CN110414764A - Micro-capacitance sensor group energy amount game dispatching method based on multi-agent system - Google Patents
Micro-capacitance sensor group energy amount game dispatching method based on multi-agent system Download PDFInfo
- Publication number
- CN110414764A CN110414764A CN201910386865.9A CN201910386865A CN110414764A CN 110414764 A CN110414764 A CN 110414764A CN 201910386865 A CN201910386865 A CN 201910386865A CN 110414764 A CN110414764 A CN 110414764A
- Authority
- CN
- China
- Prior art keywords
- energy
- buyer
- subnet
- emo
- baa
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 230000007246 mechanism Effects 0.000 claims abstract description 11
- 230000008569 process Effects 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 8
- 230000006854 communication Effects 0.000 claims abstract description 7
- 230000001174 ascending effect Effects 0.000 claims description 6
- 238000005265 energy consumption Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 2
- 230000008859 change Effects 0.000 claims description 2
- 230000000694 effects Effects 0.000 claims description 2
- 238000013139 quantization Methods 0.000 claims description 2
- 235000006508 Nelumbo nucifera Nutrition 0.000 claims 1
- 240000002853 Nelumbo nucifera Species 0.000 claims 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 claims 1
- 238000004891 communication Methods 0.000 abstract description 4
- 239000003795 chemical substances by application Substances 0.000 description 9
- 230000006870 function Effects 0.000 description 5
- 238000011161 development Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 208000006047 familial isolated pituitary adenoma Diseases 0.000 description 1
- 208000036974 gastrointestinal defects and immunodeficiency syndrome 1 Diseases 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000010977 jade Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Micro-capacitance sensor group energy amount game dispatching method based on multi-agent system, the following steps are included: step 1, establish the more proxy topologies of microgrid group, complete the modules agencyization to each subnet, dealing Proxy aggregates device SAA and BAA is set up on subnet upper layer, completes negotiation communication process by central processing unit EMO；Step 2, new microgrid energy pricing mechanism is used based on buyer MG, proposes that priority factors to the leading role of process of exchange, are found out based on best trading strategies in buyer's MG process of exchange；Step 3, new scheduling mechanism is introduced, central processing unit EMO is after receiving all buyer MG about the strategy of energy requirement, according to the superfluous energy demand of buyer MG and priority factors, by an appropriate number of energy EAiDistribute to single buyer MG；Shared weight is determined according to self-energy total surplus amount, and the strategy of buyer MG submission is verified using Energy distribution algorithm, reduces the burden of mass communication expense, improves energy is dispatched between each subnet in microgrid group fairness and stability.
Description
Technical field
The invention belongs to power electronics and technical field of power systems, and in particular to the micro-capacitance sensor group based on multi-agent system
Energy game dispatching method.
Background technique
Distributed energy is continuously improved in the permeability of power distribution network level in the whole world, the exploitation of new energy use by its zero
The conditions such as discharge, low cost and high yield, so that these technology development prospects are very wide.However, due to photovoltaic group
The non-scheduling characteristic of part, small-scale wind turbines distributed power supply, for safely and effectively operate and control microgrid cause it is huge
Big challenge, this but also there is the unmatched problem of supply and demand in micro-capacitance sensor always.Therefore, the intelligent mechanism of substitution is found to solve
The energy imbalance that MG faces is current urgent problem to be solved.
Mainstream development direction of the microgrid group as Future New Energy Source cluster, energy dynamics scheduling problem are always ground at present
Study carefully hot spot.Presently disclosed method is mostly the game between list net and main power grid, still for energy scheduling between microgrid group's subnet
In the research primary stage.
Summary of the invention
To overcome above-mentioned the deficiencies in the prior art, the object of the present invention is to provide the micro-capacitance sensor group energys based on multi-agent system
Game dispatching method is measured, solves the problems, such as each subnet energy scheduling in current microgrid group.
To achieve the above object, the technical solution adopted by the present invention is that: the micro-capacitance sensor group energy amount based on multi-agent system is rich
Dispatching method is played chess, coordinating communication completes energy scheduling between each subnet is realized on the basis of more agencies, comprising the following steps:
Step 1, the more proxy topologies of microgrid group are established, complete to set the modules agencyization of each subnet on subnet upper layer
Vertical dealing Proxy aggregates device SAA and BAA, finally completes negotiation communication process by central processing unit EMO；
Step 2, new microgrid energy pricing mechanism is used based on buyer MG, proposes priority factors to process of exchange
Leading role is found out using excess energy algorithm based on best trading strategies in buyer's MG process of exchange；
Step 3, new scheduling mechanism is introduced on the basis of game theory, central processing unit EMO is receiving all buyer MG
After the strategy of energy requirement, it will be fitted according to the superfluous energy demand of buyer MG and priority factors using Energy distribution algorithm
As the energy EA of quantityiDistribute to single buyer MG.
Other features of the invention also reside in,
The step 1 carries out agencyization to each module in each subnet using JADE platform.JADE be one completely by
The multi-Agent Development Framework that Java language is write, it then follows FIPA specification provides basic directory service, Agent management system
System, Messaging Service etc., can effectively with other JAVA development platforms and Integration ofTechnology.
The energy can not be self-supporting buyer MG be registered as buyer in dealing Proxy aggregates device BAA, energy demand based on buyer,
Power grid purchase and power grid selling price buy and sell the optimal bid amounts that Proxy aggregates device BAA determines energy purchase from seller MG, root
According to the quotation of the dealing Proxy aggregates device BAA received, seller MG, i.e. energy have remaining subnet, have been selected properly according to quotation
Strategy, such as adjust oneself energy consumption, make can energy for sale greatly increase；Then, Proxy aggregates device SAA is bought and sold
These available energy will be collected to be used to sell, and notify dealing Proxy aggregates device BAA, dealing Proxy aggregates device BAA believes this
Breath is transmitted to all buyer MG.
Priority factors described in step 2, calculation method are as follows:
C in formulaiIndicate the buyer MG in the contribution so far in power grid transaction, the i.e. energy that the passing time sells
Amount；CTotalIt is then the period all buyer MG contribution sums；DiFor the workload demand of the buyer MG, DTotalIt is total for all buyer MG
Workload demand；Formula middle front part point is measured buyer's MG past and is done by the way that extra energy is sold to other sub- buyer MG in group
Contribution, the workload demand of the quantization each buyer MG in rear portion, since past contribution and local workload demand are of equal importance,
Therefore it encourages to carry out power exchange between buyer MG, rather than trades with main power grid, use a weight factor μ (μ > 0)
Indicate importance that priority factors are traded in the energy, it be substantially it is dynamic, generated in central processing unit, for letter
Change and calculates, the μ value 1.5 of setting.
Excess energy algorithm described in step 2, the specific steps are that:
1) all-ones subnet energy is collected for demand, determines buyer and seller subnet quantity and its priority factors γi；
2) by buyer MG according to EReq, iThe arrangement of/γ i μ value ascending order, is arranged initial value j=1, N=| Q |；Dump energy Erm=
E*extra；Weight coefficient η=0；
3) gamma width value w and EReq, i/ γ i μ-η product and dump energy ErmCompare, if w (EReq, i/ γ i μ-η) < Erm, into
Row iteration calculates；
4) as w (EReq, i/ γ i μ-η) > Erm, determine that buyer MG buys strategy di=η γ ik.
The step 3, power exchange process is: for buyer MG, being collected all buyer's energy requirements by EMO
And pay the utmost attention to factor γi, according to sale EextraThe required total surplus energy and individual pay the utmost attention to factor, each buyer MG
Using optimal energy algorithm, determine that it buys strategy d using the non-cooperative game between buyer MGi, BAA transmits these information
To EMO, EMO is after receiving all buyer MG about the strategy of energy requirement, according to the superfluous energy of buyer MG, demand and preferential
Factor, using Energy distribution algorithm, the energy that seller MG is provided, with right quantity (EAi) distribute to each buyer MG.
Energy allocation algorithm described in step 3, the specific steps are that:
1) the purchase strategy d of each buyer MG is collected on the basis of superfluous energy algorithmi；
2) all buyer MG are arranged according to di/ γ μ i value ascending order, initial value j=1, k=2,1 μ of w=γ is set, it is remaining
ENERGY Erm=E*extra, weight coefficient h=d1/γ1μ；
3) gamma width value w and 2di/ γ μ j- η product and dump energy ErmCompare, if w (EReq, i/ γ i μ-η) < Erm, into
Row iteration calculates, and pays attention to distinguishing 2d at this timei/ γ μ j- η and dkThe size of/γ μ k- η carries out parallel iteration；
4) as w (EReq, i/γiμ-η)≥Erm, at this time to the ENERGY E A of each subnet distribution in EMOiFor
Compared with prior art, the beneficial effects of the present invention are:
New pricing mechanism is introduced based on seller's level, on the basis of more agency communication systems, introduces both parties agency
Mechanism, buyer MG act on behalf of the strategy for determining its energy demand using noncooperative mode according to its energy surplus, seller MG agency
Determine that shared weight, the interests of itself are protected according to self-energy total surplus amount, energy scheduling center EMO uses the energy
Allocation algorithm verifies the strategy of buyer MG submission, and seller MG formulated new energy pricing algorithm, reduced mass communication and open
The burden of pin, while this method improves the fairness and stability that energy is dispatched between each subnet in microgrid group.
Detailed description of the invention
Fig. 1 is multi-agent system model.
Fig. 2 is buyer's surplus energy algorithm flow chart of the invention.
Fig. 3 is energy allocation algorithm flow chart of the invention.
Specific embodiment
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
The method that energy game is dispatched between each subnet in a kind of micro-capacitance sensor group of the invention, gives this hair as shown in Figure 1
Bright middle microgrid group multi-agent system topological diagram includes each subnet and upper layer agency plant.
Specific operation process includes the following steps:
Step 1, multi-agent system model is established
The model bottom is the subnet agency plant of each distributed generation resource composition, and each subnet is in the case where there is energy requirement
It sends instructions to buyer's polymerizer and acts on behalf of BAA, the price which proposes buyer MG summarize being sent to energy pipe
Reason center EMO, EMO issue an order after being calculated by priority factors of itself algorithm to buyer MG to be acted on behalf of to seller's polymerizer
SAA, SAA main function are to transmit to instruct and collect information to seller MG, and last BAA completes the distribution task of energy.
Step 2, the new pricing strategy of the one kind proposed for buyer MG.
It is assumed that there are N number of subnets in microgrid group's system, subnet i ∈ N is defined, each subnet includes wind in the set
The equipment such as electricity, photovoltaic, the energy that subnet i ∈ N is generated in specific time are Gi, each subnet all needs to meet basic
Load minimum charge Lmin.Work as Gi< Lmin, subnet Q={ i ∈ N Shu G is defined at this timei< Lmin, then Q is buyer's subnet set；Work as Gi
> Lmin, subnet P={ i ∈ N Shu G is defined at this timei> Lmin, then P is seller's subnet set.For buyer MGi, energy requirement
Are as follows:
Seller MGi is on the basis of meeting the basic energy requirement of itself, salable energy are as follows:
We assume that can manage its energy consumption for each seller MG and make Li >=Limin, so as to
Energy is sold to buyer MG or power grid under this paper mechanism of exchange.
Therefore after improving to formula (2), the dump energy of seller MG after load adjustment is carried out are as follows:
Remaining gross energy after all seller MG Load adjustment consumption are as follows:
Here, we are by GbpAnd GspIt is respectively seen as power grid buying price and power grid selling price.We assume that BAA is paid
To each seller MG unit source cost in GbpAnd GspBetween.Therefore, each seller MG be more likely to sell the extra energy to
Other subnets, rather than trade with main power grid.For each buyer MG, we will be according to its contribution to the history of and local load need
It asks and calculates its priority factor.γ i indicates the top-priority factor of i-th of buyer MG, and weight coefficient μ embodies each factor
Importance.
As shown in Figure 1, BAA collects the total energy demand E of all buyer MGreq, determine to buy required list from seller MG
Position energy cost ρ, i.e. Gbp≤ρ≤Gsp.For the price that BAA is provided, seller MG adjusts its optimal energy consumption Li, and makes remaining
Energy E* ex,iCan be for sale, and notify EMO and BAA, BAA that this information is passed to all buyer MG by SAA.
Each buyer of MG is the participant of rationality, there is different energy requirements.In addition, each buyer MG to the greatest extent may be used
More energy can be obtained by EMO.Therefore, the utility function of all buyer MG is as follows:
Wherein di is the demand strategy of i-th of buyer MG.Competition between buyer MG is substantially the continuous plan of non-cooperation
Slightly form can use G=(di, ui) Qi=1, and wherein Q is buyer MG sum, and ui is effectiveness formula.
Ui∈P=ziln(1+Li)+ρ(Gi-Li), zi> 0 (6)
In formula (6), logarithm is used to measure the satisfaction to decreasing returns, and preceding part indicates that seller MG is consumed from self-energy
The effectiveness obtained in Li, zi are expressed as preference parameter, it indicates the preference consumed to self-energy.Rear portion point, which gives, sells
Square MG with the extra energy of adjacent buyer MG transaction by can be obtained taking in；
The selection of ρ value plays a key effect for seller's decision, if ρ value is too small, i.e. ρ≤Gbp, extra at this time energy
It will be used by seller MG oneself rather than trade with other subnets in microgrid group；If ρ value is excessive, ρ >=Gsp, the seller at this time
The energy cost that MG is provided is insufficient for the demand of buyer MG, and BAA will be with GspPrice buy remaining energy from main power grid；
Polymerizer of the BAA as buyer MG, objective function are as follows:
Now, the First Order Optimality Condition of BAA objective function in (5) is utilized, we obtain:
Solving (8) can obtain:
A is constant in formula (9), in the non-cooperative game between the buyer MG proposed, only exists a unique NE.
Algorithm 1 gives the solution d of NEi, the as optimal strategy of each buyer MG；
Step 3, power exchange is carried out
All buyer's energy requirements are collected by EMO and pay the utmost attention to factor γ i, according to sale EextraRequired
The total surplus energy and individual pay the utmost attention to factor, and each buyer MG is using superfluous energy algorithm such as Fig. 2, using between buyer MG
Non-cooperative game determines that it is bought strategy di, BAA and these information are passed to EMO, and EMO is receiving all buyer MG about energy
It, as shown in figure 3, will using Energy distribution algorithm according to the superfluous energy, demand and the priority factors of buyer MG after the strategy of demand
An appropriate number of energy EAi distributes to single buyer MG,
The major function of EMO is that the extra reasonable energy for providing seller MG distributes to buyer MG.The target of EMO is microgrid
The maximization of group's overall efficiency SW, i.e., the summation of the satisfaction of all buyers.From the perspective of EMO, Ui(EAi) it is buyer MG
Satisfaction, to buyer MG distribution energy optimization problem it is as follows:
EA is distributed by optimal energy*={ EA*ii ∈ Q } provides following formula:
H is the real number for meeting EA*i=E*extra.
Power exchange mechanism shown in FIG. 1 ensure that simple, fair, stable power exchange between MG cluster.Due to energy
Amount demand is the strategy of buyer MG, diBe not always the actual energy demand equal to buyer MG, i.e., each buyer MG [0,
Ei,req] on determine its strategy di, to maximize its energy distribution EAi。
Referring to fig. 2, excess energy algorithm described in step 2, the specific steps are that:
1) all-ones subnet energy is collected for demand, determines buyer and seller subnet quantity and its priority factors γi；
2) by buyer MG according to EReq, iThe arrangement of/γ i μ value ascending order, is arranged initial value j=1, N=| Q |；Dump energy Erm=
E*extra；Weight coefficient η=0；
3) gamma width value w and EReq, i/ γ i μ-η product and dump energy ErmCompare, if w (EReq, i/ γ i μ-η) < Erm, into
Row iteration calculates；
4) as w (EReq, i/ γ i μ-η) > Erm, determine that buyer MG buys strategy di=η γ ik.
Referring to Fig. 3, energy allocation algorithm described in step 3, the specific steps are that:
1) the purchase strategy d of each buyer MG is collected on the basis of superfluous energy algorithmi；
2) all buyer MG are arranged according to di/ γ μ i value ascending order, initial value j=1, k=2,1 μ of w=γ is set, it is remaining
ENERGY Erm=E*extra, weight coefficient h=d1/γ1μ；
3) gamma width value w and 2di/ γ μ j- η product and dump energy ErmCompare, if w (EReq, i/ γ i μ-η) < Erm, into
Row iteration calculates, and pays attention to distinguishing 2d at this timei/ γ μ j- η and dkThe size of/γ μ k- η carries out parallel iteration；
4) as w (EReq, i/γiμ-η)≥Erm, at this time to the ENERGY E A of each subnet distribution in EMOiFor
Claims (6)
1. the micro-capacitance sensor group energy amount game dispatching method based on multi-agent system, which comprises the following steps:
Step 1, the more proxy topologies of microgrid group are established, complete that the modules agencyization of each subnet is set up on subnet upper layer and bought
Proxy aggregates device SAA and BAA are sold, finally completes negotiation communication process by central processing unit EMO；
Step 2, new microgrid energy pricing mechanism is used based on buyer MG, proposes that priority factors dominate process of exchange
Effect, is found out using excess energy algorithm based on best trading strategies in buyer's MG process of exchange；
Step 3, introduce new scheduling mechanism on the basis of game theory, central processing unit EMO receive all buyer MG about
After the strategy of energy requirement, will suitably it be counted according to the superfluous energy demand of buyer MG and priority factors using Energy distribution algorithm
The energy EA of amountiDistribute to single buyer MG.
2. the micro-capacitance sensor group energy amount game dispatching method according to claim 1 based on multi-agent system, which is characterized in that
The step 1 carries out agencyization to each module in each subnet using JADE platform；
The energy can not be self-supporting buyer MG be registered as buyer in dealing Proxy aggregates device BAA, energy demand, power grid based on buyer
Purchase and power grid selling price buy and sell the optimal bid amounts that Proxy aggregates device BAA determines energy purchase from seller MG, according to receipts
The quotation of the dealing Proxy aggregates device BAA arrived, seller MG, i.e. energy have remaining subnet, have selected suitable plan according to quotation
Slightly, such as adjust oneself energy consumption, make can energy for sale greatly increase；Then, dealing Proxy aggregates device SAA will be received
Collect these available energy for selling, and dealing Proxy aggregates device BAA, dealing Proxy aggregates device BAA is notified to turn this information
Issue all buyer MG.
3. the micro-capacitance sensor group energy amount game dispatching method according to claim 1 based on multi-agent system, which is characterized in that
Priority factors described in step 2, calculation method are as follows:
C in formulaiIndicate the buyer MG in the contribution so far in power grid transaction, the i.e. energy that the passing time sells；
CTotalIt is then the period all buyer MG contribution sums；DiFor the workload demand of the buyer MG, DTotalIt is always born for all buyer MG
Lotus demand；Formula middle front part point measures buyer's MG past by the way that extra energy is sold to what other sub- buyer MG in group were done
Contribution, the workload demand of the quantization each buyer MG in rear portion, since past contribution and local workload demand are of equal importance, because
This encourages to carry out power exchange between buyer MG, rather than trades with main power grid, is come using a weight factor μ (μ > 0)
Indicate the importance that priority factors are traded in the energy, it is substantially dynamically, to generate in central processing unit, for simplification
It calculates, the μ value 1.5 of setting.
4. the micro-capacitance sensor group energy amount game dispatching method according to claim 1 based on multi-agent system, which is characterized in that
Excess energy algorithm described in step 2, the specific steps are that:
1) all-ones subnet energy is collected for demand, determines buyer and seller subnet quantity and its priority factors γi；
2) by buyer MG according to EReq, iThe arrangement of/γ i μ value ascending order, is arranged initial value j=1, N=| Q |；Dump energy Erm=E*
extra；Weight coefficient η=0；
3) gamma width value w and EReq, i/ γ i μ-η product and dump energy ErmCompare, if w (EReq, i/ γ i μ-η) < Erm, change
In generation, calculates；
4) as w (EReq, i/ γ i μ-η) > Erm, determine that buyer MG buys strategy di=η γ ik.
5. the micro-capacitance sensor group energy amount game dispatching method according to claim 1 based on multi-agent system, which is characterized in that
The step 3, power exchange process is: for buyer MG, being collected all buyer's energy requirements and preferential by EMO
Consideration γi, according to sale EextraThe required total surplus energy and individual pay the utmost attention to factor, and each buyer MG uses superfluous
Energy algorithm determines that it buys strategy d using the non-cooperative game between buyer MGi, these information pass to EMO by BAA,
EMO is after receiving all buyer MG about the strategy of energy requirement, according to the superfluous energy, demand and the priority factors of buyer MG,
Using Energy distribution algorithm, the energy that seller MG is provided, with right quantity (EAi) distribute to each buyer MG.
6. the micro-capacitance sensor group energy amount game dispatching method according to claim 1 based on multi-agent system, which is characterized in that
Energy allocation algorithm described in step 3, the specific steps are that:
1) the purchase strategy d of each buyer MG is collected on the basis of superfluous energy algorithmi；
2) all buyer MG are arranged according to di/ γ μ i value ascending order, initial value j=1, k=2,1 μ of w=γ, dump energy is set
Erm=E*extra, weight coefficient h=d1/γ1μ；
3) gamma width value w and 2di/ γ μ j- η product and dump energy ErmCompare, if w (EReq, i/ γ i μ-η) < Erm, it is iterated
It calculates, pays attention to distinguishing 2d at this timei/ γ μ j- η and dkThe size of/γ μ k- η carries out parallel iteration；
4) as w (EReq, i/γiμ-η)≥Erm, at this time to the ENERGY E A of each subnet distribution in EMOiFor
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910386865.9A CN110414764A (en) | 2019-05-10 | 2019-05-10 | Micro-capacitance sensor group energy amount game dispatching method based on multi-agent system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910386865.9A CN110414764A (en) | 2019-05-10 | 2019-05-10 | Micro-capacitance sensor group energy amount game dispatching method based on multi-agent system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110414764A true CN110414764A (en) | 2019-11-05 |
Family
ID=68358181
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910386865.9A Pending CN110414764A (en) | 2019-05-10 | 2019-05-10 | Micro-capacitance sensor group energy amount game dispatching method based on multi-agent system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110414764A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626569A (en) * | 2020-05-06 | 2020-09-04 | 云南电网有限责任公司怒江供电局 | Micro-grid group electric power energy trading method |
| CN112508241A (en) * | 2020-11-23 | 2021-03-16 | 洪东涛 | Energy optimization scheduling method for smart power grid |
| CN114529373A (en) * | 2022-04-22 | 2022-05-24 | 西华大学 | Priority matching-based dynamic microgrid group P2P transaction method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0617454D0 (en) * | 2006-09-06 | 2006-10-18 | Guaranteed Markets Ltd | Apparatus & method for prioritising sellers in electronic markets |
| US20100318435A1 (en) * | 2009-06-10 | 2010-12-16 | Ramanathan Ramanathan | System of facilitating a sales transaction between a buying group and multiple sellers over and electronic network |
| US20120029992A1 (en) * | 2010-07-27 | 2012-02-02 | Pasquale De Facendis | System and method for maximizing value through collaboration and pooling of buyers, sellers and parties with common interests |
| CN106815687A (en) * | 2017-01-22 | 2017-06-09 | 电子科技大学中山学院 | Micro-grid optimal and elastic energy trading method and system based on double-layer game |
| CN107292515A (en) * | 2017-06-21 | 2017-10-24 | 重庆大学 | A kind of power network multi-agent system dispatching method based on Demand-side scheduling capacity reporting policy game |
| CN108738118A (en) * | 2018-05-22 | 2018-11-02 | 广东石油化工学院 | Power exchange method in wireless chargeable sensor network |
| CN109255526A (en) * | 2018-08-21 | 2019-01-22 | 电子科技大学 | It is a kind of meter and game risk the end-to-end method of commerce of micro-capacitance sensor user |
-
2019
- 2019-05-10 CN CN201910386865.9A patent/CN110414764A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0617454D0 (en) * | 2006-09-06 | 2006-10-18 | Guaranteed Markets Ltd | Apparatus & method for prioritising sellers in electronic markets |
| US20100318435A1 (en) * | 2009-06-10 | 2010-12-16 | Ramanathan Ramanathan | System of facilitating a sales transaction between a buying group and multiple sellers over and electronic network |
| US20120029992A1 (en) * | 2010-07-27 | 2012-02-02 | Pasquale De Facendis | System and method for maximizing value through collaboration and pooling of buyers, sellers and parties with common interests |
| CN106815687A (en) * | 2017-01-22 | 2017-06-09 | 电子科技大学中山学院 | Micro-grid optimal and elastic energy trading method and system based on double-layer game |
| CN107292515A (en) * | 2017-06-21 | 2017-10-24 | 重庆大学 | A kind of power network multi-agent system dispatching method based on Demand-side scheduling capacity reporting policy game |
| CN108738118A (en) * | 2018-05-22 | 2018-11-02 | 广东石油化工学院 | Power exchange method in wireless chargeable sensor network |
| CN109255526A (en) * | 2018-08-21 | 2019-01-22 | 电子科技大学 | It is a kind of meter and game risk the end-to-end method of commerce of micro-capacitance sensor user |
Non-Patent Citations (1)
| Title |
|---|
| 李鑫滨等: "基于Stackelberg博弈的双层水下传感器网络功率分配算法", 《计算机应用》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626569A (en) * | 2020-05-06 | 2020-09-04 | 云南电网有限责任公司怒江供电局 | Micro-grid group electric power energy trading method |
| CN111626569B (en) * | 2020-05-06 | 2023-06-13 | 云南电网有限责任公司怒江供电局 | Micro-grid group electric power energy trading method |
| CN112508241A (en) * | 2020-11-23 | 2021-03-16 | 洪东涛 | Energy optimization scheduling method for smart power grid |
| CN114529373A (en) * | 2022-04-22 | 2022-05-24 | 西华大学 | Priority matching-based dynamic microgrid group P2P transaction method |
| CN114529373B (en) * | 2022-04-22 | 2022-07-01 | 西华大学 | Priority matching-based dynamic microgrid group P2P transaction method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mengelkamp et al. | Trading on local energy markets: A comparison of market designs and bidding strategies | |
| CN106845807B (en) | Settlement method and device based on peak regulation auxiliary service | |
| Bahramirad et al. | Distribution markets | |
| CN112488744A (en) | Transaction-driven virtual power plant customization construction method | |
| CN110414764A (en) | Micro-capacitance sensor group energy amount game dispatching method based on multi-agent system | |
| Zhou et al. | Distribution network congestion management considering time sequence of peer-to-peer energy trading | |
| Hussain et al. | Flexibility: Literature review on concepts, modeling, and provision method in smart grid | |
| CN109087153A (en) | Peak-shaving auxiliary service transaction clearing method and system based on bilateral market | |
| CN113052631A (en) | Competitive electricity selling market multi-producer and consumer P2P day-ahead transaction mechanism | |
| CN107492886A (en) | A kind of power network monthly electricity purchasing scheme optimization method containing wind-powered electricity generation under Regional Electric Market | |
| CN111311012A (en) | Multi-agent-based micro-grid power market double-layer bidding optimization method | |
| CN117217841B (en) | Multi-element market clearing system optimization method considering generalized energy constraint of virtual power plant | |
| Johnathon et al. | A proposed hedge-based energy market model to manage renewable intermittency | |
| Yang et al. | Coordination and optimization of CCHP microgrid group game based on the interaction of electric and thermal energy considering conditional value at risk | |
| Jin et al. | An overview of virtual power plant development from the perspective of market participation | |
| CN115526550A (en) | Spot market organization method suitable for new energy power and conventional thermal power to compete on same station | |
| Yan et al. | Optimal scheduling strategy and benefit allocation of multiple virtual power plants based on general nash bargaining theory | |
| CN107611968B (en) | The active distribution network economical operation method of distributed energy interaction consumption | |
| Zhang et al. | Shared energy storage-assisted and tolerance-based alliance strategy for wind power generators based on cooperative game and resource dependence theories | |
| CN110556821B (en) | Multi-microgrid double-layer optimization scheduling method considering interactive power control and bilateral bidding transaction | |
| CN113162066A (en) | Game behavior analysis method considering participation of electrolytic aluminum industrial users in frequency modulation market | |
| CN117114877A (en) | Medium-and-long-term power transaction method and system based on virtual power plant | |
| CN115375481A (en) | Block chain technology-based hierarchical power transaction optimization method | |
| CN112215612B (en) | Photovoltaic absorption optimization method and system based on block chain | |
| CN113870030A (en) | Multi-microgrid energy transaction mechanism design method based on improved Nash bargaining method |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20191105 |
|
| WD01 | Invention patent application deemed withdrawn after publication |