CN116011624B - Method and system for acquiring optimal supply curve of generator considering segmentation point optimization - Google Patents
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Abstract
The invention discloses a method and a system for acquiring an optimal supply curve of a generator taking segmentation point optimization into account, belonging to the technical field of operation and control of a power system, and comprising the following steps: establishing a competitor behavior model based on a Markov process; estimating opponent quotation, establishing a double-layer optimization model of a quotation strategy of a generator, simulating a market clearing result, and optimizing a quotation strategy; and constructing constraints of a supply curve segmentation point optimization strategy, and optimizing the supply curve segmentation points and quotations by combining a double-layer optimization model. According to the method, a competitor behavior model is built based on a BSV finite rationality model in behavior finance, the reality that a competitor has cognitive deviation as a real person is reflected, bidding behaviors of different opponents are modeled more reasonably, a generator segmentation point optimization strategy quotation model based on a double-layer optimization model is built, a targeted quotation strategy is formulated, supply curve segmentation points are optimized, and the generator benefits are effectively improved.
Description
Technical Field
The invention relates to the technical field of operation and control of power systems, in particular to a method and a system for acquiring an optimal supply curve of a generator taking segmentation point optimization into account.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The actual electricity wholesale market is generally not completely competitive and the generator can increase the revenue by affecting the price of electricity clearing through strategic quotes. The existing research works are mainly based on a completely rational 'economic person' model to model bidding behaviors of opponents, and then strategy quotations are carried out. But the actual market participants are "real people": not completely, the quotation can be influenced by the states of the user such as insufficient reaction, excessive reaction and the like, and when the bidding behavior of the opponent is modeled by adopting an economic person model to carry out strategy quotation, the obtained opponent quotation is inaccurate, and the optimal quotation can not be obtained.
In addition, existing markets typically employ a "power generation side volume offer" model: the market participants can not only lock the electric quantity segments and only adjust the declared electricity price, but also optimize the electric quantity segment division of the supply curve while optimizing the declared electricity price, namely, the 'segment point optimization' quotation mode. The studies on segment point optimization are mainly divided into three categories: the first kind of simulation market clearing result corrects the segmentation point, the method is greatly influenced by a market simulation algorithm, the optimal segmentation point is difficult to directly obtain, and the total number of segments of the quotation curve is not considered. The second category optimizes the supply curve based on the price of electricity discharged, but it is generally assumed that market participants are price acceptors, with certain limitations. The third analogization proves the optimal bidding strategy and finds all the segment points of the optimal bidding curve, but this method is not applicable to markets that accept segment step function bids.
The inventors believe that the existing methods do not achieve optimal quotations for the generator nor optimal generator supply curves.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a system for acquiring the optimal supply curve of the generator taking the optimization of the segmentation points into account, which more reasonably models bidding behaviors of different opponents, formulates a targeted bidding strategy, and effectively adjusts the total segmentation number, segment capacity and bidding of the supply curve so as to maximize profit of the generator.
In order to achieve the above purpose, the invention adopts the following technical scheme:
in a first aspect, a method for acquiring an optimal supply curve of a generator considering segmentation point optimization is provided, including:
obtaining historical transaction results of a mobile phone unit, marginal cost of a generator set, historical quotations of the mobile phone unit, unit parameters and initial output of the generator set and the mobile phone unit of a supply curve;
determining a supply curve of the opponent set according to the historical transaction result of the opponent set, the historical quotation and the constructed opponent bidding behavior model, wherein the opponent bidding behavior model considers two heart states of insufficient reaction and excessive reaction of the opponent, and is constructed and obtained based on a Markov model;
and obtaining an optimal supply curve of the generator according to the supply curve of the mobile phone unit, the marginal cost of the generator unit, the generator unit parameters and initial output of the generator unit and the generator segmentation point optimization strategy quotation model.
Further, the power producer segmentation point optimization strategy quotation model comprises an upper model, a lower model and a constraint of a supply curve above a segmentation constant incremental cost curve, wherein the capacity of each segment of the supply curve cannot be smaller than the set proportion constraint of the difference between the rated active power of a machine set and the output of the lowest machine set, the final segment of the declaration electric quantity of the supply curve is not higher than the rated active power constraint of the machine set, and the lower model determines the winning bid electric quantity and the node electricity price of the power producer set under different quotation strategies according to a real-time market; and the upper model determines an optimal supply curve of the generator according to the winning bid amount and the node electricity price of the generator set obtained by the lower model and by taking the maximum profit of the generator as a target.
Further, the upper model is specifically: the method aims at the maximum profit of the generator set, and takes the constraint that the quotation of the generator set is within the highest and lowest limit price, the quotation of the generator set is an incremental quotation and is higher than the marginal cost.
Further, the objective function targeting the maximum profit of the generator is:
where the power generation cost u is a piecewise convex function,
supply the power of winning bid of curve for section b of generator set i,/, for>
Marginal of supply curve for section i and section b of generator setCost (S)/(S)>
Price of m-th section of cost curve is increased slightly for generator set i segment constant, +.>
For the sum of the capacities of the first m segments of the piecewise constant incremental cost curve, when b=0,/is>
For minimum technical output of the machine set, +.>
Zero (zero)/(zero)>
Is the ratio of the starting cost of the unit to the minimum technical output.
Further, the lower model is specifically: the minimum price reporting cost of the generator is targeted, and supply and demand balance constraint, power upper and lower limit constraint, climbing constraint and line transmission power capacity constraint are used as constraint conditions.
Further, constraining the supply curve above the piecewise constant incremental cost curve includes: the k-th section declaration electric quantity can only be located on one capacity section of the piecewise constant incremental cost curve to restrict, offer the price not to be lower than the cost restriction, the upper and lower limit restriction of the sum of the capacities of the first k sections and the declaration electric quantity state restriction.
Further, the opponent bidding behavior model is:
in the method, in the process of the invention,
offer for the b-th section cost curve of the handset j at time t,/>
Offer +.f. for the b-th cost curve of the handset j at time t-1>
Estimating expectation, y, of bid adjustment values for a mobile phone set segment b supply curve t For the adjustment quantity at time t, phi t-1 Market trade, gamma, including time t-1 T ＝[-K,…,K,-K,…,K]Is a coefficient of variation with respect to the offer; q, p t-1 The state variable is a state transition matrix and at the time t-1, is a known quantity, and has the following specific expression:
wherein,,
wherein beta is 1 、β 2 The probability of converting insufficient reaction into excessive reaction and converting excessive reaction into insufficient reaction; introducing random variable s t ＝{1,2}，s t ＝1、s t =2 indicates that the trader is in a hypo-and over-reaction state, let P 1 t Pr(s) t ＝1,ω t = -K), t-1 time information Φ is known t-1 Under the conditions that the trader has insufficient reaction and the price reduces the probability of Ky; q (Q) 2 、Q 3 Omega when the reaction is insufficient and excessive t A transition probability matrix of states; pi Li 、π Hi Respectively, when the transactor is under-reacted and over-reactedProbability.
In a second aspect, a generator optimal supply curve acquisition system that accounts for segment point optimization is provided, comprising:
the data acquisition module is used for acquiring historical transaction results of opponents of the supply curve, marginal cost of the generator set, historical quotations of the opponent set, set parameters and initial output of the generator set and the opponent set;
the opponent bidding acquisition module is used for determining a supply curve of the opponent unit according to the historical transaction result of the opponent unit, the historical bidding and the constructed opponent bidding behavior model, wherein the opponent bidding behavior model considers two heart states of insufficient reaction and excessive reaction of the opponent, and is constructed and obtained based on a Markov model;
the optimal supply curve obtaining module of the generator is used for obtaining the optimal supply curve of the generator according to the supply curve of the mobile phone set, the marginal cost of the generator set, the set parameters and initial output of the generator set and the mobile phone set and the segment point optimization strategy quotation model of the generator.
In a third aspect, an electronic device is provided that includes a memory and a processor, and computer instructions stored on the memory and running on the processor that, when executed by the processor, perform the steps described for a generator optimal supply curve acquisition method that accounts for segmentation point optimization.
In a fourth aspect, a computer readable storage medium is provided for storing computer instructions that, when executed by a processor, perform the steps recited in a generator optimal supply curve acquisition method that accounts for segment point optimization.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention adopts the limited rationality model to estimate the bidding behaviors of the opponents, can more reasonably model the bidding behaviors of different opponents, and establishes a targeted quotation strategy. In addition, the invention considers the segmentation point optimization strategy, improves the traditional strategy quotation model, builds the generator strategy quotation model which takes the BSV limited rationality model and the supply curve segmentation point optimization strategy into account, effectively adjusts the total segmentation number, the segment capacity and the quotation of the supply curve of the generator, further improves the profit of the generator and maximizes the profit of the generator.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application.
FIG. 1 is a diagram of a process for generating a quotation strategy for a generator in the method disclosed in example 1;
FIG. 2 is a graph of supply curves versus segment constant incremental cost in segment point optimization mode in the method disclosed in example 1;
fig. 3 is a flow chart of the method disclosed in example 1.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
Example 1
The actual electricity wholesale market is generally not completely competitive and the generator can increase the revenue by affecting the price of electricity clearing through strategic quotes. How to optimize the quotation strategy of the generator according to the market rules has attracted attention of many students. The existing research works are mainly based on a completely rational 'economic person' model to model bidding behaviors of opponents, and then strategy quotations are carried out. But the actual market participants are "real people": rather than being completely rational, offers may be affected by self-mind states such as "under-reacted" and "over-reacted", the decision-making behavior of which may be described as a "finite rational model". Modeling bidding behavior of market participants with a limited rationality model is closer to reality than an "economic man" model.
There are two main classes of finite rationality models in existing research: the first type adopts an evolution game model, but is mainly used for multi-group decision-making, and the opponents in the same group are assumed to be homogeneous, so that the method cannot be directly used for estimating bidding behaviors of different opponents, and the influence of a plurality of factors on the evolution game balance is ignored. The second class is modeling for three decision features of limited rationality: 1) With varying risk preferences, but is primarily applicable to price-accepting market participants; 2) The decision is in accordance with the satisfaction principle, and the strategy quotation of the generator only aims at maximizing economic benefit, and the satisfaction principle is not needed to be considered; 3) With unavoidable predictive bias, i.e., cognitive bias, the market participants are described with models such as anchoring behavior, barbiers, shifter, vishny (BSV) finite rationality, etc. The BSV limited rational model proposed by the Berberis scholars, and the like, starts from two large experience deviations of a representative experience rule and an anchoring and adjusting rule, fully considers heuristic deviations of participants in the market prediction process, and can be directly used for estimating bidding behaviors of opponents.
In addition, existing markets typically employ a "power generation side volume offer" model: the market participants can not only lock the electric quantity segments and only adjust the declared electricity price, but also optimize the electric quantity segment division of the supply curve while optimizing the declared electricity price, namely, the 'segment point optimization' quotation mode. The studies on segment point optimization are mainly divided into three categories: the first kind of simulation market clearing result corrects the segmentation point, the method is greatly influenced by a market simulation algorithm, the optimal segmentation point is difficult to directly obtain, and the total number of segments of the quotation curve is not considered. The second category optimizes the supply curve based on the price of electricity discharged, but it is generally assumed that market participants are price acceptors, with certain limitations. The third analogization proves the optimal bidding strategy and finds all the segment points of the optimal bidding curve, but this method is not applicable to markets that accept segment step function bids.
In view of this, the present embodiment proposes a method for obtaining an optimal supply curve of a generator in consideration of optimization of segment points, which, compared with a traditional strategy quotation model of the generator, more reasonably models bidding behaviors of different opponents, formulates a targeted quotation strategy, and effectively adjusts the total segment number, segment capacity and quotation of the supply curve, thereby further improving the profit of the generator and maximizing the profit of the generator.
The method for acquiring the optimal supply curve of the generator, which is disclosed in the embodiment and takes the optimization of the segmentation points into account, as shown in fig. 1 and 3, comprises the following steps:
s1: and acquiring historical transaction results of the mobile phone unit, marginal cost of the generator unit, historical quotations of the mobile phone unit, unit parameters and initial output of the generator unit and the mobile phone unit of the supply curve.
The unit parameters comprise upper and lower output limits, upper climbing speed limit and the like.
S2: and determining a supply curve of the opponent set according to the historical transaction result of the opponent set, the historical quotation and the constructed opponent bidding behavior model, wherein the opponent bidding behavior model considers two heart states of insufficient reaction and excessive reaction of the opponent, and is constructed and obtained based on a Markov model.
Specifically, based on the behavioral finance theory, establishing an opponent bidding behavior model based on the BSV limited rationality model, and modeling the opponent bidding behavior by adopting the BSV limited rationality model is as follows: over time, the winning results published by the generator through Independent System Operators (ISO) may gradually accumulate data of participation of other generators in the market. For example, observing the bidding and bid history of the handset group j, the generator may guess the bidding rule that the handset group j will correct the bid at time t-1 based on the bid result at time t-1 to form the current bid at time t, and estimate the bid of the handset group j at the b-th segment supply curve at time t-1
The constructed opponent bidding behavior model can be expressed mathematically as:
in the method, in the process of the invention,
offer a curve price for section b of unit j at time t-1, y t For the adjustment quantity at time t, phi t-1 Comprises market trading information at the time t-1, including market trading results, quotations of all market participants and the like,
the estimated expectation of the bid adjustment value for the provisioning curve for segment b of the handset group. While estimating the desire->
The mathematical essence of (a) is to construct a Markov decision process for two typical mental models, namely a BSV finite rational model, so as to reasonably estimate the quotation adjustment of an adversary, which is influenced by the mind state after observing the dynamic change of the market.
Mental model for opponents: suppose there is a message Z at time t-1 t-1 Arrival, e.g. message Z t-1 Results were obtained for the market at time t-1, where Z t-1 =g and B represent interest and empty, respectively, interest is winning, and use is not winning. "hypo-and" hyper-reactions "are established from the observer's point of view with respect to the investor receiving message Z t-1 Later return on investment r at future time t t Is a model of the two typical heart states. Wherein "under-reacting" means that the expected investment return of investors after receipt of interest messages is not lower than the expected investment return of investors after receipt of interest messages, i.e
The opposite is true for "overreaction", i.e. +.>
Since the actual market participants are not completely rational "economics," they receive Z t Post market quotation N t Will be influenced by the above-mentioned state of mind, and the price N at the last moment t-1 And (5) performing adjustment. This relationship can be expressed as:
N t ＝N t-1 +y t (2)
wherein y is t The variation of the quotation for the period t is compared with the period t-1. In view of reality, assume N t Compared with N t-1 Only an integer number of monetary units y can be increased or decreased, e.g. y=1 cents, so y t Is a discrete variable.
For the observer, y t Is a discrete random variable affected by the current message, investor's mind state, and can be expressed as y t ＝ω t y, where the random variable ω t E { -K, …, -1,0,1, …, K } represents the number of quotation changes, e.g., ω t ＝-K、ω t =k represents a decrease in bid, an increase in K units, respectively. Is provided with
Is known as phi t-1 Expectation of the period t quotation in the case
Calculated based on Markov model
Can be expressed as:
wherein, gamma T ＝[-K,…,K,-K,…,K]Is a coefficient of variation with respect to the offer; q, p t-1 The state variable is a state transition matrix and at the time t-1, is a known quantity, and has the following specific expression:
wherein,,
wherein beta is 1 、β 2 The probability of converting insufficient reaction into excessive reaction and converting excessive reaction into insufficient reaction; introducing random variable s t ＝{1,2}，s t ＝1、s t =2 indicates that the trader is in a hypo-and over-reaction state, let P 1 t Pr(s) t ＝1,ω t = -K), t-1 time information Φ is known t-1 If trader has insufficient response and the price decreases by Ky, then p t ＝[Pr(s t ＝1,ω t ＝-K),…,Pr(s t ＝1,ω t ＝K),Pr(s t ＝2,ω t ＝-K),…,Pr(s t ＝2,ω t ＝K)] T Can be expressed as
Q 2 、Q 3 Omega when the reaction is insufficient and excessive t A transition probability matrix of states; pi Li 、π Hi The transition probabilities when the trader is under-reacted and over-reacted, respectively.
S3: and obtaining an optimal supply curve of the generator according to the supply curve of the mobile phone unit, the marginal cost of the generator unit, the generator unit parameters and initial output of the generator unit and the generator segmentation point optimization strategy quotation model.
The power generator subsection point optimization strategy quotation model comprises an upper model, a lower model and a constraint that a supply curve is above a subsection constant incremental cost curve, the capacity of each section of the supply curve cannot be smaller than the set proportion constraint of the difference between rated active power of a unit and the output of the lowest unit, the final section of the supply curve declares that the electric quantity is not higher than the rated active power constraint of the unit, and the lower model determines the winning electric quantity and the node electric price of the power generator unit under different quotation strategies according to a real-time market; and the upper model determines an optimal supply curve of the generator according to the winning bid amount and the node electricity price of the generator set obtained by the lower model and by taking the maximum profit of the generator as a target.
The embodiment builds a power producer strategy quotation model considering the BSV limited rationality model based on the double-layer optimization model. In the lower model, the generator simulates real-time market clearing based on known market information; in the upper model, based on the simulation market result of the lower model, the optimal supply curve is determined with the maximum profit of the generator as the target.
In the upper layer problem, the generator having multiple units maximizes the total revenue by optimizing the supply curve for each unit i. The upper layer model is specifically as follows: the method aims at the maximum profit of the generator, and takes the constraint that the unit quotation of the generator is within the highest and lowest limit price, the unit quotation of the generator is an incremental quotation and is higher than the marginal cost. This can be generally expressed as follows:
wherein, the superscript SG represents the related variable of the strategy generator;
and->
Respectively supplying quotation, winning capacity and marginal cost of a curve for a section b of the unit i; v n For the node electricity price of the node n where the unit i is located, which is equal to +.>
The product of the (a) is the income brought by the bid amount in the b-th section of the unit i; alpha min 、α max The upper and lower limits of the bid, respectively. The objective function (6) is used for maximizing profit of a generator, and is determined by the scalar quantity of each unit in the real-time market, the node electricity price of the unit i and the marginal cost; constraint (7) represents that the unit i offers are within the highest minimum price; constraint (8) represents the price of group i as an incremental price; constraint (9) represents that the price of unit i is higher than the marginal cost.
In the lower layer problem, the generator simulates the real-time market of the ISO level according to the existing information to determine the bid amount of the generator under different bid strategies
Node electricity price v n Thus, the upper layer problem determines an optimal quotation strategy, which can be considered to some extent as a "prediction" of node electricity prices and strategy generator pricing relationships. The lower layer model is specifically as follows: the minimum price reporting cost of the generator is targeted, and supply and demand balance constraint, power upper and lower limit constraint, climbing constraint and line transmission power capacity constraint are used as constraint conditions.
Based on the segment quotation function model of the common real-time market, the underlying problem can be modeled as follows:
wherein, the superscript OG represents related variables of competitors;
the generated energy and the power consumption of the load d are respectively quoted for the b section of the mobile phone set j; v is a dual variable of the supply-demand balance constraint; />
The upper power limit of the b section quotation of the generator set i and the mobile phone set j is respectively set; />
The lower power limits of the b-th section quotations of the generator set i and the mobile phone set j are respectively set to zero except that the first section takes the minimum set output of the set; />
The initial output of the b section quotation of the generator set i and the b section quotation of the mobile phone set j, namely the generated energy of the previous period, is known parameter at the current moment; />
The upper limit of power increase and power decrease of each period of the unit i and the unit j is respectively set; />
A distribution factor of the line l to the node n; psi n The set is a set of units and loads on the node n; p (P) l max 、P l min The maximum and minimum transmission capacities of the line l, respectively. The objective function (10) minimizes the price reported by the generator; constraining (11) to be a supply-demand balance; the constraints (12) and (13) are the upper and lower power limits of the generator set i and the mobile phone set j; the constraints (14) and (15) are climbing constraints of the generator set i and the mobile phone set j; the constraint (16) is a line transmission power capacity constraint.
Node electricity price v in upper layer problem objective function n The dual variables to the underlying problem have the following relationship:
wherein,,
the dual variables of the constraint (16) are respectively.
And establishing a generator segmentation point optimization strategy quotation model based on the generator strategy quotation model. The generator obtains larger benefits by optimizing the segmentation points, and the length of each segment of the supply curve, namely the segment capacity
Changing from fixed parameters to optimized variables. As shown in fig. 2, this mode has the following four features:
1) The supply curve is above the piecewise constant incremental cost curve.
2) The corresponding capacity segment labels of the same output force on the supply curve and the segment constant incremental cost curve are inconsistent.
3) The capacity difference between two consecutive declared charges of the supply curve needs to be larger than a certain determined parameter value, and the last segment declared charge is not higher than the rated active power.
4) In calculating profits, the capacity segment corresponding to the bid amount is marked as the segment marking of the supply curve, e.g. kth 1 Segments. However, as shown in FIG. 2, the segment supply curve may be slightly increased in cost curve segments across two segment constants, e.g., mth 1 、m 2 Segments, which create some difficulty in calculating revenue.
To express the above characteristics, introduce
The sum of the capacities of the first k sections and the first m sections of the supply curve and the piecewise constant incremental cost curve is represented respectively, wherein k is less than or equal to L. The number of segments of the supply curve does not necessarily match the number of segments of the segment constant incremental cost curve as long as the number of segments does not exceed the maximum number of segments allowed by the market.
As shown in FIG. 2, features 1, 2 indicate the declared electricity prices for the kth segment of the supply curve
(i.e., in formulae (7) - (9)) ->
) Price lambda of mth segment of the lower segment constant incremental cost curve should be no lower than i,m It is known that the constraint of the supply curve above the piecewise constant incremental cost curve includes: the k-th section declaration electric quantity can only be positioned on one capacity section constraint of the piecewise constant incremental cost curve, the quotation is not lower than the cost constraint, the upper and lower limit constraint of the sum of the capacities of the first k sections and the declaration electric quantity state constraint,
this can be expressed as:
according to mathematical modeling theory, this constraint of equation (18) can be written as mixed integer forms (19) - (22):
wherein delta i,k,m A variable of 0-1, a value of 1 representing that the kth declared electric quantity is in the interval
In, zero means the opposite. Equation (19) indicates that the kth declared electric quantity can only be located on one capacity segment of the piecewise constant incremental cost curve; equation (20) indicates that the price quoted is not lower than the cost; equation (21) represents the upper and lower limits of the sum of the capacities of the first k segments; the expression (22) indicates two states of reporting electric quantity only "on or off a certain capacity segment".
Aiming at the characteristic 3, the capacity of each section of the supply curve cannot be smaller than the set proportion constraint of the difference between the rated active power of the unit and the output of the lowest unit and the final section of the supply curve declares that the electric quantity is not higher than the rated active power constraint of the unit,
it is required to satisfy the formula (23) (24):
equation (23) is that the last declared electric quantity of the supply curve is not higher than the active power P of the unit i SGmax The method comprises the steps of carrying out a first treatment on the surface of the The (24) represents that each declared electric quantity should be differentiated to a certain degree, namely the capacity of each section cannot be smaller than the rated active power and the minimum technical output P of the unit i SGmin The ratio eta of the differences.
Finally, for the upper layer problem in feature 4
The marginal cost section label and the winning capacity section label are not consistent, and the variable u is used for replacing +.>
The objective function (6) can be rewritten as:
where the generation cost u is a piecewise convex function and therefore lies above the linear function that makes up the segments of the cost curve, as shown in equation (26). The original objective function (6) can thus be converted (25) into an additional linear inequality constraint (26).
Where the power generation cost u is a piecewise convex function,
supply the power generator set i with the winning capacity of the curve, i.e. the segment capacity of segment b,/for segment b>
Marginal cost of supply curve for section b of generator set i, < >>
Price of m-th section of cost curve is increased slightly for generator set i segment constant, +.>
For the sum of the capacities of the first m segments of the piecewise constant incremental cost curve, when b=0,/is>
For minimum technical output of the machine set, +.>
Zero (zero)/(zero)>
Is the ratio of the starting cost of the unit to the minimum technical output.
The quotation of each section of supply curve of the mobile phone unit, the marginal cost of the generator unit, the unit parameters of the mobile phone unit and the mobile phone unit, and the initial output are substituted into a generator segmentation point optimization strategy quotation model constructed, and the optimal supply curve of the generator and the node electricity price v of the node n where the unit i is located are solved n Thereby determining an optimal quotation strategy
In the method for acquiring the optimal supply curve of the generator considering the segmentation point optimization disclosed by the embodiment, the generator is firstly based on historical transaction results (such as node electricity price v n,t-m ) And estimating historical quotations of the opponents, analyzing bidding rules of the opponents, and establishing a heart model of the opponents in the bidding process. Then, estimating the quotation of the opponent t period according to the mental model, further simulating the market clearing process, and estimating the clear electricity price v n,t Thereby determining an optimal quotation strategy
The embodiment discloses a method for constructing a competitor behavior model based on a BSV finite rationality model in behavior financeReflecting the reality that the competitors have cognitive deviation as 'real people', modeling bidding behaviors of different opponents more reasonably, simultaneously establishing a power producer quotation model based on a double-layer optimization model, and formulating a targeted quotation strategy. In addition, the characteristics of the segment point optimized quotation mode are considered, the segment points of the supply curve are directly optimized, and the benefits of the generator are further improved. The method has reference significance for improving market rules for designers.
Example 2
In this embodiment, a generator optimal supply curve acquisition system is disclosed that accounts for segment point optimization, comprising:
the data acquisition module is used for acquiring historical transaction results of the mobile phone unit, marginal cost of the generator set, historical quotation of the mobile phone unit, unit parameters and initial output of the generator set and the mobile phone unit;
the opponent bidding acquisition module is used for determining a supply curve of the opponent unit according to the historical transaction result of the opponent unit, the historical bidding and the constructed opponent bidding behavior model, wherein the opponent bidding behavior model considers two heart states of insufficient reaction and excessive reaction of the opponent, and is constructed and obtained based on a Markov model;
the optimal supply curve obtaining module of the generator is used for obtaining the optimal supply curve of the generator according to the supply curve of the mobile phone set, the marginal cost of the generator set, the set parameters and initial output of the generator set and the mobile phone set and the segment point optimization strategy quotation model of the generator.
Example 3
In this embodiment, an electronic device is disclosed that includes a memory and a processor, and computer instructions stored on the memory and running on the processor that, when executed by the processor, perform the steps recited in the generator optimal supply curve acquisition method disclosed in embodiment 1 that accounts for segment point optimization.
Example 4
In this embodiment, a computer readable storage medium is disclosed for storing computer instructions that, when executed by a processor, perform the steps described in the generator optimal supply curve acquisition method that accounts for segment point optimization disclosed in embodiment 1.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (8)
1. The method for acquiring the optimal supply curve of the generator considering the segmentation point optimization is characterized by comprising the following steps:
obtaining historical transaction results of a mobile phone unit, marginal cost of a generator set, historical quotations of the mobile phone unit, unit parameters and initial output of the generator set and the mobile phone unit of a supply curve;
determining a supply curve of the opponent set according to the historical transaction result of the opponent set, the historical quotation and the constructed opponent bidding behavior model, wherein the opponent bidding behavior model considers two heart states of insufficient reaction and excessive reaction of the opponent, is constructed and obtained based on a Markov model, and is:
in the method, in the process of the invention,
offer for the b-th section cost curve of the handset j at time t,/>
Offer +.f. for the b-th cost curve of the handset j at time t-1>
Estimating expectation, y, of bid adjustment values for a mobile phone set segment b supply curve t For the adjustment quantity at time t, phi t-1 Market trade information including t-1 time, y being the monetary unit, gamma T ＝[-K,…,K,-K,…,K]Is a coefficient of variation with respect to the offer; q, p t-1 The state transition matrix and the state variable at the time t-1 are respectively adopted;
obtaining an optimal supply curve of a generator according to a supply curve of a mobile phone unit, marginal cost of the generator unit, historical quotation of the mobile phone unit, unit parameters and initial output of the generator unit and a generator segmentation point optimization strategy quotation model of the mobile phone unit, wherein the generator segmentation point optimization strategy quotation model comprises an upper model, a lower model and a constraint that the supply curve is above a segmentation constant incremental cost curve, the capacity of each section of the supply curve cannot be smaller than the set proportion constraint that the difference between rated active power of the mobile phone unit and the lowest unit output is not higher than the rated active power constraint of the mobile phone unit, and the last section of reporting electric quantity of the supply curve is not higher than the nominal active power constraint of the mobile phone unit; the upper model determines an optimal supply curve of a generator according to the winning bid amount and the node electricity price of the generator set obtained by the lower model and by taking the maximum profit of the generator as a target; the objective function targeting the maximum profit of the generator is:
in the method, in the process of the invention,
is the quotation of the supply curve of section b of the unit i, the generation cost u is a piecewise convex function,/>
The power generation unit i and b sections are supplied with the winning power quantity v of the curve n For the node electricity price of the node n where the unit i is located, < +.>
Marginal cost of supply curve for section b of generator set i, < >>
Price of m-th section of cost curve is increased slightly for generator set i segment constant, +.>
For the sum of the capacities of the first m segments of the piecewise constant incremental cost curve, when b=0,/is>
For minimum technical output of the machine set, +.>
Zero (zero)/(zero)>
Is the ratio of the starting cost of the unit to the minimum technical output.
2. The method for obtaining the optimal supply curve of the generator taking the segmentation point optimization into account according to claim 1, wherein the upper model is specifically: the method aims at the maximum profit of the generator, and takes the constraint that the unit quotation of the generator is within the highest and lowest limit price, the unit quotation of the generator is an incremental quotation and is higher than the marginal cost.
3. The method for obtaining the optimal supply curve of the generator taking the segmentation point optimization into account according to claim 1, wherein the lower model is specifically: the minimum price reporting cost of the generator is targeted, and supply and demand balance constraint, power upper and lower limit constraint, climbing constraint and line transmission power capacity constraint are used as constraint conditions.
4. The method for obtaining a generator optimal supply curve taking into account segment point optimization of claim 1, wherein constraining the supply curve above the segment constant incremental cost curve comprises: the k-th section declaration electric quantity can only be located on one capacity section of the piecewise constant incremental cost curve to restrict, offer the price not to be lower than the cost restriction, the upper and lower limit restriction of the sum of the capacities of the first k sections and the declaration electric quantity state restriction.
5. The method for obtaining a generator optimal supply curve taking into account segment point optimization as recited in claim 1, wherein Q, p t-1 The state transition matrix and the state variable at the time t-1 are respectively represented as follows:
wherein,,
wherein beta is 1 、β 2 The probability of converting insufficient reaction into excessive reaction and converting excessive reaction into insufficient reaction; q (Q) 2 、Q 3 Omega when the reaction is insufficient and excessive t A transition probability matrix of states; pi Li 、π Hi Respectively at the transactorTransition probabilities when insufficient and excessive reactions are required.
6. The system for acquiring the optimal supply curve of the generator taking the optimization of the segmentation points into account is characterized by comprising the following steps:
the data acquisition module is used for acquiring historical transaction results of the mobile phone unit, marginal cost of the generator set, historical quotation of the mobile phone unit, unit parameters and initial output of the generator set and the mobile phone unit;
the opponent bidding acquisition module is used for determining a supply curve of the opponent unit according to the historical transaction result of the opponent unit, the historical bidding and the constructed opponent bidding behavior model, wherein the opponent bidding behavior model considers two heart states of insufficient opponent reaction and excessive opponent reaction, is constructed and obtained based on a Markov model, and is:
in the method, in the process of the invention,
offer for the b-th section cost curve of the handset j at time t,/>
Offer +.f. for the b-th cost curve of the handset j at time t-1>
Estimating expectation, y, of bid adjustment values for a mobile phone set segment b supply curve t For the adjustment quantity at time t, phi t-1 Market trade information including t-1 time, y being the monetary unit, gamma T ＝[-K,…,K,-K,…,K]Is a coefficient of variation with respect to the offer; q, p t-1 The state transition matrix and the state variable at the time t-1 are respectively adopted;
the system comprises a generator optimal supply curve acquisition module, a generator optimal supply curve judgment module and a power supply module, wherein the generator optimal supply curve acquisition module is used for acquiring an optimal supply curve of a generator according to a mobile phone unit supply curve, marginal cost of the generator unit, unit parameters and initial output of the generator unit and a generator segmentation point optimization strategy quotation model of the mobile phone unit, the generator segmentation point optimization strategy quotation model comprises an upper model, a lower model, constraint of the supply curve above a segmentation constant incremental cost curve, set proportion constraint that each section of capacity of the supply curve cannot be smaller than the difference between rated active power of the unit and the output of the lowest unit, and final section of declaration electric quantity of the supply curve is not higher than rated active power constraint of the unit, and the lower model determines the winning electric quantity and node electric price of the generator unit under different quotation strategies according to a real-time market; the upper model determines an optimal supply curve of a generator according to the winning bid amount and the node electricity price of the generator set obtained by the lower model and by taking the maximum profit of the generator as a target; the objective function targeting the maximum profit of the generator is:
in the method, in the process of the invention,
is the quotation of the supply curve of section b of the unit i, the generation cost u is a piecewise convex function,/>
The power generation unit i and b sections are supplied with the winning power quantity v of the curve n For the node electricity price of the node n where the unit i is located, < +.>
Marginal cost of supply curve for section b of generator set i, < >>
Price of m-th section of cost curve is increased slightly for generator set i segment constant, +.>
For the sum of the capacities of the first m segments of the piecewise constant incremental cost curve, when b=0,/is>
For minimum technical output of the machine set, +.>
Zero (zero)/(zero)>
Is the ratio of the starting cost of the unit to the minimum technical output.
7. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of the generator optimum supply curve acquisition method according to any one of claims 1-5, taking into account segmentation point optimization.
8. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the generator optimum supply curve acquisition method accounting for piecewise point optimization of any one of claims 1-5.
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