CN106099964B  A kind of energystorage system participation active distribution network runing adjustment computational methods  Google Patents
A kind of energystorage system participation active distribution network runing adjustment computational methods Download PDFInfo
 Publication number
 CN106099964B CN106099964B CN201610430803.XA CN201610430803A CN106099964B CN 106099964 B CN106099964 B CN 106099964B CN 201610430803 A CN201610430803 A CN 201610430803A CN 106099964 B CN106099964 B CN 106099964B
 Authority
 CN
 China
 Prior art keywords
 energy
 storage
 distribution network
 active
 power
 Prior art date
Links
 238000004146 energy storage Methods 0.000 title claims abstract description 105
 238000004422 calculation algorithm Methods 0.000 claims abstract description 12
 239000002245 particle Substances 0.000 claims abstract description 11
 238000002402 nanowire electron scattering Methods 0.000 claims description 15
 239000000243 solution Substances 0.000 claims description 9
 238000004364 calculation method Methods 0.000 claims description 8
 238000000034 method Methods 0.000 claims description 7
 239000011159 matrix material Substances 0.000 claims description 6
 238000004088 simulation Methods 0.000 claims description 6
 238000005457 optimization Methods 0.000 claims description 4
 238000009825 accumulation Methods 0.000 claims description 3
 238000002347 injection Methods 0.000 claims description 3
 239000007924 injection Substances 0.000 claims description 3
 238000007796 conventional method Methods 0.000 abstract description 2
 239000000203 mixture Substances 0.000 abstract description 2
 230000005611 electricity Effects 0.000 description 6
 238000005516 engineering process Methods 0.000 description 5
 235000006508 Nelumbo nucifera Nutrition 0.000 description 2
 240000002853 Nelumbo nucifera Species 0.000 description 2
 235000006510 Nelumbo pentapetala Nutrition 0.000 description 2
 238000007599 discharging Methods 0.000 description 2
 230000000694 effects Effects 0.000 description 2
 230000002123 temporal effect Effects 0.000 description 2
 238000010586 diagram Methods 0.000 description 1
 230000029087 digestion Effects 0.000 description 1
 230000004048 modification Effects 0.000 description 1
 238000006011 modification reaction Methods 0.000 description 1
 230000001105 regulatory Effects 0.000 description 1
Classifications

 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
 H02J3/00—Circuit arrangements for ac mains or ac distribution networks
 H02J3/28—Arrangements for balancing of the load in a network by storage of energy
 H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means

 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
 H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
 H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

 Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSSSECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
 Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
 Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
 Y02E10/00—Energy generation through renewable energy sources
 Y02E10/50—Photovoltaic [PV] energy
 Y02E10/56—Power conversion systems, e.g. maximum power point trackers

 Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSSSECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
 Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
 Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
 Y02E70/00—Other energy conversion or management systems reducing GHG emissions
 Y02E70/30—Systems combining energy storage with energy generation of nonfossil origin
Abstract
The present invention provides a kind of energystorage system participation active distribution network runing adjustment computational methods, for the energystorage system of accumulator composition, with the minimum object function of distribution network system active loss, the operation of consideration system itself constrains, including system load flow constraint, working voltage constraint, branch current constraint and energystorage system operation constraint, example is solved using particle cluster algorithm, the chargedischarge electric power of final output energystorage system day part under the premise of meeting system reliability is as optimal solution.The present invention can effectively reduce the active power loss of distribution network system compared to conventional method, reduce operation of power networks cost, increase the utilization ratio of photovoltaic energy.
Description
Technical field
The present invention relates to a kind of energystorage systems to participate in active distribution network runing adjustment technology, and in particular to a kind of accumulator storage
It can system participation active distribution network runing adjustment computational methods.
Background technology
It is the distributed generation technology of core in world's model using renewable energy utilization by the dualpressure of energy and environment
Interior extensive rise is enclosed, the application and development of energy storage technology in the power system are greatly promoted.On the one hand, by means of energy storage system
System can efficiently reduce distributed generation resource and contribute to be influenced caused by intermittent and randomness, is formed using microcapacitance sensor as core
Selfgovernment system；On the other hand, the energystorage system of large capacity also provides new means and side to the runing adjustment of power distribution network
Method.In terms of distribution system angle, the application of energy storage technology can not only improve the digestion capability of distributed energy, additionally it is possible to actively
The effective adjusting and optimization for participating in system load flow, can greatly improve the economy and reliability of distribution system operation.
How energystorage system is made full use of, realizes the emphasis paid close attention at present when the high efficient and reliable of distribution system is run, it is domestic
Outer correlation it is studied, and achieve the achievement in terms of some theory and practice, such as analyze accumulator position
Set distribution and the influence of amount of capacity, and to positive effect that peak regulation is played；It has studied and contains distributed generation resource and accumulator
Power distribution network/microcapacitance sensor running optimizatin problem, give storage battery active power and reactive power be carried out at the same time the mathematical model of optimization；
And schedulable characteristic and quantity of electric charge information according to accumulator, it is proposed that one kind being based on constant currentconstant voltage control strategy
Accumulator cell charging and discharging mathematical model.
Different from distributed generation resource, there are apparent temporal characteristics, running optimizatin no longer to limit to for the operation of energystorage system
The discontinuity surface when single, but expand in longer time scale, there are problems that sequential running optimizatin, and then it is caused to determine
Discontinuity surface number increases and increases rapidly plan dimension at any time.
Invention content
The goal of the invention of the present invention is to solve the above problems, participating in active distribution network operation for energystorage system of accumulator
The Optimal Operation Model of adjusting provides a kind of energystorage system participation active distribution network runing adjustment computational methods.
In order to solve the above technical problems, the embodiment of the present invention provides a kind of energystorage system participation active distribution network operation tune
Computational methods are saved, are included the following steps：
(1) the active and idle characteristic for considering energystorage system, establishes energystorage system moving model；
(2) determine that the object function that energystorage system participates in active distribution network runing adjustment is：The active damage of distribution network system
Consumption is minimum；
(3) the operation constraint of distribution network system itself is taken into account in calculating process, including system load flow constraint, working voltage are about
Beam, branch current constraint and energystorage system operation constraint；
(4) PSO Algorithm energystorage system is utilized to participate in the Optimized model of power distribution network runing adjustment；
(5) optimal solution is exported：Under the premise of meeting distribution network system reliability, the chargedischarge electric power of energystorage system day part
As optimal solution.
By taking typical energystorage system of accumulator as an example, it is mainly made of accumulator and transverter, and transverter is mainly responsible for
Monitoring operation of power networks situation sends out the work such as control signal.The electric interfaces that transverter is connected as accumulator with power grid are to store
Battery energy storage system carries out the hinge of energy exchange with power distribution network, can realize the charge and discharge control of active power, also, the change of current
Utensil has certain idle miscellaneous function, can be power distribution network by idle control while executing charging and discharging function
Voltage support is provided.
Wherein, when establishing energystorage system of accumulator moving model in step (1), it is assumed that energystorage system of accumulator is with to distribution
Net output power is positive direction, considers its active and idle characteristic, and running boundary constraint is as follows：
In formula：K=1,2 ..., N_{ESS}, wherein N_{ESS}For energystorage system of accumulator number；WithWhen respectively t
Carve the active power and reactive power of the output of kth of transverter；WithThe rated capacity of respectively kth transverter and
The active power upper limit；WithThe respectively chargedischarge electric power of energystorage system of accumulator.
Step (1), it is assumed that energystorage system of accumulator using to power distribution network output power as positive direction, then energystorage system of accumulator
Input power is negative direction, naturally it is also possible to it is assumed that energystorage system of accumulator to power distribution network output power as negative direction, then to store
Battery energy storage system input power is positive direction, is suitable for this optimizing regulation computational methods.
The stateofcharge of the energystorage system of accumulator has absolute continuity in sequential, suitable in strict accordance with the time
Sequence carries out accumulation calculating according to chargedischarge electric power size, and calculation formula is as follows：
In formula：K=1,2 ..., N_{ESS}；Δ t is simulation step length；For the lotus of kth of energystorage system of accumulator of t moment
Electricity condition；
The energy storage capacity of the energystorage system of accumulator each time point should meet the requirement of stateofcharge bound, expression formula
It is as follows：
In formula,The capacity and stateofcharge of respectively kth energystorage system of accumulator
Upper lower limit value.
Power distribution network running optimizatin problem containing energystorage system is usually contributed with cost of electricitygenerating, the whole network active loss, substation
Minimum, new energy receives ability maximum and the combination etc. of plurality of target function is optimization aim, and the distribution network system has
The active power that work(loss is injected by entire distribution network system subtracts the active power that load is consumed, i.e. distribution network system is each
The sum of the active power of a node injection, with the minimum object function of distribution network system active loss in abovementioned step (2),
Mathematic(al) representation is：
In formula, N is system node number；N_{T}For when discontinuity surface number；Pi (t) is the active power injected at t moment node i；
Δ t is steplength.
Wherein, in step (3), the operation constraint of the distribution network system itself includes system load flow constraint, working voltage
Constraint, branch current constraint and energystorage system of accumulator operation constraint, it is specific as follows：
(31) system load flow constrains
In formula：I=2,3 ..., N；Ω (i) is the set of the adjacent node of node i；U_{i}(t), U_{j}(t), θ_{ij}(t) it is respectively
The voltage magnitude and phase angle difference of t moment node i and j；G_{ii}, B_{ii}, G_{ij}, B_{ij}Selfconductance respectively in node admittance matrix, from electricity
It receives, transconductance and mutual susceptance；P_{i} ^{PV}(t), P_{i} ^{ESS}(t), P_{i} ^{L}(t),Respectively t moment node i
The active power and reactive power that upper PV, accumulator, load inject；
(32) working voltage constrains
U_{imin}≤U_{i}(t)≤U_{imax}Formula (9), i=1,2 ..., N,
In formula, U_{imin}And U_{imax}The respectively bound of node i voltage magnitude；
(33) branch current constrains
In formula, I_{ij}(t) current amplitude of branch between node i and node j is flowed through for t moment；U_{i}(t), U_{j}(t), θ_{ij}(t)
The respectively voltage magnitude and phase angle difference of t moment node i and j；G_{ii}, B_{ii}, G_{ij}, B_{ij}Electricity certainly respectively in node admittance matrix
It leads, from susceptance, transconductance and mutual susceptance；I_{ijmax}For the current amplitude upper limit of branch ij；
The operation constraint of (34) energystorage system of accumulator
In formula, k=1,2 ..., N_{ESS}, wherein N_{ESS}For energystorage system of accumulator number；WithWhen respectively t
Carve the active power and reactive power of the output of kth of transverter；WithThe rated capacity of respectively kth transverter and
The active power upper limit；WithThe respectively chargedischarge electric power of accumulator；Δ t is simulation step length；For t when
Carve the stateofcharge of kth of energystorage system of accumulator；Respectively kth of energystorage system of accumulator
Capacity and stateofcharge upper lower limit value.
Wherein, in step (4), by matlab software for calculation, distribution is participated in using PSO Algorithm energystorage system
The Optimized model that network operation is adjusted.Each particle is in an iterative process as the following formula to the speed of particle in the particle cluster algorithm
It is updated with position：
v_{t+1}=ω v_{t}+c_{1}rand()(P_{t}x_{t})+c_{2}rand()(G_{t}x_{t}) formula (11),
x_{t+1}=x_{i}+v_{t}Formula (12),
In formula, i is evolutionary generation；ω is inertia weight；c_{1}、c_{2}For accelerated factor；Rand () is random between [0,1]
Number.
The abovementioned technical proposal of the present invention has the beneficial effect that：A kind of energystorage system provided by the invention participates in actively matching
Operation of power networks regulating calculation method can effectively reduce the active power loss of distribution network system compared to conventional method, reduce power grid fortune
Row cost increases the utilization ratio of photovoltaic energy.
Description of the drawings
Fig. 1 is the calculation flow chart of the embodiment of the present invention one；
Fig. 2 is IEEE33 node power distribution web frame figures in embodiment one；
Fig. 3 is lightpreserved system structural schematic diagram in embodiment one；
Fig. 4 is photovoltaic and load day operation curve in embodiment one；
Fig. 5 is energystorage system of accumulator chargedischarge electric power curve in embodiment one；
Fig. 6 is energystorage system of accumulator reactive capability curve in embodiment one；
Fig. 7 is energystorage system of accumulator stateofcharge change curve in embodiment one.
Specific implementation mode
To keep the technical problem to be solved in the present invention, technical solution and advantage clearer, below in conjunction with attached drawing and tool
Body embodiment is described in detail.
The present invention provides a kind of energystorage system participation active distribution network runing adjustment calculating by taking energystorage system of accumulator as an example
Method includes the following steps：
(1) the active and idle characteristic for considering energystorage system, establishes energystorage system of accumulator moving model；
(2) determine that the object function that energystorage system of accumulator participates in active distribution network runing adjustment is：Distribution network system
Active loss is minimum；
(3) the operation constraint of distribution network system itself is taken into account in calculating process, including system load flow constraint, working voltage are about
Beam, branch current constraint and energystorage system of accumulator operation constraint；
(4) PSO Algorithm energystorage system is utilized to participate in the Optimized model of power distribution network runing adjustment；
(5) optimal solution is exported：Under the premise of meeting distribution network system reliability, the charge and discharge of energystorage system of accumulator day part
Electrical power is optimal solution.
Energystorage system participates in the calculation process of active distribution network runing adjustment computational methods as shown in Figure 1, being embodied
Journey is as follows：
Different from distributed generation resource, there are apparent temporal characteristics, running optimizatin no longer to limit to for the operation of energystorage system
The discontinuity surface when single, but expand in longer time scale, sequential running optimizatin problem is formd, and then it is caused to determine
Discontinuity surface number increases and increases rapidly plan dimension at any time.For this purpose, the present invention participates in actively for energystorage system of accumulator
A kind of Optimal Operation Model of power distribution network runing adjustment, it is proposed that energystorage system participation active distribution network runing adjustment calculating side
Method.
Hereafter with the solution of IEEE33 nodes example (structure such as Fig. 2) to the power distribution network running optimizatin algorithm containing energystorage system
Validity and rapidity verified.8 groups of lightpreserved systems, system structure and basic configuration parameter are accessed in example as schemed
3 and table 1 shown in.Consider that the energy storage for carrying out one day optimizes, load day operation curve is obtained using load forecasting method, takes 30min
One point, the processing mode of photovoltaic are identical.The photovoltaic of whole system is contributed and load variations situation is as shown in Figure 4.
1 lightpreserved system of table configures parameter
1, energystorage system of accumulator moving model is established
It is assumed that energystorage system of accumulator to be, as positive direction, to consider its active and idle characteristic to power grid output power,
Running boundary constraint is as follows：
In formula：K=1,2 ..., N_{ESS}, wherein N_{ESS}For energystorage system of accumulator number；WithWhen respectively t
Carve the active power and reactive power of the output of kth of transverter；WithThe rated capacity of respectively kth transverter and
The active power upper limit；WithThe respectively chargedischarge electric power of energystorage system of accumulator.
On the other hand, the stateofcharge of energystorage system of accumulator in sequential have absolute continuity, it in strict accordance with
Time sequencing carries out accumulation calculating according to chargedischarge electric power size, and the energy storage capacity of each time point should meet on stateofcharge
The requirement of lower limit,
In formula：K=1,2 ..., N_{ESS}；Δ t is simulation step length；For the lotus of kth of energystorage system of accumulator of t moment
Electricity condition；The capacity of respectively kth energystorage system of accumulator and stateofcharge up and down
Limit value.
2, with the minimum object function of distribution network system active loss
The active power that the active loss of the distribution network system is injected by entire distribution network system subtracts load and is disappeared
The sum of the active power of each node injection of the active power of consumption, i.e. distribution network system, mathematic(al) representation is：
In formula, N is system node number；N_{T}For when discontinuity surface number；Pi (t) is the active power injected at t moment node i；
Δ t is steplength.
3, consider the operation constraint of distribution network system itself, including system load flow constraint, working voltage constraint, branch current
Constraint and energystorage system of accumulator operation constraint, it is specific as follows：
(31) system load flow constrains
In formula：I=2,3 ..., N；Ω (i) is the set of the adjacent node of node i；U_{i}(t), U_{j}(t), θ_{ij}(t) it is respectively
The voltage magnitude and phase angle difference of t moment node i and j；G_{ii}, B_{ii}, G_{ij}, B_{ij}Selfconductance respectively in node admittance matrix, from electricity
It receives, transconductance and mutual susceptance；P_{i} ^{PV}(t), P_{i} ^{ESS}(t), P_{i} ^{L}(t),Respectively t moment node i
The active power and reactive power that upper PV, accumulator, load inject；
(32) working voltage constrains
U_{imin}≤U_{i}(t)≤U_{imax}Formula (9), i=1,2 ..., N,
In formula, U_{imin}And U_{imax}The respectively bound of node i voltage magnitude；
(33) branch current constrains
In formula, I_{ij}(t) current amplitude of branch between node i and node j is flowed through for t moment；U_{i}(t), U_{j}(t), θ_{ij}(t)
The respectively voltage magnitude and phase angle difference of t moment node i and j；G_{ii}, B_{ii}, G_{ij}, B_{ij}Electricity certainly respectively in node admittance matrix
It leads, from susceptance, transconductance and mutual susceptance；I_{ijmax}For the current amplitude upper limit of branch ij；
The operation constraint of (34) energystorage system of accumulator
In formula, k=1,2 ..., N_{ESS}, wherein N_{ESS}For energystorage system of accumulator number；WithWhen respectively t
Carve the active power and reactive power of the output of kth of transverter；WithThe rated capacity of respectively kth transverter and
The active power upper limit；WithThe respectively chargedischarge electric power of accumulator；Δ t is simulation step length；For t when
Carve the stateofcharge of kth of energystorage system of accumulator；Respectively kth of energystorage system of accumulator
Capacity and stateofcharge upper lower limit value.
4, with formula (6) for object function, formula (1)formula (5), formula (7)(10) are constraints, using by formula (11) and formula
(12) modified particle swarm optiziation by matlab software for calculation and substitutes into concrete numerical value, utilizes PSO Algorithm energy storage
System participates in the Optimized model of power distribution network runing adjustment, wherein each particle is in an iterative process in the particle cluster algorithm
The speed of particle and position are updated as the following formula：
v_{t+1}=ω v_{t}+c_{1}rand()(P_{t}x_{t})+c_{2}rand()(G_{t}x_{t}) formula (11),
x_{t+1}=x_{i}+v_{t}Formula (12),
In formula, i is evolutionary generation；ω is inertia weight；c_{1}、c_{2}For accelerated factor；Rand () is random between [0,1]
Number.
5, export optimal solution, as under the premise of meeting system reliability energystorage system day part chargedischarge electric power.
In the present embodiment, photovoltaic according to Fig.4, and load day operation curve participate in master using abovementioned energystorage system
Dynamic power distribution network runing adjustment computational methods optimize distribution network system, as a result as shown in Fig. 5~Fig. 7.
PSO Algorithm Optimized model is used in MATLAB, participating in power distribution network in energystorage system of accumulator optimizes it
Before, system loss 1316.05kWh.Energystorage system of accumulator is by planning as a whole day part photovoltaic output situation and load
Power demand to realize peak load shifting, and provides certain reactive power support, may finally be reduced to system loss
390.64kW·h。
The above is the preferred embodiment of the present invention, it is noted that for those skilled in the art
For, without departing from the principles of the present invention, it can also make several improvements and retouch, these improvements and modifications
It should be regarded as protection scope of the present invention.
Claims (5)
1. a kind of energystorage system participates in active distribution network runing adjustment computational methods, which is characterized in that include the following steps：
(1) the active and idle characteristic for considering energystorage system, establishes energystorage system moving model；
(2) determine that the object function that energystorage system participates in active distribution network runing adjustment is：The active loss of distribution network system is most
It is small；
(3) the operation constraint of distribution network system itself is taken into account in calculating process, including system load flow constrains, working voltage constrains,
Branch current constrains and energystorage system operation constraint, specific as follows：
(31) system load flow constrains
In formula：I=2,3 ..., N；Ω (i) is the set of the adjacent node of node i；U_{i}(t), U_{j}(t), θ_{ij}(t) it is respectively t moment
The voltage magnitude and phase angle difference of node i and j；G_{ii}, B_{ii}, G_{ij}, B_{ij}Selfconductance respectively in node admittance matrix, from susceptance, mutually
Conductance and mutual susceptance；P_{i} ^{PV}(t), P_{i} ^{ESS}(t), P_{i} ^{L}(t),Respectively photovoltaic in t moment node i
The active power and reactive power that power station, accumulator, load inject；
(32) working voltage constrains
U_{imin}≤U_{i}(t)≤U_{imax}Formula (9), i=1,2 ..., N,
In formula, U_{imin}And U_{imax}The respectively bound of node i voltage magnitude；
(33) branch current constrains
In formula, I_{ij}(t) current amplitude of branch between node i and node j is flowed through for t moment；U_{i}(t), U_{j}(t), θ_{ij}(t) respectively
For the voltage magnitude and phase angle difference of t moment node i and j；G_{ii}, B_{ii}, G_{ij}, B_{ij}Selfconductance respectively in node admittance matrix, from
Susceptance, transconductance and mutual susceptance；I_{ijmax}For the current amplitude upper limit of branch ij；
The operation constraint of (34) energystorage system
In formula, k=1,2 ..., N_{ESS}, wherein N_{ESS}For energystorage system number；WithRespectively kth of change of current of t moment
The active power and reactive power of device output；WithIn the rated capacity and active power of respectively kth transverter
Limit；WithThe respectively chargedischarge electric power of accumulator；Δ t is simulation step length；For kth of energy storage of t moment
The stateofcharge of system；The capacity of respectively kth energystorage system and stateofcharge it is upper
Lower limiting value；
(4) PSO Algorithm energystorage system is utilized to participate in the Optimized model of power distribution network runing adjustment；
(5) optimal solution is exported：Under the premise of meeting distribution network system reliability, the chargedischarge electric power of energystorage system day part is
Optimal solution.
2. energystorage system according to claim 1 participates in active distribution network runing adjustment computational methods, which is characterized in that step
Suddenly in (1), when establishing energystorage system moving model, it is assumed that energystorage system is positive direction to power distribution network output power, is considered
Characteristic that its is active and idle, running boundary constraint are as follows：
In formula：K=1,2 ..., N_{ESS}, wherein N_{ESS}For energystorage system number；WithRespectively kth of change of current of t moment
The active power and reactive power of device output；WithIn the rated capacity and active power of respectively kth transverter
Limit；WithThe respectively chargedischarge electric power of energystorage system；
The stateofcharge of the energystorage system has absolute continuity in sequential, in strict accordance with time sequencing according to charge and discharge
Electrical power size carries out accumulation calculating, and calculation formula is as follows：
In formula：K=1,2 ..., N_{ESS}；Δ t is simulation step length；For the stateofcharge of kth of energystorage system of t moment；
For the energy storage capacity of the energystorage system each time point between the bound of stateofcharge, expression formula is as follows：
In formula,The capacity of respectively kth energystorage system and the upper lower limit value of stateofcharge.
3. energystorage system according to claim 1 participates in active distribution network runing adjustment computational methods, which is characterized in that step
Suddenly in (2), active power that the active loss of the distribution network system is injected by entire distribution network system subtracts load and is disappeared
The sum of the active power of each node injection of the active power of consumption, i.e. distribution network system, mathematic(al) representation is：
In formula, N is system node number；N_{T}For when discontinuity surface number；Pi (t) is the active power injected at t moment node i；Δ t is
Steplength.
4. energystorage system according to claim 1 participates in active distribution network runing adjustment computational methods, which is characterized in that step
Suddenly each particle in an iterative process as the following formula carries out more the speed of particle and position in the particle cluster algorithm described in (4)
Newly：
v_{t+1}=ω v_{t}+c_{1}rand()(P_{t}x_{t})+c_{2}rand()(G_{t}x_{t}) formula (11),
x_{t+1}=x_{i}+v_{t}Formula (12),
In formula, i is evolutionary generation；ω is inertia weight；c_{1}、c_{2}For accelerated factor；Random numbers of the rand () between [0,1].
5. energystorage system according to claim 1 participates in active distribution network runing adjustment computational methods, which is characterized in that step
Suddenly in (4), by matlab software for calculation, the optimization of power distribution network runing adjustment is participated in using PSO Algorithm energystorage system
Model.
Priority Applications (1)
Application Number  Priority Date  Filing Date  Title 

CN201610430803.XA CN106099964B (en)  20160616  20160616  A kind of energystorage system participation active distribution network runing adjustment computational methods 
Applications Claiming Priority (1)
Application Number  Priority Date  Filing Date  Title 

CN201610430803.XA CN106099964B (en)  20160616  20160616  A kind of energystorage system participation active distribution network runing adjustment computational methods 
Publications (2)
Publication Number  Publication Date 

CN106099964A CN106099964A (en)  20161109 
CN106099964B true CN106099964B (en)  20180911 
Family
ID=57235658
Family Applications (1)
Application Number  Title  Priority Date  Filing Date 

CN201610430803.XA CN106099964B (en)  20160616  20160616  A kind of energystorage system participation active distribution network runing adjustment computational methods 
Country Status (1)
Country  Link 

CN (1)  CN106099964B (en) 
Families Citing this family (5)
Publication number  Priority date  Publication date  Assignee  Title 

CN106998072A (en) *  20170515  20170801  国网江苏省电力公司电力科学研究院  A kind of mixed energy storage system capacity configuration optimizing method for optimizing operation towards power distribution network 
CN107104433B (en) *  20170515  20200814  国网江苏省电力公司电力科学研究院  Method for acquiring optimal operation strategy of optical storage system participating in power distribution network 
CN107403239B (en) *  20170725  20210212  南京工程学院  Parameter analysis method for control equipment in power system 
CN107341623A (en) *  20170831  20171110  国家电网公司  A kind of active power distribution network source storage lotus islet operation method of meter and network reconfiguration 
CN107947231B (en) *  20171201  20200421  国网江苏省电力有限公司电力科学研究院  Hybrid energy storage system control method for optimized operation of power distribution network 
Citations (2)
Publication number  Priority date  Publication date  Assignee  Title 

CN103956758A (en) *  20140520  20140730  电子科技大学  Energy storage SOC optimization control method in wind storage system 
CN104779630A (en) *  20150508  20150715  武汉大学  Capacity allocation method for hybrid energy storage system capable of restraining wind power output power fluctuation 
Family Cites Families (1)
Publication number  Priority date  Publication date  Assignee  Title 

US20160043548A1 (en) *  20130815  20160211  Nec Laboratories America, Inc.  Rolling stochastic optimization based operation of distributed energy systems with energy storage systems and renewable energy resources 

2016
 20160616 CN CN201610430803.XA patent/CN106099964B/en active IP Right Grant
Patent Citations (2)
Publication number  Priority date  Publication date  Assignee  Title 

CN103956758A (en) *  20140520  20140730  电子科技大学  Energy storage SOC optimization control method in wind storage system 
CN104779630A (en) *  20150508  20150715  武汉大学  Capacity allocation method for hybrid energy storage system capable of restraining wind power output power fluctuation 
Also Published As
Publication number  Publication date 

CN106099964A (en)  20161109 
Similar Documents
Publication  Publication Date  Title 

CN103840457B (en)  Consider DG Optimal Configuration Method in the power distribution network that electric automobile discharge and recharge affects  
CN104701871B (en)  One kind is containing the honourable complementary microgrid hybrid energystoring capacity optimum proportioning method of water multisource  
CN102931687B (en)  Power adjustment method for hybrid energy storage photovoltaic power station  
CN103441531B (en)  Area highpermeability photovoltaic energy storage system and energy management method thereof  
CN103311942B (en)  Control method of battery energy storage system for peak clipping and valley filling in distribution network  
CN106410861B (en)  A kind of microcapacitance sensor optimization operation realtime control method based on schedulable ability  
CN103248064B (en)  A kind of compound energy charging energystoring system and method thereof  
CN101777769B (en)  Multiagent optimized coordination control method of electric network  
Wu et al.  A multiagentbased energycoordination control system for gridconnected largescale wind–photovoltaic energy storage powergeneration units  
Wu et al.  Optimal coordinate operation control for wind–photovoltaic–battery storage powergeneration units  
Etxeberria et al.  Hybrid energy storage systems for renewable energy sources integration in microgrids: A review  
CN105406518B (en)  Energy storage participates in the AGC control methods and control system of electric grid secondary frequency modulation  
CN104037793B (en)  A kind of energystorage units capacity collocation method being applied to active distribution network  
CN106451550B (en)  A kind of microgrid connection Optimization Scheduling based on improvement subgradient population  
CN102545250B (en)  Power slide control method, device and working method of wind farm utilizing lithium ion battery to store energy  
CN103366314B (en)  Consider the wind energy turbine set composite energy storage method for planning capacity that goal decomposition and complementation thereof are stabilized  
CN103151797A (en)  Multiobjective dispatching modelbased microgrid energy control method under gridconnected operation mode  
CN105811409B (en)  A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile  
CN106099965B (en)  Exchange the control method for coordinating of COMPLEX MIXED energystorage system under microgrid connection state  
CN106026169B (en)  A kind of composition decomposition optimization method that power distribution network is incorporated to based on more microcapacitance sensors  
CN105787605A (en)  Microgrid economic and optimal operation and scheduling method based on improved quantum genetic algorithm  
Neto et al.  A dualbattery storage bank configuration for isolated microgrids based on renewable sources  
Kong et al.  Hierarchical distributed model predictive control of standalone wind/solar/battery power system  
CN105281360B (en)  A kind of distributed photovoltaic automatic power generation control method based on sensitivity  
CN104659804A (en)  Micro power grid with hybrid energy storage, and control method of micro power grid 
Legal Events
Date  Code  Title  Description 

PB01  Publication  
C06  Publication  
SE01  Entry into force of request for substantive examination  
C10  Entry into substantive examination  
GR01  Patent grant  
GR01  Patent grant 