CN110334856A - A kind of wind-light storage method for planning capacity based on carbon transaction mechanism - Google Patents
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Abstract
The invention proposes a kind of wind-light storage method for planning capacity based on carbon transaction mechanism.The present invention is in the regional power system containing wind-light storage, the method for being primarily based on mathematical statistics and Probability, and the random power output model of photovoltaic generating system and wind generator system is set up according to the Variation Features of solar radiation and wind speed；Then consider carbon transaction cost, using system minimum total cost as economic optimization target, the optimal solution for meeting constraint condition is solved according to plan model, output meets the optimal solution of constraint condition, realizes regional power grid wind-light storage capacity and distribute rationally.
Description
Technical field
The invention belongs to electric power system power source planning technology fields, disclose a kind of wind-light storage appearance based on carbon transaction mechanism
Measure planing method.
Background technique
Currently, the new energy such as wind-powered electricity generation, photovoltaic have been more and more widely used in the power system, and importance is also increasingly
It is promoted.Different from conventional power source, the generations of electricity by new energy such as wind-powered electricity generation, photovoltaic are often larger by such environmental effects, have significantly with
Machine, fluctuation and intermittence, and centralized new energy power supply compares distributed new power supply, power output is stablized, peak clipping effect
Obviously, it can be more convenient to carry out the control of idle and voltage, Yi Shixian mains frequency is adjusted, meanwhile, convenient for centralized management, by sky
Between limitation it is small, dilatation can be easily carried out, therefore, centralized new energy power supply is more conducive to electric system and unites
One planning.
When wind-powered electricity generation, photovoltaic are connected to the grid, often the power quality of power grid is impacted.Currently, for concentrating
Formula new energy power supply, how in reasonable disposition system wind-light storage power supply capacity, guaranteeing systematic economy, safe and stable, reliable
While, moreover it is possible to the utilization rate for guaranteeing new energy is current research hotspot.Large-scale wind-light storage access electricity in system at present
Net, wind storage is more with the research of light-preserved system, and the research of wind-light storage is mainly small-scale wind and solar hybrid generating system, wind-light storage
Power grid is accessed by common bus rod, and the research that wind-powered electricity generation in system, photovoltaic, energy storage are independently accessed power grid is less.
Current honourable capacity planning allocation models is often with system operation cost is minimum or the economy mesh such as Income Maximum
It is denoted as objective function.The research for considering that carbon emission carries out planning and configuration to system wind-light storage capacity is also fewer, or only
It is limited as a constraint condition, and is not considered the economic performance of unit carbon emission.Therefore green in view of electrical energy production
Color, low rowization and new energy run the strong feature of randomness, fluctuation, it is necessary to study a kind of wind based on carbon transaction mechanism
Light stores up method for planning capacity.
Summary of the invention
In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a kind of wind-light storage capacity based on carbon transaction mechanism
Planing method.
The technical scheme is that a kind of wind-light storage method for planning capacity based on carbon transaction mechanism, specifically includes following
Step:
Step 1: building photovoltaic power output model；
Step 2: building blower power output model；
Step 3: building energy storage battery model；
Step 4: establishing carbon transaction strategy；
Step 5: determining capacity planning model；
Step 6: using empire's Competitive Algorithms solving optimization model；
The model preferably, building photovoltaic described in step 1 is contributed specifically:
Remember that the latitude wait model area isEarth's surface solar irradiance when one Nian Zhong m d days t of the month of this areaAre as follows:
In formula,The small hourly value of earth's surface solar irradiance when for fine day m d days t of the month, α (m) are m
The irradiation level attenuation coefficient of the rainy days moon；Discrete random variable C (m) indicate the annual m in this area (m=1,2 ... the 12) moons it is fine
Rain situation；
Following empirical equation can be used to calculate:
In formula, H is remembered0For day solar irradiation total amount outside ground, belonged to the d days m month in 1 year along the N days (m=counted
1, d=1 corresponds to N=1；M=12, d=31 correspond to N=365):
I0=1367 [1+0.033cos (360N/365)]
Wherein, δ is day drift angle；ωstSmall hour angle when being risen for day；I0For ground external irradiation degree base value；
KtEarth's surface irradiation is subsisted iunction for curve when for m d days t of the month, and earth's surface irradiation is subsisted iunction for curve KtSuch as
Shown in formula:
Wherein, tstFor sunrise time, tssFor sunset time, it is calculated using the following equation:
hss=24-hst+2Δtm
Wherein, Δ tmFor time complexity curve coefficient；CsFor the sunshine clearness index of fine day, earth's surface horizontal plane is incident in reflection
The ratio between solar irradiance and ground external irradiation degree；
After determining all weathers, scales transforming of the solar irradiance mean value R through following formula hourly is at R' and in [0,1]
It is distributed on section in Beta:
In formula, rminFor the minimum value of solar irradiance, rmaxFor the maximum value of solar irradiance；
The probability density function of Beta distribution is the continuous function two-parameter about α and β (α >=0, β >=0):
In formula, fbIt (R') is Beta distribution function；Γ (Z) is Gamma function, and α is the first parameter of Beta distribution function,
β is the second parameter of Beta distribution function；
The characteristics of being distributed according to Beta can obtain R and parameter rminAnd rmaxRelationship are as follows:
The output power P of photo-voltaic power supply t momentPV(t) are as follows:
In formula, PsIt is photo-voltaic power supply in standard solar irradiance 1000W/m2With the actual power at 25 DEG C；αTFor photovoltaic electric
The temperature power coefficient in pond；Temperature when T is operation；
The model preferably, building blower described in step 2 is contributed specifically:
The regional wind speed of modeling is treated using Random time sequence model modeling, i.e. autoregressive moving-average model ARMA (p,
Q):
In formula, vtFor t moment wind speed time series；P is AR model order；Q is MA model order；εtFor t moment interference
?.
Relationship between blower electromotive power output and wind speed can be indicated with piecewise function are as follows:
In formula, PWIt (t) is t moment blower actual power, PWTFor blower rated power, vinTo cut wind speed, vrIt is specified
Wind speed, voutFor cut-out wind speed；
Preferably, constructing energy storage battery model described in step 3 are as follows:
When electric power storage tank discharge, the expression formula of carrying capacity is as follows, and PSB(t) 0 >:
When battery charging, the expression formula of carrying capacity is as follows, and PSB(t) 0 <:
S (t)=S (t-1) (1- σ)-PSB(t)Δtηc/Emax
When battery attonity, the expression formula of carrying capacity is as follows, and PSB(t)=0:
S (t)=S (t-1) (1- σ)
In formula, S (t) is the carrying capacity of battery t moment, and S (t-1) is the carrying capacity at battery (t-1) moment, and σ is to store
The self-discharge rate of battery, PSBIt (t) is charge-discharge electric power of the battery in t moment, EmaxFor the maximum capacity of battery, ηdTo store
The discharging efficiency of battery, ηcFor the charge efficiency of battery；
Preferably, establishing carbon transaction strategy described in step 4 are as follows:
Choose the free carbon emission quota that reference line method determines the system are as follows:
In formula, EQFor system, always free carbon emission quota, T are 1 year in project period, and τ is that unit electricity carbon emission standard refers to
Number, L (t) are system t moment load value；
Carbon transaction strategy are as follows: work as EG< EQWhen, it can be by extra quota EQ-EGIt sells；Work as EG> EQWhen, excess need to be bought
EG-EQ, it may be assumed that
In formula, EGFor conventional power unit total carbon emissions amount, EQFor the total free carbon emission quota of system, δiFor conventional power unit unit
Electricity EIC Carbon Emission Index, PGn(t) it contributing for conventional power unit, N is the quantity of conventional power unit,For carbon transaction price,For
It is positive to indicate that payment cost is needed to buy carbon quota,Being negative expression can be by selling carbon quota to make a profit；
Preferably, determining capacity planning model described in step 5 are as follows:
Photovoltaic power output is to contribute in the power output model of photovoltaic described in step 1 in capacity planning model constraint condition, capacity rule
Drawing wind power output in model constraint condition is to contribute in wind power output model described in step 2, capacity planning model constraint condition
Middle energy storage power output is the power output of energy storage model described in step 3；
When wind-light storage carries out capacity planning in power grid, consider that the economic optimization target of model is the totle drilling cost F of system,
Initial stage including photovoltaic, blower, energy-storage battery invests to build cost and operation expense, the operation expense of conventional power unit, carbon
Transaction cost, totle drilling cost F are as follows:
In formula, C1Initial stage for blower, photovoltaic, energy storage invests to build cost, C2For blower photovoltaic and conventional power unit operation and maintenance at
This present worth,For carbon transaction cost；
Calculate the present worth of all kinds of expenses of wind-solar power supply:
In formula, CinsExpense is always invested to build for equipment,For honourable fiery unit quantity of electricity O&M cost,For energy-storage battery list
Bit capacity O&M cost,For honourable fire year generated energy, E is energy-storage battery capacity, and q is the service life of equipment, and r is discount
Rate；
The objective function of Optimized model are as follows:
The constraint condition distributed rationally:
Power-balance constraint are as follows:
In formula, L (t) is the load value of t moment system, InIt (t) is the start and stop state of t moment conventional power unit, PGnIt (t) is t
Moment conventional power unit n power output, PW(t) it contributes for t moment blower, PPV(t) it contributes for t moment photovoltaic, PSBIt (t) is t moment energy storage
Battery power output；
The constraint of wind-light storage installation number are as follows:
In formula,For blower minimum installation number,For blower maximum installation number,For photovoltaic minimum peace
Quantity is filled,For photovoltaic maximum installation number,For battery minimum installation number,Number is installed for battery maximum
Amount；
Conventional power unit Reserve Constraint are as follows:
In formula, PGn,max(t) it contributes for conventional power unit n maximum technology, InIt (t) is the start and stop state of t moment conventional power unit, PW
(t) it contributes for t moment blower, PPV(t) it contributes for t moment photovoltaic, PSB(t) it contributes for t moment energy-storage battery, L (t) is t moment
The load value of system, R (t) are the stand-by requirement of t moment system；
Conventional power unit Climing constant are as follows:
PGn,down≤PGn(t)-PGn(t-1)≤PGn,up
In formula, PGn,upFor maximum ascending power in the conventional power unit n unit time, PGn,downWhen for conventional power unit n unit
Interior maximum decline power, PGn(t) power output for being t moment conventional power unit n, PGn(t-1) going out for t-1 moment conventional power unit n
Power；
Power of the assembling unit constraint are as follows:
In formula,For blower minimum load,For blower maximum output,For photovoltaic minimum load,For
Photovoltaic maximum output,For battery minimum load,For battery maximum output,Go out for fired power generating unit minimum
Power,For fired power generating unit maximum output；
Preferably, the step of empire's Competitive Algorithms described in step 6 are as follows:
Mode input parameter, including system load value L (t), the stand-by requirement R (t) of system, the start and stop of fired power generating unit are set
State In(t), wind-light storage installation number boundWind-light storage ignition source
Power output boundThe creep speed of fired power generating unit
PGn,down、PGn,up；
According to the installation number N of wind-light storageW、NPV、NSBAnd the power output P of wind-light storage fireW(t)、PPV(t)、PSB(t)、PG(t)
Coding forms [1 × (4T+3)] dimensional vector, and initialization forms the population of N number of country.For each country, it is each to initialize its
The value of a dimension is to meet the random number of constraint condition；
To the All Countries that initialization generates, its fitness function value i.e. totle drilling cost F is calculated；
Multiple countries are picked out as empire according to calculated fitness function value, and form multiple groups, empire；
It is g=0 that current iteration number, which is arranged, to index；
Step 6.1, according to empire's Competitive Algorithms by all colonies towards group internal empire shift position；
Step 6.2, to All Countries (empire and colony) inspection constraint, being allowed to be in using constraint Processing Algorithm can
Within row domain；
Step 6.3, for each group, empire, checking wherein has with the presence or absence of one or more colonies than empire
Smaller fitness function value.If so, carrying out step step 6.4；Conversely, then jumping to step step 6.5；
Step 6.4, the position in the colony and empire in group with the smallest fitness function value is exchanged；
Step 6.5, the force value of all groups, empire is calculated；
Step 6.6, it is selected from the group, empire with maximum adaptation degree functional value with maximum adaptation degree functional value
Most weak colony in the Ji Ruo empire of colony, and assign them to the empire that probability is occupied with maximum；
Step 6.7, it has checked for empire and has lost all colonies.If so, step 6.8 is carried out, conversely,
Then jump to step 6.9；
Step 6.8, it eliminates and has lost all colonial empires；
Step 6.9, check that the All Countries whether only remained in next group, empire and the group in population all have phase
Same fitness function value.If so, step 6.11 is jumped to, conversely, then carrying out step 6.10；
Step 6.10, check whether algorithm has reached maximum number of iterations.If so, carrying out step 6.11；Conversely, then setting
Set g=g+1 and return step 6.1；
Step 6.11, the national information in population with minimum fitness function value is exported as optimizing decision variable,
Fitness function value is as optimal objective function value.
Compared with the immediate prior art, technical solution provided by the invention is had the advantages that
Based under carbon transaction mechanism, wind-light storage capacity planning model can take into account economy and low-carbon, reasonable scene
Blower, photovoltaic and the energy-storage battery etc. that storage proportion is invested to build needed for capable of substantially reducing invest to build quantity, have significant economic benefit
Advantage.When considering carbon transaction, wind-light storage is invested to build quantity and is increased slightly, and benefit is evident for energy-saving and emission-reduction, and model is rationally effective, to reality
Planning has directive significance.
Detailed description of the invention
Fig. 1: for the wind-light storage method for planning capacity flow chart based on carbon transaction mechanism；
Fig. 2: modeling procedure figure of contributing at random for photovoltaic；
Fig. 3: for wind power output model flow figure；
Fig. 4: for objective function and constraint condition.
Specific embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, with reference to the accompanying drawings and embodiments to this hair
It is bright to be described in further detail, it should be understood that implementation example described herein is merely to illustrate and explain the present invention, not
For limiting the present invention.
Below with reference to Fig. 1 to Fig. 4, a specific embodiment of the invention is introduced.As shown in Figure 1, of the present invention be based on
The wind-light storage method for planning capacity of carbon transaction mechanism, comprising the following steps:
Step 1: building photovoltaic power output model；
The power output model of building photovoltaic described in step 1 specifically:
Remember the latitude wait model areaIt is 30.88 ° of north latitude, earth's surface sun when one Nian Zhong m d days t of the month of this area
Irradiation levelAre as follows:
In formula,The small hourly value of earth's surface solar irradiance when for fine day m d days t of the month, α (m) are m
The irradiation level attenuation coefficient of the rainy days moon；Discrete random variable C (m) indicate the annual m in this area (m=1,2 ... the 12) moons it is fine
Rain situation；
Following empirical equation can be used to calculate:
In formula, H is remembered0For day solar irradiation total amount outside ground, belonged to the d days m month in 1 year along the N days (m=counted
1, d=1 corresponds to N=1；M=12, d=31 correspond to N=365):
I0=1367 [1+0.033cos (360N/365)]
Wherein, δ is day drift angle；ωstSmall hour angle when being risen for day；I0For ground external irradiation degree base value；
KtEarth's surface irradiation is subsisted iunction for curve when for m d days t of the month, and earth's surface irradiation is subsisted iunction for curve KtSuch as
Shown in formula:
Wherein, tstFor sunrise time, tssFor sunset time, it is calculated using the following equation:
hss=24-hst+2Δtm
Wherein, Δ tmFor time complexity curve coefficient；CsFor the sunshine clearness index of fine day, earth's surface horizontal plane is incident in reflection
The ratio between solar irradiance and ground external irradiation degree；
After determining all weathers, scales transforming of the solar irradiance mean value R through following formula hourly is at R' and in [0,1]
It is distributed on section in Beta:
In formula, rminFor the minimum value of solar irradiance, rmaxFor the maximum value of solar irradiance；
The probability density function of Beta distribution is the continuous function two-parameter about α and β (α >=0, β >=0):
In formula, fbIt (R') is Beta distribution function；Γ (Z) is Gamma function, and α is the first parameter of Beta distribution function,
β is the second parameter of Beta distribution function；
The characteristics of being distributed according to Beta can obtain R and parameter rminAnd rmaxRelationship are as follows:
The output power P of photo-voltaic power supply t momentPV(t) are as follows:
In formula, PsIt is photo-voltaic power supply in standard solar irradiance 1000W/m2With the actual power at 25 DEG C；αTFor photovoltaic electric
The temperature power coefficient in pond is -0.42%；Temperature when T is operation, the model SSM250P-60 of photovoltaic module, rated power are
250W；
Step 2: building blower power output model；
The power output model of building blower described in step 2 specifically:
The regional wind speed of modeling is treated using Random time sequence model modeling, i.e. autoregressive moving-average model ARMA (p,
Q):
In formula, vtFor t moment wind speed time series；P is AR model order；Q is MA model order；εtFor t moment interference
?.
Relationship between blower electromotive power output and wind speed can be indicated with piecewise function are as follows:
In formula, PWIt (t) is t moment blower actual power, PWTFor blower rated power, vinTo cut wind speed, vrIt is specified
Wind speed, voutModel for cut-out wind speed, blower selects BZD80-2000, rated power 2MW, cuts wind speed 3m/s, cut-out wind speed
25m/s, rated wind speed 13.5m/s；
Step 3: building energy storage battery model；
Energy storage battery model is constructed described in step 3 are as follows:
When electric power storage tank discharge, the expression formula of carrying capacity is as follows, and PSB(t) 0 >:
When battery charging, the expression formula of carrying capacity is as follows, and PSB(t) 0 <:
S (t)=S (t-1) (1- σ)-PSB(t)Δtηc/Emax
When battery attonity, the expression formula of carrying capacity is as follows, and PSB(t)=0:
S (t)=S (t-1) (1- σ)
In formula, S (t) is the carrying capacity of battery t moment, and S (t-1) is the carrying capacity at battery (t-1) moment, and σ is to store
The self-discharge rate 0.01% of battery, PSBIt (t) is charge-discharge electric power of the battery in t moment, EmaxIt is for the maximum capacity of battery
Rated capacity, ηdFor the discharging efficiency 75% of battery, ηcFor the charge efficiency 75% of battery, the model of battery
512V100AH-PX01；
Step 4: establishing carbon transaction strategy；
Carbon transaction strategy is established described in step 4 are as follows:
Choose the free carbon emission quota that reference line method determines the system are as follows:
In formula, EQFor system, always free carbon emission quota, T take 1 year 8760 hour, and τ is unit electricity carbon emission standard
It is system t moment load value that index, which takes 0.789kg/kWh, L (t),；
Carbon transaction strategy are as follows: work as EG< EQWhen, it can be by extra quota EQ-EGIt sells；Work as EG> EQWhen, excess need to be bought
EG-EQ, it may be assumed that
In formula, EGFor conventional power unit total carbon emissions amount, EQFor the total free carbon emission quota of system, δiFor conventional power unit unit
Electricity EIC Carbon Emission Index, PGn(t) it contributing for conventional power unit, N is the quantity of conventional power unit,For carbon transaction price take 50 yuan/
T,The expression that is positive needs payment cost to buy carbon quota,Being negative expression can be by selling carbon quota to make a profit；
Step 5: determining capacity planning model；
Capacity planning model is determined described in step 5 are as follows:
Photovoltaic power output is to contribute in the power output model of photovoltaic described in step 1 in capacity planning model constraint condition, capacity rule
Drawing wind power output in model constraint condition is to contribute in wind power output model described in step 2, capacity planning model constraint condition
Middle energy storage power output is the power output of energy storage model described in step 3；
When wind-light storage carries out capacity planning in power grid, consider that the economic optimization target of model is the totle drilling cost F of system,
Initial stage including photovoltaic, blower, energy-storage battery invests to build cost and operation expense, the operation expense of conventional power unit, carbon
Transaction cost, totle drilling cost F are as follows:
In formula, C1Initial stage for blower, photovoltaic, energy storage invests to build cost, C2For blower photovoltaic and conventional power unit operation and maintenance at
This present worth,For carbon transaction cost；
Calculate the present worth of all kinds of expenses of wind-solar power supply:
In formula, CinsExpense is always invested to build for equipment, wind power plant invests to build cost CinsWAbout 11360 yuan/kW, photovoltaic
Power plant invests to build cost CinsPVFor 10020 yuan/kW, energy-accumulating power station invests to build cost CinsSBFor 3000 yuan/kWh,For honourable fire
Unit quantity of electricity O&M cost, the O&M cost of wind power plantFor 0.05 yuan/kWh, the O&M cost of photovoltaic plantIt is 0.12
Member/kWh, the standard degree electricity cost of thermal power plantFor 0.2 yuan/kWh,It is for energy-storage battery unit capacity O&M cost
50 yuan/kWh,For honourable fire year generated energy, E is energy-storage battery capacity, and q is the service life of equipment, photovoltaic, blower, energy storage
Battery service life is respectively 25 years, 20 years, 15 years, and r is that discount rate takes 7%；
The objective function of Optimized model are as follows:
The constraint condition distributed rationally:
Power-balance constraint are as follows:
In formula, L (t) is the load value of t moment system, InIt (t) is the start and stop state of t moment conventional power unit, PGnIt (t) is t
Moment conventional power unit n power output, PW(t) it contributes for t moment blower, PPV(t) it contributes for t moment photovoltaic, PSBIt (t) is t moment energy storage
Battery power output；
The constraint of wind-light storage installation number are as follows:
In formula,For blower minimum installation number,For blower maximum installation number,For the installation of photovoltaic minimum
Quantity,For photovoltaic maximum installation number,For battery minimum installation number,Number is installed for battery maximum
Amount；
Conventional power unit Reserve Constraint are as follows:
In formula, PGn,max(t) it contributes for conventional power unit n maximum technology, InIt (t) is the start and stop state of t moment conventional power unit, PW
(t) it contributes for t moment blower, PPV(t) it contributes for t moment photovoltaic, PSB(t) it contributes for t moment energy-storage battery, L (t) is t moment
The load value of system, R (t) are that the stand-by requirement of t moment system is taken as the 10% of the moment load；
Conventional power unit Climing constant are as follows:
PGn,down≤PGn(t)-PGn(t-1)≤PGn,up
In formula, PGn,upFor maximum ascending power in the conventional power unit n unit time, PGn,downWhen for conventional power unit n unit
Interior maximum decline power, PGn(t) power output for being t moment conventional power unit n, PGn(t-1) going out for t-1 moment conventional power unit n
Power；
Power of the assembling unit constraint are as follows:
In formula,It is 0 for blower minimum load,Rated capacity is taken for blower maximum output,For photovoltaic minimum
Power output is 0,Rated capacity is taken for photovoltaic maximum output,It is 0 for battery minimum load,For battery maximum
Power output takes rated capacity,Take rated capacity for fired power generating unit minimum load 50%,For fired power generating unit maximum output
Take rated capacity；
Step 6: using empire's Competitive Algorithms solving optimization model；
The step of empire's Competitive Algorithms described in step 6 are as follows:
Mode input parameter, including system load value L (t), the stand-by requirement R (t) of system, the start and stop of fired power generating unit are set
State In(t), wind-light storage installation number boundWind-light storage ignition source
Power output boundThe creep speed of fired power generating unit
PGn,down、PGn,up；
According to the installation number N of wind-light storageW、NPV、NSBAnd the power output P of wind-light storage fireW(t)、PPV(t)、PSB(t)、PG(t)
Coding forms [1 × (4T+3)] dimensional vector, and initialization forms the population of N number of country.For each country, it is each to initialize its
The value of a dimension is to meet the random number of constraint condition；
To the All Countries that initialization generates, its fitness function value i.e. totle drilling cost F is calculated；
Multiple countries are picked out as empire according to calculated fitness function value, and form multiple groups, empire；
It is g=0 that current iteration number, which is arranged, to index；
Step 6.1, according to empire's Competitive Algorithms by all colonies towards group internal empire shift position；
Step 6.2, to All Countries (empire and colony) inspection constraint, being allowed to be in using constraint Processing Algorithm can
Within row domain；
Step 6.3, for each group, empire, checking wherein has with the presence or absence of one or more colonies than empire
Smaller fitness function value.If so, carrying out step step 6.4；Conversely, then jumping to step step 6.5；
Step 6.4, the position in the colony and empire in group with the smallest fitness function value is exchanged；
Step 6.5, the force value of all groups, empire is calculated；
Step 6.6, it is selected from the group, empire with maximum adaptation degree functional value with maximum adaptation degree functional value
Most weak colony in the Ji Ruo empire of colony, and assign them to the empire that probability is occupied with maximum；
Step 6.7, it has checked for empire and has lost all colonies.If so, step 6.8 is carried out, conversely,
Then jump to step 6.9；
Step 6.8, it eliminates and has lost all colonial empires；
Step 6.9, check that the All Countries whether only remained in next group, empire and the group in population all have phase
Same fitness function value.If so, step 6.11 is jumped to, conversely, then carrying out step 6.10；
Step 6.10, check whether algorithm has reached maximum number of iterations.If so, carrying out step 6.11；Conversely, then setting
Set g=g+1 and return step 6.1；
Step 6.11, the national information in population with minimum fitness function value is exported as optimizing decision variable,
Fitness function value is as optimal objective function value.
It should be understood that the above-mentioned description for preferred embodiment is more detailed, can not therefore be considered to this
The limitation of invention patent protection range, those skilled in the art under the inspiration of the present invention, are not departing from power of the present invention
Benefit requires to make replacement or deformation under protected ambit, fall within the scope of protection of the present invention, this hair
It is bright range is claimed to be determined by the appended claims.
Claims (7)
1. a kind of wind-light storage method for planning capacity based on carbon transaction mechanism, which comprises the following steps:
Step 1: building photovoltaic power output model；
Step 2: building blower power output model；
Step 3: building energy storage battery model；
Step 4: establishing carbon transaction strategy；
Step 5: determining capacity planning model；
Step 6: using empire's Competitive Algorithms solving optimization model.
2. the wind-light storage method for planning capacity according to claim 1 based on carbon transaction mechanism, it is characterised in that: step 1
Described in building photovoltaic contribute model specifically:
Remember that the latitude wait model area isEarth's surface solar irradiance when one Nian Zhong m d days t of the month of this areaAre as follows:
In formula,The small hourly value of earth's surface solar irradiance when for fine day m d days t of the month, α (m) are that m month is negative
The irradiation level attenuation coefficient of rainy day；Discrete random variable C (m) indicates the annual m in this area (m=1, the weather shape of 2 ... the 12) moons
Condition；
Following empirical equation can be used to calculate:
In formula, H is remembered0For day solar irradiation total amount outside ground, belonged to the d days m month in 1 year along N days (m=1, the d=counted
1 corresponding N=1；M=12, d=31 correspond to N=365):
I0=1367 [1+0.033cos (360N/365)]
Wherein, δ is day drift angle；ωstSmall hour angle when being risen for day；I0For ground external irradiation degree base value；
KtEarth's surface irradiation is subsisted iunction for curve when for m d days t of the month, and earth's surface irradiation is subsisted iunction for curve KtSuch as formula institute
Show:
Wherein, tstFor sunrise time, tssFor sunset time, it is calculated using the following equation:
hss=24-hst+2Δtm
Wherein, Δ tmFor time complexity curve coefficient；CsFor the sunshine clearness index of fine day, the sun of earth's surface horizontal plane is incident in reflection
The ratio between irradiation level and ground external irradiation degree；
After determining all weathers, scales transforming of the solar irradiance mean value R through following formula hourly is at R' and in [0,1] section
It is upper to be distributed in Beta:
In formula, rminFor the minimum value of solar irradiance, rmaxFor the maximum value of solar irradiance；
The probability density function of Beta distribution is the continuous function two-parameter about α and β (α >=0, β >=0):
In formula, fbIt (R') is Beta distribution function；Γ (Z) is Gamma function, and α is the first parameter of Beta distribution function, and β is
Second parameter of Beta distribution function；
The characteristics of being distributed according to Beta can obtain R and parameter rminAnd rmaxRelationship are as follows:
The output power P of photo-voltaic power supply t momentPV(t) are as follows:
In formula, PsIt is photo-voltaic power supply in standard solar irradiance 1000W/m2With the actual power at 25 DEG C；αTFor photovoltaic cell
Temperature power coefficient；Temperature when T is operation.
3. the wind-light storage method for planning capacity according to claim 1 based on carbon transaction mechanism, it is characterised in that: step 2
Described in building blower contribute model specifically:
It treats the regional wind speed of modeling and uses Random time sequence model modeling, i.e. autoregressive moving-average model ARMA (p, q):
In formula, vtFor t moment wind speed time series；P is AR model order；Q is MA model order；εtFor t moment distracter；
Relationship between blower electromotive power output and wind speed can be indicated with piecewise function are as follows:
In formula, PWIt (t) is t moment blower actual power, PWTFor blower rated power, vinTo cut wind speed, vrFor rated wind speed,
voutFor cut-out wind speed.
4. the wind-light storage method for planning capacity according to claim 1 based on carbon transaction mechanism, it is characterised in that: step 3
Described in building energy storage battery model are as follows:
When electric power storage tank discharge, the expression formula of carrying capacity is as follows, and PSB(t) 0 >:
When battery charging, the expression formula of carrying capacity is as follows, and PSB(t) 0 <:
S (t)=S (t-1) (1- σ)-PSB(t)Δtηc/Emax
When battery attonity, the expression formula of carrying capacity is as follows, and PSB(t)=0:
S (t)=S (t-1) (1- σ)
In formula, S (t) is the carrying capacity of battery t moment, and S (t-1) is the carrying capacity at battery (t-1) moment, and σ is battery
Self-discharge rate, PSBIt (t) is charge-discharge electric power of the battery in t moment, EmaxFor the maximum capacity of battery, ηdFor battery
Discharging efficiency, ηcFor the charge efficiency of battery.
5. the wind-light storage method for planning capacity according to claim 1 based on carbon transaction mechanism, it is characterised in that: step 4
Described in establish carbon transaction strategy are as follows:
Choose the free carbon emission quota that reference line method determines the system are as follows:
In formula, EQFor system, always free carbon emission quota, T are 1 year in project period, and τ is unit electricity carbon emission designation number, L
It (t) is system t moment load value；
Carbon transaction strategy are as follows: work as EG< EQWhen, it can be by extra quota EQ-EGIt sells；Work as EG> EQWhen, the E of excess need to be boughtG-EQ,
That is:
In formula, EGFor conventional power unit total carbon emissions amount, EQFor the total free carbon emission quota of system, δiFor conventional power unit unit quantity of electricity
EIC Carbon Emission Index, PGn(t) it contributing for conventional power unit, N is the quantity of conventional power unit,For carbon transaction price,Be positive table
Showing needs payment cost to buy carbon quota,Being negative expression can be by selling carbon quota to make a profit.
6. the wind-light storage method for planning capacity according to claim 1 based on carbon transaction mechanism, it is characterised in that: step 5
Described in determine capacity planning model are as follows:
Photovoltaic power output is to contribute in the power output model of photovoltaic described in step 1 in capacity planning model constraint condition, capacity planning mould
Wind power output is to contribute in wind power output model described in step 2 in type constraint condition, is stored up in capacity planning model constraint condition
It can contribute as the power output of energy storage model described in step 3；
When wind-light storage carries out capacity planning in power grid, consider that the economic optimization target of model is the totle drilling cost F of system, including
Photovoltaic, blower, energy-storage battery initial stage invest to build cost and operation expense, the operation expense of conventional power unit, carbon transaction
Cost, totle drilling cost F are as follows:
In formula, C1Initial stage for blower, photovoltaic, energy storage invests to build cost, C2For blower photovoltaic and conventional power unit operation expense
Present worth,For carbon transaction cost；
Calculate the present worth of all kinds of expenses of wind-solar power supply:
In formula, CinsExpense is always invested to build for equipment,For honourable fiery unit quantity of electricity O&M cost,For energy-storage battery unit appearance
O&M cost is measured,For honourable fire year generated energy, E is energy-storage battery capacity, and q is the service life of equipment, and r is discount rate；
The objective function of Optimized model are as follows:
The constraint condition distributed rationally:
Power-balance constraint are as follows:
In formula, L (t) is the load value of t moment system, InIt (t) is the start and stop state of t moment conventional power unit, PGnIt (t) is t moment
Conventional power unit n power output, PW(t) it contributes for t moment blower, PPV(t) it contributes for t moment photovoltaic, PSBIt (t) is t moment energy-storage battery
Power output；
The constraint of wind-light storage installation number are as follows:
In formula,For blower minimum installation number,For blower maximum installation number,Number is installed for photovoltaic minimum
Amount,For photovoltaic maximum installation number,For battery minimum installation number,For battery maximum installation number；
Conventional power unit Reserve Constraint are as follows:
In formula, PGn,max(t) it contributes for conventional power unit n maximum technology, InIt (t) is the start and stop state of t moment conventional power unit, PW(t)
For t moment blower power output, PPV(t) it contributes for t moment photovoltaic, PSB(t) it contributes for t moment energy-storage battery, L (t) is t moment system
The load value of system, R (t) are the stand-by requirement of t moment system；
Conventional power unit Climing constant are as follows:
PGn,down≤PGn(t)-PGn(t-1)≤PGn,up
In formula, PGn,upFor maximum ascending power in the conventional power unit n unit time, PGn,downFor in the conventional power unit n unit time
Maximum decline power, PGn(t) power output for being t moment conventional power unit n, PGn(t-1) power output for being t-1 moment conventional power unit n；
Power of the assembling unit constraint are as follows:
In formula,For blower minimum load,For blower maximum output,For photovoltaic minimum load,Most for photovoltaic
Big power output,For battery minimum load,For battery maximum output,For fired power generating unit minimum load,
For fired power generating unit maximum output.
7. the wind-light storage method for planning capacity according to claim 1 based on carbon transaction mechanism, it is characterised in that: step 6
Described in empire's Competitive Algorithms step are as follows:
Mode input parameter, including system load value L (t), the stand-by requirement R (t) of system, the start and stop state of fired power generating unit are set
In(t), wind-light storage installation number boundThe power output of wind-light storage ignition source
BoundThe creep speed P of fired power generating unitGn,down、
PGn,up；
According to the installation number N of wind-light storageW、NPV、NSBAnd the power output P of wind-light storage fireW(t)、PPV(t)、PSB(t)、PG(t) it encodes
[1 × (4T+3)] dimensional vector is formed, initialization forms the population of N number of country；For each country, each of which dimension is initialized
The value of degree is to meet the random number of constraint condition；
To the All Countries that initialization generates, its fitness function value i.e. totle drilling cost F is calculated；
Multiple countries are picked out as empire according to calculated fitness function value, and form multiple groups, empire；
It is g=0 that current iteration number, which is arranged, to index；
Step 6.1, according to empire's Competitive Algorithms by all colonies towards group internal empire shift position；
Step 6.2, it is constrained, is allowed in feasible zone to check All Countries (empire and colony) using constraint Processing Algorithm
Within；
Step 6.3, it for each group, empire, checks wherein with the presence or absence of one or more colonies with smaller than empire
Fitness function value；If so, carrying out step step 6.4；Conversely, then jumping to step step 6.5；
Step 6.4, the position in the colony and empire in group with the smallest fitness function value is exchanged；
Step 6.5, the force value of all groups, empire is calculated；
Step 6.6, colonizing with maximum adaptation degree functional value is selected from the group, empire with maximum adaptation degree functional value
Most weak colony in Di Jiruo empire, and assign them to the empire that probability is occupied with maximum；
Step 6.7, it has checked for empire and has lost all colonies；If so, step 6.8 is carried out, conversely, then jumping
To step 6.9；
Step 6.8, it eliminates and has lost all colonial empires；
Step 6.9, check that the All Countries whether only remained in next group, empire and the group in population are all having the same
Fitness function value；If so, step 6.11 is jumped to, conversely, then carrying out step 6.10；
Step 6.10, check whether algorithm has reached maximum number of iterations；If so, carrying out step 6.11；Conversely, g is then arranged
=g+1 and return step 6.1；
Step 6.11, the national information in population with minimum fitness function value is exported as optimizing decision variable, is adapted to
Functional value is spent as optimal objective function value.
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