CN107492886A - A kind of power network monthly electricity purchasing scheme optimization method containing wind-powered electricity generation under Regional Electric Market - Google Patents
A kind of power network monthly electricity purchasing scheme optimization method containing wind-powered electricity generation under Regional Electric Market Download PDFInfo
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Abstract
The present invention relates to a kind of power network monthly electricity purchasing scheme optimization method containing wind-powered electricity generation under Regional Electric Market, including step：（1）Extract the monthly prediction electricity of monthly load, wind-powered electricity generation, ahead market forecasted electricity market price, inside the province outsourcing electricity price, generating set quote data；（2）Establish it is comprehensive a few days ago and the power purchase Optimized model of monthly electricity purchasing benefit and risk, with total power purchase expense and the minimum target of weighted value of power purchase loss；（3）Solve provincial power network monthly thermoelectricity, wind-powered electricity generation, outer purchase of electricity and corresponding quantity division.Fluctuating characteristic of the present invention according to wind power output, monthly electricity purchasing scheme is refine to daily peak, flat, paddy period, monthly electricity purchasing plan and its quantity division scheme coordinated with wind-powered electricity generation characteristic is obtained, realizes the coordination of normal power supplies and wind-powered electricity generation；Optimized model emphasis considers the out-of-limit chance constraint of peak regulation under peak load, paddy lotus state, realizes power purchase double risks management.
Description
Technical field
The present invention relates to Electricity market analysis field, more particularly to the power network moon containing wind-powered electricity generation under a kind of Regional Electric Market
Spend power purchase scheme optimization method.
Background technology
Important component of the wind-powered electricity generation as regenerative resource, installed capacity just increase rapidly, and wind-powered electricity generation receives problem to highlight,
Its randomness contributed brings peak regulation problem for power network, more increases Trading risk.Monthly electricity purchasing plan has accounted for transaction total amount
More than 80%, operation of power networks benefit can be considered as a whole in longer time span.Wind-powered electricity generation is included into monthly electric quantity balancing
Help lend some impetus to wind-powered electricity generation receiving.On the other hand, the otherness between province allows provincial power network to coordinate inside the province and inter-provincial electric power city
, realize the optimal of economy and environmental benefit.But it may aggravate to participate in the peak regulation of the monthly network system of power purchase transprovincially and network peace
Full problem.Therefore, the risk that the market price, wind-powered electricity generation amount uncertainty are brought should be not only considered in power purchase optimization, is also taken an examination
Consider wind-powered electricity generation and be received to the risk that system safe and stable operation is brought.On the basis of risk factors are considered, research considers that wind-powered electricity generation connects
Receive and the optimization of the monthly electricity purchasing of inter-provincial power purchase can provide reference for grid company power purchase.
The content of the invention
The purpose of the present invention is to propose to a kind of power network monthly electricity purchasing scheme optimization method containing wind-powered electricity generation under Regional Electric Market,
Consider wind electricity digestion and inter-provincial power purchase simultaneously, plan as a whole day-ahead power purchase benefit in the formulation process of monthly electricity purchasing plan, more comprehensively
Ground considers the problem of provincial power network runs into power purchase, increases power purchase overall efficiency.
To achieve the above object, the technical solution adopted by the present invention is：Power network containing wind-powered electricity generation under a kind of Regional Electric Market
Monthly electricity purchasing scheme optimization method, comprises the following steps：
Step S1：Extract the monthly prediction electricity of monthly load, wind-powered electricity generation, ahead market forecasted electricity market price, outsourcing electricity price, send out inside the province
Group of motors quote data；
Step S2：Establish it is comprehensive a few days ago and the power purchase Optimized model of monthly electricity purchasing benefit and risk：Consider ahead market electricity
Valency fluctuates and wind power prediction error risk to caused by power purchase, minimum with total power purchase expense and weighted value that power purchase is lost
Target, stated with mathematical function as follows：
F=min (Fout+Fin+λFβ)
Wherein, F is monthly total power purchase expense, FoutUsed for the monthly outsourcing electricity charge, FinFor monthly interior power purchase expense, by inside the province
Monthly electricity purchasing expense and a few days ago expectation power purchase expense composition；FβFor the power purchase loss risk under confidence level β；λ is Risk rated ratio
Coefficient；
Step S3：Solve provincial power network monthly thermoelectricity, wind-powered electricity generation, outer purchase of electricity and corresponding quantity division.
Further, the step S2 specifically includes following steps：
Step S21：The monthly outsourcing electricity charge are used, and consider the power purchase optimization under Regional Electric Market environment, the monthly outsourcing electricity charge
Use FoutFor：
Wherein, D is that power purchase scheme implements moon number of days, pout.iFor from the outsourcing electricity price lattice of i-th of province, N is that outer power purchase saves number
Amount；Wout.tFor decomposition electricity of the monthly outer purchase of electricity at the t days；
Step S22：Monthly interior power purchase expense, wind-powered electricity generation power purchase expense inside the province is not considered in object function, monthly interior power purchase is taken
With it is expected power purchase expense sum, monthly interior power purchase expense F for thermoelectricity monthly electricity purchasing expense and a few days agoinFor：
Fin=Fmonth+Fday
Wherein, Fmonth、FdayRespectively monthly electricity purchasing expense and a few days ago expectation power purchase expense inside the province；
FmonthFor Contract Energy and the product of contract price, due to considering the influence of ahead market, total Contract Energy is
It is daily peak, flat, paddy period Contract Energy cumulative：
Wherein, D is that power purchase scheme implements moon number of days, PcFor thermoelectricity monthly electricity purchasing price inside the province；K is load condition sequence number,
Correspond to peak, flat, 3 kinds of load conditions of paddy respectively when k takes 1,2,3；Whc.t.kIt is the purchase of electricity of the thermoelectricity moon inside the province in the t days k periods
Decompose electricity；
FdayIt is expected power purchase expense a few days ago, added up and obtained by daily peak, flat, three period power purchase expenses of paddy：
Wherein, Pr.t.k.mFor the ahead market electricity price desired value of the t days k periods；Wr.t.k.mFor the t days k periods of thermoelectricity inside the province
In the purchase of electricity desired value of ahead market；
Step S23：Ahead market electricity price is described as it is expected normal distribution of the electricity price as average, it is believed that wind-powered electricity generation work(a few days ago
Rate prediction is accurate, monthly and wind power prediction deviation Normal Distribution a few days ago；Therefore, the actual purchase of electricity of ahead market
It is relevant with wind-powered electricity generation prediction deviation, it is the stochastic variable for meeting normal distribution, then ahead market day part purchase of electricity is described as：
Wherein,For the t days k periods monthly wind power prediction deviation；WithRespectively t
The average value and variance of its k period monthly wind power prediction deviation；Wfr.t.kFor the t days k period purchase of electricity of ahead market；
Wfc.t.kFor wind-powered electricity generation monthly electricity purchasing amount the t days k periods decomposition electricity；
Step S24：Using the actual wind power of Latin Hypercube simulation and ahead market electricity price, pass through Latin hypercube mould
The data drawn up, calculate the risk that wind-powered electricity generation and price uncertain factor are brought for power purchase, and calculation formula is as follows：
Wherein,
Zk.t.n=[f (x, yk.t.n)-αt.k]+
Wherein, Fβ(x, α) be confidence level β under power purchase loss risk, αt.kFor in the VaR value (risks of the t days k periods
Value), for the maximum loss value that may occur in the case where setting confidence level；β is confidence level；M is for calculating CVaR (conditions
Venture worth) historical data number, i.e., the wind power obtained by Latin Hypercube Sampling and a few days ago electricity price number of samples；
N is the sequence number of sample data；f(x,yk.t.n) lost for power purchase, i.e., actual power purchase expense and the difference for it is expected power purchase expense；[f
(x,yk.t.n)-αt.k]+Represent max { 0, f (x, yk.t.n)-αt.k}。
Further, the constraints included when the step S3 is solved has：Electric quantity balancing constraint, each outer electricity purchasing power electricity
Amount coupling constraint, peak load and peak regulation chance constraint, the constraint of thermoelectricity generating capacity, wind power under paddy lotus state constrain, outsourcing
Electricity saves power constraint and the constraint of inter-provincial interconnection transmission capacity.
Further, the electric quantity balancing is constrained to：Monthly electricity purchasing plan considers thermoelectricity, wind-powered electricity generation and outer Transaction algorithm inside the province
The balance of purchase of electricity and power load, stated with mathematical function as follows：
Wload.t.k=Whc.t.k+Wout.t.k+Wfc.t.k
Wherein, Wload.t.kFor the t days k period power loads；Wout.t.kFor monthly outer purchase of electricity the t days k periods point
Solve electricity；Whc.t.kFor the thermoelectricity moon inside the province purchase of electricity the t days k periods decomposition electricity；Wfc.t.kFor wind-powered electricity generation monthly electricity purchasing inside the province
Measure the decomposition electricity in the t days k periods；Correspond to peak, flat, 3 kinds of load conditions of paddy respectively when k takes 1,2,3；T ∈ D, D are power purchase
Scheme implements moon number of days；
Each outer power purchase quantity of electricity coupling constraint is：Inter-provincial power purchase is by superior unit for inter-provincial interconnection safety
Constraint, different periods transmission power meets that fixed relationship i.e. electric power and electricity have close coupling relation, are stated with mathematical function
It is as follows：
Wherein, WoutFor monthly outer purchase of electricity, D is that power purchase scheme implements moon number of days, Pout.i(t, k) is i-th of province in t
Transmission power under its k period；N is that outer power purchase saves quantity, TkFor k period lasts hourages；
The peak load is stated as follows with the peak regulation chance constraint under paddy lotus state with mathematical function：
Wherein, Pr{ } is probability operator；N1、N2Respectively thermoelectricity and wind-powered electricity generation generating Board Lot inside the province；Pd.t.max、Pd.t.min
Respectively the t days maximum, minimum loads；Ph.j、Pf.sRespectively j-th of thermal power generation unit, s-th of wind-power electricity generation unit power
Random value；Pout.iFor from the power of i-th of power purchase outside the province, N is that outer power purchase saves quantity；α1、α2Respectively peak load and Gu He states
Lower peak regulation risk level limiting value；
The thermoelectricity generating capacity constraint is stated as follows with mathematical function：
Wh.min≤Wh≤Wh.max
Ph.min≤Ph≤Ph.max
Wherein, WhFor thermoelectricity gross generation inside the province；Wh.max、Wh.minRespectively thermoelectricity is maximum inside the province, minimum generated energy；PhFor
The total generated output of thermoelectricity inside the province；Ph.max、Ph.minRespectively thermoelectricity is maximum inside the province, minimum generated output；
The wind power constraint is stated as follows with mathematical function：
0≤Pf≤Pf.max
Wherein, PfFor wind-powered electricity generation general power inside the province, Pf.maxFor wind-powered electricity generation peak power inside the province；
It is as follows with mathematical function statement that the outer power purchase saves power constraint：
0≤Pout.i.k≤Pout.i.k.max
Wherein, Pout.i.kFor from i-th of power purchase kth time period power outside the province；Pout.i.k.maxFor from i-th of power purchase kth outside the province
Period peak power；
The inter-provincial interconnection transmission capacity constraint constraint is stated as follows with mathematical function：
Pl.min≤Pl≤Pl.max
Wherein, PlFor inter-provincial interconnection l transmitted power；Pl.max、Pl.minRespectively inter-provincial interconnection l transmitted powers are most
Greatly, minimum value.
Compared with prior art, the invention has the advantages that：
(1) using Regional Electric Market as background, the monthly electricity purchasing benefit-risk for considering wind electricity digestion and inter-provincial power purchase is established
Administrative model；Influencing each other for monthly market and ahead market is considered in model, meter and wind power output are uncertain and a few days ago electric
Valency fluctuates, according to wind power output feature optimization monthly electricity purchasing total amount and corresponding electricity Optimal Decomposition scheme.
(2) risk caused by quantifying wind-powered electricity generation prediction and ahead market electricity price uncertainty using Conditional Lyapunov ExponentP；By
The out-of-limit risk control of peak-load regulating within the acceptable range, is realized that peak load regulation network is out-of-limit and lost with power purchase by peak regulation chance constraint
The management of double risks.
(3) realize that normal power supplies are mutually coordinated with power purchase inside the province with wind-powered electricity generation, inter-provincial power purchase inside the province, surpassed using based on Latin
The quantum particle swarm optimization solving model of cube analogue technique.
Brief description of the drawings
Fig. 1 is the principle process schematic diagram of the present invention.
Embodiment
Below in conjunction with the accompanying drawings and embodiment the present invention will be further described.
As shown in figure 1, a kind of power network monthly electricity purchasing scheme optimization method containing wind-powered electricity generation under Regional Electric Market, including it is following
Step：
Step S1：Extract the monthly prediction electricity of monthly load, wind-powered electricity generation, ahead market forecasted electricity market price, outsourcing electricity price, send out inside the province
The data such as group of motors quotation；
Step S2：Establish it is comprehensive a few days ago and the power purchase Optimized model of monthly electricity purchasing benefit and risk；Define the model：In the moon
Plan as a whole day-ahead power purchase situation when spending power purchase optimization, monthly electricity purchasing plan is refined to daily peak, flat, paddy period, considered simultaneously
Wind electricity digestion and inter-provincial power purchase.Purchased using monthly electricity purchasing expense inside the province, day-ahead power purchase expense and the outsourcing electricity charge by the use of sum as assessment
The index of electric economy.Meter and ahead market Electricity price fluctuation and wind power prediction the error risk to caused by power purchase, always to purchase
The electricity charge are stated as follows with mathematical function with the minimum target of the weighted value lost with power purchase：
F=min (Fout+Fin+λFβ)
Wherein, F is monthly total power purchase expense, FoutUsed for the monthly outsourcing electricity charge, FinFor monthly interior power purchase expense, by inside the province
Monthly electricity purchasing expense and a few days ago expectation power purchase expense composition；FβFor the power purchase loss risk under confidence level β；λ is Risk rated ratio
Coefficient；
In the present embodiment, step S2 specifically includes following steps：
Step S21：The monthly outsourcing electricity charge are used:In Regional Electric Market, the economic disparities between different provinces makes provincial
Power network can obtain the optimal of economic and energy saving emission reduction benefit by provincial electricity transaction.Consider under Regional Electric Market environment
Power purchase optimization, then monthly electricity purchasing expense FoutFor：
Wherein, D is that power purchase scheme implements moon number of days, pout.iFor from the outsourcing electricity price lattice of i-th of province, N is that outer power purchase saves number
Amount；Wout.tFor decomposition electricity of the monthly outer purchase of electricity at the t days；
Step S22：Monthly interior power purchase expense：When monthly electricity purchasing optimizes, consider that ahead market electricity price is purchased to monthly contract
The influence of electricity, become more meticulous to the monthly electricity purchasing scheme of daily day part, can played monthly and ahead market excellent by formulating
Gesture, plan as a whole power purchase overall efficiency.Due to considering fully to buy wind-powered electricity generation, wind-powered electricity generation monthly electricity purchasing expense and day-ahead power purchase to fix electricity price
Expense sum is directly proportional to actual wind-powered electricity generation amount, not in optimization range.Therefore, wind-powered electricity generation power purchase expense inside the province is not considered in object function
With power purchase expense is thermoelectricity monthly electricity purchasing expense and day-ahead power purchase expense sum inside the province, monthly interior power purchase expense FinFor：
Fin=Fmonth+Fday
Wherein, Fmonth、FdayRespectively monthly electricity purchasing expense and a few days ago expectation power purchase expense inside the province；
FmonthFor Contract Energy and the product of contract price, due to considering the influence of ahead market, total Contract Energy is
It is daily peak, flat, paddy period Contract Energy cumulative：
Wherein, D is that power purchase scheme implements moon number of days, PcFor thermoelectricity monthly electricity purchasing price inside the province；K is load condition sequence number,
Correspond to peak, flat, 3 kinds of load conditions of paddy respectively when k takes 1,2,3；Whc.t.kIt is the purchase of electricity of the thermoelectricity moon inside the province in the t days k periods
Decompose electricity；
FdayIt is expected power purchase expense a few days ago, added up and obtained by daily peak, flat, three period power purchase expenses of paddy：
Wherein, Pr.t.k.mFor the ahead market electricity price desired value of the t days k periods；Wr.t.k.mFor the t days k periods of thermoelectricity inside the province
In the purchase of electricity desired value of ahead market；
Step S23：Ahead market electricity price is described as it is expected normal distribution of the electricity price as average, it is believed that wind-powered electricity generation work(a few days ago
Rate prediction be it is accurate, it is monthly with wind power output prediction deviation Normal Distribution a few days ago；Wind-powered electricity generation and other normal power supplies
The total purchase of electricity needs of monthly and ahead market are equal with workload demand, in the environment of " with the fixed electricity of wind ", wind power output fluctuation
Very important, wind-powered electricity generation monthly electricity purchasing amount (i.e. reserved electricity) will influence Day-ahead Electricity Purchase amount.Therefore, the actual power purchase of ahead market
Amount is relevant with wind-powered electricity generation prediction deviation, is the stochastic variable for meeting normal distribution, then ahead market day part purchase of electricity is described as：
Wherein,For the t days k periods monthly wind power prediction deviation；WithRespectively t
The average value and variance of its k period monthly wind power prediction deviation；Wfr.t.kFor the t days k period purchase of electricity of ahead market；
Wfc.t.kFor wind-powered electricity generation monthly electricity purchasing amount the t days k periods decomposition electricity；
Step S24：Using the actual wind power of Latin Hypercube simulation and ahead market electricity price, pass through Latin hypercube mould
The data drawn up, calculate the risk that wind-powered electricity generation and price uncertain factor are brought for power purchase.Using Conditional Value at Risk power purchase
Economic risk, there are two, and the comprehensive distribution of its product by the present invention puies forward the enchancement factor that Trading risk is influenceed in model
Expression formula is difficult to obtain, therefore uses the form simulation electricity price and wind-powered electricity generation historical data of random sampling, near in a manner of numerical integration
Like power purchase loss risk is calculated, calculation formula is as follows：
Wherein,
Zk.t.n=[f (x, yk.t.n)-αt.k]+
Wherein, Fβ(x, α) be confidence level β under power purchase loss risk, αt.kFor in the VaR values of the t days k periods, be
Set the maximum loss value that may occur under confidence level；β is confidence level；M is the historical data number for calculating CVaR,
The wind power that is obtained by Latin Hypercube Sampling and a few days ago electricity price number of samples；N is the sequence number of sample data；f(x,
yk.t.n) lost for power purchase, i.e., actual power purchase expense and the difference for it is expected power purchase expense；[f(x,yk.t.n)-αt.k]+Expression max 0,
f(x,yk.t.n)-αt.k}。
Step S3：Solve provincial power network monthly thermoelectricity, wind-powered electricity generation, outer purchase of electricity and corresponding quantity division.
In the present embodiment, the constraints included when step S3 is solved has：Electric quantity balancing constraint, each outer electricity purchasing power electricity
Amount coupling constraint, peak load and peak regulation chance constraint, the constraint of thermoelectricity generating capacity, wind power under paddy lotus state constrain, outsourcing
Electricity saves power constraint and the constraint of inter-provincial interconnection transmission capacity.
Electric quantity balancing is constrained to：Monthly electricity purchasing plan considers thermoelectricity, wind-powered electricity generation and outer Transaction algorithm purchase of electricity and load inside the province
The balance of electricity, stated with mathematical function as follows：
Wload.t.k=Whc.t.k+Wout.t.k+Wfc.t.k
Wherein, Wload.t.kFor the t days k period power loads；Wout.t.kFor monthly outer purchase of electricity the t days k periods point
Solve electricity；Whc.t.kFor the thermoelectricity moon inside the province purchase of electricity the t days k periods decomposition electricity；Wfc.t.kFor wind-powered electricity generation monthly electricity purchasing inside the province
Measure the decomposition electricity in the t days k periods；Correspond to peak, flat, 3 kinds of load conditions of paddy respectively when k takes 1,2,3；T ∈ D, D are power purchase
Scheme implements moon number of days；
Each outer power purchase quantity of electricity coupling constraint is：Pact of the inter-provincial power purchase by superior unit for inter-provincial interconnection safety
Beam, different periods transmission power general satisfaction fixed relationship are that electric power has stronger coupled relation with electricity, use mathematical function
It is expressed as follows：
Wherein, Wout.iFor monthly outer purchase of electricity, D is that power purchase scheme implements moon number of days, Pout.i(t, k) is i-th province the
Transmission power under t days k periods；N is that outer power purchase saves quantity, TkFor k period lasts hourages；
Peak load is with the peak regulation chance constraint under paddy lotus state：Quantity of electricity close coupling relation, the wind power output of outer power purchase
Uncertainty peaking problem when may all trigger system peak load and Gu He.Due to monthly electricity purchasing plan using meet electric quantity balancing as
It is main, specific generation schedule is not directed to, and wind power output randomness is stronger, it is difficult to meet strict tune in the works in monthly electricity purchasing
Peak equality constraint.Therefore, using the method for chance constraint, the peak regulation of all kinds of power supplys is considered in advance in monthly electricity purchasing solution formulation
Characteristic, by the probability limitation of the out-of-limit generation of peak regulation within the acceptable range, both met that system safety operation requirement in turn ensure that
The feasibility of monthly electricity purchasing solution formulation, stated with mathematical function as follows：
Wherein, Pr{ } is probability operator；N1、N2Respectively thermoelectricity and wind-powered electricity generation generating Board Lot inside the province；Pd.t.max、Pd.t.min
Respectively the t days maximum, minimum loads；Ph.j、Pf.sRespectively j-th of thermal power generation unit, s-th of wind-power electricity generation unit power
Random value；Pout.iFor from the power of i-th of power purchase outside the province, N is that outer power purchase saves quantity；α1、α2Respectively peak load and Gu He states
Lower peak regulation risk level limiting value；
The constraint of thermoelectricity generating capacity is stated as follows with mathematical function：
Wh.min≤Wh≤Wh.max
Ph.min≤Ph≤Ph.max
Wherein, WhFor thermoelectricity gross generation inside the province；Wh.max、Wh.minRespectively thermoelectricity is maximum inside the province, minimum generated energy；PhFor
The total generated output of thermoelectricity inside the province；Ph.max、Ph.minRespectively thermoelectricity is maximum inside the province, minimum generated output；
Wind power constraint is stated as follows with mathematical function：
0≤Pf≤Pf.max
Wherein, PfFor wind-powered electricity generation general power inside the province, Pf.maxFor wind-powered electricity generation peak power inside the province；
It is as follows with mathematical function statement that the outer power purchase saves power constraint：
0≤Pout.i.k≤Pout.i.k.max
Wherein, Pout.i.kFor from i-th of power purchase kth time period power outside the province；Pout.i.k.maxFor from i-th of power purchase kth outside the province
Period peak power；
Inter-provincial interconnection transmission capacity constraint constraint is stated as follows with mathematical function：
Pl.min≤Pl≤Pl.max
Wherein, PlFor inter-provincial interconnection l transmitted power；Pl.max、Pl.minRespectively inter-provincial interconnection l transmitted powers are most
Greatly, minimum value.
In the present embodiment, power purchase expense it is expected with monthly inside the province and ahead market, inter-provincial power purchase and corresponds to adding for risk
The minimum object function of weights, model emphasis consider the out-of-limit chance constraint of quantity of electricity coupling constraint, peak regulation of inter-provincial power purchase, and
Using based on Latin Hypercube simulation technology quanta particle swarm optimization solve, obtain transprovincially monthly electricity purchasing scheme, inside the province thermoelectricity,
Wind-powered electricity generation monthly electricity purchasing amount and corresponding quantity division scheme.
The foregoing is only presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification, it should all belong to the covering scope of the present invention.
Claims (4)
1. a kind of power network monthly electricity purchasing scheme optimization method containing wind-powered electricity generation under Regional Electric Market, it is characterised in that including following
Step：
Step S1：Extract the monthly prediction electricity of monthly load, wind-powered electricity generation, ahead market forecasted electricity market price, inside the province outsourcing electricity price, generator
Group quote data；
Step S2：Establish it is comprehensive a few days ago and the power purchase Optimized model of monthly electricity purchasing benefit and risk：With total power purchase expense and power purchase
The minimum target of weighted value of loss, stated with mathematical function as follows：
F=min (Fout+Fin+λFβ)
Wherein, F is monthly total power purchase expense, FoutUsed for the monthly outsourcing electricity charge, FinFor monthly interior power purchase expense, by monthly inside the province
Power purchase expense and a few days ago expectation power purchase expense composition；FβFor the power purchase loss risk under confidence level β；λ is Risk rated ratio coefficient；
Step S3：Solve provincial power network monthly thermoelectricity, wind-powered electricity generation, outer purchase of electricity and corresponding quantity division.
2. the power network monthly electricity purchasing scheme optimization method containing wind-powered electricity generation under a kind of Regional Electric Market according to claim 1,
Characterized in that, the step S2 specifically includes following steps：
Step S21：Monthly outsourcing electricity charge FoutFor：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>=</mo>
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<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
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<mi>N</mi>
</munderover>
<munderover>
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<mi>t</mi>
<mo>=</mo>
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<mi>D</mi>
</munderover>
<msub>
<mi>p</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mo>.</mo>
<mi>i</mi>
</mrow>
</msub>
<msub>
<mi>W</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mo>.</mo>
<mi>t</mi>
</mrow>
</msub>
</mrow>
Wherein, D is that power purchase scheme implements moon number of days, pout.iFor from the outsourcing electricity price lattice of i-th of province, N is that outer power purchase saves quantity；
Wout.tFor decomposition electricity of the monthly outer purchase of electricity at the t days；
Step S22：Monthly interior power purchase expense, wind-powered electricity generation power purchase expense inside the province is not considered in object function, monthly interior power purchase expense is
Thermoelectricity monthly electricity purchasing expense and power purchase expense sum, then monthly interior power purchase expense F it is expected a few days agoinIt is expressed as：
Fin=Fmonth+Fday
Wherein, Fmonth、FdayRespectively monthly electricity purchasing expense and a few days ago expectation power purchase expense inside the province；
FmonthFor total Contract Energy and the product of contract price, total Contract Energy is daily peak, flat, paddy period Contract Energy tired
Add：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>m</mi>
<mi>o</mi>
<mi>n</mi>
<mi>t</mi>
<mi>h</mi>
</mrow>
</msub>
<mo>=</mo>
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<mo>&Sigma;</mo>
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<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>D</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>3</mn>
</munderover>
<msub>
<mi>P</mi>
<mi>c</mi>
</msub>
<msub>
<mi>W</mi>
<mrow>
<mi>h</mi>
<mi>c</mi>
<mo>.</mo>
<mi>t</mi>
<mo>.</mo>
<mi>k</mi>
</mrow>
</msub>
</mrow>
Wherein, D is that power purchase scheme implements moon number of days, PcFor thermoelectricity monthly electricity purchasing price inside the province；K is load condition sequence number, when k takes
Peak, flat, 3 kinds of load conditions of paddy are corresponded to when 1,2,3 respectively；Whc.t.kFor the thermoelectricity moon inside the province purchase of electricity the t days k periods decomposition
Electricity；
FdayIt is expected power purchase expense a few days ago, added up and obtained by daily peak, flat, three period power purchase expenses of paddy：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>d</mi>
<mi>a</mi>
<mi>y</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>D</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>3</mn>
</munderover>
<msub>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mo>.</mo>
<mi>t</mi>
<mo>.</mo>
<mi>k</mi>
<mo>.</mo>
<mi>m</mi>
</mrow>
</msub>
<msub>
<mi>W</mi>
<mrow>
<mi>r</mi>
<mo>.</mo>
<mi>t</mi>
<mo>.</mo>
<mi>k</mi>
<mo>.</mo>
<mi>m</mi>
</mrow>
</msub>
<mo>;</mo>
</mrow>
Wherein, Pr.t.k.mFor the ahead market electricity price desired value of the t days k periods；Wr.t.k.mIt it is the t days k periods of thermoelectricity inside the province in day
The purchase of electricity desired value in preceding market；
Step S23：Ahead market electricity price is described as it is expected normal distribution of the electricity price as average, it is believed that wind power is pre- a few days ago
Survey is accurate, monthly and wind power prediction deviation Normal Distribution a few days ago；Therefore, the actual purchase of electricity of ahead market and wind
Electric prediction deviation is relevant, is the stochastic variable for meeting normal distribution, then ahead market day part purchase of electricity is described as：
<mrow>
<msub>
<mi>W</mi>
<mrow>
<mi>f</mi>
<mi>r</mi>
<mo>.</mo>
<mi>t</mi>
<mo>.</mo>
<mi>k</mi>
</mrow>
</msub>
<mo>~</mo>
<mi>N</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>W</mi>
<mrow>
<mi>f</mi>
<mo>.</mo>
<mi>t</mi>
<mo>.</mo>
<mi>k</mi>
</mrow>
<mrow>
<mi>f</mi>
<mi>o</mi>
<mi>r</mi>
<mi>e</mi>
<mi>c</mi>
<mi>a</mi>
<mi>s</mi>
<mi>t</mi>
</mrow>
</msubsup>
<mo>-</mo>
<msub>
<mi>W</mi>
<mrow>
<mi>f</mi>
<mi>c</mi>
<mo>.</mo>
<mi>t</mi>
<mo>.</mo>
<mi>k</mi>
</mrow>
</msub>
<mo>+</mo>
<msubsup>
<mi>W</mi>
<mrow>
<mi>f</mi>
<mo>.</mo>
<mi>t</mi>
<mo>.</mo>
<mi>k</mi>
</mrow>
<mrow>
<mi>d</mi>
<mi>e</mi>
<mi>v</mi>
<mi>i</mi>
<mi>a</mi>
<mi>t</mi>
<mi>i</mi>
<mi>o</mi>
<mi>n</mi>
</mrow>
</msubsup>
<mo>,</mo>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>f</mi>
<mo>.</mo>
<mi>t</mi>
<mo>.</mo>
<mi>k</mi>
</mrow>
<mn>2</mn>
</msubsup>
<mo>)</mo>
</mrow>
</mrow>
Wherein,For the t days k periods monthly wind power prediction deviation；WithRespectively the t days k
The average value and variance of period monthly wind power prediction deviation；Wfr.t.kFor the t days k period purchase of electricity of ahead market；Wfc.t.k
For wind-powered electricity generation monthly electricity purchasing amount the t days k periods decomposition electricity；
Step S24：Using the actual wind power of Latin Hypercube simulation and ahead market electricity price, gone out by Latin Hypercube simulation
Data, it is the risk that power purchase is brought to calculate wind-powered electricity generation and price uncertain factor, and calculation formula is as follows：
<mrow>
<msub>
<mi>F</mi>
<mi>&beta;</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>,</mo>
<mi>&alpha;</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
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<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>D</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>3</mn>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>&alpha;</mi>
<mrow>
<mi>t</mi>
<mo>.</mo>
<mi>k</mi>
</mrow>
</msub>
<mo>+</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<mi>&beta;</mi>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>n</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>Z</mi>
<mrow>
<mi>k</mi>
<mo>.</mo>
<mi>t</mi>
<mo>.</mo>
<mi>n</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
Wherein,
Zk.t.n=[f (x, yk.t.n)-αt.k]+
Wherein, Fβ(x, α) be confidence level β under power purchase loss risk, αt.kFor in the VaR values of the t days k periods, to set
The maximum loss value that may occur under confidence level；β is confidence level；M is the historical data number for calculating CVaR, i.e., logical
Cross wind power that Latin Hypercube Sampling obtains and a few days ago number of samples of electricity price；N is the sequence number of sample data；f(x,
yk.t.n) lost for power purchase, i.e., actual power purchase expense and the difference for it is expected power purchase expense；[f(x,yk.t.n)-αt.k]+Expression max 0,
f(x,yk.t.n)-αt.k}。
3. the power network monthly electricity purchasing scheme optimization method containing wind-powered electricity generation under a kind of Regional Electric Market according to claim 1,
It is characterized in that：The step S3, which solves the constraints included, to be had：Electric quantity balancing constraint, each outer power purchase quantity of electricity coupling are about
Beam, peak load and peak regulation chance constraint, the constraint of thermoelectricity generating capacity, wind power constraint, outer power purchase province power under paddy lotus state
Constraint and the constraint of inter-provincial interconnection transmission capacity.
4. the power network monthly electricity purchasing scheme optimization method containing wind-powered electricity generation under a kind of Regional Electric Market according to claim 3,
Characterized in that, the electric quantity balancing is constrained to：Monthly electricity purchasing plan considers thermoelectricity, wind-powered electricity generation and outer Transaction algorithm purchase of electricity inside the province
With the balance of power load, stated with mathematical function as follows：
Wload.t.k=Whc.t.k+Wout.t.k+Wfc.t.k
Wherein, Wload.t.kFor the t days k period power loads；Wout.t.kIt is electric in the decomposition of the t days k periods for monthly outer purchase of electricity
Amount；Whc.t.kFor the thermoelectricity moon inside the province purchase of electricity the t days k periods decomposition electricity；Wfc.t.kExist for wind-powered electricity generation monthly electricity purchasing amount inside the province
The decomposition electricity of the t days k periods；Correspond to peak, flat, 3 kinds of load conditions of paddy respectively when k takes 1,2,3；T ∈ D, D are power purchase scheme
Implement moon number of days；
Each outer power purchase quantity of electricity coupling constraint is：Pact of the inter-provincial power purchase by superior unit for inter-provincial interconnection safety
Beam, different periods transmission power meet that fixed relationship i.e. electric power has close coupling relation with electricity, are stated with mathematical function as follows：
<mrow>
<msub>
<mi>W</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>D</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>3</mn>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mo>.</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>(</mo>
<mrow>
<mi>t</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
<mo>)</mo>
<mo>&times;</mo>
<msub>
<mi>T</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
Wherein, WoutFor monthly outer purchase of electricity, D is that power purchase scheme implements moon number of days, Pout.i(t, k) is i-th of province in t days k
Transmission power under section；N is that outer power purchase saves quantity, TkFor k period lasts hourages；
The peak load is stated as follows with the peak regulation chance constraint under paddy lotus state with mathematical function：
<mrow>
<msub>
<mi>P</mi>
<mi>r</mi>
</msub>
<mo>{</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>d</mi>
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<mi>t</mi>
<mo>.</mo>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mn>1</mn>
</msub>
</munderover>
<msub>
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<mi>j</mi>
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</mrow>
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<mi>N</mi>
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</munderover>
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</mrow>
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<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
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<mi>o</mi>
<mi>u</mi>
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<mo>.</mo>
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<mo>&le;</mo>
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<mi>&alpha;</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
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<mn>1</mn>
</mrow>
2
<mrow>
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<mi>P</mi>
<mi>r</mi>
</msub>
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<msub>
<mi>P</mi>
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<mi>d</mi>
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<mi>t</mi>
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<mi>min</mi>
</mrow>
</msub>
<mo>&le;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mn>1</mn>
</msub>
</munderover>
<msub>
<mi>P</mi>
<mrow>
<mi>h</mi>
<mo>.</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mn>2</mn>
</msub>
</munderover>
<msub>
<mi>P</mi>
<mrow>
<mi>f</mi>
<mo>.</mo>
<mi>s</mi>
</mrow>
</msub>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msub>
<mi>P</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mo>.</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>}</mo>
<mo>&le;</mo>
<msub>
<mi>&alpha;</mi>
<mn>2</mn>
</msub>
<mo>,</mo>
<mi>k</mi>
<mo>=</mo>
<mn>3</mn>
</mrow>
Wherein, Pr{ } is probability operator；N1、N2Respectively thermoelectricity and wind-powered electricity generation generating Board Lot inside the province；Pd.t.max、Pd.t.minRespectively
For the t days maximum, minimum loads；Ph.j、Pf.sRespectively j-th of thermal power generation unit, s-th of wind-power electricity generation unit power are random
Value；Pout.iFor from the power of i-th of power purchase outside the province, N is that outer power purchase saves quantity；α1、α2Respectively peak load and Gu He states are lowered
Peak risk level limiting value；
The thermoelectricity generating capacity constraint is stated as follows with mathematical function：
Wh.min≤Wh≤Wh.max
Ph.min≤Ph≤Ph.max
Wherein, WhFor thermoelectricity gross generation inside the province；Wh.max、Wh.minRespectively thermoelectricity is maximum inside the province, minimum generated energy；PhFor inside the province
The total generated output of thermoelectricity；Ph.max、Ph.minRespectively thermoelectricity is maximum inside the province, minimum generated output；
The wind power constraint is stated as follows with mathematical function：
0≤Pf≤Pf.max
Wherein, PfFor wind-powered electricity generation general power inside the province, Pf.maxFor wind-powered electricity generation peak power inside the province；
It is as follows with mathematical function statement that the outer power purchase saves power constraint：
0≤Pout.i.k≤Pout.i.k.max
Wherein, Pout.i.kFor from i-th of power purchase kth time period power outside the province；Pout.i.k.maxFor from i-th of power purchase kth time period outside the province
Peak power；
The inter-provincial interconnection transmission capacity constraint constraint is stated as follows with mathematical function：
Pl.min≤Pl≤Pl.max
Wherein, PlFor inter-provincial interconnection l transmitted power；Pl.max、Pl.minThe maximum of respectively inter-provincial interconnection l transmitted powers,
Minimum value.
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| CN108767854A (en) * | 2018-06-20 | 2018-11-06 | 北京科东电力控制***有限责任公司 | Power purchase scheme optimization method, apparatus and electronic equipment |
| CN108923417A (en) * | 2018-07-09 | 2018-11-30 | 国网上海市电力公司 | The maximum demand optimization method of source user containing distributed electrical |
| CN110674986A (en) * | 2019-09-23 | 2020-01-10 | 广西电网有限责任公司 | Peak regulation influence evaluation method for outsourcing electricity sale annual decomposition scheme and related equipment |
| CN110826763A (en) * | 2019-09-19 | 2020-02-21 | 国网浙江省电力有限公司台州供电公司 | Middle-long term contract electric quantity decomposition method based on guided learning strategy |
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| CN108767854A (en) * | 2018-06-20 | 2018-11-06 | 北京科东电力控制***有限责任公司 | Power purchase scheme optimization method, apparatus and electronic equipment |
| CN108923417A (en) * | 2018-07-09 | 2018-11-30 | 国网上海市电力公司 | The maximum demand optimization method of source user containing distributed electrical |
| CN110826763A (en) * | 2019-09-19 | 2020-02-21 | 国网浙江省电力有限公司台州供电公司 | Middle-long term contract electric quantity decomposition method based on guided learning strategy |
| CN110826763B (en) * | 2019-09-19 | 2022-07-26 | 国网浙江省电力有限公司台州供电公司 | Middle-long term contract electric quantity decomposition method based on guided learning strategy |
| CN110674986A (en) * | 2019-09-23 | 2020-01-10 | 广西电网有限责任公司 | Peak regulation influence evaluation method for outsourcing electricity sale annual decomposition scheme and related equipment |
| CN110838076A (en) * | 2019-09-26 | 2020-02-25 | 国家电网公司华中分部 | Monthly inter-provincial area renewable energy consumption method and terminal equipment |
| CN110838076B (en) * | 2019-09-26 | 2022-05-17 | 国家电网公司华中分部 | Monthly cross-provincial region renewable energy consumption method and terminal equipment |
| CN112529304B (en) * | 2020-12-15 | 2024-04-05 | 西安交通大学 | Quota system based two-stage power market optimization operation method considering risk |
| CN112529304A (en) * | 2020-12-15 | 2021-03-19 | 西安交通大学 | Two-stage power market optimized operation method considering risks based on quota system |
| CN113177692A (en) * | 2021-04-06 | 2021-07-27 | 长沙理工大学 | Annual plan completion risk assessment method for state directive electric quantity |
| CN113177692B (en) * | 2021-04-06 | 2024-05-10 | 长沙理工大学 | Annual plan completion risk assessment method for national directive electric quantity |
| WO2023048642A3 (en) * | 2021-09-23 | 2023-06-08 | Envision Digital International Pte. Ltd. | Method and apparatus for generating resource exchange policies, and device and medium thereof |
| CN114925893A (en) * | 2022-05-09 | 2022-08-19 | 重庆大学 | Cross-provincial and cross-regional whole-year electricity purchasing strategy hierarchical optimization method considering source-load time sequence simulation and reservoir inter-month coordination |
| CN114925893B (en) * | 2022-05-09 | 2024-06-04 | 重庆大学 | Cross-provincial and cross-regional annual electricity purchasing strategy layered optimization method considering source-load time sequence simulation and reservoir inter-month coordination |
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