CN109636515A - A kind of sale of electricity quotient intelligent agent Bidding system and device - Google Patents

A kind of sale of electricity quotient intelligent agent Bidding system and device Download PDF

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CN109636515A
CN109636515A CN201811448384.8A CN201811448384A CN109636515A CN 109636515 A CN109636515 A CN 109636515A CN 201811448384 A CN201811448384 A CN 201811448384A CN 109636515 A CN109636515 A CN 109636515A
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bidding strategies
bidding
strategies
sale
electricity
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王高琴
张鹏程
魏路平
肖艳炜
王颖
张凯锋
郑亚先
史新红
朱炳铨
郭艳敏
邵平
程海花
龙苏岩
陈爱林
徐骏
吕建虎
叶飞
曾丹
黄春波
杨辰星
冯凯
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Southeast University
State Grid Zhejiang Electric Power Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
State Grid Shanghai Electric Power Co Ltd
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Southeast University
State Grid Zhejiang Electric Power Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
State Grid Shanghai Electric Power Co Ltd
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Abstract

The present invention relates to a kind of sale of electricity quotient intelligent agent Bidding system and devices, which comprises S1. selects bidding strategies according to the select probability of bidding strategies each in bidding strategies set；S2. according to by the select probability of tendency each bidding strategies of coefficient update of selection bidding strategies corresponding bid income and unselected bidding strategies；S3. Optimal Bidding Strategies are obtained according to the select probability of bidding strategies each after update.Technical solution provided by the invention is able to reflect the decision behavior for concentrating different type sale of electricity company in trade at competitive price, reflects the Decision-making of Bidding behavior of different type sale of electricity quotient, further embodies the decision predisposition of different sale of electricity quotient in real market.

Description

A kind of sale of electricity quotient intelligent agent Bidding system and device
Technical field
The present invention relates to electricity market fields, and in particular to a kind of sale of electricity quotient intelligent agent Bidding system and device.
Background technique
With the propulsion of power market reform, on the basis of Generation Side competition, the competition of sale of electricity side is also being graduallyed relax control, greatly Amount sale of electricity company participates in market in succession.As new main market players, the bid of sale of electricity company bids behavior to entire electric power Risk management, market mode design, the trading rules formulation in market etc. propose some new challenges, and future will also more show It writes.Carry out power market simulation research, it is necessary first to the report for being how sale of electricity quotient complexity in effectively simulation market of solution Valence decision behavior, establish the sale of electricity Shang dynasty reason offer decision-making models, embody sale of electricity quotient bid behavior for market operation process shadow Ring effect.
In recent years, the behavior simulation of bidding for being concentrated mainly on generation side market member based on the modeling technique of agency is ground Study carefully, by market member being modeled as having the computer intelligence of certain learning decision ability act on behalf of, for particular market, according to Market rules construct market member bidding decision simulation model, by emulation experiment, assess market operating status, examine market rule Reasonability then.In comparison, it is less to act on behalf of tactics research for the behavior of bidding of sales market member, and it is specific to be based primarily upon certain Market mode study the bidding decision method based on forecasted electricity market price and Monte Carlo randomized optimization process, there are a large amount of hypothesis Premise, and for market member bid the factors such as target, market mode variation adaptability it is not strong.
Summary of the invention
In view of the deficiencies of the prior art, the purpose of the invention is to describe the sale of electricity quotient of differentiation target in real market Bid behavior is able to reflect and concentrates in trade at competitive price the present invention provides a kind of sale of electricity quotient intelligent agent Bidding system and device The decision behavior of different type sale of electricity company, reflects the Decision-making of Bidding behavior of different type sale of electricity quotient, and simulates as far as possible Actual market.
The purpose of the present invention is adopt the following technical solutions realization:
A kind of sale of electricity quotient intelligent agent Bidding system, it is improved in that the described method includes:
S1. bidding strategies are selected according to the select probability of bidding strategies each in bidding strategies set；
S2. according to each by the tendency coefficient update of selection bidding strategies corresponding bid income and unselected bidding strategies The select probability of bidding strategies；
S3. Optimal Bidding Strategies are obtained according to the select probability of bidding strategies each after update.
Preferably, in the step S1, the initial selected probability of each bidding strategies in bidding strategies set, as the following formula really It is fixed:
In above formula, p1It (s) is the initial selected probability of s-th of bidding strategies in bidding strategies set, s ∈ [1, M], M are Bidding strategies sum.
Preferably, the step S1, comprising:
According to the select probability of each bidding strategies in the bidding strategies set, using roulette algorithm from bidding strategies collection Bidding strategies are selected in conjunction.
Preferably, the step S2, comprising:
The corresponding receipts of bidding of the bidding strategies are determined according to by the corresponding e-commerce operation target value of selling of selection bidding strategies Benefit；
It is updated according to the income of bidding by the tendency coefficient of selection bidding strategies；
The tendency coefficient of unselected bidding strategies is updated according to the forgetting factor of unselected bidding strategies；
According to the select probability of the corresponding bidding strategies of tendency coefficient update of each bidding strategies.
Further, described to determine the bidding strategies according to by the corresponding e-commerce operation target value of selling of selection bidding strategies Corresponding income of bidding, comprising:
It determines as the following formula and determines that the bidding strategies are corresponding according to by the corresponding e-commerce operation target of selling of selection bidding strategies Income of bidding:
In above formula, R is the corresponding income of bidding of the bidding strategies, αnTo be described by the corresponding sale of electricity of selection bidding strategies Quotient n-th runs target value, θnFor the weight for being runed target value by the corresponding sale of electricity quotient n-th of selection bidding strategies, βnIt is described The conversion coefficient of target value, n ∈ [1,9] are runed by the corresponding sale of electricity quotient n-th of selection bidding strategies.
Specifically, determining that the corresponding sale of electricity quotient first of the bidding strategies runs target value α as the following formula1:
α1=max [(psell*qsell)-(pclear*qclear)]
The second operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula2:
α2=max [qload*(pset-pclear)]
The third operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula3:
α3=max (qclear*pclear)
The 4th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula4:
α4=max [(qsell*psell)-Δpenalty]
The 5th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula5:
α5=max [(qsell*psell)-Δpenalty']
The 6th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula6:
α6=maxqclear
The 7th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula7:
α7=max (qclear-Δpenalty)
The 8th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula8:
α8=max (qclear-Δpenalty')
The 9th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula9:
α9=max (qclear-qclear')
In above formula, psellAnd qsellThe respectively price and electricity of the sale of electricity contract of sale of electricity Shang Yuqi user signing, pclear For the corresponding cleaing price of the bidding strategies, qclearFor the corresponding acceptance of the bid electricity of the bidding strategies, psetFor listed power price, qloadFor load prediction electricity, ΔpenaltyPenalty term when for practical loss of capital more than the receptible loss of capital amount of money, Δpenalty' be It not can guarantee the penalty term when condition got a profit substantially, qclear' be rival conclusion of the business electricity；
Wherein, penalty term Δ when practical loss of capital is more than the receptible loss of capital amount of money is determined as the following formulapenalty:
Penalty term Δ when not can guarantee the condition got a profit substantially is determined as the following formulapenalty':
In above formula, δpenaltyIt is penalty factor, πlossFor the receptible loss of capital amount of money.
Further, the income of bidding according to is updated by the tendency coefficient of selection bidding strategies, comprising:
It is updated as the following formula by the tendency coefficient of selection bidding strategies:
qt+1(m)=(1-r) qt(m)+(1-e)R
In above formula, qt+1(m) for, by the tendency coefficient of selection bidding strategies, r is in the t+1 times iteration bidding strategies set Forgetting factor, e are empirical parameter, qtIt (m) is the tendency coefficient of m-th of bidding strategies in the t times iteration bidding strategies set, t ∈ [1, T], T are iteration total degree, and m ∈ [1, M], M are bidding strategies sum.
Further, the forgetting factor according to unselected bidding strategies updates the tendency of unselected bidding strategies Coefficient, comprising:
The tendency coefficient of unselected bidding strategies is updated as the following formula:
In above formula, x ∈ [1, M] and x ≠ m, m ∈ [1, M], M are bidding strategies sum, and m is the bidding strategies selected, x For non-selected bidding strategies,；qt+1It (x) is the tendency system of x-th of bidding strategies in the t+1 times iteration bidding strategies set Number, qtIt (x) is the tendency coefficient of x-th of bidding strategies in the t times iteration bidding strategies set.
Further, the select probability of the corresponding bidding strategies of tendency coefficient update according to each bidding strategies, comprising:
The select probability p of s-th of bidding strategies in the t+1 times iteration bidding strategies set is determined as the following formulat+1(s):
In above formula, s ∈ [1, M], M are bidding strategies sum；qt+1It (s) is s in the t+1 times iteration bidding strategies set The tendency coefficient of a bidding strategies, c are cooling ratio；
Wherein, cooling ratio c is determined as the following formula:
In above formula, qtIt (s) is the tendency coefficient of m-th of bidding strategies in the t times iteration bidding strategies set, ε is greater than 0 Real number.
Preferably, the step S3, comprising:
If there are the select probabilities of bidding strategies to be greater than 0.99 in the bidding strategies set, which is optimal Bidding strategies；Otherwise, the step S1 is returned.
A kind of sale of electricity quotient intelligent agent bid device, it is improved in that described device includes:
Selecting unit, for selecting bidding strategies according to the select probability of bidding strategies each in bidding strategies set；
Updating unit, for according to by the tendency of selection bidding strategies corresponding bid income and unselected bidding strategies The select probability of each bidding strategies of coefficient update；
Acquiring unit, for obtaining Optimal Bidding Strategies according to the select probability of bidding strategies each after update.
Compared with the immediate prior art, the invention has the benefit that
Technical solution provided by the invention, it is competing by being selected according to the select probability of bidding strategies each in bidding strategies set Valence strategy is respectively bidded according to by the tendency coefficient update of selection bidding strategies corresponding bid income and unselected bidding strategies The select probability of strategy obtains Optimal Bidding Strategies according to the select probability of bidding strategies each after update, it is competing to be able to reflect concentration The decision behavior of different type sale of electricity company, reflects the Decision-making of Bidding behavior of different type sale of electricity quotient, further body in valence transaction The decision predisposition of different sale of electricity quotient in real market is showed.
Detailed description of the invention
Fig. 1 is a kind of flow chart of sale of electricity quotient intelligent agent Bidding system in the embodiment of the present invention；
Fig. 2 is a kind of structural schematic diagram of sale of electricity quotient intelligent agent bid device in the embodiment of the present invention.
Specific embodiment
Specific embodiments of the present invention will be described in further detail with reference to the accompanying drawing.
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art All other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
The present invention provides a kind of sale of electricity quotient intelligent agent Bidding systems, as shown in Figure 1, which comprises
101. selecting bidding strategies according to the select probability of bidding strategies each in bidding strategies set；
102. according to by the tendency coefficient update of selection bidding strategies corresponding bid income and unselected bidding strategies The select probability of each bidding strategies；
103. obtaining Optimal Bidding Strategies according to the select probability of bidding strategies each after update.
Further, in the step 101, the initial selected probability of each bidding strategies in bidding strategies set, as the following formula It determines:
In above formula, p1It (s) is the initial selected probability of s-th of bidding strategies in bidding strategies set, s ∈ [1, M], M are Bidding strategies sum.
Further, the step 101, comprising:
According to the select probability of each bidding strategies in the bidding strategies set, using roulette algorithm from bidding strategies collection Bidding strategies are selected in conjunction.
Further, the step 102, comprising:
The corresponding receipts of bidding of the bidding strategies are determined according to by the corresponding e-commerce operation target value of selling of selection bidding strategies Benefit；
It is updated according to the income of bidding by the tendency coefficient of selection bidding strategies；
The tendency coefficient of unselected bidding strategies is updated according to the forgetting factor of unselected bidding strategies；
According to the select probability of the corresponding bidding strategies of tendency coefficient update of each bidding strategies.
Specifically, described determine the bidding strategies pair according to by the corresponding e-commerce operation target value of selling of selection bidding strategies The income of bidding answered, comprising:
It determines as the following formula and determines that the bidding strategies are corresponding according to by the corresponding e-commerce operation target of selling of selection bidding strategies Income of bidding:
In above formula, R is the corresponding income of bidding of the bidding strategies, αnTo be described by the corresponding sale of electricity of selection bidding strategies Quotient n-th runs target value, θnFor the weight for being runed target value by the corresponding sale of electricity quotient n-th of selection bidding strategies, βnIt is described The conversion coefficient of target value, n ∈ [1,9] are runed by the corresponding sale of electricity quotient n-th of selection bidding strategies.
Specifically, determining that the corresponding sale of electricity quotient first of the bidding strategies runs target value α as the following formula1:
α1=max [(psell*qsell)-(pclear*qclear)]
The second operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula2:
α2=max [qload*(pset-pclear)]
The third operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula3:
α3=max (qclear*pclear)
The 4th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula4:
α4=max [(qsell*psell)-Δpenalty]
The 5th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula5:
α5=max [(qsell*psell)-Δpenalty']
The 6th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula6:
α6=maxqclear
The 7th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula7:
α7=max (qclear-Δpenalty)
The 8th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula8:
α8=max (qclear-Δpenalty')
The 9th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula9:
α9=max (qclear-qclear')
In above formula, psellAnd qsellThe respectively price and electricity of the sale of electricity contract of sale of electricity Shang Yuqi user signing, pclear For the corresponding cleaing price of the bidding strategies, qclearFor the corresponding acceptance of the bid electricity of the bidding strategies, psetFor listed power price, qloadFor load prediction electricity, ΔpenaltyPenalty term when for practical loss of capital more than the receptible loss of capital amount of money, Δpenalty' be It not can guarantee the penalty term when condition got a profit substantially, qclear' be rival conclusion of the business electricity；
Wherein, penalty term Δ when practical loss of capital is more than the receptible loss of capital amount of money is determined as the following formulapenalty:
Penalty term Δ when not can guarantee the condition got a profit substantially is determined as the following formulapenalty':
In above formula, δpenaltyIt is penalty factor, πlossFor the receptible loss of capital amount of money.
Specifically, the income of bidding according to is updated by the tendency coefficient of selection bidding strategies, comprising:
It is updated as the following formula by the tendency coefficient of selection bidding strategies:
qt+1(m)=(1-r) qt(m)+(1-e)R
In above formula, qt+1(m) for, by the tendency coefficient of selection bidding strategies, r is in the t+1 times iteration bidding strategies set Forgetting factor, e are empirical parameter, qtIt (m) is the tendency coefficient of m-th of bidding strategies in the t times iteration bidding strategies set, t ∈ [1, T], T are iteration total degree, and m ∈ [1, M], M are bidding strategies sum.
Specifically, the forgetting factor according to unselected bidding strategies updates the tendency system of unselected bidding strategies Number, comprising:
The tendency coefficient of unselected bidding strategies is updated as the following formula:
In above formula, x ∈ [1, M] and x ≠ m, m ∈ [1, M], M are bidding strategies sum, and m is the bidding strategies selected, x For non-selected bidding strategies,；qt+1It (x) is the tendency system of x-th of bidding strategies in the t+1 times iteration bidding strategies set Number, qtIt (x) is the tendency coefficient of x-th of bidding strategies in the t times iteration bidding strategies set.
Wherein, as t=1, qt(m) or qt(x) it is the initial tendency coefficient of each bidding strategies in bidding strategies set, enables Initial tendency coefficient is 6000, and the initial coefficient that is inclined to is an empirical parameter, guarantees that each strategy has positive choosing at the beginning Probability is selected so as to global convergence.
Specifically, the select probability of the corresponding bidding strategies of tendency coefficient update according to each bidding strategies, comprising:
The select probability p of s-th of bidding strategies in the t+1 times iteration bidding strategies set is determined as the following formulat+1(s):
In above formula, s ∈ [1, M], s both can be m, or x, M are bidding strategies sum；qt+1(s) it is the t+1 times The tendency coefficient of s-th of bidding strategies, c are cooling ratio in iteration bidding strategies set；
Wherein, cooling ratio c is determined as the following formula:
In above formula, qtIt (s) is the tendency coefficient of m-th of bidding strategies in the t times iteration bidding strategies set, ε is greater than 0 Real number.
Further, the step 103, comprising:
If there are the select probabilities of bidding strategies to be greater than 0.99 in the bidding strategies set, which is optimal Bidding strategies；Otherwise, the step S101 is returned.
The present invention also provides a kind of sale of electricity quotient intelligent agent bid devices, as shown in Fig. 2, described device includes:
Selecting unit, for selecting bidding strategies according to the select probability of bidding strategies each in bidding strategies set；
Updating unit, for according to by the tendency of selection bidding strategies corresponding bid income and unselected bidding strategies The select probability of each bidding strategies of coefficient update；
Acquiring unit, for obtaining Optimal Bidding Strategies according to the select probability of bidding strategies each after update.
Further, in the selecting unit, the initial selected probability of each bidding strategies in bidding strategies set, as the following formula It determines:
In above formula, p1It (s) is the initial selected probability of s-th of bidding strategies in bidding strategies set, s ∈ [1, M], M are Bidding strategies sum.
Further, the selecting unit, is used for:
According to the select probability of each bidding strategies in the bidding strategies set, using roulette algorithm from bidding strategies collection Bidding strategies are selected in conjunction.
Further, the updating unit, comprising:
Determining module, for determining the bidding strategies according to by the corresponding e-commerce operation target value of selling of selection bidding strategies Corresponding income of bidding；
First update module, for bidding according to, income is updated by the tendency coefficient of selection bidding strategies；
Second update module, for updating unselected bidding strategies according to the forgetting factor of unselected bidding strategies It is inclined to coefficient；
Third update module, for the select probability for being inclined to the corresponding bidding strategies of coefficient update according to each bidding strategies.
Specifically, the determining module, e-commerce operation is sold according to by selection bidding strategies are corresponding for determining as the following formula Target determines the corresponding income of bidding of the bidding strategies:
In above formula, R is the corresponding income of bidding of the bidding strategies, αnTo be described by the corresponding sale of electricity of selection bidding strategies Quotient n-th runs target value, θnFor the weight for being runed target value by the corresponding sale of electricity quotient n-th of selection bidding strategies, βnIt is described The conversion coefficient of target value, n ∈ [1,9] are runed by the corresponding sale of electricity quotient n-th of selection bidding strategies.
Specifically, the determining module, further includes:
First determines submodule, for determining that the corresponding sale of electricity quotient first of the bidding strategies runs target value as the following formula α1:
α1=max [(psell*qsell)-(pclear*qclear)]
Second determines submodule, for determining the second operation target value of the corresponding sale of electricity quotient of the bidding strategies as the following formula α2:
α2=max [qload*(pset-pclear)]
Third determines submodule, for determining that the third of the corresponding sale of electricity quotient of the bidding strategies runs target value as the following formula α3:
α3=max (qclear*pclear)
4th determines submodule, for determining the 4th operation target value of the corresponding sale of electricity quotient of the bidding strategies as the following formula α4:
α4=max [(qsell*psell)-Δpenalty]
5th determines submodule, for determining the 5th operation target value of the corresponding sale of electricity quotient of the bidding strategies as the following formula α5:
α5=max [(qsell*psell)-Δpenalty']
6th determines submodule, for determining the 6th operation target value of the corresponding sale of electricity quotient of the bidding strategies as the following formula α6:
α6=maxqclear
7th determines submodule, for determining the 7th operation target value of the corresponding sale of electricity quotient of the bidding strategies as the following formula α7:
α7=max (qclear-Δpenalty)
8th determines submodule, for determining the 8th operation target value of the corresponding sale of electricity quotient of the bidding strategies as the following formula α8:
α8=max (qclear-Δpenalty')
9th determines submodule, for determining the 9th operation target value of the corresponding sale of electricity quotient of the bidding strategies as the following formula α9:
α9=max (qclear-qclear')
In above formula, psellAnd qsellThe respectively price and electricity of the sale of electricity contract of sale of electricity Shang Yuqi user signing, pclear For the corresponding cleaing price of the bidding strategies, qclearFor the corresponding acceptance of the bid electricity of the bidding strategies, psetFor listed power price, qloadFor load prediction electricity, ΔpenaltyPenalty term when for practical loss of capital more than the receptible loss of capital amount of money, Δpenalty' be It not can guarantee the penalty term when condition got a profit substantially, qclear' be rival conclusion of the business electricity；
Tenth determines submodule, penalty term when for determining that practical loss of capital is more than the receptible loss of capital amount of money as the following formula Δpenalty:
11st determines submodule, for determining penalty term when not can guarantee the condition got a profit substantially as the following formula Δpenalty':
In above formula, δpenaltyIt is penalty factor, πlossFor the receptible loss of capital amount of money.
Specifically, first update module, for being updated as the following formula by the tendency coefficient of selection bidding strategies:
qt+1(m)=(1-r) qt(m)+(1-e)R
In above formula, qt+1(m) for, by the tendency coefficient of selection bidding strategies, r is in the t+1 times iteration bidding strategies set Forgetting factor, e are empirical parameter, qtIt (m) is the tendency coefficient of m-th of bidding strategies in the t times iteration bidding strategies set, t ∈ [1, T], T are iteration total degree, and m ∈ [1, M], M are bidding strategies sum.
Specifically, second update module is used to update the tendency coefficient of unselected bidding strategies as the following formula:
In above formula, x ∈ [1, M] and x ≠ m, m ∈ [1, M], M are bidding strategies sum, and m is the bidding strategies selected, x For non-selected bidding strategies,；qt+1It (x) is the tendency system of x-th of bidding strategies in the t+1 times iteration bidding strategies set Number, qtIt (x) is the tendency coefficient of x-th of bidding strategies in the t times iteration bidding strategies set.
Wherein, as t=1, qt(m) or qt(x) it is the initial tendency coefficient of each bidding strategies in bidding strategies set, enables Initial tendency coefficient is 6000, and the initial coefficient that is inclined to is an empirical parameter, guarantees that each strategy has positive choosing at the beginning Probability is selected so as to global convergence.
Specifically, the third update module for determine in the t+1 times iteration bidding strategies set as the following formula s-th it is competing The select probability p of valence strategyt+1(s):
In above formula, s ∈ [1, M], s both can be m, or x, M are bidding strategies sum；qt+1(s) it is the t+1 times The tendency coefficient of s-th of bidding strategies, c are cooling ratio in iteration bidding strategies set；
The third update module, further includes:
12nd determines submodule, for determining cooling ratio c as the following formula:
In above formula, qtIt (s) is the tendency coefficient of m-th of bidding strategies in the t times iteration bidding strategies set, ε is greater than 0 Real number.
Further, the acquiring unit, if for there are the select probabilities of bidding strategies in the bidding strategies set Greater than 0.99, then the bidding strategies are Optimal Bidding Strategies；Otherwise, selecting unit is returned.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more, The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates, Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one The step of function of being specified in a box or multiple boxes.
Finally it should be noted that: the above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, to the greatest extent Invention is explained in detail referring to above-described embodiment for pipe, it should be understood by those ordinary skilled in the art that: still It can be with modifications or equivalent substitutions are made to specific embodiments of the invention, and without departing from any of spirit and scope of the invention Modification or equivalent replacement, should all cover within the scope of the claims of the present invention.

Claims (11)

1. a kind of sale of electricity quotient intelligent agent Bidding system, which is characterized in that the described method includes:
S1. bidding strategies are selected according to the select probability of bidding strategies each in bidding strategies set；
S2. it respectively bids according to by the tendency coefficient update of selection bidding strategies corresponding bid income and unselected bidding strategies The select probability of strategy；
S3. Optimal Bidding Strategies are obtained according to the select probability of bidding strategies each after update.
2. the method as described in claim 1, which is characterized in that in the step S1, each bidding strategies in bidding strategies set Initial selected probability, determine as the following formula:
In above formula, p1It (s) is the initial selected probability of s-th of bidding strategies in bidding strategies set, s ∈ [1, M], M are plan of bidding It is slightly total.
3. the method as described in claim 1, which is characterized in that the step S1, comprising:
According to the select probability of each bidding strategies in the bidding strategies set, using roulette algorithm from bidding strategies set Select bidding strategies.
4. the method as described in claim 1, which is characterized in that the step S2, comprising:
The income of bidding by selection bidding strategies is determined according to by the corresponding e-commerce operation target value of selling of selection bidding strategies；
It is updated by the income of bidding of selection bidding strategies by the tendency coefficient of selection bidding strategies according to described；
The tendency coefficient of unselected bidding strategies is updated according to the forgetting factor of unselected bidding strategies；
According to the select probability of the corresponding bidding strategies of tendency coefficient update of each bidding strategies.
5. method as claimed in claim 4, which is characterized in that described to sell e-commerce operation according to by selection bidding strategies are corresponding Target value determines the corresponding income of bidding of the bidding strategies, comprising:
According to e-commerce operation target is sold by selection bidding strategies are corresponding, determine as the following formula described corresponding by selection bidding strategies It bids income:
In above formula, R is the corresponding income of bidding of the bidding strategies, αnTo be described by the corresponding sale of electricity quotient n-th of selection bidding strategies Run target value, θnFor the weight for being runed target value by the corresponding sale of electricity quotient n-th of selection bidding strategies, βnIt is described selected Select the conversion coefficient that the corresponding sale of electricity quotient n-th of bidding strategies runs target value, n ∈ [1,9].
6. method as claimed in claim 5, which is characterized in that determine the corresponding sale of electricity quotient first of the bidding strategies as the following formula Run target value α1:
α1=max [(psell*qsell)-(pclear*qclear)]
The second operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula2:
α2=max [qload*(pset-pclear)]
The third operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula3:
α3=max (qclear*pclear)
The 4th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula4:
α4=max [(qsell*psell)-Δpenalty]
The 5th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula5:
α5=max [(qsell*psell)-Δpenalty']
The 6th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula6:
α6=maxqclear
The 7th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula7:
α7=max (qclear-Δpenalty)
The 8th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula8:
α8=max (qclear-Δpenalty')
The 9th operation target value α of the corresponding sale of electricity quotient of the bidding strategies is determined as the following formula9:
α9=max (qclear-qclear')
In above formula, psellAnd qsellThe respectively price and electricity of the sale of electricity contract of sale of electricity Shang Yuqi user signing, pclearIt is described The corresponding cleaing price of bidding strategies, qclearFor the corresponding acceptance of the bid electricity of the bidding strategies, psetFor listed power price, qloadFor Load prediction electricity, ΔpenaltyPenalty term when for practical loss of capital more than the receptible loss of capital amount of money, Δpenalty' it is to fail to protect Demonstrate,prove the penalty term when condition got a profit substantially, qclear' be rival conclusion of the business electricity；
Wherein, penalty term Δ when practical loss of capital is more than the receptible loss of capital amount of money is determined as the following formulapenalty:
Penalty term Δ when not can guarantee the condition got a profit substantially is determined as the following formulapenalty':
In above formula, δpenaltyIt is penalty factor, πlossFor the receptible loss of capital amount of money.
7. method as claimed in claim 4, which is characterized in that the income of bidding according to is updated by selection bidding strategies Tendency coefficient, comprising:
It is updated as the following formula by the tendency coefficient of selection bidding strategies:
qt+1(m)=(1-r) qt(m)+(1-e)R
In above formula, qt+1(m) for, by the tendency coefficient of selection bidding strategies, r is to forget in the t+1 times iteration bidding strategies set The factor, e are empirical parameter, qtIt (m) is the tendency coefficient of m-th of bidding strategies in the t times iteration bidding strategies set, t ∈ [1, T], T is iteration total degree, and m ∈ [1, M], M are bidding strategies sum.
8. method as claimed in claim 4, which is characterized in that described to be updated according to the forgetting factor of unselected bidding strategies The tendency coefficient of unselected bidding strategies, comprising:
The tendency coefficient of unselected bidding strategies is updated as the following formula:
In above formula, x ∈ [1, M] and x ≠ m, m ∈ [1, M], m are the bidding strategies selected, and x is non-selected bidding strategies, M is bidding strategies sum；qt+1It (x) is the tendency coefficient of x-th of bidding strategies in the t+1 times iteration bidding strategies set, qt It (x) is the tendency coefficient of x-th of bidding strategies in the t times iteration bidding strategies set.
9. method as claimed in claim 4, which is characterized in that described accordingly competing according to the tendency coefficient update of each bidding strategies The select probability of valence strategy, comprising:
The select probability p of s-th of bidding strategies in the t+1 times iteration bidding strategies set is determined as the following formulat+1(s):
In above formula, s ∈ [1, M], M are bidding strategies sum；qt+1(s) competing for s-th in the t+1 times iteration bidding strategies set The tendency coefficient of valence strategy, c are cooling ratio；
Wherein, cooling ratio c is determined as the following formula:
In above formula, qtIt (s) is the tendency coefficient of m-th of bidding strategies in the t times iteration bidding strategies set, ε is the reality greater than 0 Number.
10. the method as described in claim 1, which is characterized in that the step S3, comprising:
If there are the select probabilities of bidding strategies to be greater than 0.99 in the bidding strategies set, which is optimal bid Strategy；Otherwise, the step S1 is returned.
11. a kind of sale of electricity quotient intelligent agent bid device, which is characterized in that described device includes:
Selecting unit, for selecting bidding strategies according to the select probability of bidding strategies each in bidding strategies set；
Updating unit, for according to by the tendency coefficient of selection bidding strategies corresponding bid income and unselected bidding strategies Update the select probability of each bidding strategies；
Acquiring unit, for obtaining Optimal Bidding Strategies according to the select probability of bidding strategies each after update.
CN201811448384.8A 2018-11-30 2018-11-30 A kind of sale of electricity quotient intelligent agent Bidding system and device Pending CN109636515A (en)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030233315A1 (en) * 2002-02-26 2003-12-18 Byde Andrew Robert Bidding in multiple on-line auctions
CN107644370A (en) * 2017-09-29 2018-01-30 中国电力科学研究院 Price competing method and system are brought in a kind of self-reinforcing study together

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030233315A1 (en) * 2002-02-26 2003-12-18 Byde Andrew Robert Bidding in multiple on-line auctions
CN107644370A (en) * 2017-09-29 2018-01-30 中国电力科学研究院 Price competing method and system are brought in a kind of self-reinforcing study together

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王野平等: ""智能代理模拟在电力市场中的运用"", 《华南理工大学学报（自然科学版）》 *

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Application publication date: 20190416