CN109934476A - A kind of more tactful evolutionary Game Analysis methods of the micro-capacitance sensor source based on main body bounded rationality decision-storage joint planning - Google Patents
A kind of more tactful evolutionary Game Analysis methods of the micro-capacitance sensor source based on main body bounded rationality decision-storage joint planning Download PDFInfo
- Publication number
- CN109934476A CN109934476A CN201910165023.0A CN201910165023A CN109934476A CN 109934476 A CN109934476 A CN 109934476A CN 201910165023 A CN201910165023 A CN 201910165023A CN 109934476 A CN109934476 A CN 109934476A
- Authority
- CN
- China
- Prior art keywords
- strategy
- population
- micro
- capacitance sensor
- individual
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
A kind of more tactful evolutionary Game Analysis methods of micro-capacitance sensor source based on main body bounded rationality decision of the invention-storage joint planning, its main feature is that: including establishing more Interest Main Body Evolutionary Game Models, establishing evolutionary Game replicator equation, the seeking of Evolutionarily Stable Strategy, solution based on more set of strategies evolutionary Game Evolutionarily Stable Strategies, it solves in current micro-capacitance sensor planning and is difficult to balance the problem of conflict of interest and traditional game method assume the limitation of participant's rational between micro-capacitance sensor operator and grid operator.Use the method for the present invention that can improve renewable energy utilization rate in micro-capacitance sensor, and rationally reduce grid net loss with income between active balance micro-capacitance sensor operator and grid operator.The advantages that reasonable with methodological science, strong applicability, effect is good, can be improved micro-capacitance sensor intelligent planning efficiency, balances interest relations between different operators.
Description
Technical field
The present invention relates to micro-capacitance sensor source-storage capacity planning fields, are a kind of micro-capacitance sensors based on main body bounded rationality decision
The more tactful evolutionary Game Analysis methods of source-storage joint planning, the micro-capacitance sensor distributed power source applied to more Interest Main Bodies
(Distribution Generation, DG) and energy storage (Battery energy storage system, BESS) joint rule
It draws.
Background technique
To improve renewable energy utilization, meeting zonule power supply flexibility demand, the region based on distributed power generation is micro-
Network system becomes research hotspot.Micro-capacitance sensor is as the small-sized electric system containing distributed generation resource and energy-storage system, to big
Network system has carried out effective supplement, can effectively improve block supply reliability, reduce long distance power transmission cost.Reasonable configuration
Bulk power grid peace is ensured when micro battery and stored energy capacitance are micro-capacitance sensor the safe and economic operation premise and micro-grid connection in micro-capacitance sensor
The basis of row for the national games.
Currently, about micro-grid system distributed generation resource capacity planning research mainly include self micro-capacitance sensor with it is grid-connected
Type micro-capacitance sensor.Influence when wherein the research of self micro-capacitance sensor does not consider micro-grid connection to bulk power grid, does not meet practical work
Journey.The optimum programming under different planing method optimizing different scenes can be used according to the difference of the object of planning for grid type
Scheme.Existing planing method is broadly divided into classical way and intelligent algorithm two major classes.But as distribution scale increases,
Carrying out planning using classical way, time-consuming, convergence rate is slow；Intelligent algorithm is widely used to micro-capacitance sensor distribution
In power supply capacity planning, but existing research ignores micro-capacitance sensor to bulk power grid using the interests of micro-capacitance sensor itself as the object of planning
It influences, and does not consider influence of the energy storage charge and discharge operation reserve to planning.
In addition to traditional economy factor, micro-capacitance sensor operator and grid operator that micro-capacitance sensor source-storage capacity planning is related to
There is conflict between the two golden eggs principal sectors of the economy.Game method can be complicated between active balance different subjects economic relation, but pass
Game of uniting assumes participant's rational, and therefore, when analyzing Interest Main Body conflict, solution is excessively idealized.And at it
His field is using evolutionary Game, mostly only with two set of strategies, it is difficult to which determining user, there are more decisions to decision conclusions
It influences.
Summary of the invention
The object of the present invention is to overcome the shortcomings of existing micro-capacitance sensor source-storage planning technology, provide a kind of scientific and reasonable, fits
Strong with property, effect is good, can take into account the interests of grid operator Yu micro-capacitance sensor operator, and that improves planning efficiency is had based on main body
Limit the more tactful evolutionary Game Analysis methods of micro-capacitance sensor source-storage joint planning of rational decision making.
Realize the object of the invention the technical solution adopted is that, a kind of micro-capacitance sensor source-storage based on main body bounded rationality decision
The more tactful evolutionary Game Analysis methods of joint planning, characterized in that it the following steps are included:
1) more Interest Main Body Evolutionary Game Models are established
Three elements based on game, it may be assumed that participant, set of strategies, pay off function establish Evolutionary Game Model；
(1.1) participant: micro-capacitance sensor operator, grid operator
In evolutionary Game Analysis, game participant is biocenose, by micro-capacitance sensor operator and power grid operation L mappings
For two populations, it is denoted as P1With P2, have multiple individuals in population, each individual generates respective strategy, and is repeated at random
Game；
(1.2) more set of strategies
It proposes a kind of evolutionary Game method based on more set of strategies, reflects under Long-term planning comprehensively, potential optimal solution set, entirely
The shifty Evolution States of surface analysis finally determine optimal Evolution States strategy, by each population under constraint condition, at random
N strategy is generated, n is natural number, is combined into strategy set, micro-capacitance sensor operator population with the collection in each micro- source and the installation number of energy storage
P1Set of strategies be denoted as S1, grid operator population P2Set of strategies be denoted as S2, set of strategies characterization are as follows:
Wherein,For population P1I-th of set of strategies；For population P2J-th of set of strategies；Ni DGFor population P1I-th
In a strategy, the installation number of distributed generation resource；Nj BESSFor population P1I-th of strategy in, the installation number of energy-storage battery；
Nj DGFor population P2J-th strategy in, the installation number of distributed generation resource；Nj BESSFor population P2J-th of strategy in, storage
The installation number of energy battery；
Using evolutionary Game principle, under the restriction of maximum evolution time-constrain, adaptation of the Different Strategies in group is analyzed
Property, finally determine Evolutionarily Stable Strategy (Evolutionary stable strategy, ESS)；
(1.3) pay off function
Pay off function (Payoff Function) refers to the target that each participant pursues in game, pay off function generation
The economic benefit of table micro-capacitance sensor operator and grid operator under respective strategy, by population P1The payment of acquirement is denoted as U1, population
P2The payment of acquirement is denoted as U2, then
P1→U1(NDG,NBESS)
P2→U2(NDG,NBESS)
Wherein, NDGNumber is installed for micro-capacitance sensor distributed power source；NBESSNumber is installed for micro-capacitance sensor energy-storage battery；
2) evolutionary Game replicator equation is established
(2.1) fitness function is established, p is enabledi(t) indicate that t moment uses strategy SiIndividual amount, SiIt is in population i-th
A strategy, then group's sum N be
If selection strategy SiNumber of individuals account for total individual number ratio be xi, then have
AndIf fiIt (s) is using strategy SiIndividual fitness function, then micro-capacitance sensor operator population P1With
Grid operator population P2Fitness function respectively indicate are as follows:
Wherein, f1 iFor population P1Fitness function,For population P1In the tactful proportion of i-th of individual choice；For population P1The pay off function value of i-th of strategy of middle individual choice；For population P2Fitness function；For for population P2
In the tactful proportion of j-th of individual choice；For population P2The pay off function value of j-th of strategy of middle individual choice；
Then population P1With population P2Average fitness are as follows:
Wherein,For population P1Average fitness function,For population P2Average fitness function；
(2.2) replicator equation is established, total ratio x is accounted for using number of individualsiAs state variable, then population P1With
Population P2Replicator equation be expressed as
Wherein,For state variableDifferential；For state variableDifferential；
If individual choice strategy SiIncome be less than population average yield, then select the number of individuals growth rate of the strategy for
It is negative；Otherwise it is positive, if number of individuals selects the strategy profit to be exactly equal to group's average yield, selects the individual of the strategy
Number remains unchanged, and replicator equation presents each individual in population and constantly selects in game environment constantly changes with situation
The dynamic process of the evolutionary process of strategy, i.e. Different Individual in continuous random repeated game；
3) Evolutionarily Stable Strategy is sought
S is strategy set, ifAnd y ≠ Si, there is some positive numberSo that being S about strategyiGroup
The fitness function f of body meets:
Then claim Si∈ S is Evolutionarily Stable Strategy；
If nearly all individual all uses S in populationiStrategy, then the fitness of these individuals must may go out higher than other
The fitness of existing mutated individual, at this point, SiIt is stable strategy；Otherwise, mutated individual will encroach on entire population, SiIt can not be steady
Calmly, the fact that, indicates strategy SiIt is more excellent than tactful y；
4) solution procedure based on more set of strategies evolutionary Game Evolutionarily Stable Strategies
(a) it is based on initial data, it is random to generate initial population P1、P2, random to generate n group policy group；
(b) in population P1、P2It is middle that 1 individual is randomly generated respectivelyAnd 1 group policy is randomly choosed in set of strategiesCalculate payoff under this strategy
(c) individual is calculatedIn strategyUnder fitness function value；
(d) step (b), (c) are repeated, until n strategy group is selected；
(e) population P is calculated1、P2Total fitness function and average fitness；
(f) according to replicator equation, the ratio that each individual takes strategy is calculated
(g) step (b)-(f) is repeated, that is, re-starts tactful selection process, is developed the time until reaching maximum；
(h) each strategy proportion in individual choice is exportedAnd xiEvolution StatesIt obtains
In the maximum most stable of strategy of Evolution States to develop under the time, as Evolutionarily Stable Strategy.
A kind of more tactful evolutionary Games point of micro-capacitance sensor source based on main body bounded rationality decision of the invention-storage joint planning
Analysis method is rushed its main feature is that solving and being difficult to balance interests between micro-capacitance sensor operator and grid operator in current micro-capacitance sensor planning
Prominent and traditional game method assumes the problem of limitation of participant's rational.It can effectively be put down using the method for the present invention
Weigh income between micro-capacitance sensor operator and grid operator, improves renewable energy utilization rate in micro-capacitance sensor, and rationally reduce power grid
Network loss.Reasonable with methodological science, strong applicability, effect is good, can be improved micro-capacitance sensor intelligent planning efficiency, balances different operations
Between quotient the advantages that interest relations.
Detailed description of the invention
A kind of micro-capacitance sensor source based on main body bounded rationality decision Fig. 1 of the invention-more strategies of storage joint planning develop rich
Play chess analysis method flow chart；
Fig. 2 micro-capacitance sensor connects the practical 37 node 10kV distribution system schematic diagrames in one city of Northeast China；
Fig. 3 example micro-grid system structure chart；
Fig. 4 population P1Evolution States；
Fig. 5 population P2Evolution States.
Specific embodiment
Referring to Fig. 2 and Fig. 3, it is with the practical distribution network system in one city of Northeast China and shown micro-grid system structure chart
Example plans more tactful evolutionary Game Analysis methods to analyze the micro-capacitance sensor source based on main body bounded rationality decision-storage joint, wherein
In micro-capacitance sensor to be planned main power source by miniature gas turbine (Microturbine, MT) system, photovoltaic (Photovoltaic,
PV) 3 parts such as system, lead-acid accumulator energy-storage system (Battery energy storage system, BESS) form.
Referring to Fig.1, a kind of micro-capacitance sensor source based on main body bounded rationality decision of the invention-storage joint plans that more strategies are drilled
Change game analysis method, it include in have:
1) more Interest Main Body Evolutionary Game Models are established
(1.1) it generates game and participates in ethnic group
Micro-capacitance sensor operator and grid operator are mapped as two populations, are denoted as P1With P2.In population P1、P2Middle difference
1 individual is randomly generated
(1.2) the more set of strategies of game are generated
According under power distribution network network parameter, micro-capacitance sensor distributed power source and energy storage parameter and corresponding constraint condition, each
N=1000 strategy is randomly generated in individual, is combined into strategy set with the collection in each micro- source and the installation number of energy storage.Micro-capacitance sensor operator
Population P1Set of strategies be denoted as S1, grid operator population P2Set of strategies be denoted as S2, set of strategies characterization are as follows:
Wherein,For population P1I-th of set of strategies；For population P2J-th of set of strategies；Ni PVFor population P1I-th
In a strategy, the installation number of photovoltaic cell；Ni MTFor population P1I-th of strategy in, the installation number of miniature gas turbine；
Nj BESSFor population P1I-th of strategy in, the installation number of energy-storage battery；Nj PVFor population P2J-th strategy in, photovoltaic
The installation number of battery；Nj MTFor population P2J-th strategy in, the installation number of miniature gas turbine；Nj BESSFor population P2
J-th of strategy in, the installation number of energy-storage battery.
Each individualIn set of strategiesIt is middle to randomly choose 1 group policy respectively
(1.3) game pay off function is sought
By population P1The payment of acquirement is denoted as U1, population P2The payment of acquirement is denoted as U2, then
P1→U1(NPV,NMT,NBESS)
P2→U2(NPV,NMT,NBESS)
Wherein, NPVNumber is installed for micro-capacitance sensor photovoltaic cell；NMTNumber is installed for micro-capacitance sensor photovoltaic cell；NBESSFor micro- electricity
Net energy-storage battery installs number.
It calculates in strategyUnder payoff
2) evolutionary Game replicator equation is established
(2.1) fitness function is established.Enable pi(t) indicate that t moment uses strategy SiIndividual amount, then group's sum be
If selection strategy SiNumber of individuals account for total individual number ratio be xi, then have
AndIf fiIt (s) is using strategy SiIndividual fitness function, then micro-capacitance sensor operator population P1With
Grid operator population P2Fitness function respectively indicate are as follows:
Wherein, f1 iFor population P1Fitness function,For population P1In the tactful proportion of i-th of individual choice；For population P1The pay off function value of i-th of strategy of middle individual choice；For population P2Fitness function；For population P2In
The tactful proportion of j-th of individual choice；For population P2The pay off function value of j-th of strategy of middle individual choice.
Then population P1With population P2Average fitness are as follows:
Wherein,For population P1Average fitness function,For population P2Average fitness function.
Individual is calculated according to formula (3), formula (4)In strategyUnder fitness function value.
(2.2) it repeats " to generate individual, individual choice strategy, calculative strategy pay off function value, calculative strategy fitness function
The process of value ", i.e. step (1.1), (1.2), (1.3), (2.1).
(2.3) population P is calculated according to formula (3), (4), (5), (6)1、P2Total fitness function and average fitness.
(2.4) total ratio x is accounted for using number of individualsiAs state variable, then population P1With population P2Replicator Dynamics side
Journey is expressed as
The ratio for the strategy that each individual is taken is calculated according to formula (7), formula (8)
(2.5) individual reselects strategy again, calculates the replicator equation of each individual, i.e., each individual is adopted
The ratio for taking strategy, until reaching the maximum time T=30s that develops, evolution time interval is 0.001s.
3) Evolutionarily Stable Strategy is sought
S is strategy set, ifAnd y ≠ Si, there is some positive numberSo that being S about strategyiGroup
The fitness function f of body meets:
Then claim Si∈ S is Evolutionarily Stable Strategy.
4) solution procedure based on more set of strategies evolutionary Game Evolutionarily Stable Strategies
(a) it is based on initial data, it is random to generate initial population P1、P2, random to generate n=1000 group policy group；
(b) in population P1、P2It is middle that 1 individual is randomly generated respectivelyAnd 1 group policy is randomly choosed in set of strategies Calculate payoff under this strategy
(c) individual is calculatedIn strategyUnder fitness function value；
(d) step (b), (c) are repeated, until n strategy group is selected；
(e) population P is calculated1、P2Total fitness function and average fitness；
(f) according to replicator equation, the ratio that each individual takes strategy is calculated
(g) step (b)-(f) is repeated, that is, re-starts tactful selection process, is developed the time until reaching maximum；
(h) each strategy proportion in individual choice is exportedAnd xiEvolution StatesIt obtains
In the maximum most stable of strategy of Evolution States to develop under the time, i.e. the strategy of x=1 is Evolutionarily Stable Strategy, according to establishing plan
Foundation slightly, the installation number comprising each micro- source and energy storage in each strategy, the program results as obtained.
Fig. 4 is to represent micro-capacitance sensor operator main body population P11000 set of strategies Evolution States curve.Fig. 4 can be seen
It arrives, is carried out with evolutionary Game, finally only have the Evolution States of 1 set of strategies gradually to level off in set of strategies and 1 and be finally reached steady
It surely is the 227th strategy, and other strategies then gradually level off to 0.Evolution States, which level off to, 1 to be represent in game situation and environment
It is constantly changing down, which has eventually become the stable strategy in population, i.e. Evolutionarily Stable Strategy.
Fig. 5 is to represent grid operator main body population P21000 set of strategies Evolution States curve.Fig. 5 can be seen
It arrives, the variation of strategy was generated between 0s to 0.05s, but also there is after 0.1s 1 strategy gradually level off to 1, i.e., the 227th
Strategy is the Evolutionarily Stable Strategy of grid operator's main body.
1 program results of table and with other methods compare
In table 1, in evolutionary Game, non-cooperative game and the multi-objective particle for considering more Interest Main Bodies
It, can be in the optimum results that (Multi-objective particle swarm optimization, MOPSO) method obtains
See, in the result obtained by evolutionary Game, the year payment expense of micro-capacitance sensor operator is obtained compared to Non-cooperative
Result reduce 300,000 yuan, reduce 500,000 yuan than MOPSO method；And the year payment more non-cooperation of expense of grid operator is rich
The method of playing chess reduces 3.771 ten thousand yuan, reduces 2.62 ten thousand yuan compared with MOPSO method, meanwhile, network loss also compared with non-cooperative game and
MOPSO method reduces 3.5%~4%.
Obviously, the above embodiments are merely examples for clarifying the description, and does not limit the embodiments, right
For those of ordinary skill in the art, can also make on the basis of the above description it is other it is various forms of variation or
Change, there is no necessity and possibility to exhaust all the enbodiments, and it is extended from this it is obvious variation or
It changes still within the protection scope of the invention.
Claims (1)
1. a kind of more tactful evolutionary Game Analysis methods of micro-capacitance sensor source based on main body bounded rationality decision-storage joint planning,
Be characterized in, it the following steps are included:
1) more Interest Main Body Evolutionary Game Models are established
Three elements based on game, it may be assumed that participant, set of strategies, pay off function establish Evolutionary Game Model；
(1.1) participant: micro-capacitance sensor operator, grid operator
In evolutionary Game Analysis, game participant is biocenose, and micro-capacitance sensor operator and grid operator are mapped as two
A population, is denoted as P1With P2, have multiple individuals in population, each individual generates respective strategy, and carries out repeating to win at random
It plays chess；
(1.2) more set of strategies
It proposes a kind of evolutionary Game method based on more set of strategies, reflects under Long-term planning comprehensively, potential optimal solution set, Quan Mianfen
Shifty Evolution States are analysed, finally determine optimal Evolution States strategy, by each population under constraint condition, are randomly generated
N strategy, n is natural number, is combined into strategy set, micro-capacitance sensor operator population P with the collection in each micro- source and the installation number of energy storage1's
Set of strategies is denoted as S1, grid operator population P2Set of strategies be denoted as S2, set of strategies characterization are as follows:
Wherein,For population P1I-th of set of strategies；For population P2J-th of set of strategies；Ni DGFor population P1I-th of plan
In slightly, the installation number of distributed generation resource；Nj BESSFor population P1I-th of strategy in, the installation number of energy-storage battery；Nj DGFor
Population P2J-th strategy in, the installation number of distributed generation resource；Nj BESSFor population P2J-th of strategy in, energy-storage battery
Installation number；
Using evolutionary Game principle, under the restriction of maximum evolution time-constrain, adaptability of the Different Strategies in group is analyzed, most
Determine Evolutionarily Stable Strategy (Evolutionary stable strategy, ESS) eventually；
(1.3) pay off function
Pay off function (Payoff Function) refers to the target that each participant pursues in game, and pay off function represents micro-
The economic benefit of grid operator and grid operator under respective strategy, by population P1The payment of acquirement is denoted as U1, population P2It takes
The payment obtained is denoted as U2, then
P1→U1(NDG,NBESS)
P2→U2(NDG,NBESS)
Wherein, NDGNumber is installed for micro-capacitance sensor distributed power source；NBESSNumber is installed for micro-capacitance sensor energy-storage battery；
2) evolutionary Game replicator equation is established
(2.1) fitness function is established, p is enabledi(t) indicate that t moment uses strategy SiIndividual amount, SiFor i-th of plan in population
Slightly, then group's sum N is
If selection strategy SiNumber of individuals account for total individual number ratio be xi, then have
AndIf fiIt (s) is using strategy SiIndividual fitness function, then micro-capacitance sensor operator population P1With power grid
Operator population P2Fitness function respectively indicate are as follows:
Wherein, f1 iFor population P1Fitness function,For population P1In the tactful proportion of i-th of individual choice；For kind
Group P1The pay off function value of i-th of strategy of middle individual choice；For population P2Fitness function；For for population P2In
Body selects j-th of tactful proportion；For population P2The pay off function value of j-th of strategy of middle individual choice；
Then population P1With population P2Average fitness are as follows:
Wherein,For population P1Average fitness function,For population P2Average fitness function；
(2.2) replicator equation is established, total ratio x is accounted for using number of individualsiAs state variable, then population P1With population
P2Replicator equation be expressed as
Wherein,For state variableDifferential；For state variableDifferential；
If individual choice strategy SiIncome be less than population average yield, then select the number of individuals growth rate of the strategy to be negative；It is on the contrary
It is positive, if number of individuals selects the strategy profit to be exactly equal to group's average yield, the number of individuals of the strategy is selected to keep
Constant, replicator equation presents each individual continuous selection strategy in game environment and situation constantly change in population
The dynamic process of evolutionary process, i.e. Different Individual in continuous random repeated game；
3) Evolutionarily Stable Strategy is sought
S is strategy set, ifAnd y ≠ Si, there is some positive numberSo that being S about strategyiGroup
Fitness function f meets:
Then claim Si∈ S is Evolutionarily Stable Strategy；
If nearly all individual all uses S in populationiStrategy, then fitness of these individuals must be higher than other be likely to occur
The fitness of mutated individual, at this point, SiIt is stable strategy；Otherwise, mutated individual will encroach on entire population, SiIt can not stablize, this
One true expression strategy SiIt is more excellent than tactful y；
4) solution procedure based on more set of strategies evolutionary Game Evolutionarily Stable Strategies
(a) it is based on initial data, it is random to generate initial population P1、P2, random to generate n group policy group；
(b) in population P1、P2It is middle that 1 individual is randomly generated respectivelyAnd 1 group policy is randomly choosed in set of strategiesCalculate payoff under this strategy
(c) individual is calculatedIn strategyUnder fitness function value；
(d) step (b), (c) are repeated, until n strategy group is selected；
(e) population P is calculated1、P2Total fitness function and average fitness；
(f) according to replicator equation, the ratio that each individual takes strategy is calculated
(g) step (b)-(f) is repeated, that is, re-starts tactful selection process, is developed the time until reaching maximum；
(h) each strategy proportion in individual choice is exportedAnd xiEvolution StatesIt obtains most
The most stable of strategy of Evolution States under the macroevolution time, as Evolutionarily Stable Strategy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910165023.0A CN109934476B (en) | 2019-03-05 | 2019-03-05 | Micro-grid source-storage joint planning multi-strategy evolution game analysis method based on subject limited rational decision |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910165023.0A CN109934476B (en) | 2019-03-05 | 2019-03-05 | Micro-grid source-storage joint planning multi-strategy evolution game analysis method based on subject limited rational decision |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109934476A true CN109934476A (en) | 2019-06-25 |
| CN109934476B CN109934476B (en) | 2022-05-24 |
Family
ID=66986314
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910165023.0A Active CN109934476B (en) | 2019-03-05 | 2019-03-05 | Micro-grid source-storage joint planning multi-strategy evolution game analysis method based on subject limited rational decision |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109934476B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113570221A (en) * | 2021-07-15 | 2021-10-29 | 国网浙江省电力有限公司经济技术研究院 | Power grid enterprise comprehensive energy service market expanding auxiliary decision-making method based on dynamic evolution visual angle |
| CN113656123A (en) * | 2021-07-28 | 2021-11-16 | 上海纽盾科技股份有限公司 | Information evaluation method, device and system for equal protection evaluation |
| CN113807569A (en) * | 2021-08-12 | 2021-12-17 | 华南理工大学 | Fully distributed cooperative optimization method for multi-source energy storage type microgrid |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160179923A1 (en) * | 2014-12-19 | 2016-06-23 | Xerox Corporation | Adaptive trajectory analysis of replicator dynamics for data clustering |
| CN107566387A (en) * | 2017-09-14 | 2018-01-09 | 中国人民解放军信息工程大学 | Cyber-defence action decision method based on attacking and defending evolutionary Game Analysis |
| CN108985897A (en) * | 2018-07-12 | 2018-12-11 | 华东交通大学 | A kind of smart grid Generation Side Differential evolution game price competing method |
-
2019
- 2019-03-05 CN CN201910165023.0A patent/CN109934476B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160179923A1 (en) * | 2014-12-19 | 2016-06-23 | Xerox Corporation | Adaptive trajectory analysis of replicator dynamics for data clustering |
| CN107566387A (en) * | 2017-09-14 | 2018-01-09 | 中国人民解放军信息工程大学 | Cyber-defence action decision method based on attacking and defending evolutionary Game Analysis |
| CN108985897A (en) * | 2018-07-12 | 2018-12-11 | 华东交通大学 | A kind of smart grid Generation Side Differential evolution game price competing method |
Non-Patent Citations (7)
| Title |
|---|
| LEFENG CHENG 等: "Game-Theoretic Approaches Applied to Transactions in the Open and Ever-Growing Electricity Markets From the Perspective of Power Demand Response：An Overview", 《IEEE ACCESS》 * |
| WANG JIANHUI 等: "An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand", 《ENERGY》 * |
| 乔根·W.威布尔著: "《演化博弈论》", 31 May 2015, 上海：上海人民出版社；上海：格致出版社 * |
| 何建佳等: "基于供需网企业合作博弈模型的演化路径分析", 《运筹与管理》 * |
| 梅生伟，刘锋，魏韡著: "《工程博弈论基础及电力系统应用》", 30 September 2016, 北京：科学出版社 * |
| 钱锟: "智能电网中发电侧演化博弈竞价策略研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 顾洪超: "基于演化博弈论的发电商竞价行为的研究", 《中国优秀硕士学位论文全文数据库 经济与管理科学辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113570221A (en) * | 2021-07-15 | 2021-10-29 | 国网浙江省电力有限公司经济技术研究院 | Power grid enterprise comprehensive energy service market expanding auxiliary decision-making method based on dynamic evolution visual angle |
| CN113656123A (en) * | 2021-07-28 | 2021-11-16 | 上海纽盾科技股份有限公司 | Information evaluation method, device and system for equal protection evaluation |
| CN113656123B (en) * | 2021-07-28 | 2023-05-16 | 上海纽盾科技股份有限公司 | Information evaluation method, device and system for equal-protection evaluation |
| CN113807569A (en) * | 2021-08-12 | 2021-12-17 | 华南理工大学 | Fully distributed cooperative optimization method for multi-source energy storage type microgrid |
| CN113807569B (en) * | 2021-08-12 | 2024-04-16 | 华南理工大学 | Complete distributed collaborative optimization method for multi-source energy storage type micro-grid |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109934476B (en) | 2022-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113746144B (en) | Source-grid-load real-time interaction electric carbon control method and intelligent management system thereof | |
| CN109934487A (en) | A kind of active distribution network coordinated planning method considering multiagent interest game | |
| CN109934476A (en) | A kind of more tactful evolutionary Game Analysis methods of the micro-capacitance sensor source based on main body bounded rationality decision-storage joint planning | |
| CN107423852A (en) | A kind of light storage combined plant optimizing management method of meter and typical scene | |
| CN107578182A (en) | Micro-grid operational control method is stored up based on light under Demand Side Response | |
| CN106228462B (en) | Multi-energy-storage-system optimal scheduling method based on genetic algorithm | |
| CN106130066B (en) | A kind of Multi-objective Robust control method for frequency for independent micro-grid system | |
| CN107565585B (en) | Energy storage device peak regulation report-back time prediction technique and its model creation method | |
| CN110119888A (en) | A kind of active gridding planing method based on distributed generation resource access | |
| CN114938035B (en) | Shared energy storage energy scheduling method and system considering energy storage degradation cost | |
| CN105896596B (en) | A kind of the wind power layering smoothing system and its method of consideration Demand Side Response | |
| Li et al. | Identifying effective operating rules for large hydro–solar–wind hybrid systems based on an implicit stochastic optimization framework | |
| CN116976598A (en) | Demand response low-carbon scheduling method and system based on carbon responsibility allocation | |
| CN116402307A (en) | Power grid planning capacity analysis method considering operation characteristics of schedulable flexible resources | |
| Zou et al. | The dynamic economic emission dispatch of the combined heat and power system integrated with a wind farm and a photovoltaic plant | |
| CN110991928B (en) | Energy management method and system for comprehensive energy system of multiple micro energy networks | |
| CN108921368A (en) | Balanced cooperative game controller based on virtual power plant | |
| CN108764737A (en) | Economic benefit evaluation method and device for battery energy storage system at user side of industrial park | |
| CN111211576B (en) | Method for measuring and calculating new energy bearing capacity of regional power grid in consideration of energy storage | |
| Zhang et al. | Self-optimization simulation model of short-term cascaded hydroelectric system dispatching based on the daily load curve | |
| Jin et al. | Research on energy management of microgrid in power supply system using deep reinforcement learning | |
| CN114677064B (en) | Cascade reservoir scheduling decision support method coupling optimality and stability | |
| CN107591794A (en) | Active distribution network source storage capacity configuration optimizing method based on load classification | |
| CN115939538A (en) | Comprehensive evaluation method and device for performance of battery energy storage system and computer equipment | |
| CN114330938B (en) | Distributed energy storage planning method and system for power distribution network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |