CN110084446A - The cross-domain coordination energy of microgrid group is dispatched and is adapted to optimization cooperation operation method - Google Patents
The cross-domain coordination energy of microgrid group is dispatched and is adapted to optimization cooperation operation method Download PDFInfo
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Abstract
The cross-domain coordination energy of microgrid group of the present invention is dispatched and is adapted to optimization cooperation operation method, " three-level layer (tertiary level) " in micro-grid system considers through the monitoring device at electric system connection switch, such as multiple agent, it obtains system status information and controls interconnection switch using it, and then microgrid topological structure is reconstructed from logic level, the electricity supplying and using system of multiple autonomous micro-grid systems is subjected to re-optimization combination, the Optimized Operation of electric energy is realized by cooperation；Being somebody's turn to do " cauline leaf generation strategy " mainly includes two parts algorithm, i.e. MST searches for optimal DG-CL power supply relationship, determines network primary structure；LMI determines adding or deleting for NL node, guarantees that the maximum of DG electric energy utilizes.
Description
Technical field
Invention is related to distributed power generation and the energy storage device field of micro-grid system, more particularly to it is a kind of containing wind, light, storage,
The cross-domain coordination energy of the microgrid group of bavin power supply is dispatched and is adapted to optimization cooperation operation method.
Background technique
Along with the increasingly mature of micro-grid system, it is this can independent operating or the electric power network system that is incorporated into the power networks it is more
Using renewable energy technologies.Although however having internal combustion engine generator group, the power generation of combustion gas wheel in currently used distributed generation resource
The relatively stable reliable power supply system such as machine, but due to needing to consume traditional energy, scale and power supply volume when its power supply
It will receive certain restrictions, can not fully meet in microgrid the power requirement of whole power loads, and solar energy, wind power generation system
Because it has the characteristics that cleaning, renewable also more penetrates into micro-grid system.But the latter is due to by weather, ring
The influence of the factors such as border, power supply have the characteristics that therefore intermittent, fluctuation can not provide continual and steady power supply.
This has resulted in that the operational efficiency of autonomous micro-grid system will be reduced in terms of two:
On the one hand, when DG powers abundance, a large amount of electric energy will be unable to obtain effective utilization.Although energy storage device at this time
A part of extra electricity can be received, but its effect is limited and needs a large amount of energy-storage units, to considerably increase microgrid
The investment and maintenance cost of system；
On the other hand, when DG electricity shortage, load is unable to get sufficient power supply and is restricted, especially when
In system when CL electricity shortage, caused by loss will be more serious.
Optimize cooperation operation method therefore, it is necessary to provide a kind of microgrid group cross-domain coordination energy scheduling and solve with being adapted to
The above problem.
Summary of the invention
It is dispatched the object of the present invention is to provide a kind of cross-domain coordination energy of microgrid group and is adapted to optimization cooperation operation method.
Technical solution is as follows:
A kind of cross-domain coordination energy scheduling of microgrid group optimizes cooperation operation method with being adapted to, and steps are as follows:
Network is divided using 33 node topology of IEEE:
(1) Δ t is sampled at timed intervals, obtains the status information of current time system DGs, CLs, NLs, Ss.
(2) judge whetherAnd if so, executing
(3), (18) otherwise are executed；
(3) the microgrid topological structure matrix A (t) of current t moment is constructed；
(4) judge whether the total power generation of DGs in system is greater than the aggregate demand of all power loads: judgementIt is to execute (step 5), otherwise executes (step 6)；
(51)Si∈ x | x is load }, i=1 ..., NsAnd execute (step 9)；
(6) judgeIt is to execute (step 7), otherwise executes (step 8)；
(7)Si∈ x | x is load } ∪ y | y is generater }, i=1 ..., NsAnd execute (step 9) and (step
It is rapid 10)；
(8)Si∈ y | y is generater }, i=1 ..., NsAnd execute (step 10)；All energy storage devices are considered as
DG；
(9) judge each SiSOCi>=80%, i=1 ..., Ns, it is to execute (step 11), otherwise executes (step
12)；
(10) judge each SiSOCi≤ 20%, i=1 ..., Ns, it is to execute (step 11), otherwise executes (step
13)；
(11)The SOC of energy storage devicei>=80% or SOCi≤
20%, then the energy storage device is failure to actuate, both not as power supply or not as electrical equipment；
(12)Execute (step 14)；
(13)Execute (step 14)；
(14) calculate fromIt arrivesMinimum weight and: Min [sum
(weights)]ij, obtain NG×NCLMinimum tree；
(15) minimum weight the smallest Min [sum (weights)] in is selectedijCorresponding GiAs jth important load
CLjSupply node；
(16) whole N is determinedG×NCLA set { Gi,CLj},i∈(1,NG),j∈(1,NCL)；
(17) judge each set { Gi,CLj},i∈(1,NG),j∈(1,NCL) in Gi-CLj> 0 is to execute (step
19) (step 18), is otherwise executed；
(18) selection time small weight and Submin [sum (weights)]ijCorresponding GiAs jth important load CLj
Supply node and execution (step 19)；
(19) G is determinediAnd CLjBetween Municipal in NLs, the number of Tertiary, Light is simultaneously stored in variable nM,
nTAnd nLIn；
(20) LMI: ε=min { xM (t)+yT (t)+zL (t)-(G is constructedi-CLj)}
s.t.:0≤ε
x≤nM；y≤nT；z≤nL
(21) value of x, y and z are determined.
(22) G is selectedi, CLjAnd between themY and z Municipal, Tertiary, Light constitute set { k
|kth Autonomous MG,k∈(1,NG×NCL), i.e., k-th autonomous microgrid.
Compared with prior art, the present invention passes through for " three-level layer (tertiary level) " consideration in micro-grid system
Monitoring device at electric system connection switch, such as multiple agent are obtained system status information and are opened using its control contact
It closes, and then reconstructs microgrid topological structure from logic level, the electricity supplying and using system of multiple autonomous micro-grid systems is subjected to re-optimization
The Optimized Operation of electric energy is realized in combination by cooperation；Being somebody's turn to do " cauline leaf generation strategy " mainly includes two parts algorithm, i.e. MST search
Optimal DG-CL power supply relationship, determines network primary structure；LMI determines adding or deleting for NL node, guarantees DG electric energy most
It is big to utilize；Specifically:
(1) consider multifactor building microgrid topological structure matrix weight, and the low metric of correlation is organically unified
Get up, so that the simplification that the number for greatly reducing constraint condition is algorithm is laid a good foundation；
(2) MST searching algorithm and LMI optimization algorithm are used in combination with and are constructed under new microgrid topological structure respectively
" trunk " and " leaf ", the respective advantage of algorithm had not only been utilized but also combining at any time for algorithm is facilitated to use with splitting (can be according to being
" leaf " node is repartitioned or only increased and decreased to system state), so as to avoid after each sampling all to this algorithm again
Operation, enormously simplifies calculation amount, improves efficiency of algorithm；
(3) charging and discharging state of reasonable arrangement energy-storage units enables the strategy from two dimensions of room and time to electricity
It can be carried out Optimum, so that the utilization rate of electric energy be made greatly to be improved；Meanwhile energy storage device is reduced to the greatest extent
Charge and discharge number also avoids the charge and discharge movement of S to S, to reduce the use cost of energy-storage units, extends its use
Service life.
Detailed description of the invention
Fig. 1 microgrid topological structure matrix method
Fig. 2 energy storage device working region
Fig. 3 scheduling strategy flow chart
Fig. 4 IEEE 33-bus system testing topology and initial network divide
Fig. 5 DG and load characteristic curve
Fig. 6 (a) 00:00 moment is with DG1The MSTs generated for root node
Fig. 6 (b) 00:00 moment is with DG2The MSTs generated for root node
Fig. 6 (c) 00:00 moment is with DG3The MSTs generated for root node
Fig. 7 (a) 05:00 moment is with DG1The MSTs generated for root node
Fig. 7 (b) 05:00 moment is with DG2The MSTs generated for root node
Fig. 7 (c) 05:00 moment is with DG3The MSTs generated for root node
Fig. 7 (d) 05:00 moment is with S1The MSTs generated for root node
Fig. 7 (e) 05:00 moment is with S2The MSTs generated for root node
Fig. 7 (f) 05:00 moment is with S3The MSTs generated for root node
Fig. 8 (a) 00:00 moment cooperative scheduling result
Fig. 8 (b) 05:00 moment cooperative scheduling result
CLs powers comparison for 24 hours under Fig. 9 initial configuration comparison scheduling structure
Figure 10 DG utilization rate correlation curve
Figure 11 (a) does not use tactful load to meet condition diagram
Figure 11 (b) in the strategy but system using not having energy storage device load to meet condition diagram
Figure 11 (c) meets condition diagram using the strategy load
24 hours cooperative scheduling results of Figure 12
Specific embodiment
Embodiment:
Referring to Fig. 3, using 33 node topology of IEEE to divide network, topological structure is as shown in Figure 4.
It can be seen that, there are 3 DG in whole network, wherein setting DG by Fig. 41For photovoltaic (0-624.205MW), DG2With
DG3For wind energy (82.01-419.50MW), power characteristic comes from Belgian electricity transmission
operator Elias(May 13th, 2014), as shown in Fig. 5 (a).Each energy-storage units setting is held with the maximum of 900MWh
Amount, then the maximum total capacity of energy-storage system is 2700MWh in system shown in Figure 4.In addition, including 6 weights in system shown in Figure 4
Load and 21 insignificant loads are wanted, shown in typical characteristic working curve such as Fig. 5 (b) for 24 hours.Each node connection type such as Table I.
TABLEI
NODES CONNECTED WITH DGS CLS AND NLS IN33-BUS TEST SYSTEM
(1) Δ t is sampled at timed intervals, obtains the status information of current time system DGs, CLs, NLs, Ss.
(2) judge whetherAnd if so, executing
(3), (18) otherwise are executed；Under the structural system that a upper sampling period determines, in each autonomous microgrid i (DG-CL)iVariation
Than having, repartitioning, only do not increase and decrease NLs then either with or without the division trigger door threshold θ for being more than setting；
(3) the microgrid topological structure matrix A (t) of current t moment is constructed,.Its branch weight is true by expression formula (1)-(6)
It is fixed.
A certain power load j in microgrid is set, active power and reactive power are respectively PjAnd Qj, upstream power supply section
Point be i, then from node i to the power supply line of node j on all-in resistance and total reactance be respectively RijAnd Xij.Set the electricity of node j
Pressure remains Uj, then may be expressed as: from the route network loss in the power supply line that node i is transferred to node j
The real time power loss value in network between any two node can be obtained using expression formula (1).
During Model in Reliability Evaluation of Power Systems, the degree of unavailability K of route and device is a common measurement index,
It is determined by year failure-frequency and repair time, i.e.,
Wherein, f is year failure-frequency number；R is fault correction time.
It should be pointed out that a kind of method is used alone or statistical data is likely difficult to the practical wind of effective assessment system
Dangerous state or operating condition.In fact, in addition to use it is calculated to the failure-frequency of route and the statistical value of repair time can not
With being outside one's consideration, the practical engineering experience of expert is also an important factor.Therefore, in conjunction with the two trade-off evaluation system each line
The risk factor on road:
Wherein, KijThe degree of unavailability of route between node i and node j is acquired by expression formula (2)；EijFor node i and
Expert's assessed value of route between node j；η is regulatory factor, both adjustable actual count data and expertise assessment
Specific gravity in risk assessment processes obtains more reasonable assessed value.
In order to preferably unify the two under a Measure Indexes, we normalize firstly the need of by the two.
If the route between node i and node j is Lij, then its normalized route network loss and degree of unavailability are respectively as follows:
Wherein, N is the number of nodes in entire microgrid；
Using after normalization route network loss and route degree of unavailability can obtain route LijSynthesis weight evaluation index
Are as follows:
If thering is route to be connected directly between node i and node j, acquired by expression formula (6), weight aij；If otherwise section
Point i and node j are not connected directly, then aij=0；Diagonal entry aii=0.Then microgrid topological structure matrix construction methods such as Fig. 1
It is shown.
(4) judge whether the total power generation of DGs in system is greater than the aggregate demand of all power loads: judgementIt is to execute (step 5), otherwise executes (step 6)；
(5)Si∈ x | x is load }, i=1 ..., NsAnd execute (step 9)；All energy storage devices are considered as electricity consumption
Load；
(6) whether the total power generation of DGs is greater than the aggregate demand of all CLs in system: judgementIt is then
(step 7) is executed, (step 8) is otherwise executed；
(7)Si∈ x | x is load } ∪ y | y is generater }, i=1 ..., NsAnd execute (step 9) and (step
It is rapid 10)；Energy storage device is considered as power load or power supply (Generater)；
(8)Si∈ y | y is generater }, i=1 ..., NsAnd execute (step 10)；All energy storage devices are considered as
DG；
(9) judge each SiSOCi>=80%, i=1 ..., Ns, it is to execute (step 11), otherwise executes (step
12)；
(10) judge each SiSOCi≤ 20%, i=1 ..., Ns, it is to execute (step 11), otherwise executes (step
13)；
(11)The SOC of energy storage devicei>=80% or SOCi≤
20%, then the energy storage device is failure to actuate, both not as power supply or not as electrical equipment；
(12)Execute (step 14)；Energy storage device is considered as power supply；
(13)Execute (step 14)；Energy storage device is considered as load；
(14) calculate fromIt arrivesMinimum weight and: Min [sum
(weights)]ij, obtain NG×NCLMinimum tree；
(15) minimum weight the smallest Min [sum (weights)] in is selectedijCorresponding GiAs jth important load
CLjSupply node；
(16) whole N is determinedG×NCLA set { Gi,CLj},i∈(1,NG),j∈(1,NCL)；
(17) judge each set { Gi,CLj},i∈(1,NG),j∈(1,NCL) in Gi-CLj> 0 is to execute (step
19) (step 18), is otherwise executed；
(18) selection time small weight and Submin [sum (weights)]ijCorresponding GiAs jth important load CLj
Supply node and execution (step 19).
(19) G is determinediAnd CLjBetween Municipal in NLs, the number of Tertiary, Light is simultaneously stored in variable nM,
nTAnd nLIn.
(20) LMI: ε=min { xM (t)+yT (t)+zL (t)-(G is constructedi-CLj)}
s.t.:0≤ε
x≤nM；y≤nT；z≤nL
(21) value of x, y and z are determined.
(22) G is selectedi, CLjAnd between themY andzA Municipal, Tertiary, Light constitute set { k
|kth Autonomous MG,k∈(1,NG×NCL), i.e.,kA autonomy microgrid.
Therefore, DG load curve as shown in Figure 4 is it is found that at the 00:00 moment, systemI.e.
Total output is greater than aggregate demand, and therefore, whole energy storage devices are treated as power load and according to its own SOC feelings in system at this time
Condition decides whether to carry out it charging operations, and (by step 9), and system only searches for MSTs for DGs as root node to determine CL's
Power supply.According to the MSTs such as Fig. 6 institute for slave DG to the CLs that the microgrid topological structure matrix A (00:00) at 00:00 moment obtains
Show.MSTs according to figure 6, calculate weight from each DG to each CL and, as shown in Table II.Wherein overstriking font
Represented is weight and the smallest, the i.e. power supply root node of the CL in certain CL to 3 root node.It should be noted, however, that
00:00 moment DG1Be 0 for electric output power, therefore, according to mentioned strategy, be responsible for 7,8 and 21CLs of power supply according to secondary
Small weight and principle (press step 18), arrange to be responsible for power supply by other two DGs respectively, as shown in underscore numerical value in Table II.
Accordingly, 00:00 micro-grid system is reconfigured as two microgrid subsystems, and according to DG at this time2And DG3Power supply capacity and other NLs
Burden requirement, to make full use of DGs remaining capacity as target, according to LMI algorithm (by step 20) determination be added to every height
Insignificant load bus in microgrid.Shown in 00:00 moment cooperative scheduling result such as Fig. 8 (a).
TABLEII
WEIGHT SUMS FROM DGS TO CLS
It is different from the 00:00 moment, the etching system in 05:00At this point, according to mentioned strategy
Energy storage device (S) all decides whether to discharge as power supply and according to itself SOC state (Step 10) in system.According to 05:00
The microgrid topological structure matrix A (05:00) at moment obtains as shown in Figure 7 as the MSTs of root node using DGs and Ss.At this point, from
Each root node to each CL weight and, as shown in Table III.According to Table III as a result, 05:00 micro-grid system is weighed
Structure is five subsystems, and according to DG at this time2、DG3、S1-S3Power supply capacity and other NLs burden requirement, calculated according to LMI
Method (Step 20) adds " leaf " insignificant load into each autonomous microgrid, to utilize the more of power supply to greatest extent
Remaining electric energy is to realize making full use of for electric energy.Shown in 05:00 moment cooperative scheduling result such as Fig. 8 (b).
It is made of altogether 3 autonomous micro-grid systems under setting system initial state, as shown in Figure 4.It is each in each autonomy microgrid
There are a DG, an energy-storage units and two CLs.Under the initial configuration and under proposed scheduling, system CLs is whole
The comparative situation for obtaining power supply is as shown in Figure 9.Result can be seen that CLs in system and adjust in the unified of coordination strategy as shown in Figure 9
The lower Service Efficiency for obtaining power supply of degree is apparently higher than the Service Efficiency of CLs under original autonomous micro-grid system structure.Due to CLs load for
System has a meaning and value bigger than NLs, therefore this is from proving the strategy with effective economic value on one side.
It is system DG capacity factor correlation curve shown in Figure 10.From Figure 10 this it appears that this paper mentioned from
Controlling microgrid cooperative scheduling strategy acts on the utilization rate of the power generation of DG to be higher than the utilization rate under no cooperative scheduling strategy scenarios
(being the gas-to electricity Percent efficiency of DG shown in Figure 10 wicket).By reconstructing the architecture of micro-grid system, close simultaneously
The charge and discharge movement of reason scheduling energy storage device, the electric energy that DG is issued is utilized or stored by energy storage device by power load, and is being
System is released when lacking power supply, this can effectively carry out electric power from two dimensions of room and time excellent in systems in practice
Change and use, thus improve DG efficiency, this has great importance in actual electric network.
It is the curve of all load Service Efficiency comparisons shown in Figure 11.As seen from Figure 11, when including energy storage in system
When unit, which can ensure the power demand of system entirety load in the most of the time, as shown in Figure 11 (a).And if
Energy storage device has sufficiently large capacity in system, then can guarantee in all the period of time in conjunction with reasonable autonomous microgrid cooperative scheduling strategy
All load electricity consumptions.Even secondly, in systems without energy storage device in the case where, due to the strategy coordinated scheduling make
With the power demand of all loads is also available satisfaction in most of the time section, only when system is total in part-time
When power output is less than electric energy aggregate demand, just having part NLs cannot power, as shown in Figure 11 (b).It is distinct right to be formed therewith
Ratio, even if there are energy storage devices in system, in the case where no rational management, system in most of time still without
Method meets the power requirement of all loads, as shown in Figure 11 (c).Figure 11 absolutely proved autonomous microgrid rational management meaning and
Value, while illustrating that reasonable cooperative scheduling strategy makes the optimization of electric power resource than only increasing energy storage device in systems
With with prior meaning.
Figure 12 show the peace of the power supply all to system in one day of proposed cooperative scheduling strategy and load electricity consumption
Arrange result.Result as shown in Figure 12 can be seen that is dispatched by the reasonable charge and discharge of energy storage device, 00:00-04:30, and 07:
The extra electricity that DGs is generated in 30-08:30 the and 11:00-13:00 period is fully absorbed, and makes 05:00-07:00,
The insufficient electrical energy demands of 09:00-10:30 and 18:30-21:00 have obtained effective supplement；Meanwhile in 00:00-22:30
Between in section, whole important load electricity consumptions is all fully used, only the important load electricity within the 22:30-24:00 time
For demand since DG and energy storage are exported without electric power, i.e., total electricity is for should be less than CLs aggregate demand in system, therefore adjusts anyway
Degree is all unable to satisfy and the power supply for abandoning part CL of having to.But in actual operation, this some electrical power notch can be by public affairs
Common-battery net is bought electricity and is compensated.In addition, for insignificant load, by the cooperative scheduling of this paper, when making most of in system
Between NLs power requirement in section can attain full and complete satisfaction, only when systematic electricity aggregate demand is greater than aggregate supply, just can not
Met by scheduling, and this some electrical power still can be by buying electric acquisition.
It should be understood that (1), in order to make energy storage device have longer service life, there are certain for its usual charge and discharge
Remaining, this programme choose the 20%-80% that its charge and discharge range is maximum capacitance of storage.
(2) all energy storage devices are uniformly considered as " electricity consumption " according to power supply situation whole in system or " put by the strategy
Electricity " equipment, this is with certain realistic meaning: this strategy can be avoided effectively from an energy storage device to another energy storage
The movement of equipment charge, so as to avoid the repeated charge " concussion " between battery.
(3) strategy first guarantees that all CL and NL have obtained sufficient electric power and supplied when charging to energy storage device
It answers, i.e., is carried out according to CL > NL > S power supply priority, this can reduce the movement of the charge and discharge to energy storage device to the greatest extent, to prolong
The service life of long battery reduces operating cost；The scheduling strategy can be realized effectively under the conditions of output power deficiency
Input-output power matching, realizes that more microgrids unify the harmony of electricity consumption.On the basis of ensure that important load is sufficiently powered,
The safety of whole system important load electricity consumption is also improved to a certain extent, while can also be realized well between more microgrids
The effective use of electric energy.
Compared with prior art, the present embodiment considers logical for " three-level layer (the tertiary level) " in micro-grid system
The monitoring device at electric system connection switch, such as multiple agent are crossed, system status information is obtained and utilizes its control contact
Switch, and then microgrid topological structure is reconstructed from logic level, the electricity supplying and using system of multiple autonomous micro-grid systems is carried out again excellent
Change combination, the Optimized Operation of electric energy is realized by cooperation；Being somebody's turn to do " cauline leaf generation strategy " mainly includes two parts algorithm, i.e. MST is searched
The optimal DG-CL power supply relationship of rope, determines network primary structure；LMI determines adding or deleting for NL node, guarantees DG electric energy
Maximum utilizes；Specifically:
(1) consider multifactor building microgrid topological structure matrix weight, and the lower metric of these correlations is organic
Unite, thus greatly reduce constraint condition number be algorithm simplification lay a good foundation；
(2) MST searching algorithm and LMI optimization algorithm are used in combination with and are constructed under new microgrid topological structure respectively
" trunk " and " leaf ", the respective advantage of algorithm had not only been utilized but also combining at any time for algorithm is facilitated to use with splitting (can be according to being
" leaf " node is repartitioned or only increased and decreased to system state), so as to avoid after each sampling all to this algorithm again
Operation, enormously simplifies calculation amount, improves efficiency of algorithm；
(3) charging and discharging state of reasonable arrangement energy-storage units enables the strategy from two dimensions of room and time to electricity
It can be carried out Optimum, so that the utilization rate of electric energy be made greatly to be improved；Meanwhile energy storage device is reduced to the greatest extent
Charge and discharge number also avoids the charge and discharge movement of S to S, to reduce the use cost of energy-storage units, extends its use
Service life.
Above-described is only some embodiments of the present invention.For those of ordinary skill in the art, not
Under the premise of being detached from the invention design, various modifications and improvements can be made, these belong to protection model of the invention
It encloses.
Claims (1)
1. a kind of cross-domain coordination energy of microgrid group is dispatched and is adapted to optimization cooperation operation method, it is characterised in that: steps are as follows:
Network is divided using IEEE33 node topology:
(1) Δ t is sampled at timed intervals, obtains the status information of current time system DGs, CLs, NLs, Ss.
(2) judge whetherAnd if so, (3) are executed,
Otherwise (18) are executed；
(3) the microgrid topological structure matrix A (t) of current t moment is constructed；
(4) judge whether the total power generation of DGs in system is greater than the aggregate demand of all power loads: judgementIt is to execute (step 5), otherwise executes (step 6)；
(5)Si∈ x | x is load }, i=1 ..., NsAnd execute (step 9)；
(6) judgeIt is to execute (step 7), otherwise executes (step 8)；
(7)Si∈ { x x is load } i ∪ { y y is generater }, i=1 ..., NsAnd execute (step 9) and (step
10)；
(8)Si∈ y | y is generater }, i=1 ..., NsAnd execute (step 10)；All energy storage devices are considered as DG；
(9) judge each SiSOCi>=80%, i=1 ..., Ns, it is to execute (step 11), otherwise executes (step 12)；
(10) judge each SiSOCi≤ 20%, i=1 ..., Ns, it is to execute (step 11), otherwise executes (step 13)；
(11)The SOC of energy storage devicei>=80% or SOCi≤ 20%,
Then the energy storage device is failure to actuate, both not as power supply or not as electrical equipment；
(12)Execute (step 14)；
(13)Execute (step 14)；
(14) calculate fromIt arrivesMinimum weight and: Min [sum
(weights)]ij, obtain NG×NCLMinimum tree；
(15) minimum weight the smallest Min [sum (weights)] in is selectedijCorresponding GiAs jth important load CLj's
Supply node；
(16) whole N is determinedG×NCLA set { Gi,CLj},i∈(1,NG),j∈(1,NCL)；
(17) judge each set { Gi,CLj},i∈(1,NG),j∈(1,NCL) in Gi-CLj> 0 is to execute (step 19), no
Then execute (step 18)；
(18) selection time small weight and Submin [sum (weights)]ijCorresponding GiAs jth important load CLjConfession
Electrical nodes simultaneously execute (step 19)；
(19) G is determinediAnd CLjBetween Municipal in NLs, the number of Tertiary, Light is simultaneously stored in variable nM, nTAnd nL
In；
(20) LMI: ε=min { xM (t)+yT (t)+zL (t)-(G is constructedi-CLj)}
s.t.:0≤ε
x≤nM；y≤nT；z≤nL
(21) value of x, y and z are determined.
(22) G is selectedi, CLjAnd between themY and z Municipal, Tertiary, Light composition set k |
kth Autonomous MG,k∈(1,NG×NCL), i.e., k-th autonomous microgrid.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577891A (en) * | 2013-10-21 | 2014-02-12 | 浙江大学 | Multi-island micro-grid optimization cooperation running method containing distributed power source |
| CN104200296A (en) * | 2014-07-10 | 2014-12-10 | 浙江大学 | Wind-solar-stored energy-firewood autonomous micro-grid group cross-domain collaboration energy dispatching and fitting method |
| CN107221937A (en) * | 2017-06-27 | 2017-09-29 | 四川大学 | Distribution network failure reconstruct and voltage control method and system based on distributed energy storage |
| CN109460880A (en) * | 2017-12-15 | 2019-03-12 | 国网浙江省电力公司湖州供电公司 | A kind of mesolow distribution risk management and control method containing wind-light storage bavin |
-
2019
- 2019-06-07 CN CN201910493866.3A patent/CN110084446A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577891A (en) * | 2013-10-21 | 2014-02-12 | 浙江大学 | Multi-island micro-grid optimization cooperation running method containing distributed power source |
| CN104200296A (en) * | 2014-07-10 | 2014-12-10 | 浙江大学 | Wind-solar-stored energy-firewood autonomous micro-grid group cross-domain collaboration energy dispatching and fitting method |
| CN107221937A (en) * | 2017-06-27 | 2017-09-29 | 四川大学 | Distribution network failure reconstruct and voltage control method and system based on distributed energy storage |
| CN109460880A (en) * | 2017-12-15 | 2019-03-12 | 国网浙江省电力公司湖州供电公司 | A kind of mesolow distribution risk management and control method containing wind-light storage bavin |
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