CN110266057A - A kind of cross-domain collaboration interaction of wind-light storage bavin autonomy microgrid group and consumption method - Google Patents
A kind of cross-domain collaboration interaction of wind-light storage bavin autonomy microgrid group and consumption method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract
The invention discloses a kind of cross-domain collaboration interactions of wind-light storage bavin autonomy microgrid group and consumption method, consider multifactor building network topology matrix weight, and the lower metric of these correlations is organically united, it lays a good foundation to greatly reduce the simplification that the number of constraint condition is algorithm, MST searching algorithm and LMI optimization algorithm are used in combination with to " trunk " and " leaf " constructed under new microgrid topological structure respectively, not only the respective advantage of algorithm had been utilized but also has facilitated combining and split at any time and using for algorithm, so as to avoid all being reruned to this algorithm after each sampling, enormously simplify calculation amount, improve efficiency of algorithm, the charging and discharging state of reasonable arrangement energy-storage units, the strategy is set to optimize arrangement to electric energy from two dimensions of room and time, to make the utilization rate of electric energy obtain It is a greater degree of to improve.
Description
Technical field
The present invention relates to the distributed power generation of micro-grid system and energy storage device field, more particularly to it is a kind of containing wind, light,
It stores up, optimization cooperation operation method is dispatched and be adapted to the cross-domain coordination energy of the microgrid group of bavin power supply.
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
Use renewable energy technologies.Although however having internal combustion engine generator group, combustion gas wheel hair in currently used distributed generation resource
The relatively stable reliable power supply system such as motor, but due to needing to consume traditional energy, scale and power supply when its power supply
Amount will receive certain restrictions, can not fully meet in microgrid the power requirement of whole power loads, and solar energy, wind power generation
System is because it has the characteristics that clean, renewable also more penetrates into micro-grid system.But the latter is due to by day
The influence of the factors such as gas, environment, power supply have the characteristics that therefore intermittent, fluctuation can not provide continual and steady electric 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 can receive a part of extra electricity at this time,
Its effect is limited and needs a large amount of energy-storage units, to considerably increase the investment and maintenance cost of micro-grid system；Another party
Face, when DG electricity shortage, load is unable to get sufficient power supply and is restricted, especially when CL power supply in system
When insufficient, caused by loss will be more serious.
Summary of the invention
The technical problem to be solved by the invention is to provide a kind of wind-light storage bavin autonomy microgrid group cross-domain collaboration interaction with
The electricity supplying and using system of multiple autonomous micro-grid systems is carried out re-optimization combination, realizes the excellent of electric energy by cooperation by consumption method
Change scheduling.
In order to solve the above technical problems, the present invention adopts the following technical scheme: a kind of wind-light storage bavin autonomy microgrid group is cross-domain
Collaboration interaction and consumption method, include the following steps:
(1) Δ t is sampled at timed intervals, obtains current time system distributed generation resource DGs, important load CLs, common
The status information of load NLs, energy storage device Ss；
(2) judge whetherI=1 ..., NAMG(t- Δ t), and if so, holding
Row (3) otherwise executes (18), whereinWithRespectively (t- Δ t) moment and t moment are autonomous
The difference of the power of DG and CL, N in microgrid i_AMGAMG(t- Δ t) is (number of autonomy microgrid existing for the t- Δ t) moment；
(3) the network topology matrix A (t) of current t moment is constructed；
(4) judge whetherIt is to execute (5), otherwise executes (6), wherein NDGIt is micro-
Net the total number of DG in group, NCLIt is the total number of CL, NNLIt is the total number of NL；
(5)Si∈ x | x is load }, i=1 ..., NsAnd execute (9), wherein SiFor i-th of energy storage device, NsIt is micro-
Net the total number of energy storage device in group；
(6) judge whetherIt is to execute (7), otherwise executes (8)；
(7)And (9) and (10) are executed,；
(8)Si∈ y | y is generater }, i=1 ..., NsAnd execute (10)；
(9) judge whether each SiSOCi>=80%, i=1 ..., Ns, it is to execute (11), otherwise executes (12),
Middle SOCi, it is the state-of-charge of i-th of energy storage device；
(10) judge whether each SiSOCi≤ 20%, i=1 ..., Ns, it is to execute (11), otherwise executes (13)；
(11)Wherein, SiFor i-th of energy storage device, GiFor
I-th of power supply, LiFor i-th of electrical equipment；NGFor the total number of power generating source in microgrid group, NLIt is negative for electricity in microgrid group
The total number of lotus；
(12)It executes (14), wherein SiFor i-th of energy storage device, GiFor
I-th of power supply,For the total number that energy storage device is regarded as to power generating source in microgrid group；
(13)It executes (14), wherein SiFor i-th of energy storage device, LiFor
I-th of electrical equipment,For the total number that energy storage device is regarded as to power load in microgrid group；
(14) calculate fromIt arrivesMinimum weight and: Min [sum
(weights)]ij, obtain NG×NCLMinimum tree, wherein GiFor i-th of power supply, NGFor in microgrid group power generating source it is total
Number, CLjFor j-th of important load, NCLFor the total number of important load in microgrid group, sum () is summing function,
Weights is network topology matrix weight；
(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), wherein GiIt is supplied for i-th
Power supply, CLjFor j-th of important load；
(17) judge whether each set { Gi,CLj},i∈(1,NG),j∈(1,NCL) in Gi-CLj> 0 is to execute
(19), (18) otherwise are executed；
(18) selection time small weight and Submin [sum (weights)]ijCorresponding GiAs jth important load CLj
Supply node and execution (19), wherein time minimum value is sought in Submin () expression, and sum () is summing function, weights
For network topology matrix weight；
(19) G is determinediAnd CLjBetween municipal electricity consumption Municipal in common load NLs, tertiary industry electricity consumption
Tertiary, the number of light industry electricity consumption Light are simultaneously stored in variable nM, nTAnd nLIn；
(20) linear moments: ε=min { xM (t)+yT (t)+zL (t)-(G are constructedi-CLj)}
s.t.:0≤ε
x≤nM；y≤nT；z≤nL
Wherein, M (t), T (t) and L (t) are respectively load power value of three kinds of NLs in moment t, and x, y, z is power supply GiWith
Important load CLjBetween wish connection three kinds of NLs quantity；
(21) by solving above-mentioned LMI optimization problem, the value of x, y and z are determined；
(22) G is selectedi, CLjAnd between the twoY and z Municipal, Tertiary, Light constitute collection
Close k | kth AutonomousMG, k ∈ (1, NG×NCL), i.e., k-th autonomous microgrid.
Optionally, the determination method of network topology matrix A (t) branch weight are as follows:
Assuming that a certain power load j in microgrid, 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, it is assumed that node j's
Voltage 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；
Other than using and calculating its degree of unavailability to the failure-frequency of route and the statistical value of repair time, in conjunction with expert's
The risk factor of practical engineering experience trade-off evaluation system each route,
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,
In order to the two preferably be unified under a Measure Indexes, it is necessary first to the two is normalized,
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.
The present invention by adopting the above technical scheme, has the following beneficial effects:
(1) consider multifactor building network topology matrix weight, and the lower metric of these correlations is organically united
It comes together, 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 has facilitated combining at any time and splitting that use (can basis for algorithm
" leaf " node is repartitioned or only increased and decreased to system mode), so as to avoid after each sampling all to the weight of this algorithm
New 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 avoid the charge and discharge of energy storage (S) to energy storage (S) and act, thus reduce the uses of energy-storage units at
This, extends its service life.A series of simulation architecture is shown under IEEE 33-bus standard testing platform, and the strategy is effective
The electricity consumption that ensure that CL, the service efficiency for improving DG electric energy also greatly ensure that the power requirement of NL.
The specific technical solution of the present invention and its advantages will in the following detailed description in conjunction with attached drawing into
Row detailed description.
Detailed description of the invention
Present invention will be further described below with reference to the accompanying drawings and specific embodiments:
Fig. 1 is network topology matrix method；
Fig. 2 is energy storage device working region；
Fig. 3 is scheduling strategy flow chart；
Fig. 4 is that IEEE 33-bus system testing topology and initial network divide；
Fig. 5 is DG and load characteristic curve；
Fig. 6 (a) is the 00:00 moment with DG1The MSTs generated for root node；
Fig. 6 (b) is the 00:00 moment with DG2The MSTs generated for root node；
Fig. 6 (c) is the 00:00 moment with DG3The MSTs generated for root node；
Fig. 7 (a) is the 05:00 moment with DG1The MSTs generated for root node；
Fig. 7 (b) is the 05:00 moment with DG2The MSTs generated for root node；
Fig. 7 (c) is the 05:00 moment with DG3The MSTs generated for root node；
Fig. 7 (d) is the 05:00 moment with S1The MSTs generated for root node；
Fig. 7 (e) is the 05:00 moment with S2The MSTs generated for root node；
Fig. 7 (f) is the 05:00 moment with S3The MSTs generated for root node；
Fig. 8 (a) is 00:00 moment cooperative scheduling result；
Fig. 8 (b) is 05:00 moment cooperative scheduling result；
Fig. 9 is that initial configuration compares CLs under scheduling structure and powers for 24 hours comparison；
Figure 10 is DG utilization rate correlation curve；
Figure 11 (a) is that tactful load is not used to meet condition diagram；
Figure 11 (b) is using not having energy storage device load to meet condition diagram in the strategy but system；
Figure 11 (c) is to meet condition diagram using the strategy load；
Figure 12 is 24 hours cooperative scheduling results.
Specific embodiment
The present invention considers to open by electric system connection for " three-level layer (the tertiary level) " in micro-grid system
Monitoring device at pass, such as multiple agent obtain system status information and simultaneously control interconnection switch using its, and then from logic
The electricity supplying and using system of multiple autonomous micro-grid systems is carried out re-optimization combination, is realized by cooperation by level reconstructed network topology
The Optimized Operation of electric energy.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 and closes
System, determines network primary structure；LMI determines adding or deleting for NL node, guarantees that the maximum of DG electric energy utilizes.
It should be noted that (1), in order to make energy storage device have longer service life, there are one for its usual charge and discharge
Determine remaining, chooses 20%-80%, workspace B as shown in Figure 2 that its charge and discharge range is maximum capacitance of storage herein. (2)
All energy storage devices are uniformly considered as " electricity consumption " or " electric discharge " equipment according to power supply situation whole in system by the strategy, this
With certain realistic meaning: this strategy can effectively avoid charging from an energy storage device to another energy storage device
Movement, so as to avoid the repeated charge " concussion " between battery.(3) strategy to energy storage device when charging
First guarantee that all CL and NL have obtained sufficient power supply, i.e., carried out according to CL > NL > S power supply priority, this can be with
Reducing the charge and discharge movement to energy storage device to the greatest extent reduces operating cost to extend the service life of battery.The scheduling plan
It slightly can effectively realize input-output power matching under the conditions of output power deficiency, realize that more microgrids unify electricity consumption
Harmony.On the basis of ensure that important load is sufficiently powered, whole system important load is also improved to a certain extent
The safety of electricity consumption, while the effective use of electric energy between more microgrids can also be realized well.
Shown in Fig. 3 is the process flow diagram of the invention.Its specific implementation will be described as follows in conjunction with specific example.With
Under will use 33 node topology of IEEE for divide network its specific steps is described, topological structure is as shown in Fig. 4.
It can be seen that, there are 3 DG in whole network, wherein assuming 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 assume the maximum appearance with 900MWh
Amount, then the maximum total capacity of energy-storage system is 2700MWh in system shown in Figure 4.In addition, including 6 in system shown in Figure 4
Important load and 21 insignificant loads, shown in typical characteristic working curve such as Fig. 5 (b) for 24 hours.Each node connection type is such as
Table I.
Table I
Network node (the NODES of distributed generation resource, important load and insignificant load is connected in 33 node test networks
CONNECTED WITH DGS CLS AND NLS IN 33-BUS TEST SYSTEM)
Refering to what is shown in Fig. 3, a kind of cross-domain collaboration interaction of wind-light storage bavin autonomy microgrid group and consumption method, including walk as follows
It is rapid:
(1) Δ t is sampled at timed intervals, obtains current time system distributed generation resource DGs, important load CLs, common
The status information of load NLs, energy storage device Ss.
(2) judge whetherI=1 ..., NAMG(T- Δ t), and if so, holding
Row (3) otherwise executes (18).Meaning: under the structural system that a upper sampling period determines, (the DG- in each autonomous microgrid i
CL)iVariation than either with or without be more than setting division trigger door threshold θ, have, repartition, only do not increase and decrease NLs then.Its
In,WithRespectively (the function of DG and CL in t- Δ t) moment and t moment autonomy microgrid i_AMG
The difference of rate, NAMG(t- Δ t) is (number of autonomy microgrid existing for the t- Δ t) moment.
(3) the network topology matrix A (t) of current t moment is constructed.Its branch weight is determined by expression formula (1)-(6).
Assuming that a certain power load j in microgrid, 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.Assuming that node j
Voltage 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
The risk factor [15] of route.
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 ∈ [0,1] is regulatory factor (0.6 is set as in this), adjustable actual count
The specific gravity of both data and expertise assessment 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, wherein β ∈
[0,1]；If otherwise node i and node j are not connected directly, aij=0；Diagonal entry aii=0.Then network topology matrix structure
Construction method is as shown in Figure 1.
(4) judgeIt is to execute (5), otherwise executes (6).Meaning: DGs in system
Total power generation whether be greater than the aggregate demands of all power loads.Wherein, NDGIt is the total number of DG in microgrid group, NCLIt is CL
Total number, NNLIt is the total number of NL.
(5)Si∈ x | x is load }, i=1 ..., NsAnd execute (9).Meaning: it is negative that all energy storage devices are considered as electricity consumption
Lotus.Wherein, SiFor i-th of energy storage device, NsFor the total number of energy storage device in microgrid group.
(6) judgeIt is to execute (7), otherwise executes (8).Meaning: total hair of DGs in system
Whether electricity is greater than the aggregate demand of all CLs.Wherein, NDGIt is the total number of DG in microgrid group, NCLIt is the total number of CL.
(7)Si∈ x | x is load } ∪ y | y is generater }, i=1 ..., NsAnd execute (9) and (10).Meaning
Justice: energy storage device is considered as power load or power supply (Generater).Wherein, SiFor i-th of energy storage device, NsIt is micro-
Net the total number of energy storage device in group.
(8)Si∈ y | y is generater }, i=1 ..., NsAnd execute (10).Meaning: all energy storage devices are considered as
DG.Wherein, SiFor i-th of energy storage device, NsFor the total number of energy storage device in microgrid group.
(9) judge each SiSOCi>=80%, i=1 ..., Ns, (11) are executed if being (such as the region C in Fig. 2), otherwise
It executes (12).Wherein, SiFor i-th of energy storage device, SOCi, it is the state-of-charge of i-th of energy storage device, NsTo be stored up in microgrid group
The total number of energy equipment.
(10) judge each SiSOCi≤ 20%, i=1 ..., Ns, (11) are executed if being (such as a-quadrant in Fig. 2), otherwise
It executes (13).Wherein, SiIt isiA energy storage device, SOCi, it is the state-of-charge of i-th of energy storage device, NsTo be stored up in microgrid group
The total number of energy equipment.
(11)(meaning: 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).Wherein, SiIt is stored up for i-th
Energy equipment, GiFor i-th of power supply, LiFor i-th of electrical equipment；NGFor the total number of power generating source in microgrid group, NLFor
The total number of electric load in microgrid group.
(12)Executing (14), (meaning: energy storage device is considered as power supply.) its
In, SiFor i-th of energy storage device, GiFor i-th of power supply,For energy storage device is regarded as power generating source in microgrid group
Total number.
(13)Executing (14), (meaning: energy storage device is considered as load.) its
In, SiFor i-th of energy storage device, LiFor i-th of electrical equipment；For energy storage device is regarded as power load in microgrid group
Total number.
(14) calculate fromIt arrivesMinimum weight and: Min [sum
(weights)]ij, obtain NG×NCLMinimum tree.Wherein, GiFor i-th of power supply, NGFor in microgrid group power generating source it is total
Number, CLjFor j-th of important load, NCLFor the total number of important load in microgrid group, sum () is summing function,
Weights is network topology matrix weight.
(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).Wherein, GiIt is supplied for i-th
Power supply, CLjFor j-th of important load.
(17) judge each set { Gi,CLj},i∈(1,NG),j∈(1,NCL) in Gi-CLj> 0? it is to execute (19),
Otherwise (18) are executed.
(18) selection time small weight and Submin [sum (weights)]ijCorresponding GiAs jth important load CLj
Supply node and execution (19).Wherein, time minimum value is sought in Submin () expression, and sum () is summing function, weights
For network topology matrix weight.
(19) G is determinediAnd CLjBetween municipal electricity consumption Municipal in common load NLs, tertiary industry electricity consumption
Tertiary, the number of light industry electricity consumption Light are simultaneously stored in variable nM, nTAnd nLIn.
(20) linear moments are constructed:
s.t.:0≤ε
x≤nM；y≤nT；z≤nL
Optimization aim is to add under given three kinds common load NLs maximum node quantity constraints into each autonomous microgrid
Add " leaf " insignificant load, thus to greatest extent using the extra electric energy of power supply to realize making full use of for electric energy.Its
In, M (t), T (t) and L (t) they are respectively load power value of three kinds of NLs in moment t, and x, y, z is power supply GiWith important load CLj
Between wish connection three kinds of NLs quantity.
(21) by solving above-mentioned LMI optimization problem, so that it is determined that the value of x, y and z.
(22) G is selectedi, CLjAnd between themY and z Municipal, Tertiary, Light constitute collection
Close k | kth Autonomous MG, k ∈ (1, NG×NCL), i.e., k-th autonomous microgrid.
Therefore, DG load curve as shown in Figure 4 is it is found that at the 00:00 moment, system
I.e. total output is greater than aggregate demand, and therefore, whole energy storage devices are treated as power load and according to its own in system at this time
SOC situation decides whether to carry out it charging operations, and (by step 9), and system only searches for MSTs for DGs as root node with true
Determine the power supply of CL.According to the MSTs such as Fig. 6 for slave DG to the CLs that the network topology matrix A (00:00) at 00:00 moment obtains
(a-c) shown in.According to MSTs shown in Fig. 6 (a-c), weight of the calculating from each DG to each CL and such as Table II institute
Show.It is wherein weight and the smallest, the i.e. power supply root node of the CL in certain CL to 3 root node represented by overstriking font.So
And, it should be pointed out that 00:00 moment DG1Be 0 for electric output power, therefore, according to mentioned strategy, be responsible for power supply
7,8 and 21CLs (presses step 18), arranges to be responsible for power supply, such as Table II by other two DGs respectively according to secondary small weight and principle
Shown in middle underscore numerical value.Accordingly, 00:00 micro-grid system is reconfigured as two microgrid subsystems, and according to DG at this time2And DG3
Power supply capacity and other NLs burden requirement, to make full use of DGs remaining capacity as target, according to LMI algorithm (press step
20) the insignificant load bus being added in every sub- microgrid is determined.Shown in 00:00 moment cooperative scheduling result such as Fig. 8 (a).
Table II
From distributed generation resource to the weight of important load supply path and (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:
The network topology matrix A (05:00) at 00 moment obtain using DGs and Ss as shown in the MSTs of root node such as Fig. 7 (a-f).This
When, from each root node to the weight of each CL, and, 05:00 micro-grid system is reconfigured as five subsystems, and according to this
When DG2、DG3、S1-S3Power supply capacity and other NLs burden requirement, according to LMI algorithm (Step 20) to each autonomy
" leaf " insignificant load is added in microgrid, thus to greatest extent using the extra electric energy of power supply to realize the abundant of electric energy
It utilizes.Shown in 05:00 moment cooperative scheduling result such as Fig. 8 (b).
Assuming that being made of altogether 3 autonomous micro-grid systems under 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 that body obtains power supply is as shown in Figure 9.Result can be seen that in system CLs in the system of coordination strategy as shown in Figure 9
The lower Service Efficiency for obtaining power supply of one scheduling is apparently higher than the Service Efficiency of CLs under original autonomous micro-grid system structure.Since CLs is negative
Lotus has a meaning and value bigger than NLs for system, therefore this is from proving that the strategy has effective economy on one side
Value.
It is system DG capacity factor correlation curve shown in Figure 10.This it appears that being mentioned in this paper from Figure 10
The utilization rate that autonomous microgrid cooperative scheduling strategy acts on the power generation of lower DG is higher than the utilization under no cooperative scheduling strategy scenarios
Rate (being the gas-to electricity Percent efficiency of DG shown in Figure 10 wicket).By reconstructing the architecture of micro-grid system, simultaneously
The charge and discharge of rational management energy storage device act, and the electric energy that DG is issued is utilized or stored by energy storage device by power load, and
System lack power supply when release, this in systems in practice can effectively from two dimensions of room and time to electric power into
Row optimization uses, thus improves DG efficiency, this has great importance in actual electric network.
It is the curve of all load Service Efficiency comparisons shown in Figure 11 (a-c).As seen from Figure 11, when including in system
When having energy-storage units, 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, it can guarantee if in conjunction with reasonable autonomous microgrid cooperative scheduling strategy
All load electricity consumptions in all the period of time.Even secondly, in systems without energy storage device in the case where, due to the strategy
Coordinated scheduling effect, the power demand of all loads is also available satisfaction in the most of the time section, only part-time
It is interior when the total power output of system be less than electric energy aggregate demand when, just having part NLs cannot power, as shown in Figure 11 (b).With
Formation sharp contrast, even if there are energy storage devices in system, in the case where no rational management, system is most
Still the power requirement of all loads is unable to satisfy in the number time, as shown in Figure 11 (c).Figure 11 has absolutely proved autonomous microgrid
The meaning and value of rational management, while illustrating reasonable cooperative scheduling strategy than only increasing energy storage device pair in systems
Using in the optimization of electric power resource has 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:
The insufficient electrical energy demands of 00,09:00-10:30 and 18:30-21:00 have obtained effective supplement；Meanwhile in 00:00-22:
In 30 periods, whole important load electricity consumptions is all fully used, only important negative within the 22:30-24:00 time
Carrying capacity demand is since DG and energy storage are exported without electric power, i.e., total electricity is for should be less than CLs aggregate demand, therefore nothing in system
It is all unable to satisfy and the power supply for abandoning part CL of having to by how to dispatch.But in actual operation, this some electrical power notch can
It is compensated and buying electricity to public electric wire net.In addition, by the cooperative scheduling of this paper, making in system for insignificant load
NLs power requirement in most of time section can attain full and complete satisfaction, and only be greater than aggregate supply in systematic electricity aggregate demand
When, it can not just be met by scheduling, and this some electrical power still can be by buying electric acquisition.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, ripe
Knowing the those skilled in the art should be understood that the present invention includes but is not limited to content described in specific embodiment above.
Any modification without departing from function and structure principle of the invention is intended to be included in the range of claims.
Claims (2)
1. a kind of cross-domain collaboration interaction of wind-light storage bavin autonomy microgrid group and consumption method, it is characterized in that including the following steps:
(1) Δ t is sampled at timed intervals, obtains current time system distributed generation resource DGs, important load CLs, common load
The status information of NLs, energy storage device Ss；
(2) judge whetherAnd if so, (3) are executed,
Otherwise (18) are executed, whereinWithRespectively (t- Δ t) moment and t moment autonomy microgrid i_
The difference of the power of DG and CL, N in AMGAMG(t- Δ t) is (number of autonomy microgrid existing for the t- Δ t) moment；
(3) the network topology matrix A (t) of current t moment is constructed；
(4) judge whetherIt is to execute (5), otherwise executes (6), wherein NDGIt is microgrid group
The total number of middle DG, NCLIt is the total number of CL, NNLIt is the total number of NL；
(5)Si∈ x | x is load }, i=1 ..., NsAnd execute (9), wherein SiFor i-th of energy storage device, NsFor microgrid group
The total number of middle energy storage device；
(6) judge whetherIt is to execute (7), otherwise executes (8)；
(7)Si∈ x | and x | is | load } ∪ y | y is generater }, i=1 ..., NsAnd (9) and (10) are executed,；
(8)Si∈ y | y is generater }, i=1 ..., NsAnd execute (10)；
(9) judge whether each SiSOCi>=80%, i=1 ..., Ns, it is to execute (11), otherwise executes (12), wherein
SOCi, it is the state-of-charge of i-th of energy storage device；
(10) judge whether each SiSOCi≤ 20%, i=1 ..., Ns, it is to execute (11), otherwise executes (13)；
(11)Wherein, SiFor i-th of energy storage device, GiIt is i-th
Power supply, LiFor i-th of electrical equipment；NGFor the total number of power generating source in microgrid group, NLFor in microgrid group electric load it is total
Number；
(12)It executes (14), wherein SiFor i-th of energy storage device, GiIt is i-th
Power supply,For the total number that energy storage device is regarded as to power generating source in microgrid group；
(13)It executes (14), wherein SiFor i-th of energy storage device, LiIt is i-th
Electrical equipment,For the total number that energy storage device is regarded as to power load in microgrid group；
(14) calculate fromIt arrivesMinimum weight and: Min [sum
(weights)]ij, obtain NG×NCLMinimum tree, wherein GiFor i-th of power supply, NGFor in microgrid group power generating source it is total
Number, CLjFor j-th of important load, NCLFor the total number of important load in microgrid group, sum () is summing function,
Weights is network topology matrix weight；
(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), wherein GiFor i-th of power supply electricity
Source, CLjFor j-th of important load；
(17) judge whether each set { Gi,CLj},i∈(1,NG),j∈(1,NCL) in Gi-CLj> 0 is to execute (19), no
Then execute (18)；
(18) selection time small weight and Submin [sum (weights)]ijCorresponding GiAs jth important load CLjConfession
Electrical nodes simultaneously execute (19), wherein time minimum value is sought in Submin () expression, and sum () is summing function, and weights is net
Network topological matrix weight；
(19) G is determinediAnd CLjBetween municipal electricity consumption Municipal, tertiary industry electricity consumption Tertiary in common load NLs, gently
The number of commercial power Light is simultaneously stored in variable nM, nTAnd nLIn；
(20) linear moments: ε=min { xM (t)+yT (t)+zL (t)-(G are constructedi-CLj)}
s.t.:0≤ε
x≤nM；y≤nT；z≤nL
Wherein, M (t), T (t) and L (t) are respectively load power value of three kinds of NLs in moment t, and x, y, z is power supply GiIt is born with important
Lotus CLjBetween wish connection three kinds of NLs quantity；
(21) by solving above-mentioned LMI optimization problem, the value of x, y and z are determined；
(22) G is selectedi, CLjAnd between the twoY and z Municipal, Tertiary, Light composition set k |
kth|Autonomous MG,k∈(1,NG×NCL), i.e., k-th autonomous microgrid.
2. the cross-domain collaboration interaction of a kind of wind-light storage bavin autonomy microgrid group according to claim 1 and consumption method, feature
It is: the determination method of network topology matrix A (t) branch weight are as follows:
Assuming that a certain power load j in microgrid, active power and reactive power are respectively PjAnd Qj, upstream supply node is
I, then from node i to the power supply line of node j on all-in resistance and total reactance be respectively RijAnd Xij, it is assumed that the voltage of node j is protected
It holds as 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
It is determined by year failure-frequency and repair time, i.e.,
Wherein, f is year failure-frequency number；R is fault correction time；
Other than using and calculating its degree of unavailability to the failure-frequency of route and the statistical value of repair time, in conjunction with the reality of expert
The risk factor of engineering experience trade-off evaluation system each route,
Wherein, KijThe degree of unavailability of route between node i and node j is acquired by expression formula (2)；EijFor node i and nodej
Between route expert's assessed value；η is regulatory factor,
In order to the two preferably be unified under a Measure Indexes, it is necessary first to the two is normalized,
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 node i and
Node j is not connected directly, then aij=0；Diagonal entry aii=0.
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