, and the expression is shown in formula (15).
V＝{v i |v i Axle key (15) C middle maximum element } (
The method can be used for solving the decision problem of the multi-attribute power-to-hydrogen planning scheme by quantifying the importance degree of each standard, calculating the weight of each decision scheme standard and evaluating the quality sequence of each scheme based on the weight. FIG. 1 is a hierarchical structure diagram of a power to hydrogen system planning, which includes three levels, namely a target level, a standard level and a decision scheme level, wherein the target level is optimal for comprehensive evaluation of the system; the standard layer is used for comprehensively measuring a plurality of indexes, and the decision scheme layer is used for different electricity-to-hydrogen configuration schemes.
The objective function employs the following constraints:
node power balance constraints
In the formula: p i Active power injected into the node i;
active power generated by a generator on a node i;
active power generated by the wind farm at node i;
the consumed power for converting the electricity into the hydrogen for the node i;
is the active load power on node i;
if all transmission lines of the whole network adopt the same constraint value, alpha is ij α is not required. Accordingly, the inequality constraint reduces to:
P ij ≤αP ij,max (18)
unit output constraint
In the formula:
the minimum output value of the generator on the node i is obtained;
the maximum output value of the generator on the node i is obtained;
the minimum output value of the wind power plant above the node i is obtained;
the maximum output value of the wind farm above the node i.
Electric to hydrogen output constraints
0≤P ELC ≤P ELC，MAX (21)
The unit climbing constraint, thermal power unit and electricity change hydrogen device need satisfy power climbing constraint as follows:
|P elc,t -P elc,t-1 ≤ΔP elc,max (23)
in the formula: delta P elc,max The maximum climbing power is the maximum climbing power of the P2H in the unit time interval in the starting state. Cap g The total capacity of the thermoelectric generator set in the system.
The ramp rate is adjusted up and down for thermal power.
The invention solves the problem of comprehensive evaluation of the power-to-hydrogen comprehensive value, mainly calculates the weight of each decision scheme standard (the result of formula 12 is the final weight) by quantifying the importance degree of each standard, evaluates the quality sequence of each scheme based on the weight, establishes a comprehensive evaluation index mathematical model based on an analytic hierarchy process, and takes the comprehensive evaluation index value as the basis of planning the power-to-hydrogen scheme. The system value evaluation method can effectively avoid the limitations of the existing evaluation methods such as single value evaluation, multiple value evaluation, standard cost and the like, and can evaluate the value of converting electricity into hydrogen more objectively and comprehensively. Meanwhile, the method can also solve the decision problem of the planning scheme with multiple attributes of other resources.
The embodiment is as follows:
the invention selects a typical daily curve of the northeast as a system scheduling curve, the time interval is 15min, and 96 time periods are total. The load power and the output power of the wind turbine generator are shown in fig. 2, and are respectively selected from 1 thermoelectric generator set in the system, the power is 200MW, and one wind turbine generator set is 150MW. In this example, 1 battery of electricity was used to convert hydrogen into electricity of 10MW,2 MW,30MW,40MW,50MW. The unit capacity cost of electricity-to-hydrogen is 3500 yuan/KW, the life cycle is 10 years, and the conversion is carried out by taking 330d as one year. The price of hydrogen sale is 2.7 yuan/Nm 3 . And the electricity-to-hydrogen participation requires side response, optimizes a system load curve and obtains benefits by preparing hydrogen. The power-to-hydrogen conversion is used as a system flexible resource to participate in power grid dispatching, and the value of the power-to-hydrogen conversion is linearly related to the power-to-hydrogen conversion capacity according to a value quantification model. When the electricity-to-hydrogen is involved in the power grid dispatching application, the configuration capacity of the electricity-to-hydrogen is not large enough relative to the load, so that the system value of the electricity-to-hydrogen is in direct proportion to the configuration capacity of the electricity-to-hydrogen. However, when the configured capacity of the power-to-hydrogen converter is continuously increased to the valley and cannot be operated at full power or completely shut down at the peak, the slope of the value curve of the power-to-hydrogen converter system is reduced. With the increase of the scale of electricity-to-hydrogen, the investment cost is in an increasing trend. The calculation is performed with reference to the flowchart of fig. 9.
When the scale of the electricity-to-hydrogen conversion is 10MW, the investment cost is the lowest, about 140 ten thousand yuan;
when the scale of electro-hydrogen conversion is 50MW, the investment cost is the highest, about 22400 ten thousand yuan.
When electricity-to-hydrogen is used as system flexibility load to participate in peak clipping and valley filling, static consumption and dynamic consumption of new energy are shown in fig. 3, the consumption rate of the new energy of the system is reduced in 23-24 hours, the thermoelectric unit is influenced by other factors at the moment, the electricity output is obviously increased, and in order to meet the balance of system flexibility and the balance of system power, the system chooses to give up the wind electricity output to ensure the safe and stable operation of the system.
As shown in fig. 4, the power consumption of the electric power to hydrogen increases gradually as the capacity increases. The system is limited by wind power output at 19-24 moments and cannot meet the full-power operation of the electric hydrogen conversion device. The electric hydrogen conversion device at this moment is used as a flexible load to follow the output of wind power, flexibly adjust the output of the unit and maintain the stable operation of the system.
The change trends of the new energy dynamic consumption, the electricity-to-hydrogen profitability index, the system carbon emission rate and other indexes of the electricity-to-hydrogen system are shown in fig. 5.
It can be seen from fig. 5 that as the capacity of the power to hydrogen increases, the dynamic consumption of new energy of the system gradually increases, the air curtailment amount gradually decreases, the profitability index of the power to hydrogen gradually decreases as the capacity of the power to hydrogen increases, and the air curtailment amount of the system is not always the same as that of the power to hydrogen full-load operation.
Investors of commercial operation electricity-to-hydrogen projects pay attention to the multiple indexes, projects with low cost, long service life, high investment return rate and high net income are easier to popularize and apply in practical projects, and the calculation result of the graph 4 shows that the electricity-to-hydrogen scale obtained based on single index evaluation cannot meet the requirements of the indexes at the same time.
FIG. 6 compares the evaluation conditions of the indexes of the 10MW/50MW power-to-hydrogen system with the lowest investment cost and the highest wind curtailment consumption rate.
It can be seen more intuitively from the system 3 technical and economic indicators radar chart in fig. 7 that:
the investment return rate is the highest, namely when the scale of the electricity-to-hydrogen conversion is 10MW, the system investment cost is low, the investment recovery period is short, the investment return rate is high, but the wind abandoning rate is high, and the system flexibility is insufficient;
the net benefit is the largest, namely when the scale of converting electricity into hydrogen is 50MW, the wind curtailment and absorption capacity of the system is the strongest, the flexibility is sufficient, the net benefit is high, but the investment cost is high, and the return on investment is low.
The comprehensive evaluation indexes of the scale of the electric hydrogen conversion system under different weights are shown in fig. 8:
in the figure, R1R2 represents two kinds of targets, namely dynamic wind curtailment rate and CO in R1 2 The emission is a grade 1 index, R2 is opposite, and the profitability of electricity-to-hydrogen is a grade 1 index. Analysis shows that the change of the comprehensive index tends to be smooth along with the increase of the scale of converting electricity into hydrogen, and when the capacity is 50MW, the value of the comprehensive evaluation index established by the invention is optimal.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (4)
1. A multidimensional value evaluation method for a hydrogen electrolysis-to-conversion device based on fuzzy hierarchical analysis is characterized by comprising the following steps:
establishing a multidimensional value evaluation index mathematical model taking electricity-to-hydrogen as a newly added flexible resource:
max R i ＝λ T λ i ，i＝1,2,...,n
wherein: r i Is a comprehensive evaluation index; n is the number of decision schemes; lambda is a feature vector between standards; lambda [ alpha ] i Is the feature vector of the ith scheme;
constructing a comprehensive evaluation matrix:
wherein r is ij So that the ith influence factor accounts for the importance degree of the jth comment in the comment set;
carrying out fuzzy estimation on risk probability by adopting a weight fuzzy set of factors and comprehensive evaluation matrix operation:
C＝A×R＝(C 1 ,C 2 ,C 3 …C j )
wherein C represents the comprehensive judgment result of the risk probability, C j Representing the importance degree of the result of the comprehensive evaluation in the jth comment in the comment set;
the maximum evaluation index C is j The corresponding comment set is used as a final evaluation result of the comprehensive analysis matrix C:
V＝{v i |v i and ← C maximum element }.
2. The method of claim 1, wherein the composite evaluation index comprises: the system comprises an electricity-to-hydrogen economic value evaluation index, a system level flexibility evaluation index, a novel power system new energy consumption evaluation index and a novel power system electricity-to-hydrogen carbon emission index.
3. The method according to claim 2, wherein the electricity-to-hydrogen economic value evaluation index is a profitability as the electricity-to-hydrogen economic value evaluation index:
in the formula: v y The yield of electricity-to-hydrogen on the y day; rho is the daily interest rate of the bank;
converting the current value of the system cost into the annual value cost by using the equal-amount series capital recovery coefficients, wherein the expression is
In the formula: c NPC The system present cost; c AC The daily cost; n is the number of days of cost conversion; i is the daily rate of interest;
the calculation formula of the investment cost of the electricity-to-hydrogen system is
C NPC ＝γ h P elc,max (3)
In the formula: c NPC Investment cost for electric to hydrogen system, gamma h Investment cost per unit capacity of electric hydrogen conversion equipment, P elc,max Capacity of an electricity-to-hydrogen plant;
the system-level flexibility evaluation index is the square sum of upward and downward flexibility of the system in a time period t and the proportion of the square sum to the maximum adjustable flexibility of the system;
the new energy consumption evaluation index of the novel power system takes the sum of squares of the air curtailment rate of change of all adjacent time periods of new energy as a dynamic consumption index, and is calculated as follows:
wherein:
P rescur,t ＝[(1+μ)P load.max -U G C G -P cl -kC self )-[(P load,t -L G C G -kC self -P elc,t )-P res,t )]；
P elc,t part of the flexibility requirements for the electricity-to-hydrogen plant, P rescur,t Indicates the new energy margin at time t, L G Is the minimum technical output coefficient, P, of a conventional power supply res,t Indicates new energy contribution, U G Is the maximum technical output coefficient, C, of a conventional power supply G The starting capacity of a conventional power supply; k is the average output coefficient of the self-contained power plant; c self For the starting-up capacity of the self-contained power plant, P cl Is the confidence capacity of renewable energy, the spare coefficient is mu;
the calculation of the emission index of hydrogen and carbon converted from electricity of the novel power system is as follows:
in the formula: rho m As coal fuel CO 2 Coefficient of emission, F m The coal consumption of the thermal power generating unit is increased,
the coal consumption of the coal electricity hydrogen production under the same condition.
4. The method of claim 3,
the constraint conditions of the multidimensional value evaluation index mathematical model comprise:
node power balance constraint:
in the formula: p i Active power injected into the node i;
active power generated by a generator on a node i;
active power generated by the wind farm at node i;
the consumed power for converting the electricity into the hydrogen for the node i;
is the active load power on node i;
unit output restraint:
in the formula:
the minimum output value of the generator on the node i is obtained;
the maximum output value of the generator on the node i is obtained;
the minimum output value of the wind power plant above the node i is obtained;
the maximum output value of the wind farm at node i is:
electric-to-hydrogen output constraint:
0≤P ELC ≤P ELC，MAX
the unit climbing restraint, thermal power unit and electricity change hydrogen device need satisfy power climbing restraint as follows:
|P elc,t -P elc,t-1 |≤ΔP elc,max
in the formula: delta P elc,max Cap is the maximum power of P2H in the power-on state in unit time interval g Is the total capacity of the internal combustion engine set in the system,
respectively the down-regulation climbing speed and the up-regulation climbing speed of the thermal power.
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