CN109919472A - A kind of GENERATION MARKET iteration price competing method considering more Interest Main Body games - Google Patents
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Abstract
The invention discloses a kind of GENERATION MARKET iteration price competing methods for considering more Interest Main Body games, comprising steps of step 1: obtaining unit and network parameter, scalar in each Power Generation is arrangedPrice cap initial value and Lagrangian penalty function multiplier initial value and the number of iterations；Step 2: the Power Generation objective function and constraint condition obtained according to the Lagrangian penalty function of introducing constitutes Power Generation Competitive Bidding Model and is solved, and the generated energy of optimization and quotation coefficient are passed to ISO；Step 3: going out clear model objective function and its constraint condition solution market clearing model according to what introducing I Lagrangian penalty function obtained；Step 4: obtaining optimal power generation market iteration bid price scheme if meeting convergence；Go out last round of ISO to settle accounts fruit if being unsatisfactory for and pass to Power Generation, while updating penalty function multiplier, price cap and return step 2 after making the number of iterations k value increase 1；Step 5: being submitted a tender on electricity market according to optimal power generation market iteration bid price scheme.
Description
Technical field
The present invention relates to GENERATION MARKET price competing methods more particularly to a kind of GENERATION MARKET for considering more Interest Main Body games to change
For price competing method.
Background technique
Power market reform has vital effect, electric power crisis in California to the safety, economy, reliability service of electric power
It is exactly the typical case of electricity market reform failure, and a major reason of its failure is exactly to have in Bidding Mechanism design
Serious loophole.Reasonable Bidding Mechanism is to improving the interests of market efficiency, Maintenance Market participant, guarantee fair competition, steady
The operation and development for determining market can all generate direct influence.One of key problem as electricity market reform, it is each at present
The Bidding Mechanism that state carries out the use of electricity market operative practice mainly includes going out clear valence payment by quotation payment and by Unified Margin
Two kinds.Practical studies show that both mechanism all do not have the incentive compatibility for promoting Power Generation to offer according to bona fide cost,
Power Generation is inclined to serious high quoting, influences the economic load dispatching and operation of system.Incentive electric power bidding mechanism be in order to
The new market price bidding mechanism of the one kind for inhibiting the market forces of Generation Side to design, so that Generation Side participant is by power generation in market
Maximum profit is obtained when Cost Offer, cannot get additional profit instead when deviateing Cost Offer, thus effective solution
Not the problem of Bidding Mechanism in Power Market design not can be effectively controlled rate for incorporation into the power network.Economize on electricity Marginal Pricing (locational
Marginal price, lmp) theory can sufficiently reflect electricity supply and demand relationship, reflect the bona fide cost of electric power, and can effectively into
Row congestion management provides strong tool for the design of electric power bidding mechanism.
ITERATIVE COMPETITIVE BIDDING MECHANISM IN is substantially the bilateral multiple rounds of negotiations process between Power Generation and ISO about cleaing price.Hair
Electric business expresses the price lowest limit for oneself being ready to receive by offering, and ISO is shown as power purchase side by the fixing the price upper limit
Oneself it is ready the ceiling price received.In this dynamic negotiation process, Power Generation can seize market opportunity, and obtain maximum
Profit, ISO reduce purchases strategies by negotiating a price, and Maintenance Market is stablized.ITERATIVE COMPETITIVE BIDDING MECHANISM IN can give full play to generation assets
Market value, under the market environment of perfect competition, the market price is converged near traditional System Margin cost, electricity generation system
Close to economical operation state, there is higher market efficiency.
It mainly includes by quotation payment and by unified that various countries, which carry out the Bidding Mechanism of electricity market operative practice use, at present
Limit goes out clear valence and pays two kinds, and clearing method mainly uses single-wheel to submit a tender out clearly out, is seldom submitted a tender out clearly using iteration, and can
The number of iterations can occur and excessively be difficult to the case where applying.
With the development of electricity market, in the electricity market of market pricing Bidding Mechanism, repeat offer day by day
So that the generated energy of market clear price and each Power Generation easily becomes public information, so that the tacit agreement between Power Generation is total
Scheme is easy to occur, and in turn results in power supply shortage, electricity price rises suddenly and sharply.Tacit collusion between another aspect Power Generation is in turn
Power Generation is made to exercise market forces again more unbridled and handy.And lack by the method that payment quotation is bidded enough
Economic theory basis, cannot embody the market fair competition of " same to net, homogeneity, same to valence ", cannot provide effective guidance investment,
The market economy signal of production and consumption.In addition, there is higher Economic Withholding to be inclined to.
Single-wheel bid is a kind of imperfect information Static Game, and Power Generation does not know about the bidding strategy of other manufacturers, not yet
Determine the income of oneself, submitting a tender has very big risk and game play space.Under these uncertain factors, market is difficult to reach
To effective operating status.On the other hand, ISO is a passively price takers, form a market it is unstable because
Element.
Summary of the invention
In order to overcome above the shortcomings of the prior art, the invention proposes a kind of power generation cities for considering multi-agent Game
Field iteration Competitive Bidding Model.Using Power Generation from autonomous system operator as different Interest Main Bodies, productivity effect is realized most respectively
The excellent and the smallest optimization aim of purchases strategies；It is got in touch with and is decoupled by generated output, realize two different interests main bodys
Independent modeling and parallel coordination solve, and improve computational efficiency；Using the clearing form of deploying node, electricity is sufficiently reflected
The true market value of power resource facilitates the economical operation of electric system；Using ITERATIVE COMPETITIVE BIDDING MECHANISM IN, begged for by both parties
The process that valence is counter-offered realizes the exchange of market information, to realize the respective profit maximization of both sides, market is in efficient fortune
Row state, market member all cannot individually change strategy and increase the interests of oneself.
The purpose of the present invention is realized by the following technical solution:
A kind of GENERATION MARKET iteration price competing method considering more Interest Main Body games, comprising steps of
Step 1: obtaining unit and network parameter, scalar in each Power Generation is setPrice cap initial value and Lagrange
Penalty function multiplier initial value, enables the number of iterations k=1；
Step 2: each subsystem indicates generated energy P according to introducingGiWith middle scalarBetween deviation Lagrangian penalty function
Obtained Power Generation objective function and its constraint condition constitutes Power Generation strategy Competitive Bidding Model and carries out itself optimization problem solving, and
Generated energy after gained is optimizedWith quotation factor alphaiAnd βiISO is passed to, is made while realizing itself profit maximization
Target variable is close to design objective；
Step 3: indicating purchase of electricity P according to introducingiWith the generated energy after the optimization of each Power GenerationBetween deviation I
The objective function and its constraint condition for the market clearing model that Lagrangian penalty function obtains solve market clearing model, in minimum
Make competitive bidding result close to design objective while changing power purchase expense；
Step 4: judging whether system convergence meets setting condition, terminate iteration if all meeting, export optimal competitive bidding
As a result PiAnd each target function value, obtain optimal power generation market iteration bid price scheme；Last round of ISO is gone out to settle accounts if being unsatisfactory for
Fruit, i.e., scalar in each Power GenerationAnd deploying node (LMP) passes to Power Generation, while updating Lagrangian penalty function
Multiplier updates price cap and makes return step 2 after the number of iterations k value increasing 1；
Step 5: being submitted a tender on electricity market according to resulting optimal power generation market iteration bid price scheme.
Further, in step 1, the unit parameter includes unit cost coefficient ai、biAnd ci, unit linear bidding
Factor alphaiAnd βi；The network parameter includes the impedance and admittance of power circuit and transformer.
Further, in step 2, generated energy P is indicated according to introducingGiWith middle scalarBetween the Lagrange of deviation penalize letter
The Power Generation objective function that number obtains specifically includes:
Acquire the objective function of conventional power generation quotient's strategy Competitive Bidding Model are as follows:
minfi=fcosti-fselli
In formula: fiFor the negative value of Power Generation i profit；
fcostiFor cost of electricity-generating, fselliFor by the income of quotation sale of electricity, in which:
In formula: T is to bid the period；PGiIt (t) is power output of i-th conventional power unit in period t；ai、bi、ciFor it is corresponding at
This coefficient；αiAnd βiFor using the quotation coefficient of linear bidding curve, αi(t)*PGi(t)+βiFor a corresponding offer curve；
Consider generated energy PGiWith middle scalarBetween coordination, in conventional power generation quotient's objective function introduce Lagrange penalize
Function representation generated energy PGiWith middle scalarBetween deviation, then the objective function of Power Generation strategy Competitive Bidding Model can indicate
Are as follows:
In formula: viAnd wiThe first order and quadratic term multiplier of respectively Lagrangian penalty function.
Further, in step 2, the Power Generation bound for objective function includes:
The constraint of unit output bound:
PGi,min≤PGi(t)≤PGi,max；
The constraint of Bidding lower limit:
αi(t)*PGi(t)+βi(t)≥2*ai*PGi(t)+bi
The constraint of the Bidding upper limit:
αi(t)*PGi(t)+βi(t)≤(1-di)*qi(t)
Wherein, PGi,minAnd PGi,maxFor the active power output bound of unit i；diFor price reduction rate；qiClear economize on electricity i is taken turns out on
Marginal Pricing；The Bidding lower limit is used to guarantee the bidding higher than marginal cost of Power Generation, makes its income；It is described
This wheel of Bidding upper limit constraint representation is bidded should be not higher than the upper limit of ISO proposition.
Further, described that purchase of electricity P is indicated according to introducingiWith the generated energy after the optimization of each Power GenerationBetween it is inclined
The objective function for the market clearing model that the Lagrangian penalty function of I of difference obtains specifically includes:
Acquire the objective function that conventional market goes out clear model are as follows:
In formula: f is ISO power purchase totle drilling cost；I is competitive bidding unit sum；
When being associated when the Power Generation that ISO participates in bidding with I, in the objective function that the conventional market goes out clear model
I Lagrangian penalty function should be added, indicate purchase of electricity PiWith the generated energy after the optimization of each Power GenerationBetween deviation,
The objective function of market clearing model can indicate are as follows:
Further, in step 3, the bound for objective function of the market clearing model includes:
The constraint of unit output bound:
PGi,min≤Pi(t)≤PGi,max；
System power Constraints of Equilibrium:
Climing constant above and below unit:
-ri≤Pi(t)-Pi(t-1)≤ri；
Line transmission power constraint:
PL,min≤PL(t)≤PL,max；
Wherein, Pi(t) for ISO in period t to the power purchase power of Power Generation i；N is load node total number；PdnIt (t) is economize on electricity
Load value of the n in period t；riFor the creep speed of unit i；PL,minAnd PL,maxFor the transimission power bound of route.
Further, described to judge whether system convergence meets the convergence criterion of setting condition and include: in step 4
A) as the purchase of electricity of coupling variable and generated energy, its difference should meet precision and want in last time iterative process
It asks:
Wherein,For the generated energy of the Power Generation i of kth time iteration period t；For kth time iteration period t
Purchase of electricity of the ISO to Power Generation i；ε1For precision.
B) system overall benefit, the negative value including ISO purchases strategies Yu Power Generation profit, if reached optimal:
Wherein, fkFor kth time iteration ISO power purchase totle drilling cost；For the negative value of kth time iteration Power Generation i profit；ε2For essence
Degree, when upper two formula meets simultaneously, system has reached convergence.
Further, in step 4, when updating Lagrangian penalty function multiplier, first and second of Lagrangian penalty function multiplies
Sub- viAnd wiUpdate rule are as follows:
Wherein, k indicates the number of iterations, and ρ indicates accelerating convergence factors, and value is 1~3, to further increase convergence rate,
It can be taken as 2~3.
Further, in step 4, the step of the update price cap with specific reference to the Bidding upper limit constrain into
Row updates.
Further, in step 1, when the setting Lagrangian penalty function multiplier initial value, it is bright that the glug is set
The first order multiplier v of day penalty functioniFor constant 1.5, quadratic term multiplier wiFor constant 1.5.
Compared with prior art, the beneficial effects of the present invention are:
(1) deploying node is introduced as complementary analysis tool, has been given full play to it in electricity transaction and has been handled defeated
The important function having in resistance plug.
(2) while inheriting ITERATIVE COMPETITIVE BIDDING MECHANISM IN advantage, by Power Generation benefit different from autonomous system operator two
The Optimized model of beneficial main body carries out decoupling and Parallel implementation, improves computational efficiency, can be used for the power generation city of extensive perfect competition
Field bid process.
(3) model considers the influence of unit and network constraint, more closing to reality, and iterative process can be motivated effectively
Power Generation reports its bona fide cost, is advantageously implemented social benefit maximization.
Detailed description of the invention
Fig. 1 is multiple electric business iteration bidding frame schematic diagram.
Fig. 2 is the iteration Competitive Bidding Model schematic illustration based on multi-agent Game.
Fig. 3 is the GENERATION MARKET iteration price competing method flow diagram based on multi-agent Game.
Specific embodiment
The object of the invention will be described in further detail combined with specific embodiments below, wherein identical zero
Part is presented with like reference characters.It should be noted that word "front", "rear" used in the following description, "left", "right",
"up" and "down" refers to that the direction in attached drawing, word "inner" and "outside" are referred respectively to towards or away from particular elements geometry
The direction of the heart.
Fig. 1 gives the structure chart that multiple electric business is bidded under pow erpool bulk-mode.Power Generation is in defined price cap
In constraint, optimizes unit operating scheme by maximizing itself profit and bid, and be reported to ISO；ISO is according to Power Generation
Quotation, the capacity of selection report low price sends out cleaing price, the competitive bidding result of each Power Generation and price reduction rate to meet load
Cloth is to manufacturer.Every price cap bidded of taking turns is made of the additional price reduction rate of last round of cleaing price, the report for the first time of Power Generation
Valence allows much higher than cost.Power Generation and ISO have different optimization aims as different Interest Main Bodies；Simultaneously by upper
Report capacity and competitive bidding result make the two model solution influence each other, and there is strong coupling in system operation.
As shown in figure 3, a kind of GENERATION MARKET iteration price competing method for considering more Interest Main Body games, comprising steps of
Step 1: obtaining unit and network parameter, scalar in each Power Generation is setPrice cap initial value and Lagrange
Penalty function multiplier initial value, wherein the first order multiplier v of the Lagrange penalty functioniFor constant 1.5, quadratic term multiplier wiIt is normal
Number 1.5.The number of iterations k=1 is enabled, the unit parameter includes unit cost coefficient ai、biAnd ci, unit linear bidding coefficient
αiAnd βi；The network parameter includes the impedance and admittance of power circuit and transformer.
Step 2: each subsystem indicates generated energy P according to introducingGiWith middle scalarBetween deviation Lagrangian penalty function
Obtained Power Generation objective function and its constraint condition constitutes Power Generation strategy Competitive Bidding Model and carries out itself optimization problem solving, and
Generated energy after gained is optimizedWith quotation factor alphaiAnd βiISO is passed to, is made while realizing itself profit maximization
Target variable is close to design objective；
Step 3: indicating purchase of electricity P according to introducingiWith the generated energy after the optimization of each Power GenerationBetween deviation I
The objective function and its constraint condition for the market clearing model that Lagrangian penalty function obtains solve market clearing model, in minimum
Make competitive bidding result close to design objective while changing power purchase expense；
Step 4: judging whether system convergence meets setting condition, terminate iteration if all meeting, export optimal competitive bidding
As a result PiAnd each target function value, obtain optimal power generation market iteration bid price scheme；Last round of ISO is gone out to settle accounts if being unsatisfactory for
Fruit, i.e., scalar in each Power GenerationAnd deploying node (LMP) passes to Power Generation, while updating Lagrangian penalty function
Multiplier constrains update price cap according to the Bidding upper limit and makes return step 2 after the number of iterations k value increasing 1；
Step 5: being submitted a tender on electricity market according to resulting optimal power generation market iteration bid price scheme.
Specifically, indicating generated energy P according to introducing in step 2GiWith middle scalarBetween the Lagrange of deviation penalize letter
The Power Generation objective function that number obtains specifically includes:
Acquire the objective function of conventional power generation quotient's strategy Competitive Bidding Model are as follows:
minfi=fcosti-fselli
In formula: fiFor the negative value of Power Generation i profit；
fcostiFor cost of electricity-generating, fselliFor by the income of quotation sale of electricity, in which:
In formula: T is to bid the period；PGiIt (t) is power output of i-th conventional power unit in period t；ai、bi、ciFor it is corresponding at
This coefficient；αiAnd βiFor using the quotation coefficient of linear bidding curve, αi(t)*PGi(t)+βiFor a corresponding offer curve；
Fig. 2 gives the schematic diagram for the iteration Competitive Bidding Model that the present invention uses.Its basic thought is by system design objective
It is from top to bottom shunted step by step according to system, subsystem, component etc., while response signal at different levels is from bottom to top constantly fed back, it is at different levels
Unit optimization problem separate it is independent solve and it is overlapping carry out, until between the input and output between system, subsystem and component
Deviation meet the requirements, that is, think to meet system convergence condition.It has can parallel optimization, series it is unrestricted and by tight
The advantages that convergence proof of lattice, is commonly used for solving the problems, such as large-scale complex systems optimization.In multilevel system, unit at the same level
Between the inconsistency of design optimization result coordinated by the optimization of upper level.
The ITERATIVE COMPETITIVE BIDDING MECHANISM IN as described in upper section and system mathematic model are it is found that Power Generation adheres to different interests separately from ISO
Main body has respective optimization aim；Meanwhile the two overlaps optimization by reporting capacity and competitive bidding result to carry out operation coupling
Until realizing that system overall benefit is optimal.This model structure and model thought described in this section are almost the same, therefore are applied
It can yet be regarded as a kind of effective method in the ITERATIVE COMPETITIVE BIDDING MECHANISM IN of GENERATION MARKET.
By comparison, can by ISO level with it is system-level corresponding, Power Generation is corresponding with subsystem irrespective of size.ISO is being solved
When market clearing model, by competitive bidding as a result, the middle scalar of i.e. each Power GenerationIt is distributed to each Power Generation as parameter, Power Generation exists
While optimizing self benefits, need to consider generated energy PGiWith middle scalarBetween coordination, therefore in conventional power generation quotient's target
Lagrangian penalty function is introduced in function indicates generated energy PGiWith middle scalarBetween deviation, then Power Generation strategy Competitive Bidding Model
Objective function can indicate are as follows:
In formula: viAnd wiThe first order and quadratic term multiplier of respectively Lagrangian penalty function, if generated energy PGiWith acceptance of the bid
AmountBetween deviation it is excessive, then target function value becomes larger to far from optimal solution.
Specifically, in step 2, the Power Generation bound for objective function includes:
The constraint of unit output bound:
PGi,min≤PGi(t)≤PGi,max；
The constraint of Bidding lower limit:
αi(t)*PGi(t)+βi(t)≥2*αi*PGi(t)+bi
The constraint of the Bidding upper limit:
αi(t)*PGi(t)+βi(t)≤(1-di)*qi(t)
Wherein, PGi,minAnd PGi,maxFor the active power output bound of unit i；diFor price reduction rate；qiClear economize on electricity i is taken turns out on
Marginal Pricing；The Bidding lower limit is used to guarantee the bidding higher than marginal cost of Power Generation, makes its income；It is described
This wheel of Bidding upper limit constraint representation is bidded should be not higher than the upper limit of ISO proposition；
Specifically, described indicate purchase of electricity P according to introducingiWith the generated energy after the optimization of each Power GenerationBetween it is inclined
The objective function for the market clearing model that the Lagrangian penalty function of I of difference obtains specifically includes:
Acquire the objective function that conventional market goes out clear model are as follows:
In formula: f is ISO power purchase totle drilling cost；I is competitive bidding unit sum；
When being associated when the Power Generation that ISO participates in bidding with I, in the objective function that the conventional market goes out clear model
I Lagrangian penalty function should be added, indicate purchase of electricity PiWith the generated energy after the optimization of each Power GenerationBetween deviation,
The objective function of market clearing model can indicate are as follows:
Specifically, in step 3, the bound for objective function of the market clearing model includes:
The constraint of unit output bound:
PGi,min≤Pi(t)≤PGi,max；
System power Constraints of Equilibrium:
Climing constant above and below unit:
-ri≤Pi(t)-Pi(t-1)≤ri；
Line transmission power constraint:
PL,min≤PL(t)≤PL,max；
Wherein, Pi(t) for ISO in period t to the power purchase power of Power Generation i；N is load node total number；PdnIt (t) is economize on electricity
Load value of the n in period t；riFor the creep speed of unit i；PL,minAnd PL,maxFor the transimission power bound of route.
Therefore, in the iteration Competitive Bidding Model of the invention based on more Interest Main Body games, Power Generation Offer Model is by introducing
Indicate generated energy PGiWith middle scalarBetween deviation the obtained Power Generation objective function of Lagrangian penalty function and its constraint item
Part is constituted；ISO, which goes out clear model, indicates purchase of electricity P by introducingiWith the generated energy after the optimization of each Power GenerationBetween deviation I
The objective function and its constraint condition for the market clearing model that a Lagrange penalty function obtains are constituted.Each Optimized model is only parallel
It is vertical to solve, it overlaps and carries out until meeting the condition of convergence.
It is described to judge whether system convergence meets the convergence criterion of setting condition and include: specifically, in step 4
A) as the purchase of electricity of coupling variable and generated energy, its difference should meet precision and want in last time iterative process
It asks:
Wherein,For the generated energy of the Power Generation i of kth time iteration period t；For kth time iteration period t
Purchase of electricity of the ISO to Power Generation i, ε1For precision.
B) system overall benefit, the negative value including ISO purchases strategies Yu Power Generation profit, if reached optimal:
Wherein, fkFor kth time iteration ISO power purchase totle drilling cost；For the negative value of kth time iteration Power Generation i profit；ε2For essence
Degree, when upper two formula meets simultaneously, system has reached convergence.
Specifically, when updating Lagrangian penalty function multiplier, first and second of Lagrangian penalty function multiplies in step 4
Sub- viAnd wiUpdate rule are as follows:
Wherein, k indicates the number of iterations, and ρ indicates accelerating convergence factors, and value is 1~3, to further increase convergence rate,
It can be taken as 2~3.
In conclusion price competing method of the invention has the advantage that
1) using Power Generation from autonomous system operator as different Interest Main Bodies, respectively with productivity effect is optimal and power purchase
Cost minimization is target, carries out decoupling and the Parallel implementation that two Optimized models are realized in contact by generated output.
2) deploying node is introduced as complementary analysis tool, is sufficiently reflected electricity supply and demand relationship, is effectively hindered
Plug management.
3) the iteration Competitive Bidding Model mentioned considers the influence of network constraint, and the mechanism can be motivated effectively on Power Generation
Its bona fide cost is reported, distributing rationally for social resources is conducive to.
The above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be to the present invention
Embodiment restriction.For those of ordinary skill in the art, it can also make on the basis of the above description
Other various forms of variations or variation.There is no necessity and possibility to exhaust all the enbodiments.It is all of the invention
Made any modifications, equivalent replacements, and improvements etc., should be included in the protection of the claims in the present invention within spirit and principle
Within the scope of.
Claims (10)
1. a kind of GENERATION MARKET iteration price competing method for considering more Interest Main Body games, which is characterized in that comprising steps of
Step 1: obtaining unit and network parameter, scalar in each Power Generation is setPrice cap initial value and Lagrange penalize letter
Number multiplier initial value, enables the number of iterations k=1；
Step 2: each subsystem indicates generated energy P according to introducingGiWith middle scalarBetween the Lagrangian penalty function of deviation obtain
Power Generation objective function and its constraint condition constitute Power Generation strategy Competitive Bidding Model and carry out itself optimization problem solving, and by institute
Generated energy after must optimizingWith quotation factor alphaiAnd βiISO is passed to, makes target while realizing itself profit maximization
Variable is close to design objective；
Step 3: indicating purchase of electricity P according to introducingiWith the generated energy after the optimization of each Power GenerationBetween deviation I glug
The objective function and its constraint condition for the market clearing model that bright day penalty function obtains solve market clearing model, purchase minimizing
Make competitive bidding result close to design objective while the electricity charge；
Step 4: judging whether system convergence meets setting condition, terminate iteration if all meeting, export optimal competitive bidding result Pi
And each target function value, obtain optimal power generation market iteration bid price scheme；Last round of ISO is gone out clearly as a result, i.e. if being unsatisfactory for
Scalar in each Power GenerationAnd deploying node passes to Power Generation, while updating Lagrangian penalty function multiplier, updating
Price cap simultaneously makes return step 2 after the number of iterations k value increasing 1；
Step 5: being submitted a tender on electricity market according to resulting optimal power generation market iteration bid price scheme.
2. the GENERATION MARKET iteration price competing method according to claim 1 for considering more Interest Main Body games, which is characterized in that
In step 1, the unit parameter includes unit cost coefficient ai、biAnd ci, unit linear bidding factor alphaiAnd βi；The net
Network parameter includes the impedance and admittance of power circuit and transformer.
3. the GENERATION MARKET iteration price competing method according to claim 1 for considering more Interest Main Body games, which is characterized in that
In step 2, generated energy P is indicated according to introducingGiWith middle scalarBetween deviation the obtained Power Generation mesh of Lagrangian penalty function
Scalar functions specifically include:
Acquire the objective function of conventional power generation quotient's strategy Competitive Bidding Model are as follows:
minfi=fcosti-fselli
In formula: fiFor the negative value of Power Generation i profit；
fcostiFor cost of electricity-generating, fselliFor by the income of quotation sale of electricity, in which:
In formula: T is to bid the period；PGiIt (t) is power output of i-th conventional power unit in period t；ai、bi、ciFor corresponding cost system
Number；αiAnd βiFor using the quotation coefficient of linear bidding curve, αi(t)*PGi(t)+βiFor a corresponding offer curve；
Consider generated energy PGiWith middle scalarBetween coordination, Lagrangian penalty function is introduced in conventional power generation quotient's objective function
Indicate generated energy PGiWith middle scalarBetween deviation, then the objective function of Power Generation strategy Competitive Bidding Model can indicate are as follows:
In formula: viAnd WiThe first order and quadratic term multiplier of respectively Lagrangian penalty function.
4. the GENERATION MARKET iteration price competing method according to claim 3 for considering more Interest Main Body games, which is characterized in that
In step 2, the Power Generation bound for objective function includes:
The constraint of unit output bound:
PGi, min≤PGi(t)≤PGi, max；
The constraint of Bidding lower limit:
αi(t)*PGi(t)+βi(t)≥2*ai*PGi(t)+bi
The constraint of the Bidding upper limit:
αi(t)*PGi(t)+βi(t)≤(1-di)*qi(t)
Wherein, PGi, minAnd PGi, maxFor the active power output bound of unit i；diFor price reduction rate；qiThe side of clear economize on electricity i is taken turns out on
Border electricity price；The Bidding lower limit is used to guarantee the bidding higher than marginal cost of Power Generation, makes its income；The unit
This wheel of price cap constraint representation is bidded should be not higher than the upper limit of ISO proposition.
5. the GENERATION MARKET iteration price competing method according to claim 4 for considering more Interest Main Body games, which is characterized in that
It is described that purchase of electricity P is indicated according to introducingiWith the generated energy after the optimization of each Power GenerationBetween I of deviation Lagrange
The objective function for the market clearing model that penalty function obtains specifically includes:
Acquire the objective function that conventional market goes out clear model are as follows:
In formula: f is ISO power purchase totle drilling cost；I is competitive bidding unit sum；
When being associated when the Power Generation that ISO participates in bidding with I, it should add in the objective function that the conventional market goes out clear model
Enter I Lagrangian penalty function, indicates purchase of electricity PiWith the generated energy after the optimization of each Power GenerationBetween deviation, market
The objective function of clear model can indicate out are as follows:
6. the GENERATION MARKET iteration price competing method according to claim 5 for considering more Interest Main Body games, which is characterized in that
In step 3, the bound for objective function of the market clearing model includes:
The constraint of unit output bound:
PGi, min≤Pi(t)≤PGi, max；
System power Constraints of Equilibrium:
Climing constant above and below unit:
-ri≤Pi(t)-Pi(t-1)≤ri；
Line transmission power constraint:
PL, min≤PL(t)≤PL, max；
Wherein, Pi(t) for ISO in period t to the power purchase power of Power Generation i；N is load node total number；Pdn(t) exist for economize on electricity n
The load value of period t；riFor the creep speed of unit i；PL, minAnd PL, maxFor the transimission power bound of route.
7. the GENERATION MARKET iteration price competing method according to claim 6 for considering more Interest Main Body games, which is characterized in that
It is described to judge whether system convergence meets the convergence criterion of setting condition and include: in step 4
A) as the purchase of electricity of coupling variable and generated energy, its difference should meet required precision in last time iterative process:
Wherein,For the generated energy of the Power Generation i of kth time iteration period t；For kth time iteration period t ISO to
The purchase of electricity of Power Generation i；ε1For precision.
B) system overall benefit, the negative value including ISO purchases strategies Yu Power Generation profit, if reached optimal:
Wherein, fkFor kth time iteration ISO power purchase totle drilling cost；fi kFor the negative value of kth time iteration Power Generation i profit；ε2For precision, when
When upper two formula meets simultaneously, system has reached convergence.
8. the GENERATION MARKET iteration price competing method according to claim 7 for considering more Interest Main Body games, which is characterized in that
In step 4, when updating Lagrangian penalty function multiplier, first and second multiplier v of Lagrangian penalty functioniAnd wiUpdate rule
Are as follows:
Wherein, k indicates the number of iterations, and ρ indicates accelerating convergence factors, and value is 1~3.
9. the GENERATION MARKET iteration price competing method according to claim 4 for considering more Interest Main Body games, which is characterized in that
In step 4, it is updated with specific reference to the Bidding upper limit constraint the step of update price cap.
10. the GENERATION MARKET iteration price competing method according to claim 1 for considering more Interest Main Body games, feature exist
In when the setting Lagrangian penalty function multiplier initial value, the primary of the Lagrangian penalty function is arranged in step 1
Item multiplier viFor constant 1.5, quadratic term multiplier wiFor constant 1.5.
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