CN109447328A - The distributed wind power plant sale of electricity income optimization method of meter and user's willingness to pay - Google Patents
The distributed wind power plant sale of electricity income optimization method of meter and user's willingness to pay Download PDFInfo
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Abstract
The present invention relates to a kind of meter and the distributed wind power plant sale of electricity income optimization methods of user's willingness to pay.Consider the uncertainty and its fluctuation for being sold to power grid electric price of wind power output, it proposes that several pairs of uncertain factors of connection is combined to carry out the method that objective statement is described comprehensively under more scenes, the willingness to pay of quantization user is characterized with user utility function, and one of clean energy resource dynamic constraint condition is bought as it, the distributed wind-powered electricity generation sale of electricity income Optimized model of meter and user's willingness to pay is established, provides reference for sale of electricity strategy of the distributed wind power plant quotient under market environment.
Description
Technical field
The present invention relates to distributed wind-powered electricity generation field, the distributed wind power plant of especially a kind of meter and user's willingness to pay is sold
Electric income optimization method.
Background technique
China's planning clearly proposes to develop simultaneously with centralization power generation in a distributed manner, and the principle based on consumption optimizes wind-powered electricity generation cloth nearby
Office.Have many advantages, such as to dissolve nearby, dispatch flexibly, the distributed wind-powered electricity generation of energy conservation and environmental protection it is receive more and more attention.Product
Distributed power generation project is developed in pole can be improved efficiency of energy utilization, optimizes power structure, is the certainty of China's sustainable development
Selection.Under the overall situation of electricity market, distributed wind power plant can surf the Internet with power consumer direct dealing, remaining capacity, with
This obtains income.The distributed wind-powered electricity generation in China is in the starting stage, and carrying out analysis to its income facilitates government department, electric power
User, investor etc. are fully recognized that the value of distributed wind-powered electricity generation, have to the development and application of realizing distributed wind-powered electricity generation important
Realistic meaning.
It is current in the income research of distributed wind power plant, it is uncertain to wind power output, load variations, Electricity price fluctuation etc.
The processing of factor is the certainty converted uncertainty under specified conditions mostly, and it is uncertain to have ignored uncertain factor
Essence in terms of consider randomness in place of Shortcomings, and mostly anticipates for the payment of user from cost-benefit angle
Hope is all qualitative analysis, does not quantify to state.Several pairs of uncertain factors of the connection that this method is introduced into theory of set pair are stated, right
Uncertainty has carried out objective description, while introducing utility function and quantifying user's willingness to pay.Fully consider distributed wind
Interact the relationship of influence between the electric field investor, power grid and user three, from the angle of distributed wind power plant quotient,
The variation of meter and wind power output, customer charge demand and electricity price is combined connection number under more scenes, is measured using utility function
User's willingness to pay constructs distributed wind power plant maximum revenue model as constraint condition, optimization wind power plant be sold to user and
The electricity of power grid, to obtain maximum return.
Summary of the invention
The purpose of the present invention is to provide the distributed wind power plant sale of electricity income optimizations of a kind of meter and user's willingness to pay
Method, considers the uncertainty and its fluctuation for being sold to power grid electric price of wind power output, and proposition is tied under more scenes
It closes several pairs of uncertain factors of connection and carries out the methods that objective statement is described comprehensively, quantify user's with user utility function to characterize
Willingness to pay, and one of clean energy resource dynamic constraint condition is bought as it, establish the distributed wind of meter and user's willingness to pay
Electric sale of electricity income Optimized model provides reference for sale of electricity strategy of the distributed wind power plant quotient under market environment.
To achieve the above object, the technical scheme is that the distributed wind-powered electricity generation of a kind of meter and user's willingness to pay
Field sale of electricity income optimization method, includes the following steps:
Step S1, data are extracted；It extracts distributed wind farm wind velocity, cost data, distributed wind power plant and is sold to power grid
Electricity price, the average electricity price of distributed wind power plant and user's negotiation, the sales rate of electricity of power grid, user demand data, unit coal
Charcoal is converted into co2Factor sigma, distributed wind power plant location fired power generating unit unit quantity of electricity average coal consumption mcoal, unit carbon transaction
Price δ；
Step S2, in each scene wind power output and wind power plant be sold to power grid electric electricity price with contacting several expression；
Step S3, target is turned to Income Maximum, establishes the distributed wind power plant sale of electricity income of meter and user's willingness to pay
Optimized model describes as one of constraint condition the willingness to pay utility function of user；
Step S4, with PSO Algorithm model, and evaluation index is provided.
In an embodiment of the present invention, the step S2 specifically comprises the following steps:
Step S21, to the connection number expression of the wind power output in each scene:
Distributed output of wind electric field PWIt is with value P in s-th of scene of period t with intermittent, randomnessSW t,sFor in
The heart changes within the scope of predetermined fluctuation, and with the off-centered distance P that contributesiW t,sIt becomes larger, the probability that it occurs is gradually
Become smaller；Power output uncertainty of the wind power plant under t-th of period each scene is indicated with connection number:
In formula, i is uncertainty coefficient, and size changes in [- 1,1], PiW t,sTo contribute under t-th of period scene s of wind power plant
Fluctuation amplitude, setting when value interval should be avoided to overlap, in order to avoid expression repeat, consider wind energy turbine set installed capacity
The deviation of error and predicted value of contributing is configured fluctuation amplitude；
Step S22, the connection number expression of power grid electric electricity price is sold to the wind power plant in each scene:
Carrying out connection number expression to the price uncertainty that wind power plant is sold to power grid electric has:
λGW t,s=λSGW t,s+λiGW t,si
In formula, λSGW t,sAnd λiGW t,sDistributed wind-powered electricity generation is sold to power grid respectively under s-th of period of t-th of wind power plant scene
The electricity price central value and fluctuation amplitude of electricity.
In an embodiment of the present invention, the step S3 specifically comprises the following steps:
Step S31, using Income Maximum as target, while considering that the uncertainty of distributed output of wind electric field, it is sold to
The fluctuation of power grid electric price is established Optimized model using the method that more scenes combine connection number, is expressed as with mathematical function:
In formula: QW t,s,QUW t,s,QGW t,sRespectively distributed wind power plant is under t period s scene: generated energy is sold
To the electricity of user, it is sold to the electricity of power grid；λGW t,sPower grid is sold under s scene for the distributed wind power plant t period
The price of electricity；λUWThe electricity price of user is sold to for distributed wind power plant；et,sFor the s scene of distributed wind power plant t period
Energy-saving and emission-reduction income；CAThe average unit cost to generate a kilowatt for wind power plant；Δ C is wind-powered electricity generation deviation punishment cost；pt,sFor distribution
The probability that the s scene of wind power plant t period occurs；T, S are respectively period, scene number；
The energy-saving and emission-reduction income of wind-powered electricity generation is indicated using carbon emission Trading Model are as follows:
In formula, σco2Co is converted to for unit coal2Coefficient；mcoalIt needs to consume for conventional thermal power unit production unit electric energy
Tandard coal amount, determined by the coal consumption average level of location power plant, distributed power generation factory fired power generating unit；δ is with reference to international standard
Determining unit carbon transaction price；
Wind-powered electricity generation deviation punishment cost are as follows:
In formula, λGiFor sales rate of electricity supplied to consumers；D% is negative deviation rate；
Step S32, the constraint condition of the distributed wind power plant sale of electricity income Optimized model based on more scenes connection number:
(1) distributed wind power plant generated energy Constraints of Equilibrium
QW t,s=QUW t,s+QGW t,s
(2) distributed output of wind electric field constraint
PW t,s≤Pmax
Wherein, PmaxFor wind power plant peak power output, PW t,sFor output of the wind power plant under s-th of scene of t period
Power；
(3) distributed wind power plant quotient's minimum requirements earning rate constraint
It introduces connection number and describes uncertain factor, obtained Profit Assessment is also connection coefficient form；Connection determine amount with not
Determine that the i of amount changes between [- 1,1], so that maximum return changes therewith；If distributed wind power plant requires the scheme of optimization
Earning rate is not less than rminThe ratio of %, the maximum return and cost that each scheme obtains need and minimum requirements earning rate rmin% into
Row compares:
In formula, R is income of the distributed wind power plant in one month；
(4) user's willingness to pay constrains
User will affect the income of wind power plant to the willingness to pay of wind-powered electricity generation, its payment is measured using the utility function of user
Wish；
Utility function indicates are as follows: U (x1,x2,…,xn), wherein x1,x2,…,xnIndicate that consumer consumes n kind in a period
The quantity of commodity；Utilize common CES utility function:
In formula, δiFor parameter, the degree of recognition of the consumer to commodity value, δ are characterized1+δ2+ ...+δ n=1；ρ is substitution ginseng
Number, ρ >=-1；
The electricity for buying distributed wind power plant and the electricity of selection power grid is selected to be seen as 2 kinds of special commodity user, then
Both commodity are user's bring effectiveness are as follows:
In formula: C is demand charge；
It is available that " the ratio between marginal utility of commodity is equal to commodity price ratio " can be obtained by economic theory:
Both sides take logarithm arrangement that can obtain:
In formula, elasticity of substitution=- 1/ (1+ ρ) characterizes opposite caused by the change rate of the two kinds of consumer goods its relative prices need
The change rate for the amount of asking is constant in CES utility function；δ1、δ2Think only related with commodity price herein；ε is error
?；
For a user, receive effectiveness U there are minimum on the basis of itself loadmin, when the effectiveness that Purchasing combination obtains
Less than it is minimum receive effectiveness when, user will change to distributed wind power plant and the purchase of electricity of power grid proportion, itself is promoted with this
Effectiveness；Therefore the effectiveness of user need to receive effectiveness not less than the minimum of user:
U≥Umin。
In an embodiment of the present invention, the step S4 specifically comprises the following steps:
Step S41, with PSO Algorithm model；
Step S42, the evaluation index of the distributed wind power plant sale of electricity income Optimized model based on more scenes connection number:
(1) section can be met
According to the comparative approach of connection number, available earning rate is not less than rminThe credibility interval the i I of %；Show distribution
The income of wind power plant meets the earning rate requirement of the wind power plant investor under the influence of uncertain factor in the I of section, and I is known as
Section can be met, the range size of I has a direct impact adaptability of the scheme to environment；
(2) fitness
In different sections, the project compatibility difference that the i in number is embodied is contacted, i is bigger, can meet interval width more
It is wide then adaptability is stronger, therefore blind number is introduced to state fitness；
Ifαm∈ [0,1], m=1,2...M, then blind several fitness may be expressed as:
In formula: αmFor credibility interval xmFitness i.e. indicate i in section xmThe fitness of optimum results when interior；For total fitness of f (i)；M is the order of blind several fitness；By minimum requirements earning rate rmin% obtains to expire
Sufficient section I=[IL,IR], thus obtain the fitness β of scheme:
When fitness α is bigger, then show that the ability of the variation of scheme response environment is stronger, i.e., robustness is stronger.
Compared to the prior art, the invention has the following advantages: the method for the present invention considers the uncertain of wind power output
Property and its fluctuation for being sold to power grid electric price, propose several pairs of uncertain factors of connection is combined to carry out under more scenes complete
The method that the objective statement in face is described characterizes the willingness to pay of quantization user with user utility function, and as its purchase cleaning
One of energy source and power constraint condition establishes the distributed wind-powered electricity generation sale of electricity income Optimized model of meter and user's willingness to pay, for distribution
Sale of electricity strategy of the formula wind power plant quotient under market environment provides reference.
Detailed description of the invention
Fig. 1 is a month daily optimization electricity.
Specific embodiment
With reference to the accompanying drawing, technical solution of the present invention is specifically described.
The present invention provides the distributed wind power plant sale of electricity income optimization method of a kind of meter and user's willingness to pay, including such as
Lower step:
Step S1, data are extracted；It extracts distributed wind farm wind velocity, cost data, distributed wind power plant and is sold to power grid
Electricity price, the average electricity price of distributed wind power plant and user's negotiation, the sales rate of electricity of power grid, user demand data, unit coal
Charcoal is converted into co2Factor sigma, distributed wind power plant location fired power generating unit unit quantity of electricity average coal consumption mcoal, unit carbon hand over
Easy price δ；
Step S2, in each scene wind power output and wind power plant be sold to power grid electric electricity price with contacting several expression；
Step S3, target is turned to Income Maximum, establishes the distributed wind power plant sale of electricity income of meter and user's willingness to pay
Optimized model describes as one of constraint condition the willingness to pay utility function of user；
Step S4, with PSO Algorithm model, and evaluation index is provided.
Further, the step S2 specifically comprises the following steps:
Step S21, to the connection number expression of the wind power output in each scene:
If A describes things m and n deterministic dependence, B describes things m and n uncertainty relation, and i then indicates uncertain, then
A+Bi be referred to as binary connection number, with u come indicate connection number, then have:
U=A+Bi
I is that the coefficient of uncertain component can also be used as probabilistic label under specific circumstances in formula, and i is in [- 1,1]
Section regards different situations value.In the system comprising uncertain factor, result is that the probability of some determination value is smaller, but ties
Fruit, which appears in, determines that a certain range of probability of value is larger, and connection number is not only the interval range where a determining value and its
It is associated, really quantitative in macroscopic aspect will even more be utilized simultaneously with the Uncertainty in microcosmic point, so that determining amount and not
Determine that the variation for measuring the connection that influences each other can quantitatively obtain objective expression.
Equipped with connection number u1=A1+B1I and u2=A2+B2I:
1) connection number is added, subtracts
u1+u2As the two determines that amount and Uncertainty are separately summed or subtract each other:
u1+u2=(A1+A2)+(B1+B2)i
u1-u2=(A1-A2)+(B1-B2)i
Two connection numbers, which are added (subtracting), can be generalized to n connection number addition (subtracting):
u1+u2+...+un=(A1+A2+...+An)+(B1+B2+...Bn)i
2) multiplication, the division of number are contacted
u1And u2Multiplying are as follows:
u1×u2=(A1A2-B1B2)+(A1B2+B1A2)i
Connection number u=A+Bi is identical as the plural number form of A+Bi, and the i in plural number has i4n=1, i4n+1=i, i4n+2=-1,
i4n+2The rule of=- i.The i contacted in number indicates Uncertainty, if having in same regulation connection number: i4n=1, i4n+1=i, i4n +2=-1, i4n+2=-i then available coefficient form, i.e. after two binary connection multiplications or binary contacts number.Connection
The division rule of coefficient is available by the inverse operation of connection co-efficient multiplication.
3) law of communication, associative law and the distributive law of number are contacted
Connection number meets law of communication, associative law and distributive law:
u1+u2=u2+u1；u1u2=u2u1
(u1+u2)+u3=u1+(u2+u3)=(u1+u3)+u2
u1(u2+u3)=u1u2+u1u3
4) size of simple form second line of a couplet coefficient compares
For contacting number u1, u2Have:
u1-u2=(A1-A2)+(B1-B2)i
If i ∈ I is contained in [- 1,1], u1-u2> 0 then has u in the range of i ∈ I1> u2；It is identical, u1-u2< 0
When, there is u1< u2。
Distributed output of wind electric field PWIt is with value P in s-th of scene of period t with intermittent, randomnessSW t,sFor in
The heart changes within the scope of predetermined fluctuation, and with the off-centered distance P that contributesiW t,sIt becomes larger, the probability that it occurs is gradually
Become smaller；Power output uncertainty of the wind power plant under t-th of period each scene is indicated with connection number:
In formula, i is uncertainty coefficient, and size changes in [- 1,1], PiW t,sTo contribute under t-th of period scene s of wind power plant
Fluctuation amplitude, setting when value interval should be avoided to overlap, in order to avoid expression repeat, consider wind energy turbine set installed capacity
The deviation of error and predicted value of contributing is configured fluctuation amplitude；
Step S22, the connection number expression of power grid electric electricity price is sold to the wind power plant in each scene:
Carrying out connection number expression to the price uncertainty that wind power plant is sold to power grid electric has:
λGW t,s=λSGW t,s+λiGW t,si
In formula, λSGW t,sAnd λiGW t,sDistributed wind-powered electricity generation is sold to power grid respectively under s-th of period of t-th of wind power plant scene
The electricity price central value and fluctuation amplitude of electricity.
In an embodiment of the present invention, the step S3 specifically comprises the following steps:
Step S31, using Income Maximum as target, while considering that the uncertainty of distributed output of wind electric field, it is sold to
The fluctuation of power grid electric price is established Optimized model using the method that more scenes combine connection number, is expressed as with mathematical function:
In formula: QW t,s,QUW t,s,QGW t,sRespectively distributed wind power plant is under t period s scene: generated energy is sold
To the electricity of user, it is sold to the electricity of power grid, kWh；λGW t,sIt is sold under s scene for the distributed wind power plant t period
The price of power grid electric, member/kWh；λUWThe electricity price of user, member/kWh are sold to for distributed wind power plant；et,sFor distributed wind-powered electricity generation
The energy-saving and emission-reduction income of the s scene of field t period, member；CAFor the average unit cost that wind power plant generates a kilowatt, member；Δ C is wind
Electric deviation punishment cost, member；pt,sThe probability occurred for the s scene of distributed wind power plant t period；T, S are respectively the time
Section, scene number；
The energy-saving and emission-reduction income of wind-powered electricity generation is indicated using carbon emission Trading Model are as follows:
In formula, σco2Co is converted to for unit coal2Coefficient；mcoalIt needs to consume for conventional thermal power unit production unit electric energy
Tandard coal amount, determined by the coal consumption average level of location power plant, distributed power generation factory fired power generating unit；δ is with reference to international standard
Determining unit carbon transaction price；
Wind-powered electricity generation deviation punishment cost are as follows:
In formula, λGiFor sales rate of electricity supplied to consumers；D% is negative deviation rate；
Step S32, the constraint condition of the distributed wind power plant sale of electricity income Optimized model based on more scenes connection number:
(3) distributed wind power plant generated energy Constraints of Equilibrium
QW t,s=QUW t,s+QGW t,s
(4) distributed output of wind electric field constraint
PW t,s≤Pmax
Wherein, PmaxFor wind power plant peak power output, PW t,sFor output of the wind power plant under s-th of scene of t period
Power；
(3) distributed wind power plant quotient's minimum requirements earning rate constraint
It introduces connection number and describes uncertain factor, obtained Profit Assessment is also connection coefficient form；Connection determine amount with not
Determine that the i of amount changes between [- 1,1], so that maximum return changes therewith；If distributed wind power plant requires the scheme of optimization
Earning rate is not less than rminThe ratio of %, the maximum return and cost that each scheme obtains need and minimum requirements earning rate rmin% into
Row compares:
In formula, R is income of the distributed wind power plant in one month；
(4) user's willingness to pay constrains
User will affect the income of wind power plant to the willingness to pay of wind-powered electricity generation, its payment is measured using the utility function of user
Wish；
Utility function indicates are as follows: U (x1,x2,…,xn), wherein x1,x2,…,xnIndicate that consumer consumes n kind in a period
The quantity of commodity；Utilize common CES utility function:
In formula, δiFor parameter, the degree of recognition of the consumer to commodity value, δ are characterized1+δ2+ ...+δ n=1；ρ is substitution ginseng
Number, ρ >=-1；
The electricity for buying distributed wind power plant and the electricity of selection power grid is selected to be seen as 2 kinds of special commodity user, then
Both commodity are user's bring effectiveness are as follows:
In formula: C is demand charge；
It is available that " the ratio between marginal utility of commodity is equal to commodity price ratio " can be obtained by economic theory:
Both sides take logarithm arrangement that can obtain:
In formula, elasticity of substitution=- 1/ (1+ ρ) characterizes opposite caused by the change rate of the two kinds of consumer goods its relative prices need
The change rate for the amount of asking is constant in CES utility function；δ1、δ2Think only related with commodity price herein；ε is error
?；
For a user, receive effectiveness U there are minimum on the basis of itself loadmin, when the effectiveness that Purchasing combination obtains
Less than it is minimum receive effectiveness when, user will change to distributed wind power plant and the purchase of electricity of power grid proportion, itself is promoted with this
Effectiveness；Therefore the effectiveness of user need to receive effectiveness not less than the minimum of user:
U≥Umin。
In an embodiment of the present invention, the step S4 specifically comprises the following steps:
Step S41, with PSO Algorithm model；
Step S42, the evaluation index of the distributed wind power plant sale of electricity income Optimized model based on more scenes connection number:
(1) section can be met
According to the comparative approach of connection number, available earning rate is not less than rminThe credibility interval the i I of %；Show distribution
The income of wind power plant meets the earning rate requirement of the wind power plant investor under the influence of uncertain factor in the I of section, and I is known as
Section can be met, the range size of I has a direct impact adaptability of the scheme to environment；
(2) fitness
In different sections, the project compatibility difference that the i in number is embodied is contacted, i is bigger, can meet interval width more
It is wide then adaptability is stronger, therefore blind number is introduced to state fitness；
Ifαm∈ [0,1], m=1,2...M, then blind several fitness may be expressed as:
In formula: αmFor credibility interval xmFitness i.e. indicate i in section xmThe fitness of optimum results when interior；For total fitness of f (i)；M is the order of blind several fitness；By minimum requirements earning rate rmin% obtains to expire
Sufficient section I=[IL,IR], thus obtain the fitness β of scheme:
When fitness α is bigger, then show that the ability of the variation of scheme response environment is stronger, i.e., robustness is stronger.
The following are a specific examples of the invention.
As shown in Figure 1, present embodiments provide it is a kind of meter and user's willingness to pay distributed wind power plant sale of electricity income it is excellent
Change method, specifically includes the following steps:
Step S1: data are extracted；Referring to table 1,2, distributed wind farm wind velocity, cost data, distributed wind power plant are extracted
It is sold to average electricity price, the sales rate of electricity of power grid, user demand number that power grid electric electricity price, distributed wind power plant and user negotiate
Co is converted into according to, unit coal2Factor sigma, distributed wind power plant location fired power generating unit unit quantity of electricity average coal consumption mcoal、
Unit carbon transaction price δ etc..Wherein distributed wind power plant is sold to the price λ of power grid electricGWiObeying the expectation of peak Pinggu is respectively
0.6,0.5,0.4 yuan/kWh, variance are respectively 0.065,0.055,0.045 (member/kWh)2Normal distribution, distributed wind-powered electricity generation
Field is sold to electricity price (i.e. electricity price of the user to wind power plant power purchase) λ of userUWIt is set as 0.5 yuan/kWh；The sales rate of electricity of power grid is
Peak Pinggu electricity price, respectively 0.95,0.65 and 0.55 yuan/kWh.
Step S2: in each scene of generation wind power output and wind power plant be sold to power grid electric price with contacting number
Expression.
Step S3: target is turned to Income Maximum, establishes the distributed wind power plant sale of electricity income of meter and user's willingness to pay
Optimized model describes as one of constraint condition the willingness to pay utility function of user
Step S4: PSO Algorithm model is used, and provides evaluation index.
The above are preferred embodiments of the present invention, all any changes made according to the technical solution of the present invention, and generated function is made
When with range without departing from technical solution of the present invention, all belong to the scope of protection of the present invention.
Claims (4)
1. the distributed wind power plant sale of electricity income optimization method of a kind of meter and user's willingness to pay, which is characterized in that including such as
Lower step:
Step S1, data are extracted；It extracts distributed wind farm wind velocity, cost data, distributed wind power plant and is sold to power grid electric
Electricity price, the average electricity price of distributed wind power plant and user's negotiation, the sales rate of electricity of power grid, user demand data, unit coal turn
Turn to co2Factor sigma, distributed wind power plant location fired power generating unit unit quantity of electricity average coal consumption mcoal, unit carbon transaction price
δ；
Step S2, in each scene wind power output and wind power plant be sold to power grid electric electricity price with contacting several expression；
Step S3, target is turned to Income Maximum, establishes the distributed wind power plant sale of electricity income optimization of meter and user's willingness to pay
Model describes as one of constraint condition the willingness to pay utility function of user；
Step S4, with PSO Algorithm model, and evaluation index is provided.
2. the distributed wind power plant sale of electricity income optimization method of meter according to claim 1 and user's willingness to pay,
It is characterized in that, the step S2 specifically comprises the following steps:
Step S21, to the connection number expression of the wind power output in each scene:
Distributed output of wind electric field PWIt is with value P in s-th of scene of period t with intermittent, randomnessSW t,sCentered on,
Change within the scope of predetermined fluctuation, and with the off-centered distance P that contributesiW t,sIt becomes larger, the probability that it occurs gradually becomes smaller；
Power output uncertainty of the wind power plant under t-th of period each scene is indicated with connection number:
In formula, i is uncertainty coefficient, and size changes in [- 1,1], PiW t,sFor the wave contributed under t-th of period scene s of wind power plant
Dynamic amplitude should avoid value interval from overlapping, in order to avoid expression repeats, consider the error of wind energy turbine set installed capacity in setting
And the deviation of power output predicted value is configured fluctuation amplitude；
Step S22, the connection number expression of power grid electric electricity price is sold to the wind power plant in each scene:
Carrying out connection number expression to the price uncertainty that wind power plant is sold to power grid electric has:
λGW t,s=λSGW t,s+λiGW t,si
In formula, λSGW t,sAnd λiGW t,sDistributed wind-powered electricity generation is sold to power grid electric respectively under s-th of period of t-th of wind power plant scene
Electricity price central value and fluctuation amplitude.
3. the distributed wind power plant sale of electricity income optimization method of meter according to claim 2 and user's willingness to pay,
It is characterized in that, the step S3 specifically comprises the following steps:
Step S31, using Income Maximum as target, at the same consider distributed output of wind electric field uncertainty, its be sold to power grid
The fluctuation of electricity price is established Optimized model using the method that more scenes combine connection number, is expressed as with mathematical function:
In formula: QW t,s,QUW t,s,QGW t,sRespectively distributed wind power plant is under t period s scene: generated energy is sold to use
The electricity at family is sold to the electricity of power grid；λGW t,sPower grid electric is sold under s scene for the distributed wind power plant t period
Price；λUWThe electricity price of user is sold to for distributed wind power plant；et,sFor the section of the s scene of distributed wind power plant t period
It can emission reduction income；CAThe average unit cost to generate a kilowatt for wind power plant；Δ C is wind-powered electricity generation deviation punishment cost；pt,sFor distributed wind-powered electricity generation
The probability that the s scene of field t period occurs；T, S are respectively period, scene number；
The energy-saving and emission-reduction income of wind-powered electricity generation is indicated using carbon emission Trading Model are as follows:
In formula, σco2Co is converted to for unit coal2Coefficient；mcoalThe mark that need to be consumed for conventional thermal power unit production unit electric energy
Quasi- coal amount is determined by the coal consumption average level of location power plant, distributed power generation factory fired power generating unit；δ is to determine with reference to international standard
Unit carbon transaction price；
Wind-powered electricity generation deviation punishment cost are as follows:
In formula, λGiFor sales rate of electricity supplied to consumers；D% is negative deviation rate；
Step S32, the constraint condition of the distributed wind power plant sale of electricity income Optimized model based on more scenes connection number:
(1) distributed wind power plant generated energy Constraints of Equilibrium
QW t,s=QUW t,s+QGW t,s
(2) distributed output of wind electric field constraint
PW t,s≤Pmax
Wherein, PmaxFor wind power plant peak power output, PW t,sFor output power of the wind power plant under s-th of scene of t period；
(3) distributed wind power plant quotient's minimum requirements earning rate constraint
It introduces connection number and describes uncertain factor, obtained Profit Assessment is also connection coefficient form；Connection determines amount and does not know
The i of amount changes between [- 1,1], so that maximum return changes therewith；If distributed wind power plant requires the scheme earning rate of optimization
Not less than rminThe ratio of %, the maximum return and cost that each scheme obtains need and minimum requirements earning rate rmin% is compared
Compared with:
In formula, R is income of the distributed wind power plant in one month；
(4) user's willingness to pay constrains
User will affect the income of wind power plant to the willingness to pay of wind-powered electricity generation, its payment meaning is measured using the utility function of user
It is willing to；
Utility function indicates are as follows: U (x1,x2,…,xn), wherein x1,x2,…,xnIndicate that consumer consumes n kind commodity in a period
Quantity；Utilize common CES utility function:
In formula, δiFor parameter, the degree of recognition of the consumer to commodity value, δ are characterized1+δ2+ ...+δ n=1；ρ is alternate parameter, ρ
≥-1；
Select the electricity for buying distributed wind power plant and the electricity of selection power grid to be seen as 2 kinds of special commodity user, then this two
Kind commodity are user's bring effectiveness are as follows:
In formula: C is demand charge；
It is available that " the ratio between marginal utility of commodity is equal to commodity price ratio " can be obtained by economic theory:
Both sides take logarithm arrangement that can obtain:
In formula, elasticity of substitution=- 1/ (1+ ρ) characterizes relative requirements amount caused by the change rate of the two kinds of consumer goods its relative prices
Change rate, in CES utility function be constant；δ1、δ2Think only related with commodity price herein；ε is error term；
For a user, receive effectiveness U there are minimum on the basis of itself loadmin, when the effectiveness that Purchasing combination obtains is less than
Minimum user will change to distributed wind power plant and the purchase of electricity of power grid proportion when receiving effectiveness, promote itself effectiveness with this；
Therefore the effectiveness of user need to receive effectiveness not less than the minimum of user:
U≥Umin。
4. the distributed wind power plant sale of electricity income optimization method of meter according to claim 3 and user's willingness to pay,
It is characterized in that, the step S4 specifically comprises the following steps:
Step S41, with PSO Algorithm model；
Step S42, the evaluation index of the distributed wind power plant sale of electricity income Optimized model based on more scenes connection number:
(1) section can be met
According to the comparative approach of connection number, available earning rate is not less than rminThe credibility interval the i I of %；Show distributed wind-powered electricity generation
The income of field meets the earning rate requirement of the wind power plant investor under the influence of uncertain factor in the I of section, and I is known as to expire
The range size in sufficient section, I has a direct impact adaptability of the scheme to environment；
(2) fitness
In different sections, the project compatibility that is embodied of i contacted in number is different, and i is bigger, interval width can be met more it is wide then
Adaptability is stronger, therefore blind number is introduced to state fitness；
IfThen blind several fitness may be expressed as:
In formula: αmFor credibility interval xmFitness i.e. indicate i in section xmThe fitness of optimum results when interior；For
Total fitness of f (i)；M is the order of blind several fitness；By minimum requirements earning rate rmin% obtains that section I=[I can be metL,
IR], thus obtain the fitness β of scheme:
When fitness α is bigger, then show that the ability of the variation of scheme response environment is stronger, i.e., robustness is stronger.
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| CN113507111A (en) * | 2021-06-24 | 2021-10-15 | 东北电力大学 | Blind number theory-based planning target annual power profit and loss assessment method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110311417A (en) * | 2019-05-21 | 2019-10-08 | 国网能源研究院有限公司 | A kind of decision-making technique counted and the Unit Combination of user utility is dispatched |
| CN113507111A (en) * | 2021-06-24 | 2021-10-15 | 东北电力大学 | Blind number theory-based planning target annual power profit and loss assessment method |
| CN115330144A (en) * | 2022-05-17 | 2022-11-11 | 国网江苏省电力有限公司淮安供电分公司 | Demand response mechanism model establishment method considering real-time carbon emission reduction |
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| CN115496378A (en) * | 2022-09-27 | 2022-12-20 | 四川省电力行业协会 | Power system economic dispatching method taking wind energy emission reduction benefits into account |
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