CN109919472A - A kind of GENERATION MARKET iteration price competing method considering more Interest Main Body games - Google Patents

A kind of GENERATION MARKET iteration price competing method considering more Interest Main Body games Download PDF

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CN109919472A
CN109919472A CN201910146810.0A CN201910146810A CN109919472A CN 109919472 A CN109919472 A CN 109919472A CN 201910146810 A CN201910146810 A CN 201910146810A CN 109919472 A CN109919472 A CN 109919472A
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power generation
market
iteration
price
electricity
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谢敏
胡昕彤
程培军
韦薇
张悦
刘明波
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South China University of Technology SCUT
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South China University of Technology SCUT
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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

A kind of GENERATION MARKET iteration price competing method considering more Interest Main Body games
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.
CN201910146810.0A 2019-02-27 2019-02-27 A kind of GENERATION MARKET iteration price competing method considering more Interest Main Body games Pending CN109919472A (en)

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CN110619428A (en) * 2019-08-30 2019-12-27 广东工业大学 Scheduling optimization system for intelligent agent to assist independent power generators to participate in market bidding
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CN110474320A (en) * 2019-07-24 2019-11-19 广东工业大学 The distribution optimization method that Distributed sharing is mutually cooperateed with centralization clearance
CN110474320B (en) * 2019-07-24 2023-04-07 广东工业大学 Power distribution network optimization method based on cooperation of distributed sharing and centralized clearing
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CN110619428A (en) * 2019-08-30 2019-12-27 广东工业大学 Scheduling optimization system for intelligent agent to assist independent power generators to participate in market bidding
CN111797892B (en) * 2020-05-21 2022-10-11 国电南瑞科技股份有限公司 Electric power market generator market force monitoring method based on random forest regression
CN111797892A (en) * 2020-05-21 2020-10-20 国电南瑞科技股份有限公司 Electric power market generator market force monitoring method based on random forest regression
CN111967895A (en) * 2020-07-27 2020-11-20 华南理工大学 Method for formulating power generation plan release scheme based on multi-objective and MPEC (Multi-project control System) planning
CN111967895B (en) * 2020-07-27 2024-05-28 华南理工大学 Method for making power generation plan release scheme based on multi-objective and MPEC planning
CN112036625A (en) * 2020-08-21 2020-12-04 上海电力大学 New energy consumption method based on principal and subordinate game under power market background
CN112288466A (en) * 2020-10-21 2021-01-29 国电南京自动化股份有限公司 Day-ahead reporting method of power generator considering gas-electricity coupling in power market environment
CN112288466B (en) * 2020-10-21 2024-02-02 国电南京自动化股份有限公司 Day-ahead reporting method for generator considering gas-electricity coupling in electric power market environment
CN112186768A (en) * 2020-10-22 2021-01-05 华中科技大学 Method and system for cooperatively scheduling AC/DC power distribution network with joint participation of MG, LA and DNO
CN112561329A (en) * 2020-12-16 2021-03-26 国网安徽省电力有限公司检修分公司 Optimal operation method of distribution and sale electric company considering user side interaction
CN113487089A (en) * 2021-07-07 2021-10-08 中国电力科学研究院有限公司 Optimal compensation price calculation method for excitation type demand response in unilateral market
CN113487089B (en) * 2021-07-07 2024-03-12 中国电力科学研究院有限公司 Optimal compensation price calculation method for excitation type demand response under unilateral market
CN113506156A (en) * 2021-09-09 2021-10-15 华南理工大学 Market clearing method for one-stage bidding of demand side market main body and generator set

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Application publication date: 20190621