CN110310173A - A kind of renewable energy participate in long-term electricity transaction power energy allocation method - Google Patents
A kind of renewable energy participate in long-term electricity transaction power energy allocation method Download PDFInfo
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Abstract
The present invention relates to a kind of renewable energy participate in long-term electricity transaction power energy allocation method, comprising the following steps: 1) according to energy market go out that clear data draft electricity power enterprise declare electricity price；2) according to Two-Hierarchical Programming Theory, the bilevel programming model of long-term electricity transaction in renewable energy participation is established；3) hybrid algorithm and Nonlinear Programming Method combined using discrete particle cluster and continuous population solves bilevel programming model, according to the distribution of the electricity price for the electricity and power consumer for obtaining optimal solution completion electricity power enterprise.Compared with prior art, the present invention has many advantages, such as that distribution is accurate, practical reasonable.
Description
Technical field
The present invention relates to electricity market fields, more particularly, to the electricity of long-term electricity transaction in a kind of participation of renewable energy
Measure distribution method.
Background technique
With rapid economic development, energy and environmental problem has become world today's focus of interest.Coal, petroleum,
The demand of the energy such as natural gas is growing day by day, but these energy are non-renewable, and can cause to environment sternly during utilization
Heavily contaminated, it is also increasing for being influenced caused by social sound development and stabilization, therefore renewable energy power generation receives
It pays close attention to more and more widely, wind-force and solar power generation will be as the main forces of China's future source of energy structure.
Under national governments' policy support, by years of researches, wind-powered electricity generation and photovoltaic have become more mature new energy
Source generation technology.But due to the characteristic of wind-powered electricity generation and photovoltaic power generation itself, the access electric system of both renewable energy can be given
System brings certain challenge, and at the same time, China actively pushes forward electric Power Reform at this stage, and electric system is gradually entered into the market
The change stage, but there is presently no a kind of medium-term and long-term power planning scheme participated in view of renewable energy, cause renewable
The utilization rate of the energy declines, and resource mispairing can be also caused because of information asymmetry.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of renewable energy to join
With the power energy allocation method of medium-term and long-term electricity transaction.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of renewable energy participate in long-term electricity transaction power energy allocation method, comprising the following steps:
1) according to energy market go out that clear data draft electricity power enterprise declare electricity price；
2) according to Two-Hierarchical Programming Theory, the bilevel programming model of long-term electricity transaction in renewable energy participation is established；
3) hybrid algorithm and Nonlinear Programming Method combined using discrete particle cluster and continuous population advises two layers
It draws model to be solved, according to the distribution of the electricity price for the electricity and power consumer for obtaining optimal solution completion electricity power enterprise.
In the step 2), renewable energy participate in long-term electricity transaction bilevel programming model with electricity power enterprise
The minimum objective function of negative profit establish upper layer plan model, with social welfare be up to objective function establish lower layer planning mould
Type.
The objective function of the upper layer plan model are as follows:
Wherein, NcFor electricity power enterprise/unit i sum, T is period sum, πiGo out clear electricity price, u for electricity power enterprisei,tFor
Unit respectively represents shutdown and booting, P in the 0-1 variable of period t operating statusi,tThe output that should be provided for unit in period t
Power, πswGo out clear electricity price, P for renewable energy power generation enterpriseSW,tAlways the going out in t moment for wind power plant and photovoltaic plant
Power,For the unit cost of electricity-generating of electricity power enterprise, F (Pi,t) it is power output Bidding Price Functions of the unit in period t, Si,tExist for unit
The start-up cost of period t,For the expense that unit needs to undertake when the period, t provided spare capacity, ai、bi、ciRespectively machine
Each term coefficient of the Bidding Price Functions of group i, αiFor the starting and the cost of overhaul of unit i, βiTo be cold-started cost,For unit
Time through stopping transport, λiFor unit cooling velocity,To discharge unit CO2The expense that need to be paid,It is unit i in period t
CO corresponding to unit output2Discharge amount, CD,iThe expense that unit spare capacity need to be paid, R are provided in period t for unit ii,tFor
The spare power output that unit i is provided in period t.
The constraint condition of the upper layer plan model includes:
System loading Constraints of Equilibrium:
Spinning reserve capacity constraint:
The constraint of system core section tidal current:
Unit ramp loss:
The constraint of unit output bound:
The continuous start-off time constraints of unit:
Wherein, DtFor the total load of period t, r is system reserve parameter, Pi,max、Pi,minThe power output of respectively unit is upper and lower
Limit, Pup,iIt climb per hour ratio of slope for unit, N is unit sum, Gl→iTransfer distribution factor for unit to route l, NWSFor
Wind-powered electricity generation and photovoltaic power generation unit sum, PWS,ws,tThe acceptance of the bid electricity of photovoltaic or Wind turbines in period t, Gl→wsFor wind-powered electricity generation or photovoltaic
Transfer distribution factor of the generating set to route l, K are the number of node load, Gl→kTransfer point for node load k to route l
The cloth factor, Dk,tFor the bus load of node k period t,For the active transmission capacity of section L, Pdown,iFor unit per hour to
Lower climbing rate, Ti on、Ti offRespectively unit minimum continuously open, downtime, yi,t、zi,tRespectively unit period t whether
The 0-1 variable start, shut down, ui,ttFor unit period tt operating status 0-1 variable.
The objective function of lower layer's plan model are as follows:
Wherein,It offers for the unit quantity of electricity of electricity power enterprise, Pi GFor the monthly or annual acceptance of the bid electricity of thermal power generation corporations
Amount,It offers for the unit quantity of electricity of renewable energy, PSWFor the monthly or annual acceptance of the bid electricity of renewable energy power generation enterprise,
NUFor participate in market price bidding power consumer sum,It offers for the unit quantity of electricity of power consumer j,For power consumer j
Monthly or annual acceptance of the bid electricity, diFor the quotation coefficient of electricity power enterprise's competitive bidding electricity price,For electricity power enterprise unit power generation at
This.
The constraint condition of lower layer's plan model are as follows:
Thermal power output constraint:
The constraint of renewable energy power generation amount:
Power consumer competitive bidding electricity restriction:
The constraint of the electric quantity balancing of electricity power enterprise and power consumer:
The quotation restricted coefficients of equation of electricity power enterprise:
Wherein,The respectively monthly or annual acceptance of the bid electricity upper and lower limit of thermal power generation corporations, PSW, min、
PSW, maxThe respectively monthly or annual acceptance of the bid electricity upper and lower limit of renewable energy power generation enterprise,Respectively electric power
The monthly or annual acceptance of the bid electricity upper and lower limit of user j, dI, min、dI, maxThe respectively quotation coefficient upper and lower limit of electricity power enterprise.
The step 3) specifically includes the following steps:
31) dynamic parameter for inputting initial data, initially going out clear electricity price and particle swarm algorithm, and by the number of iterations k1Set 1；
32) particle position and particle rapidity of primary group are formed, and by population number and the number of iterations k2Set 1；
33) speed of more new particle and position, make primary globally optimal solution and individual optimal solution value one
Sufficiently large number, and calculate the adaptive value of current particle, optimum load dispatch of the record unit under start and stop combination and optimal
The negative profit of electricity power enterprise；
34) for each particle, by its fitness value compared with current individual extreme value, if being less than current individual extreme value,
Updating current individual extreme value is fitness value at this time；
35) judge whether population quantity reaches population total, if having reached population total, carry out step 36), otherwise,
Population number is enabled to add 1, and return step 33)；
36) according to the particle position of adaptive value, speed and the position of population are updated, judges the number of iterations k at this time1It is
It is no to reach maximum number of iterations k1,maxIf having reached k1,maxStep 37) is then carried out, otherwise, enables the number of iterations k1Add 1, population number
Set 1, and return step 33)；
37) according to optimum particle position, optimum load dispatch and optimal power generation of the unit under this start and stop combination are calculated
The negative profit of enterprise, and the corresponding control variable value of optimal solution is saved, the unit cost of electricity-generating of Ji Ge electricity power enterprise is excellent as lower layer
Change initial parameter；
38) underlying model is optimized using Non-Linear Programming function, acquire underlying model optimal solution and
Corresponding each electricity power enterprise goes out clear electricity price, and this is gone out clear electricity price as the supreme layer model of known parameters back substitution；
39) successive ignition optimization is carried out to upper and lower layer model, and judges whether to meet termination condition, if satisfied, then terminating
It calculates and exports globally optimal solution；
310) globally optimal solution obtained according to Optimization Solution, optimum allocation electricity, unit hair including each electricity power enterprise
Electric cost and electricity price is declared, the acceptance of the bid electricity of final each electricity power enterprise goes out clear electricity price, totle drilling cost and corresponding social good fortune
Benefit, and operation is distributed with this.
Compared with prior art, the invention has the following advantages that
On the basis of the considerations of proposing in present invention renewable energy participates in electricity transaction, it is based on Two-Hierarchical Programming Theory, from
Transaction perspective is set out, establish renewable energy participate in long-term electricity transaction bilevel programming model, using discrete particle cluster and
The hybrid algorithm and Nonlinear Programming Method that continuous population combines solve bilevel programming model, obtain optimum programming
Scheme meets the premise of system stability, has the advantages that logical construction is clear, practical reasonable.
Detailed description of the invention
Fig. 1 is invention flow chart of the invention.
Fig. 2 is the flow chart of hybrid algorithm of the present invention.
Fig. 3 is improved IEEE14 node system topological diagram.
Fig. 4 is the situation of Profit under IEEE14 node system difference renewable energy accounting.
Fig. 5 is improved IEEE39 node system topological diagram.
Fig. 6 is the situation of Profit under IEEE39 node system difference renewable energy accounting.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment
As shown in Figure 1, a kind of long-term electricity transaction method in renewable energy participation based on bi-level programming method, including
Following steps:
S1 goes out clear situation according to energy market, and that drafts electricity power enterprise declares electricity price；
S2 is according to Two-Hierarchical Programming Theory, from transaction perspective, establishes long-term electricity transaction in renewable energy participation
Bilevel programming model；
The hybrid algorithm and Nonlinear Programming Method that S3 is combined using discrete particle cluster and continuous population advise two layers
It draws model to be solved, the distribution of electricity power enterprise's electricity, electricity price is completed according to obtained optimal solution.
Clear situation is gone out according to energy market in step S1, that drafts electricity power enterprise declares electricity price, the specific steps are that:
Step S11: after long-term electricity market is opened in, competitive bidding electricity and competitive bidding are declared according to own situation by electricity power enterprise
Electricity price, power consumer then declare competitive bidding electricity and competitive bidding electricity price according to electricity consumption situation on last stage；
Step S12: the information that electricity transaction mechanism is submitted according to market member, in conjunction with the operation of electric system next stage
Mode constructs the clear model out of long-term electricity transaction in renewable energy participation；
Step S13: electricity transaction mechanism uses reasonable algorithm via electronic system, renewable energy the considerations of to previous step
Optimization is clear in the clear Models Sets out of long-term electricity transaction in the participation of source；
Step S14: after being settled accounts fruit out, electricity transaction mechanism will include that system goes out clear electricity by electric power transaction platform
Valence, out clear electric quantity curve etc., which go out to settle accounts fruit, feeds back to each market member；
Step S15: each market member can adjust competitive bidding electricity price after receiving out clear information according to own situation, and will adjustment
Competitive bidding electricity price afterwards reports electricity transaction mechanism again；
Step S16: electricity transaction mechanism after receiving market member competitive bidding electricity price adjusted, repeat step S23 and it
Step afterwards, before electricity transaction closing, market member, which can be adjusted repeatedly, declares electricity price to obtain preferably market clearing
As a result；
Step S17: before electricity transaction closing, electricity power enterprise and power consumer confirm that it is participated in business, and sign transaction
Contract.
Long-term electric power in renewable energy participation is established from transaction perspective according to Two-Hierarchical Programming Theory in step S2
The bilevel programming model of transaction, specific steps are as follows:
Step S21: upper layer plan model, upper layer objective function are established with the minimum objective function of negative profit of electricity power enterprise
It is as follows:
Wherein:
In formula: πiGo out clear electricity price for electricity power enterprise i；ui,t0-1 variable for unit i in period t operating status, difference
Represent shutdown and booting；Pi,tThe output power that should be provided for unit i in period t；πswIt is clear for going out for renewable energy power generation enterprise
Electricity price；For the unit cost of electricity-generating of electricity power enterprise i；F(Pi,t) it is power output Bidding Price Functions of the unit i in period t；Si,tFor machine
Start-up cost of the group i in period t；The expense for needing to undertake when the period, t provided spare capacity for unit i；For unit
CO of the i in period t2Discharge fee；ai、bi、ciRespectively each term coefficient of the Bidding Price Functions of unit i, αiFor unit i starting and
The cost of overhaul；βiTo be cold-started cost,For the time that unit has been stopped transport, λiFor unit cooling velocity,It is single for discharge
Position CO2The expense that need to be paid；Correspond to the CO of unit output in period t for unit i2Discharge amount, CD,iIt is unit i in period t
The expense that unit spare capacity need to be paid, R are providedi,tThe spare power output provided for unit i in period t.
Upper layer model constraint condition is expressed as follows:
In formula, PSW,tFor wind power plant and photovoltaic plant t moment gross capability；DtFor the total load of period t；R is system
Backup；Pi,max、Pi,minThe respectively power output upper and lower limit of unit i；Pup,iIt climb per hour ratio of slope for unit i；N is machine
Group sum；Gl→iIndicate transfer distribution factor of the unit i to route l；NWSFor wind-powered electricity generation and photovoltaic power generation unit sum；PWS,ws,tLight
Volt or Wind turbines period t acceptance of the bid electricity；Gl→wsIndicate wind-powered electricity generation or photovoltaic power generation unit to the transfer distribution of route l because
Son；Gl→kIndicate transfer distribution factor of the node load k to route l；The number of K expression node load；Dk,tFor node k period t
Bus load；Indicate the active transmission capacity of section L；Pdown,iFor unit i climbing rate downwards per hour；Point
Not Wei unit i minimum continuously open, downtime；yi,t、zi,tThe 0-1 change whether respectively unit i starts, shuts down in period t
Amount；ui,ttFor unit i period tt operating status 0-1 variable.
It when carrying out year transaction, using year as the cycle of operation, sets T to 8760 hours, and with 1 year load of future
Prediction data brings calculating into；It was the cycle of operation with one month when carrying out monthly transaction, sets T to 720 hours, and with not
The load prediction data come one month brings calculating into, if when carrying out monthly transaction, participated in advance year transaction and at
Hand over, then when carrying out monthly transaction, unit output bound constrain in unit output lower limit need to according to annual transaction results into
The corresponding adjustment of row.
Step S22: objective function is up to social welfare and establishes lower layer's plan model, lower layer's objective function is as follows:
Wherein:
In formula,It offers for the unit quantity of electricity of electricity power enterprise i；Pi GFor the monthly or annual acceptance of the bid electricity of thermal power generation corporations i
Amount；It offers for the unit quantity of electricity of renewable energy；PSWFor the monthly or annual acceptance of the bid electricity of renewable energy power generation enterprise；
NUFor the sum of the power consumer of participation market price bidding；It offers for the unit quantity of electricity of power consumer j；For power consumer i's
Monthly or annual acceptance of the bid electricity；diFor the quotation coefficient of electricity power enterprise's competitive bidding electricity price.
Underlying model constraint condition is expressed as follows:
In formula,The monthly or annual acceptance of the bid electricity upper and lower limit of respectively thermal power generation corporations i；PSW, min、
PSW, maxThe respectively monthly or annual acceptance of the bid electricity upper and lower limit of renewable energy power generation enterprise；It is respectively electric
The monthly or annual acceptance of the bid electricity upper and lower limit of power user j；dI, min、dI, maxThe respectively quotation coefficient of electricity power enterprise i is upper and lower
Limit.The upper limit for assuming acceptance of the bid electricity herein is 0, and the lower limit for electricity of getting the bid declares electricity for each market member.
When carrying out year transaction, the bound of above-mentioned each acceptance of the bid electricity is following 1 year acceptance of the bid electricity bound；
When carrying out monthly transaction, the bound of above-mentioned each acceptance of the bid electricity is following one month acceptance of the bid electricity bound.
When solving above-mentioned Two-level Optimization model, it should be noted that following two points: the unit cost of electricity-generating of electricity power enterprise i
It is used as decision variable in the Optimized model of upper layer, a known parameters are then used as in lower layer's Optimized model, it means that one
Denier upper layer model optimization obtains the unit cost of electricity-generating of each electricity power enterpriseUnderlying model can be obtained respectively by Optimization Solution
Electricity power enterprise goes out clear electricity price, and clear electricity price is using heat-clearing method clearing are brought out together out, should go out clear electricity price again can back substitution to upper layer optimization mould
Type；The power output of each fired power generating unit, honourable gross capability are both the decision variable of lower layer's Optimized model and determining for upper layer Optimized model
Plan variable.
The hybrid algorithm and Nonlinear Programming Method pair for using discrete particle cluster and continuous population to combine in step S3
Bilevel programming model is solved, and the distribution of electricity power enterprise's electricity, electricity price, specific steps are completed according to obtained optimal solution are as follows:
Step S31: input initial data, the dynamic parameter for initially going out clear electricity price and particle swarm algorithm, and by the number of iterations
k1Set 1；
Step S32: the particle position and particle rapidity of primary group are formed, and by population number and the number of iterations k2It sets
1；
Step S33: the speed of more new particle and position make the globally optimal solution and individual optimal solution value of primary
One sufficiently large number, and calculate the adaptive value of current particle, optimum load dispatch of the record unit under start and stop combination and
The negative profit of optimal power generation enterprise；
Step S34: for each particle, by its fitness value compared with current individual extreme value, if being less than current individual pole
Value, then updating current individual extreme value is fitness value at this time；
Step S35: judging whether population quantity reaches population total, if having reached population total, runs in next step, no
Then, population number is enabled to add 1, and return step S3；
Step S36: according to the particle position of adaptive value, updating speed and the position of population, judges iteration time at this time
Number k1Whether maximum number of iterations k is reached1, maxIf having reached k1, maxIt then continues to run in next step, otherwise, enables the number of iterations k1Add
1, population number sets 1, and return step S3；
Step S37: according to optimum particle position, optimum load dispatch of the unit under this start and stop combination and optimal is calculated
The negative profit of electricity power enterprise, and the corresponding control variable value of optimal solution (the unit cost of electricity-generating of each electricity power enterprise) is saved as lower layer
Optimize initial parameter；
Step S38: optimizing underlying model using Non-Linear Programming function, acquires the optimal solution of underlying model
And the clear electricity price out of corresponding each electricity power enterprise, and this is gone out into clear electricity price as the supreme layer model of known parameters back substitution；
Step S39: successive ignition optimization is carried out to upper and lower layer model, and judges whether to meet termination condition, if satisfied, then
Terminate to calculate and exports globally optimal solution.
Step S310: it electricity power enterprise's optimum allocation electricity for being obtained according to Optimization Solution, unit cost of electricity-generating and declares
Electricity price, declaring electricity price, declare electricity in conjunction with power consumer bring the going out in clear model of energy market into, it is final solve obtain it is each
The acceptance of the bid electricity of electricity power enterprise goes out clear electricity price, totle drilling cost and corresponding social welfare.
Embodiment 1
In this example, the electric system of simulation uses IEEE14 node and IEEE39 node system.
In IEEE14 node system, according to burden with power in initial data and the distribution situation of unit, it is added to 2 volumes
The photovoltaic plant that the wind power plant and a rated capacity that constant volume is 50MW are 35MW, wind power plant and photovoltaic plant are located at section
Point 4, node 13 and node 9.Wind power plant and the total installation of generating capacity of photovoltaic plant are 135MW, the installation total capacity of fired power generating unit
For 320MW, the installed capacity of wind-powered electricity generation, photovoltaic power generation accounts for about the 30% of total installation of generating capacity.Generating set is considered as by this section example
Load bus is considered as power consumer by electricity power enterprise.
Fig. 3 is modified IEEE14 node system topological diagram；IEEE14 node system thermal power generation unit partial parameters
1 is shown in Table to table 3, table 4 declares situation for each power consumer.Consider in Optimization Solution long-term in renewable energy power generation unit
When going out to settle accounts fruit of electricity market, for spare and section tidal current confidence level η2、η3、η4Take 95%, stand-by requirement confidence water
Flat η '2Take 97%, balancing the load confidence level η1Take 70%.When arranging data, by renewable energy power generations all in this example
Enterprise is unified to be marked, number 6；The clear electricity price that goes out of this chapter example Zhong Ge electricity power enterprise is indicated with the clear electricity price that averages out of itself；It can
The minimum electricity price of declaring of routinely energy electricity power enterprise, renewable source of energy generation enterprise is declared to participate in market clearing, this example is assumed
The unit quantity of electricity of renewable energy power generation enterprise declare electricity price be 6.5 $/MWh, when the regional generation enterprise can not provide it is enough
When electric energy is to meet user demand, the renewable energy power generation enterprise that has more than needed can be introduced outside city by transregional transaction transprovincially and participate in hair
Electricity.
1 IEEE14 node system branch parameters of table
2 IEEE14 node system node parameter of table
3 IEEE14 node system fired power generating unit parameter of table
4 IEEE14 node system power consumer electricity price declaration data of table
In order to study the influence of investment renewable energy power generation unit centering long term power mechanism of exchange, table 5,6 is given respectively
It is clear as a result, table 6 gives both not to have gone out not consider and consider that the medium-term and long-term electricity market of renewable energy power generation unit goes out
With the situation of Profit under situation.By table 5, table 6 it is found that since cost of electricity-generating considers operating cost, start-up cost, spare simultaneously
The unit cost of electricity-generating of cost and carbon emission cost, electricity power enterprise can change with the variation of upper layer optimum results, each to send out
Electric enterprise can declare electricity price according to self generating cost adjustment, declare electricity price more lower easier conclusion of the business, declare electricity price more it is high more not
Be easy to strike a bargain, and height and the height for declaring electricity price for going out clear electricity price of each electricity power enterprise have no it is too big be associated with, in order to obtain
Higher profit, each electricity power enterprise should be according to self generating cost and demand and combination market situation selection preferably quotation plans
Slightly.In general, after considering renewable energy power generation, the clear electricity price that goes out of electricity market decreases to a certain extent.By
After table 7 is it is found that be added renewable energy power generation unit, electricity power enterprise's gross profit is improved extremely by 556417.49 dollars
688640.892 dollars, increase about 41.42%, at the same time, social welfare also rises to 813180 dollars by 773790 dollars.
This explanation considers that renewable energy power generation can be under the premise of guaranteeing system reliability, effectively in long-term electricity transaction mechanism
Increase the gross profit of electricity power enterprise.
5 IEEE14 node system of table does not consider market clearing result when renewable energy
6 IEEE14 node system of table considers market clearing result when renewable energy
Situation of Profit under 7 IEEE14 node system different situations of table
| Do not consider renewable energy generation | Consider renewable energy generation | |
| Electricity power enterprise's gross profit/$ | 556417.49 | 688640.892 |
| Social welfare/$ | 773790 | 813180 |
In order to study different renewable energy accountings to renewable energy participate in long-term electricity transaction go out to settle accounts fruit
It influences, taking renewable energy power generation installation to account for the ratio of total installation of generating capacity respectively is 30% to 70%, and optimization obtains IEEE14 section
Dot system is under the electricity power enterprise's gross profit and social welfare, different renewable energy accountings under different renewable energy accountings
Renewable energy power generation situation is as shown in Fig. 4, table 8.As shown in Figure 4, as renewable energy power generation installation accounts for total installation of generating capacity
The rising of ratio, electricity power enterprise's gross profit and social welfare are increased.As shown in Table 8, as renewable energy power generation fills
Machine capacity accounts for the raising of total electricity installed capacity ratio, and renewable energy power generation total amount is substantially improved, and renewable energy power output accounts for
The ratio of system gross capability is also promoted therewith.It can be seen that considering that the medium-term and long-term electricity transaction mechanism of renewable energy can be
Promote the consumption of renewable energy to a certain extent, and renewable energy generation installed capacity accounts for the ratio of total installed capacity of electricity capacity
Higher, the promotion amplitude that renewable energy power output accounts for system gross capability ratio is also bigger.
Renewable energy power generation situation under 8 IEEE14 node system difference renewable energy accounting of table
In IEEE39 system, according to burden with power in initial data and the distribution situation of unit, it is added to 4 specified appearances
Amount be respectively 800MW, 800MW, 800MW, 200MW wind power plant and 4 rated capacities be respectively 200MW, 200MW, 200MW,
The photovoltaic plant of 100MW, wind power plant and photovoltaic plant are located at node 4, node 8, node 16, node 20 and node 3, section
5, node 15, node 21.Wind power plant and the total installation of generating capacity of photovoltaic plant are 3300MW, and the installation total capacity of fired power generating unit is
7665MW, wind-powered electricity generation, photovoltaic power generation account for about the 30% of total installation of generating capacity.Improved IEEE39 node system figure such as Fig. 5；IEEE39
Node system thermal power generation unit part basic parameter is shown in Table 9 to table 11, and each power consumer declares situation such as table 12.
9 IEEE39 node system branch parameters of table
10 IEEE39 node system node parameter of table
11 IEEE39 node system fired power generating unit parameter of table
12 IEEE39 node system power consumer electricity price declaration data of table
Table 13,14 does not respectively consider and considers the market clearing of the medium-term and long-term electricity transaction of renewable energy power generation unit
As a result.This section is when Optimization Solution considers that long-term electricity transaction goes out to settle accounts fruit in renewable energy power generation unit, for standby
With and section tidal current confidence level η2、η3、η4Take 95%, stand-by requirement confidence level η '2Take 97%, balancing the load confidence water
Flat η1Take 60%.When arranging data, by the unified label of renewable energy power generations all in this example enterprise, number 11；This calculation
The unit quantity of electricity that official holiday sets renewable energy power generation enterprise declares electricity price as 13 $/MWh.By table 13, table 14 it is found that due to power generation
Cost considers operating cost, start-up cost, stand-by cost and carbon emission cost, the unit cost of electricity-generating of electricity power enterprise simultaneously
It can change with the variation of upper layer optimum results, each electricity power enterprise can declare electricity price according to self generating cost adjustment, declare
Electricity price more lower easier conclusion of the business, declares that electricity price is higher to be less susceptible to strike a bargain, and the height for going out clear electricity price of each electricity power enterprise and Shen
The height of report electricity price has no too big association, in order to obtain higher profit, each electricity power enterprise should according to self generating cost and
Demand simultaneously combines market situation to select preferably quotation strategy.In general, after considering renewable energy power generation, electricity market
Go out clear electricity price decrease to a certain extent.
13 IEEE39 node system of table does not consider market clearing result when renewable energy
14 IEEE39 node system of table considers market clearing result when renewable energy
Table 15 is not consider and consider that the medium-term and long-term electricity market of renewable energy power generation unit goes out to settle accounts corresponding to fruit
Situation of Profit.As shown in Table 15, after renewable energy power generation unit being added, electricity power enterprise's gross profit is by 16547049.93 dollars
It improves to 23401573.23 dollars, increases about 23.5%, at the same time, social welfare is also risen to by 27476400 dollars
28059000 dollars.This explanation considers that renewable energy power generation can guarantee system reliability in long-term electricity transaction mechanism
Under the premise of, the gross profit of electricity power enterprise is effectively increased, social welfare is improved.
Situation of Profit under 15 IEEE39 node system different situations of table
| Do not consider renewable energy generation | Consider renewable energy generation | |
| Electricity power enterprise's gross profit/$ | 16547049.93 | 23401573.23 |
| Social welfare/$ | 27476400 | 28059000 |
Fig. 6, table 16 be respectively renewable energy power generation installation account for total installation of generating capacity ratio be 30% to 70% when, optimization
Electricity power enterprise's gross profit and social welfare, the renewable energy power generation situation of obtained IEEE39 node system.It will be appreciated from fig. 6 that
As renewable energy power generation installation accounts for the rising of total installation of generating capacity ratio, electricity power enterprise's gross profit and social welfare are increased
It is long.As shown in Table 16, as renewable energy power generation installed capacity accounts for the raising of total electricity installed capacity ratio, renewable energy
Power generation total amount is substantially improved, and the ratio that renewable energy power output accounts for system gross capability is also promoted therewith.It can be seen that consideration can be again
The medium-term and long-term electricity transaction mechanism of the raw energy can promote the consumption of renewable energy, and renewable energy machine to a certain extent
The ratio that kludge capacity accounts for total installed capacity of electricity capacity is higher, and renewable energy, which is contributed, accounts for the promotion amplitude of system gross capability ratio
Also bigger.
Renewable energy power generation situation under 16 IEEE39 node system difference renewable energy accounting of table
Claims (7)
1. a kind of power energy allocation method of long-term electricity transaction in renewable energy participation, which comprises the following steps:
1) according to energy market go out that clear data draft electricity power enterprise declare electricity price；
2) according to Two-Hierarchical Programming Theory, the bilevel programming model of long-term electricity transaction in renewable energy participation is established；
3) hybrid algorithm and Nonlinear Programming Method combined using discrete particle cluster and continuous population is to bi-level programming mould
Type is solved, according to the distribution of the electricity price for the electricity and power consumer for obtaining optimal solution completion electricity power enterprise.
2. the power energy allocation method of long-term electricity transaction, special in a kind of renewable energy participation according to claim 1
Sign is, in the step 2), renewable energy participate in long-term electricity transaction bilevel programming model with electricity power enterprise
The minimum objective function of negative profit establish upper layer plan model, with social welfare be up to objective function establish lower layer planning mould
Type.
3. the power energy allocation method of long-term electricity transaction, special in a kind of renewable energy participation according to claim 2
Sign is, the objective function of the upper layer plan model are as follows:
Wherein, NcFor electricity power enterprise/unit i sum, T is period sum, πiGo out clear electricity price, u for electricity power enterprisei,tFor unit
In the 0-1 variable of period t operating status, shutdown and booting, P are respectively representedi,tFor the output power that unit should be provided in period t,
πswGo out clear electricity price, P for renewable energy power generation enterpriseSW,tFor wind power plant and photovoltaic plant t moment gross capability,
For the unit cost of electricity-generating of electricity power enterprise, F (Pi,t) it is power output Bidding Price Functions of the unit in period t, Si,tIt is unit period t's
Start-up cost,For the expense that unit needs to undertake when the period, t provided spare capacity, ai、bi、ciThe respectively report of unit i
Each term coefficient of valence function, αiFor the starting and the cost of overhaul of unit i, βiTo be cold-started cost,It has stopped transport for unit
Time, λiFor unit cooling velocity,To discharge unit CO2The expense that need to be paid,Correspond to machine in period t for unit i
The CO of group power output2Discharge amount, CD,iThe expense that unit spare capacity need to be paid, R are provided in period t for unit ii,tExist for unit i
The spare power output that period t provides.
4. the power energy allocation method of long-term electricity transaction, special in a kind of renewable energy participation according to claim 3
Sign is that the constraint condition of the upper layer plan model includes:
System loading Constraints of Equilibrium:
Spinning reserve capacity constraint:
The constraint of system core section tidal current:
Unit ramp loss:
The constraint of unit output bound:
The continuous start-off time constraints of unit:
Wherein, DtFor the total load of period t, r is system reserve parameter, Pi,max、Pi,minThe respectively power output upper and lower limit of unit,
Pup,iIt climb per hour ratio of slope for unit, N is unit sum, Gl→iTransfer distribution factor for unit to route l, NWSFor wind
Electricity and photovoltaic power generation unit sum, PWS,ws,tThe acceptance of the bid electricity of photovoltaic or Wind turbines in period t, Gl→wsIt is sent out for wind-powered electricity generation or photovoltaic
Transfer distribution factor of the motor group to route l, K are the number of node load, Gl→kThe transfer of route l is distributed for node load k
The factor, Dk,tFor the bus load of node k period t,For the active transmission capacity of section L, Pdown,iIt is downward per hour for unit
Climbing rate, Ti on、Ti offRespectively unit minimum continuously open, downtime, yi,t、zi,tRespectively whether unit opens in period t
The 0-1 variable move, shut down, ui,ttFor unit period tt operating status 0-1 variable.
5. the power energy allocation method of long-term electricity transaction, special in a kind of renewable energy participation according to claim 2
Sign is, the objective function of lower layer's plan model are as follows:
Wherein,It offers for the unit quantity of electricity of electricity power enterprise, Pi GFor the monthly or annual acceptance of the bid electricity of thermal power generation corporations,
It offers for the unit quantity of electricity of renewable energy, PSWFor the monthly or annual acceptance of the bid electricity of renewable energy power generation enterprise, NUFor ginseng
With the sum of the power consumer of market price bidding,It offers for the unit quantity of electricity of power consumer j,For the monthly of power consumer j
Or year acceptance of the bid electricity, diFor the quotation coefficient of electricity power enterprise's competitive bidding electricity price,For the unit cost of electricity-generating of electricity power enterprise.
6. the power energy allocation method of long-term electricity transaction, special in a kind of renewable energy participation according to claim 5
Sign is, the constraint condition of lower layer's plan model are as follows:
Thermal power output constraint:
The constraint of renewable energy power generation amount:
Power consumer competitive bidding electricity restriction:
The constraint of the electric quantity balancing of electricity power enterprise and power consumer:
The quotation restricted coefficients of equation of electricity power enterprise:
Wherein,The respectively monthly or annual acceptance of the bid electricity upper and lower limit of thermal power generation corporations, PSW, min、PSW, maxPoint
Not Wei renewable energy power generation enterprise monthly or annual acceptance of the bid electricity upper and lower limit,Respectively power consumer j
Monthly or annual acceptance of the bid electricity upper and lower limit, dI, min、dI, maxThe respectively quotation coefficient upper and lower limit of electricity power enterprise.
7. the power energy allocation method of long-term electricity transaction, special in a kind of renewable energy participation according to claim 1
Sign is, the step 3) specifically includes the following steps:
31) dynamic parameter for inputting initial data, initially going out clear electricity price and particle swarm algorithm, and by the number of iterations k1Set 1；
32) particle position and particle rapidity of primary group are formed, and by population number and the number of iterations k2Set 1；
33) speed of more new particle and position, the globally optimal solution for making primary and individual optimal solution value one are enough
Big number, and the adaptive value of current particle is calculated, optimum load dispatch and optimal power generation of the record unit under start and stop combination
The negative profit of enterprise；
34) it for each particle, by its fitness value compared with current individual extreme value, if being less than current individual extreme value, updates
Current individual extreme value is fitness value at this time；
35) judge whether population quantity reaches population total, if having reached population total, carry out step 36), otherwise, enable kind
Group's number adds 1, and return step 33)；
36) according to the particle position of adaptive value, speed and the position of population are updated, judges the number of iterations k at this time1Whether reach
To maximum number of iterations k1,maxIf having reached k1,maxStep 37) is then carried out, otherwise, enables the number of iterations k11, population number is added to set 1,
And return step 33)；
37) according to optimum particle position, optimum load dispatch and optimal power generation enterprise of the unit under this start and stop combination are calculated
Negative profit, and the corresponding control variable value of optimal solution is saved, the unit cost of electricity-generating of Ji Ge electricity power enterprise is first as lower layer's optimization
Beginning parameter；
38) underlying model is optimized using Non-Linear Programming function, acquires the optimal solution of underlying model and corresponding
Each electricity power enterprise go out clear electricity price, and this is gone out into clear electricity price as the supreme layer model of known parameters back substitution；
39) successive ignition optimization is carried out to upper and lower layer model, and judges whether to meet termination condition, if satisfied, then terminating to calculate
And export globally optimal solution；
310) globally optimal solution obtained according to Optimization Solution, optimum allocation electricity including each electricity power enterprise, unit power generation at
Originally and electricity price is declared, the acceptance of the bid electricity of final each electricity power enterprise goes out clear electricity price, totle drilling cost and corresponding social welfare, and
Operation is distributed with this.
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