CN107623337A - A kind of energy management method for micro-grid - Google Patents
A kind of energy management method for micro-grid Download PDFInfo
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Abstract
The present invention relates to intelligent power grid technology, and in particular to a kind of energy management method for micro-grid, micro-capacitance sensor include multiple distributed generation units, and each distributed generation unit has an intelligent body；The communication network of connection is designed between each distributed generation unit, intelligent body；This method comprises the following steps：Establish the optimized mathematical model of micro-capacitance sensor distributed energy management solutions；Measure and obtain distributed generation unit and access local local message at the grid entry point of micro-capacitance sensor, and carry out initialization process；The information for realizing each distributed generation unit intelligent body by communication network exchanges, and the reference value of distributed generation unit active power output is calculated, the active power output of each distributed generation unit is adjusted according to reference value；The judgement of end condition.This method considers various constraintss in micro-capacitance sensor, provides the global active demand information of load without command node, fully distributed microgrid energy management can be achieved, result of calculation is highly reliable and precision is high.
Description
Technical field
The invention belongs to intelligent power grid technology field, more particularly to a kind of energy management method for micro-grid.
Background technology
With increasingly increase of the social development to energy demand and problem of environmental pollution getting worse, distributed power generation and micro-
Electric power network technique is got growing concern for.Realize microgrid energy management and coordinate control to making full use of distributed power source,
Micro-capacitance sensor performance driving economy is improved, voltage and Frequency Index when improving micro-capacitance sensor islet operation, ensures power supply reliability and electric energy
Quality etc. is respectively provided with important theoretical and practical values.
Multi-agent system feature in system the state of each intelligent body it is different and limited in one's ability, when many individual intelligence
When energy body is linked together by communication network and communication protocol, coordination is carried out by information exchange between each other and completes list
Individual intelligent body impossible mission.Multiple agent uniformity reason body discusses the basis as coordination, is widely used in honeybee
In the problems such as gathering around control, formation control and aggregation.Current how intelligent congruity theory has obtained in terms of the economic load dispatching of power network
Correlative study, but the factor that most of algorithm considers is not comprehensive enough, does not consider power-balance, the constraint that generates electricity, communication systematically
Constraint etc..The problem of not considering distributed power source units limits and needing command node also be present.So that the practicality of various algorithms
Property is greatly limited.
The content of the invention
Consider each distributed generation unit units limits it is an object of the invention to provide one kind, micro-capacitance sensor internal power is put down
Weighing apparatus, communication constraint etc., the global active demand information of load is provided without command node, realizes fully distributed microgrid energy
The method of management.
Therefore, a kind of energy management method for micro-grid according to embodiments of the present invention, micro-capacitance sensor include multiple distributions
Generator unit, each distributed generation unit have an intelligent body；Connection is designed between each distributed generation unit, intelligent body
Communication network；This method comprises the following steps：
Step 1, the optimized mathematical model for establishing micro-capacitance sensor distributed energy management solutions；
Local local message at step 2, the grid entry point for measuring and obtaining distributed generation unit access micro-capacitance sensor, and carry out
Initialization process；
Step 3, realize that by communication network the information of each distributed generation unit intelligent body exchanges, distribution is calculated
The reference value of generator unit active power output, the active power output of each distributed generation unit is adjusted according to reference value；
The judgement of step 4, end condition.
The optimization of micro-capacitance sensor distributed energy management solutions is established in above-mentioned energy management method for micro-grid, described in step 1
Mathematical modeling specifically includes following steps：
Generator unit cost of electricity-generating minimum is used as object function to step 1.1 in a distributed manner；
The operating cost function of distributed generation unit is in micro-capacitance sensor：
(1) in formula, PG,iIt is distributed generation unit DG_i active power output reference value；CG,i(PG,i) it is distributed power generation
Cells D G_i operating cost function, ai、biAnd ciIt is distributed generation unit DG_i cost coefficient respectively；
Minimize distributed generation unit DG_i cost of electricity-generating CG,i(PG,i) so that overall cost of electricity-generating is minimum；
Active power balance is as equality constraint in step 1.2 micro-capacitance sensor；
(3) in formula, PDFor the total active power demand of load in micro-capacitance sensor；
Generator unit active power output is limited in a distributed manner is used as inequality constraints condition for step 1.3；
PG,imin≤PG,i≤PG,imax (4)
(4) in formula, PG,iminAnd PG,imaxIt is divided into distributed generation unit DG_i active power outputs PG,iLower and upper limit.
In above-mentioned energy management method for micro-grid, comprising the following steps that for step 2 is realized：
Initial time of the intelligent body of step 2.1 distributed generation unit in each active power output reference value counting period,
The initial value of distributed generation unit active power output reference value is calculated by local measurement；
(5) P in formulaD,iThe load active power of micro-capacitance sensor node, P where distributed generation unit DG_i grid entry pointsG,i
[0] it is the initial value of cloth generator unit DG_i active power outputs；
The initial value and active power output of step 2.2 calculating distributed generation unit cost tiny increment are to workload demand difference
Predict that initial value is：
(6) r in formulaG,i[0] be distributed generation unit DG_i cost tiny increments initial value, fG,i[0] it is active power output
Prediction initial value to workload demand difference.
In above-mentioned energy management method for micro-grid, comprising the following steps that for step 3 is realized：
Step 3.1 is by iterating to calculate the reference value of distributed generation unit active power output in each time interval；In t
In the individual iterative calculation cycle, intellectual Agent _ i obtains its imported field set by communication networkInterior each intelligence
Body Agent_j cost tiny increment rG,jThe predicted value f of [t] and active power output to workload demand differenceG,j[t], (j=1,2 ...,For setMiddle individual sum)；And by the cost tiny increment r of itselfG,i[t] and active power output are to workload demand difference
Predicted value fG,i[t], which is sent to its output type field, to be gatheredInterior each intellectual Agent _ k, (k=1,2 ...,
For setMiddle individual sum)；
Step 3.2 solves and obtains distributed generation unit active power output reference value, distributed power generation in each time interval
The predicted value of unit cost tiny increment and active power output to workload demand difference；
Intellectual Agent _ i solves the micro- increasing of cost of the distributed generation unit DG_i after information exchanges by the following method
The predicted value of rate, active power output reference value and active power output to workload demand difference：
(7) in formula, wijTry to achieve by the following method：
(8) in formula, ξi∈ [0,1] is intellectual Agent _ i consistent item weight；
(7) in formula, ε is feedback factor；
(7) in formula, vikTry to achieve by the following method：
(9) in formula, ξi' ∈ [0,1] be intellectual Agent _ i feedback term weight；
(7) in formula, ΦG,i(rG,i[t+1]) it is projection function, expression formula is：
(10) in formula, rG,imin=aiPG,imin+bi, rG,imax=aiPG,imax+bi。
In above-mentioned energy management method for micro-grid, ξiOptimization value be 0.5；ε optimization value is 1.5 × 10-3；, ξi' optimization value be 0.5.
In above-mentioned energy management method for micro-grid, comprising the following steps that for step 4 is realized：
Step 4.1 is when the total active power output of each distributed generation unit and load aggregate demand difference and workload demand in micro-capacitance sensor
Ratios delta P stop iteration when being less than given threshold λ, each intelligent body sends the active power output reference value being calculated to corresponding
Distributed generation unit, distributed generation unit exports corresponding active power；
Otherwise return to step 3 continues iteration to step 4.2.
In above-mentioned energy management method for micro-grid, ratioGiven threshold λ values
For 10-6。
It should be noted that it is considered herein that the active power output and its active power output reference value of distributed generation unit reality
It is identical, therefore following illustrated with active power output.
Beneficial effects of the present invention：Consider various constraintss in micro-capacitance sensor, provided without command node global negative
The active demand information of lotus, fully distributed microgrid energy management can be achieved, fully comprehensively utilize existing various based on consistent
Property theoretical energy management method the advantages of, result of calculation is highly reliable and precision is high.For making full use of distributed power generation list
The value of member, the economy of micro-capacitance sensor operation is improved, voltage and Frequency Index when improving micro-capacitance sensor islet operation, ensure that power supply can
Important technical economic benefit and practical value are respectively provided with by property and quality of power supply etc..
Brief description of the drawings
Fig. 1 is the micro-grid system structural representation of one embodiment of the invention；
Fig. 2 is the distributed energy management solutions method flow diagram of each intelligent body of one embodiment of the invention.
Embodiment
Embodiments of the invention are described below in detail, the example of the embodiment is shown in the drawings, wherein from beginning to end
Same or similar label represents same or similar element or the element with same or like function.Retouched by reference to accompanying drawing
The embodiment stated is exemplary, it is intended to for explaining the present invention, and is not considered as limiting the invention.
To make those skilled in the art more fully understand the present invention, related basic briefing is as follows：
The present embodiment realizes that a kind of energy management method for micro-grid, micro-capacitance sensor includes more using following technical scheme
Individual distributed generation unit, each distributed generation unit have an intelligent body；Set between each distributed generation unit, intelligent body
In respect of the communication network of connection；This method comprises the following steps：
Step 1, the optimized mathematical model for establishing micro-capacitance sensor distributed energy management solutions；
Local local message at step 2, the grid entry point for measuring and obtaining distributed generation unit access micro-capacitance sensor, and carry out
Initialization process；
Step 3, realize that by communication network the information of each distributed generation unit intelligent body exchanges, distribution is calculated
The reference value of generator unit active power output, the active power output of each distributed generation unit is adjusted according to reference value；
The judgement of step 4, end condition.
Further, the optimized mathematical model that micro-capacitance sensor distributed energy management solutions are established described in step 1 specifically includes following step
Suddenly：
Generator unit cost of electricity-generating minimum is used as object function to step 1.1 in a distributed manner；
The operating cost function of distributed generation unit is in micro-capacitance sensor：
(1) in formula, PG,iIt is distributed generation unit DG_i active power output reference value；CG,i(PG,i) it is distributed power generation
Cells D G_i operating cost function, ai、biAnd ciIt is distributed generation unit DG_i cost coefficient respectively；
Minimize distributed generation unit DG_i cost of electricity-generating CG,i(PG,i) so that overall cost of electricity-generating is minimum；
Active power balance is as equality constraint in step 1.2 micro-capacitance sensor；
(3) in formula, PDFor the total active power demand of load in micro-capacitance sensor；
Generator unit active power output is limited in a distributed manner is used as inequality constraints condition for step 1.3；
PG,imin≤PG,i≤PG,imax (4)
(4) in formula, PG,iminAnd PG,imaxIt is divided into distributed generation unit DG_i active power outputs PG,iLower and upper limit.
Further, comprising the following steps that for step 2 is realized：
Initial time of the intelligent body of step 2.1 distributed generation unit in each active power output reference value counting period,
The initial value of distributed generation unit active power output reference value is calculated by local measurement；
(5) P in formulaD,iThe load active power of micro-capacitance sensor node, P where distributed generation unit DG_i grid entry pointsG,i
[0] it is the initial value of cloth generator unit DG_i active power outputs；
The initial value and active power output of step 2.2 calculating distributed generation unit cost tiny increment are to workload demand difference
Predict that initial value is：
(6) r in formulaG,i[0] be distributed generation unit DG_i cost tiny increments initial value, fG,i[0] it is active power output
Prediction initial value to workload demand difference.
Further, comprising the following steps that for step 3 is realized：
Step 3.1 is by iterating to calculate the reference value of distributed generation unit active power output in each time interval；In t
In the individual iterative calculation cycle, intellectual Agent _ i obtains its imported field set by communication networkInterior each intelligence
Body Agent_j cost tiny increment rG,jThe predicted value f of [t] and active power output to workload demand differenceG,j[t], (j=1,2 ...,For setMiddle individual sum)；And by the cost tiny increment r of itselfG,i[t] and active power output are poor to workload demand
The predicted value f of volumeG,i[t], which is sent to its output type field, to be gatheredInterior each intellectual Agent _ k, (k=1,2 ...,For setMiddle individual sum)；
Step 3.2 solves and obtains distributed generation unit active power output reference value, distributed power generation in each time interval
The predicted value of unit cost tiny increment and active power output to workload demand difference；
Intellectual Agent _ i solves the micro- increasing of cost of the distributed generation unit DG_i after information exchanges by the following method
The predicted value of rate, active power output reference value and active power output to workload demand difference：
(7) in formula, wijTry to achieve by the following method：
(8) in formula, ξi∈ [0,1] is intellectual Agent _ i consistent item weight；
(7) in formula, ε is feedback factor；
(7) in formula, vikTry to achieve by the following method：
(9) in formula, ξi' ∈ [0,1] be intellectual Agent _ i feedback term weight；
(7) in formula, ΦG,i(rG,i[t+1]) it is projection function, expression formula is：
(10) in formula, rG,imin=aiPG,imin+bi, rG,imax=aiPG,imax+bi。
Further, ξiOptimization value be 0.5；ε optimization value is 1.5 × 10-3；, ξi' optimization value be 0.5.
Further, realize that step 4 comprises the following steps that：
Step 4.1 is when the total active power output of each distributed generation unit and load aggregate demand difference and workload demand in micro-capacitance sensor
Ratios delta P stop iteration when being less than given threshold λ, each intelligent body sends the active power output reference value being calculated to corresponding
Distributed generation unit, distributed generation unit exports corresponding active power；
Otherwise return to step 3 continues iteration to step 4.2.
Further, ratioGiven threshold λ values are 10-6。
When it is implemented, a kind of energy management method for micro-grid, generator unit cost of electricity-generating is minimum in a distributed manner first makees
For object function, as equality constraint, generator unit active power output is limited in a distributed manner makees active power balance using in micro-capacitance sensor
For inequality constraints, microgrid energy management optimization mathematical modeling is established；The intelligent body of distributed generation unit is each active
The initial time in output reference value counting period, distributed generation unit active power output reference value is calculated by local measurement
Initial value, the prediction of the initial value and active power output of distributed generation unit cost tiny increment to workload demand difference is initial
Value；By iterating to calculate the reference value of distributed generation unit active power output in each time interval, intelligent body passes through communication network
Network, obtain the prediction of the cost tiny increment and active power output of each intelligent body in its imported field set to workload demand difference
Value, and the cost tiny increment of itself and active power output are sent to the predicted value of workload demand difference to its output type field and gathered
Interior each intelligent body, solution obtain distributed generation unit active power output reference value in each time interval, distributed power generation list
The predicted value of first cost tiny increment and active power output to workload demand difference；Have when each distributed generation unit is always defeated in micro-capacitance sensor
Stop iteration when work(is contributed and the ratio of workload demand difference and workload demand is less than given threshold, each intelligent body will be calculated
Active power output reference value send to corresponding distributed generation unit, distributed generation unit exports corresponding active power,
Cost of electricity-generating minimum energy management target is realized while total capacity requirement is met.
As shown in figure 1, multiple distributed generation units are contained in micro-capacitance sensor, such as diesel engine, gas turbine etc. are conventional
Generator unit, also including wind power generating set, solar energy power generating etc. the generator unit based on regenerative resource, it is unified with
DG_1, DG_2 ..., DG_n is represented；Each distributed generation unit has an intelligent body, i.e. Agent_1, Agent_2 ...,
Agent_n；Intellectual Agent _ i (i=1,2 ..., n) measure and obtain distributed generation unit DG_i access micro-capacitance sensors and
Local local message at site, such as distributed generation unit DG_i active power output and the active demand of the load of the access node,
And enter row information with other intelligent bodies and exchange to update the local information of oneself, it is active that distributed generation unit DG_i is calculated
The reference value of output, distributed generation unit DG_i produce actual active power output according to the reference value, realize that micro-capacitance sensor is run
The target of cost minimization.The design of communication network can be independently of the network structure of micro-capacitance sensor in itself, it is only necessary to ensures the communication network
Topological diagram corresponding to network is connected graph.
The operating cost function of distributed generation unit is in micro-capacitance sensor：
1. in formula, PG,iIt is distributed generation unit DG_i active power output reference value.CG,i(PG,i) it is distributed power generation list
First DG_i operating cost function, ai、biAnd ciIt is distributed generation unit DG_i cost coefficient respectively, cost coefficient is asked
Solution is current ripe technology.
With the target of the minimum energy management of distributed generation unit total operating cost in micro-capacitance sensor, i.e.,：
Active power balance constraint condition is in micro-capacitance sensor：
3. in formula, PDFor the total active power demand of load in micro-capacitance sensor.
Distributed generation unit DG_i active power output constraintss are：
PG,imin≤PG,i≤PG,imax ④
4. in formula, PG,iminAnd PG,imaxIt is divided into distributed generation unit DG_i active power outputs PG,iLower and upper limit.
In order to realize complete distributed energy management solutions, intellectual Agent _ i will be by calculating distributed generation unit DG_i
Active power output PG,i, minimum distributed generation unit DG_i cost of electricity-generating CG,i(PG,i) so that overall cost of electricity-generating is 2. most
It is small, and meet the requirement of constraints 3. and 4..
As shown in Fig. 2 intellectual Agent _ i calculates distributed generation unit DG_i active power outputs PG,iIt is with certain time
What interval was carried out, normally the time interval optimal value is 5min under normal circumstances；Carved at the beginning of each time interval, intelligence
Can body Agent_i will carry out calculating active power output P in the time interval in accordance with the following stepsG,i.Intellectual Agent _ i leads to
Multiple iteration cycles are often needed to complete PG,iCalculating, it is each to iterate to calculate the cycle and be typically smaller than 10ms, much smaller than PG,i's
Update cycle.
1) measure and initialize
In a new PG,iTime interval initial time (t=0) is calculated, intellectual Agent _ i will complete following initialization
Information calculates.Measurement obtains the load active-power P of distributed generation unit DG_i grid entry points place micro-capacitance sensor node firstD,i,
The initial value P of distributed generation unit DG_i active power outputsG,i[0] value is：
The initial value r of distributed generation unit DG_i cost tiny incrementsG,i[0] and active power output is to workload demand difference
Predict initial value fG,i[0] it is respectively：
2) communication and information exchange
Iterated to calculate at t-th in the cycle, intellectual Agent _ i obtains its imported field set by communication networkInterior each intellectual Agent _ j rG,j[t] and fG,j[t] (j=1,2 ...,For setMiddle individual sum),
And by the r of itselfG,i[t] and fG,i[t], which is sent to its output type field, to be gatheredInterior each intellectual Agent _ k (k=1,
2,…,For setMiddle individual sum).
3) optimal cost tiny increment solves
Intellectual Agent _ i solves new distributed generation unit DG_i cost tiny increment by the following method, active
The predicted value contributed with active power output to workload demand difference：
7. in formula, wijTry to achieve by the following method：
8. in formula, ξi∈ [0,1] is intellectual Agent _ i consistent item weight, can be according to distributed generation unit DG_i
Concrete condition setting, higher value can be set when distributed generation unit DG_i capacity is larger, cost of electricity-generating is relatively low, optimization takes
It is worth for 0.5.
7. ε is feedback factor in formula, its value is the positive number of a very little, such as optimization value is 1.5 × 10-3；
7. in formula, vikTry to achieve by the following method：
9. in formula, ξi' ∈ [0,1] be intellectual Agent _ i feedback term weight, the coefficient mainly influence iteration convergence speed
Degree, larger value iterative convergence speed faster, but can influence stability, and optimization value is 0.5.
7. in formula, ΦG,i(rG,i[t+1]) it is projection function, expression formula is：
10. in formula, rG,imin=aiPG,imin+bi, rG,imax=aiPG,imax+bi。
4) iteration terminates to judge
When the ratio of the total active power output of each distributed generation unit and load aggregate demand difference and load aggregate demand in micro-capacitance sensor
ValueLess than given threshold λ, (λ is the positive number of a very little, such as value is 10-6) when, terminate iteration
Process；Otherwise return to step 2) continue iteration.
It should be appreciated that the part that this specification does not elaborate belongs to prior art.
Any process or method described otherwise above description in flow chart or herein is construed as, and represents to include
Module, fragment or the portion of the code of the executable instruction of one or more the step of being used to realize specific logical function or process
Point, and the scope of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discuss suitable
Sequence, including according to involved function by it is basic simultaneously in the way of or in the opposite order come perform function people, this should be sent out by this
Bright embodiment person of ordinary skill in the field is understood.
Although describing the embodiment of the present invention above in association with accompanying drawing, those of ordinary skill in the art should
Understand, these are merely illustrative of, and various deformation or modification can be made to these embodiments, without departing from the original of the present invention
Reason and essence.The scope of the present invention is only limited by the claims that follow.
Claims (7)
1. a kind of energy management method for micro-grid, it is characterized in that, micro-capacitance sensor includes multiple distributed generation units, each distribution
Formula generator unit has an intelligent body；The communication network of connection is designed between each distributed generation unit, intelligent body；This method
Comprise the following steps：
Step 1, the optimized mathematical model for establishing micro-capacitance sensor distributed energy management solutions；
Local local message at step 2, the grid entry point for measuring and obtaining distributed generation unit access micro-capacitance sensor, and carry out initial
Change is handled；
Step 3, realize that by communication network the information of each distributed generation unit intelligent body exchanges, distributed power generation is calculated
The reference value of unit active power output, the active power output of each distributed generation unit is adjusted according to reference value；
The judgement of step 4, end condition.
2. energy management method for micro-grid as claimed in claim 1, it is characterized in that, micro-capacitance sensor distribution is established described in step 1
The optimized mathematical model of energy management specifically includes following steps：
Generator unit cost of electricity-generating minimum is used as object function to step 1.1 in a distributed manner；
The operating cost function of distributed generation unit is in micro-capacitance sensor：
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(1) in formula, PG,iIt is distributed generation unit DG_i active power output reference value；CG,i(PG,i) it is distributed generation unit
DG_i operating cost function, ai、biAnd ciIt is distributed generation unit DG_i cost coefficient respectively；
Minimize distributed generation unit DG_i cost of electricity-generating CG,i(PG,i) so that overall cost of electricity-generating is minimum；
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Active power balance is as equality constraint in step 1.2 micro-capacitance sensor；
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(3) in formula, PDFor the total active power demand of load in micro-capacitance sensor；
Generator unit active power output is limited in a distributed manner is used as inequality constraints condition for step 1.3；
PG,imin≤PG,i≤PG,imax (4)
(4) in formula, PG,iminAnd PG,imaxIt is divided into distributed generation unit DG_i active power outputs PG,iLower and upper limit.
3. energy management method for micro-grid as claimed in claim 1, it is characterized in that, realize comprising the following steps that for step 2：
The intelligent body of step 2.1 distributed generation unit passes through in the initial time in each active power output reference value counting period
The initial value of distributed generation unit active power output reference value is calculated in local measurement；
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(5) P in formulaD,iThe load active power of micro-capacitance sensor node, P where distributed generation unit DG_i grid entry pointsG,i[0] it is
The initial value of cloth generator unit DG_i active power outputs；
Step 2.2 calculates the prediction of the initial value and active power output of distributed generation unit cost tiny increment to workload demand difference
Initial value is：
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(6) r in formulaG,i[0] be distributed generation unit DG_i cost tiny increments initial value, fG,i[0] it is active power output to negative
The prediction initial value of lotus demand difference.
4. energy management method for micro-grid as claimed in claim 1, it is characterized in that, realize comprising the following steps that for step 3：
Step 3.1 is by iterating to calculate the reference value of distributed generation unit active power output in each time interval；Changed at t-th
For in calculating cycle, intellectual Agent _ i obtains its imported field set by communication networkInterior each intelligent body
Agent_j cost tiny increment rG,jThe predicted value f of [t] and active power output to workload demand differenceG,j[t], (For setMiddle individual sum)；And by the cost tiny increment r of itselfG,i[t] and active power output are to negative
The predicted value f of lotus demand differenceG,i[t], which is sent to its output type field, to be gatheredInterior each intellectual Agent _ k, (For setMiddle individual sum)；
Step 3.2 solves and obtains distributed generation unit active power output reference value, distributed generation unit in each time interval
The predicted value of cost tiny increment and active power output to workload demand difference；
Intellectual Agent _ i solves the cost tiny increment of the distributed generation unit DG_i after information exchanges by the following method,
The predicted value of active power output reference value and active power output to workload demand difference：
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(7) in formula, wijTry to achieve by the following method：
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(8) in formula, ξi∈ [0,1] is intellectual Agent _ i consistent item weight；
(7) in formula, ε is feedback factor；
(7) in formula, vikTry to achieve by the following method：
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(9) in formula, ξ 'i∈ [0,1] is intellectual Agent _ i feedback term weight；
(7) in formula, ΦG,i(rG,i[t+1]) it is projection function, expression formula is：
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(10) in formula, rG,imin=aiPG,imin+bi, rG,imax=aiPG,imax+bi。
5. energy management method for micro-grid as claimed in claim 4, it is characterized in that, ξiOptimization value be 0.5；ε optimization takes
It is worth for 1.5 × 10-3；, ξ 'iOptimization value be 0.5.
6. energy management method for micro-grid as claimed in claim 1, it is characterized in that, realize comprising the following steps that for step 4：
Step 4.1 is when the total active power output of each distributed generation unit and the ratio of load aggregate demand difference and workload demand in micro-capacitance sensor
Value Δ P stops iteration when being less than given threshold λ, each intelligent body sends the active power output reference value being calculated to corresponding point
Cloth generator unit, distributed generation unit export corresponding active power；
Otherwise return to step 3 continues iteration to step 4.2.
7. energy management method for micro-grid as claimed in claim 6, it is characterized in that, ratio
Given threshold λ values are 10-6。
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| CN110858718A (en) * | 2018-08-22 | 2020-03-03 | 郑州大学 | Alternating current micro-grid distributed event-driven frequency control method considering economy |
| CN112821554A (en) * | 2021-01-12 | 2021-05-18 | 武汉大学 | Power distribution network information physical system and distributed economic dispatching feedback coefficient optimization method |
| CN114123173A (en) * | 2021-11-15 | 2022-03-01 | 南京邮电大学 | Micro-grid elastic energy management method based on event trigger mechanism under network attack |
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| CN108808668A (en) * | 2018-06-29 | 2018-11-13 | 广东电网有限责任公司 | It is a kind of meter and multiclass power supply active distribution network distributed optimization dispatching algorithm |
| WO2020000399A1 (en) * | 2018-06-29 | 2020-01-02 | 东莞理工学院 | Multi-agent deep reinforcement learning proxy method based on intelligent grid |
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| CN110858718A (en) * | 2018-08-22 | 2020-03-03 | 郑州大学 | Alternating current micro-grid distributed event-driven frequency control method considering economy |
| CN110858718B (en) * | 2018-08-22 | 2023-02-03 | 郑州大学 | Alternating current micro-grid distributed event-driven frequency control method considering economy |
| CN110265991A (en) * | 2019-05-07 | 2019-09-20 | 上海电力学院 | A kind of distributed and coordinated control method of direct-current grid |
| CN110265991B (en) * | 2019-05-07 | 2023-04-28 | 上海电力学院 | Distributed coordination control method for direct-current micro-grid |
| CN112821554A (en) * | 2021-01-12 | 2021-05-18 | 武汉大学 | Power distribution network information physical system and distributed economic dispatching feedback coefficient optimization method |
| CN112821554B (en) * | 2021-01-12 | 2023-02-03 | 武汉大学 | Power distribution network information physical system and distributed economic dispatching feedback coefficient optimization method |
| CN114123173A (en) * | 2021-11-15 | 2022-03-01 | 南京邮电大学 | Micro-grid elastic energy management method based on event trigger mechanism under network attack |
| CN114123173B (en) * | 2021-11-15 | 2024-05-14 | 南京邮电大学 | Micro-grid elastic energy management method based on event triggering mechanism under network attack |
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