CN109193729A - The site selecting method of energy-storage system in a kind of distribution automation system - Google Patents
The site selecting method of energy-storage system in a kind of distribution automation system Download PDFInfo
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- CN109193729A CN109193729A CN201811339760.XA CN201811339760A CN109193729A CN 109193729 A CN109193729 A CN 109193729A CN 201811339760 A CN201811339760 A CN 201811339760A CN 109193729 A CN109193729 A CN 109193729A
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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
- 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
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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
- 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]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE 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 non-fossil origin
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Abstract
The invention discloses a kind of site selecting methods of energy-storage system in distribution automation system, by the impact analysis for accessing distribution network systems to energy-storage system, one kind is established with node voltage fluctuation, system loading fluctuation and the minimum target of energy storage system capacity, take node voltage and power-balance as the energy storage addressing Model for Multi-Objective Optimization of constraint.During model solution, target value normalized, linear weighted function polymerization and analytic hierarchy process (AHP) first are carried out to multiple sub-goals and judge weight, it makes single-goal function, and with the PSO Algorithm of linear weighted function, finally carries out simulating, verifying in IEEE14 node power distribution net system.
Description
Technical field
The present invention relates to the addressing field of energy-storage system in distribution automation system more particularly to a kind of distribution automations
The site selecting method of energy storage device in system.
Background technique
Distributed generation resource such as photovoltaic generating system is connected to the grid on a large scale can be reduced route loss, promote node voltage water
It is flat, however the fluctuation of its output power and uncertainty exacerbate network load and node voltage fluctuation, reduce power train
The q&r of system.And energy-storage system has ability, the high speed of charge and discharge and the high efficiency of energy in bidirectional flow, is connecing
After entering distribution key node, the influence of photovoltaic generating system bring can be stabilized well, is played and is stabilized fluctuation, peak load shifting, mentions
The power quality of high power distribution network and the effect for improving system economy make distribution operation that can have more stability and safety.Pass through
Optimum choice to the addressing constant volume scheme for being equipped with energy-storage system, maximizing utilization rate promote stablizing effect, wherein access digit
The selection set is the most key problem.
The addressing constant volume of energy-storage system is a multi-objective optimization question.Currently used objective optimization algorithm have differential into
Change algorithm, genetic algorithm, Artificial Immune Algorithm etc., however above-mentioned several algorithms computational efficiency in solution procedure is lower, convergence
Speed is slower.Particle swarm algorithm concurrently scans for effective solution using efficient cluster, and every time can in iterative process
Multiple effective solutions are generated, while particle swarm algorithm has memory function, particle is complete by tracking itself history optimal solution and population
Office's optimal solution scans for, so that particle swarm algorithm has good convergence and ability of searching optimum in searching process.
It for multi-objective optimization question, can not often compare between target, or even conflict mutually, can only be assisted between each sub-goal
Tradeoff and compromise are adjusted, so that each sub-goal reaches as optimal as possible, such as each target can be made to be comparable by normalization
With weighting property, single goal is converted by multiple target, greatly simplifies computation complexity.
Summary of the invention
In view of the deficiencies of the prior art, the purpose of the present invention is to provide energy-storage systems in a kind of distribution automation system
Site selecting method.
The present invention is to realize that implementation steps are as follows by following technological means:
Step (1) obtains the topological model for the distribution network systems that energy storage device is accessed；The topological model should include net
Network topological structure, reference voltage Un, reference capacity Sn, all-network node power parameter S and each item branch road impedance ginseng
Number zi。
Step (2) determines the on-position of photovoltaic devices, obtains its power curve.
Step (3) is obtained according in the network topology structure model of distribution network systems obtained in step (1) and step (2)
Photovoltaic devices access and power output, establish objective function and constraint condition:
(3.1) objective function is established.F is fluctuated with node voltage1, system loading fluctuate f2And energy storage system capacity f3Three
A index forms multi-goal optimizing function: minF (x, y)=(f1(x,y),f2(x,y),f3(x,y)).Wherein,M is number of nodes, number at the time of T indicates total, UijIndicate the voltage value at j-th of moment of i-th of node,Indicate average voltage of i-th of node in time T； Indicate power grid input in T time
Mean power, Ps(j) in the input power of j moment power grid；N is energy storage device number, EstoreIt (k) is the
The rated capacity of k energy storage device, the selection rule of the rated capacity of single energy storage device are as follows: Estore=max { Estore,1,
Estore,2, wherein Estore,1For the maximum charge-discharge energy of energy storage device, Estore,2For the accumulation charge and discharge electric energy of energy storage device
Amount.Estore,1And Estore,2Selection rule is as follows:
By charging and discharging, the power curve of energy storage device is divided into n sections, so that every section all maintains always charge or discharge
State, the not change of state.Charge-discharge energy is denoted as E in i-th sectionsi, then the maximum charge-discharge energy E of energy storage devicestore,1
Choose EsiMaximum value, shown in formula such as formula (1):
Wherein tiAt the beginning of indicating i-th section, tieIndicate i-th section of end time, Pstore(t) t moment energy storage is indicated
The charge-discharge electric power of device, Δ t indicate i-th section of time interval, i.e. Δ t=tie-ti。
In observing time T, the accumulation charge-discharge energy in the energy storage device j period is denoted as Eadsj, then energy storage device
Accumulate charge-discharge energy Estore,2It is chosen for EadsjMaximum value, shown in formula such as formula (2):
(3.2) constraint condition is constructed:
(3.2.1) node voltage constraint: Ui,min<=Ui<=Ui,max(i=1,2, M), wherein Ui,minTable
Show the lower voltage limit of i-th of node, Ui,maxIndicate the upper voltage limit of i-th of node, M is number of nodes；
(3.2.2) power-balance constraint:What wherein each code name indicated is same
When inscribe, PloadiIndicate the load power of i-th of node, PstorekIndicate the power of k-th of energy storage device, PdgjIt indicates j-th
The power of photovoltaic power generation power supply, PsIndicate power grid input power, wherein N is the number of energy storage device, and D is photovoltaic generating system
Number；(3.2.3) energy storage power constraint: Pstorek,min<=Pstorek<=Pstorek,max(k=1,2, N), wherein
Pstorek,minIndicate the energy storage lower limit of the power of k-th of energy storage device, Pstorek,maxIt indicates on the energy storage power of k-th of energy storage device
Limit；
The constraint of (3.2.4) energy storage energy balance:；
In summary listed objective function and constraint condition, the addressing constant volume Model for Multi-Objective Optimization of energy storage device
Are as follows:
Step (4), solves above-mentioned multi-objective optimization question, is divided into two steps:
(4.1) multiple target is converted into single goal, is carried out in two steps:
(4.1.1) is standardized using deviation to each sub-goal functional value normalized, the steps include:
Step1 is within the scope of feasible solution, to each single-object problem, finds out the solution and objective function of formula (1) respectively
Value, is expressed as fi,min。
minfi(x) (i=1,2, m) (1)
Wherein, independent variable x indicates the node of distribution network, fi(x) i-th of objective optimization function is indicated.
Step2 is within the scope of feasible solution, to each single-object problem, finds out the solution and objective function of formula (2) respectively
Value, is expressed as fi,max。
maxfi(x) (i=1,2, m) (2)
Step3 is normalized using transfer function:
(4.1.2) weigthed sums approach polymerize multiple objective function:
Wherein, m indicate objective function quantity, value 3, X be the node set for meeting constraint condition, that is, constrain
Collection indicates the feasible solution range for meeting constraint condition.λiMeetλ is obtained with analytic hierarchy process (AHP)iValue.
(4.2) single-goal function that step 4.1 obtains is asked using the PSO algorithm (particle swarm algorithm) of linear weight
Solution, obtains single-goal function optimal solution, the i.e. optimal addressing of energy storage device.
Beneficial effects of the present invention are as follows: the present invention is established by the impact analysis to energy-storage system access distribution network systems
It is a kind of with node voltage fluctuation, system loading fluctuation and energy storage system capacity minimum target, it is flat with node voltage and power
Weighing apparatus is the energy storage addressing Model for Multi-Objective Optimization of constraint.During model solution, target value first is carried out to multiple sub-goals
Normalized, linear weighted function polymerization and analytic hierarchy process (AHP) judge weight, make single-goal function, and with linear weighted function
PSO Algorithm has obtained the allocation optimum position of energy-storage system.
Detailed description of the invention
Fig. 1: IEEE-14 node distribution network systems.
Fig. 2: Load flow calculation recurrence model.
Fig. 3: photovoltaic power generation curve.
Fig. 4: voltage magnitude figure under optimal on-position.
Specific embodiment
Step (1) obtains the topological model for the distribution network systems that energy-storage system is accessed.This example is chosen IEEE-14 node and is matched
Electric network is as simulating, verifying object, and shown in parameter such as table (1), node power parameter and branch impedance parameter are shown in Table (2), table
(3), typical structure is as shown in figure (1).In IEEE14 distribution network systems, chooses node 1 and be used as PV node, access photovoltaic power generation
System.
Table (1) IEEE-14 node power distribution network parameter
| Reference voltage | Reference capacity | Network total load | Feeder line number | |
| Parameter value | 23kV | 100MVA | 28.7+j7.75MVA | 3 |
Table (2) IEEE-14 node power distribution branch of a network impedance
Table (3) IEEE-14 node power distribution network power parameter
Load flow calculation is carried out to IEEE14 distribution network and obtains relevant parameter, using node 3 as balance nodes (access energy storage
System), a certain moment photovoltaic generation power PpvFor, Load flow calculation process is described:
The conversion of Step1 branch impedance per unit value
In IEEE14 distribution network systems, if reference voltage is Un, reference capacity Sn, the impedance of i-th branch road be R (i)+
JX (i), then the conversion of branch impedance per unit value is as follows:
Step2 admittance matrix Y
For IEEE14 distribution network systems, according to Distributing network structure, admittance matrix Y is as follows:
Step3 node voltage is sought
In actual operating system, it is known that service condition be frequently not actual node current but load and power generation
The power of machine, and these power do not change generally with the variation of node voltage.In the case where node power is constant, node
Injection Current change with the variation of node voltage.Therefore, in the case where known node admittance matrix, it is necessary to known to use
Node power replaces unknown node Injection Current.The injecting power equation of every PQ node are as follows:
Above formula is complex number equation, and calculating formula requires to be launched into real number form, and node voltage can use polar coordinate representation, can also
It is as follows respectively to be indicated with rectangular co-ordinate:
Power equation under available two kinds of node voltage representation methods:
Or
Herein due to using matrix operation, considers its convenience degree, use the representation method of rectangular co-ordinate.
In IEEE14 node distribution network model employed herein, No. 1 PV node is different from PQ node, the side that need to meet
Formula are as follows:
Wherein, Pi、QiPower component, the reactive component of node load are respectively indicated, is known conditions.Due to being power note
Enter equation, so regulation is just injecting power, bears to consume power.
The Nonlinear System of Equations is solved using the fsolve function in matlab.
Step4 Load flow calculation
After solution obtains all node voltages, the calculating of distribution trend can be started.Due to connecing for energy storage device
Enter position difference, the Load flow calculation process under different micro-capacitance sensor topologys is also different.The method that this section is taken is: by energy storage position
Beginning of the access node as Load flow calculation, end of each branch roadhead as branch.Its process can use figure (2) description.
According to formula
From the end up line loss of all branches of recursion step by stepAnd the apparent energy S of outflow node jj.To branch
Beginning (at energy storage device access), by node power balance can calculate energy storage device it is a certain when the absorption or release inscribed
Power.
The distributed generation system that one maximum output is 200kw is accessed IEEE-14 distribution system, access by step (2)
Node is 1, chooses the photovoltaic power output on its May 18, and (5 minutes are a sampled point, totally 288 sampled points) is as shown in figure (3).
Then in 14 nodes, energy-storage system allows to access in any node other than No.1 node, maximum installation power
For 400kw.
Step (3) is obtained according in the network topology structure model of distribution network systems obtained in step (1) and step (2)
Photovoltaic devices on-position and power output, establish objective function and constraint condition:
(3.1) objective function is established.F is fluctuated with node voltage1, system loading fluctuate f2And energy storage system capacity f3Three
A index forms multi-goal optimizing function: minF (x, y)=(f1(x,y),f2(x,y),f3(x,y)).Wherein,M is number of nodes, number at the time of T indicates total, UijIndicate the voltage value at j-th of moment of i-th of node,Indicate average voltage of i-th of node in time T； Indicate power grid input in T time
Mean power, Ps(j) in the input power of j moment power grid；N is energy storage device number, EstoreIt (k) is the
The rated capacity of k energy storage device, the selection rule of the rated capacity of single energy storage device are as follows: Estore=max { Estore,1,
Estore,2, wherein Estore,1For the maximum charge-discharge energy of energy storage device, Estore,2For the accumulation charge and discharge electric energy of energy storage device
Amount.Estore,1And Estore,2Selection rule is as follows:
By charging and discharging, the power curve of energy storage device is divided into n sections, so that every section all maintains always charge or discharge
State, the not change of state.Charge-discharge energy is denoted as E in i-th sectionsi, then the maximum charge-discharge energy E of energy storage devicestore,1
Choose EsiMaximum value, shown in formula such as formula (1):
Wherein tiAt the beginning of indicating i-th section, tieIndicate i-th section of end time, Pstore(t) t moment energy storage is indicated
The charge-discharge electric power of device, Δ t indicate i-th section of time interval, i.e. Δ t=tie-ti。
In observing time T, the accumulation charge-discharge energy in the energy storage device j period is denoted as Eadsj, then energy storage device
Accumulate charge-discharge energy Estore,2It is chosen for EadsjMaximum value, shown in formula such as formula (2):
(3.2) constraint condition is constructed:
(3.2.1) node voltage constraint: Ui,min<=Ui<=Ui,max(i=1,2, M), wherein Ui,minTable
Show the lower voltage limit of i-th of node, Ui,maxIndicate the upper voltage limit of i-th of node, M is number of nodes；
(3.2.2) power-balance constraint:What wherein each code name indicated is same
When inscribe, PloadiIndicate the load power of i-th of node, PstorekIndicate the power of k-th of energy storage device, PdgjIt indicates j-th
The power of photovoltaic power generation power supply, PsIndicate power grid input power, wherein N is the number of energy storage device, and D is photovoltaic generating system
Number；(3.2.3) energy storage power constraint: Pstorek,min<=Pstorek<=Pstorek,max(k=1,2, N), wherein
Pstorek,minIndicate the energy storage lower limit of the power of k-th of energy storage device, Pstorek,maxIt indicates on the energy storage power of k-th of energy storage device
Limit；
The constraint of (3.2.4) energy storage energy balance:；
In summary listed objective function and constraint condition, the addressing constant volume Model for Multi-Objective Optimization of energy storage device
Are as follows:
Step (4), solves above-mentioned multi-objective optimization question, is divided into two steps:
(4.1) multiple target is converted into single goal, is carried out in two steps:
(4.1.1) each sub-goal functional value normalized:
The node voltage fluctuation minimum 0.0052 when energy-storage system accesses No. 12 nodes is known, when energy-storage system accesses 5
When number node, node voltage fluctuation is up to 0.0505, and when energy-storage system accesses No. 8 nodes, energy storage system capacity is minimum
0.1635MWh, when energy-storage system accesses No. 10 nodes, energy storage system capacity is up to 1.4381MWh, because system loading takes
Constant, so system loading fluctuation is taken as 0 during model solution, then for node voltage fluctuation, fmaxIt is taken as
0.0505, fminIt is taken as 0.0052, for energy storage system capacity, fmaxIt is taken as 1.4381MWh, fminIt is taken as
0.163558MWh。
(4.1.2) weigthed sums approach polymerize multiple objective function:
Wherein, m indicate objective function quantity, value 3, X be the node set for meeting constraint condition, that is, constrain
Collection indicates the feasible solution range for meeting constraint condition.λiMeetλ is obtained with analytic hierarchy process (AHP)iValue, it is specific as follows:
The weight of each target function value is calculated using AHP, judgment matrix construction is as follows: (fluctuation of B1 node voltage, B2 are to be
System load fluctuation, B3 are energy-storage system energy)
Consistency check is carried out to matrix A, can obtain CR is 0.0133, passes through consistency check.
Acquire the weight of each objective function are as follows: it is 0.5587 that node voltage, which fluctuates weight, and system loading fluctuation weight is
0.3197, stored energy capacitance weight is 0.1216.
(4.2) single-goal function that step 4.1 obtains is asked using the PSO algorithm (particle swarm algorithm) of linear weight
Solution, obtains single-goal function optimal solution, the i.e. optimal addressing of energy storage device.Energy-storage system can be obtained using this particle swarm algorithm should
It is placed at No. 2 nodes, at this time configuration capacity 1.430MWh, node voltage fluctuation is 0.011, and node voltage amplitude curve is as schemed
(4) shown in.
Fig. 4 is voltage magnitude figure under optimal on-position, therefrom we can see that:
(1) in morning 0-6 and when night 19-24 or so, the voltage of each node is in stable state, this is because this when
Between be set to constant without power needed for photovoltaic power output and load in section.
(2) node voltage fluctuates between 0.9Un and 1.05Un always, meets node voltage constraint condition.
(3) node 1 and 2 voltage of node are always 1.05Un, this is because PV node and balance node voltage are constant, it is permanent
For 1.05Un.
(4) in addition to No. 1 node and No. 2 nodes, remaining node voltage becomes in the stable variation of stabilization-reduction-raising-
Gesture reaches extreme value and photovoltaic power curve variation tendency is exactly the opposite for 12 points or so at noon.
(5) in all nodes, the voltage fluctuation of No. 14, No. 9 and No. 10 nodes is more violent, wherein fluctuated with No. 9 again
It is maximum.This is because these three nodes are branch endpoint nodes, farther out from balance nodes when balance nodes are taken as No. 2 nodes.
Claims (1)
1. the site selecting method of energy-storage system in a kind of distribution automation system, which is characterized in that this approach includes the following steps
Step (1) obtains the topological model for the distribution network systems that energy storage device is accessed；The topological model should be opened up including network
Flutter structure, reference voltage Un, reference capacity Sn, the power parameter S of all-network node and the impedance parameter of each item branch road
zi。
Step (2) determines the on-position of photovoltaic devices, obtains its power curve.
Step (3), according to light obtained in the network topology structure model of distribution network systems obtained in step (1) and step (2)
On-position and the power output for lying prostrate device, establish objective function and constraint condition:
(3.1) objective function is established.F is fluctuated with node voltage1, system loading fluctuate f2And energy storage system capacity f3Three fingers
Mark composition multi-goal optimizing function: minF (x, y)=(f1(x,y),f2(x,y),f3(x,y)).Wherein,M is number of nodes, number at the time of T indicates total, UijIndicate the voltage value at j-th of moment of i-th of node,Indicate average voltage of i-th of node in time T； Indicate power grid input in T time
Mean power, Ps(j) in the input power of j moment power grid；N is energy storage device number, EstoreIt (k) is the
The rated capacity of k energy storage device, the selection rule of the rated capacity of single energy storage device are as follows: Estore=max { Estore,1,
Estore,2, wherein Estore,1For the maximum charge-discharge energy of energy storage device, Estore,2For the accumulation charge and discharge electric energy of energy storage device
Amount.Estore,1And Estore,2Selection rule is as follows:
By charging and discharging, the power curve of energy storage device is divided into n sections, so that every section all maintains always charge or discharge shape
State, the not change of state.Charge-discharge energy is denoted as E in i-th sectionsi, then the maximum charge-discharge energy E of energy storage devicestore,1Choosing
Take EsiMaximum value, shown in formula such as formula (1):
Wherein tiAt the beginning of indicating i-th section, tieIndicate i-th section of end time, Pstore(t) t moment energy storage device is indicated
Charge-discharge electric power, Δ t indicates i-th section of time interval, i.e. Δ t=tie-ti。
In observing time T, the accumulation charge-discharge energy in the energy storage device j period is denoted as Eadsj, then the accumulation of energy storage device is filled
Discharge energy Estore,2It is chosen for EadsjMaximum value, shown in formula such as formula (2):
(3.2) constraint condition is constructed:
(3.2.1) node voltage constraint: Ui,min<=Ui<=Ui,max(i=1,2 ..., M), wherein Ui,minIndicate i-th of section
The lower voltage limit of point, Ui,maxIndicate the upper voltage limit of i-th of node, M is number of nodes；
(3.2.2) power-balance constraint:What wherein each code name indicated is synchronization
Under, PloadiIndicate the load power of i-th of node, PstorekIndicate the power of k-th of energy storage device, PdgjIndicate j-th of photovoltaic
The power of power generating source, PsIndicate power grid input power, wherein N is the number of energy storage device, and D is of photovoltaic generating system
Number；
(3.2.3) energy storage power constraint: Pstorek,min<=Pstorek<=Pstorek,max(k=1,2 ..., N), wherein
Pstorek,minIndicate the energy storage lower limit of the power of k-th of energy storage device, Pstorek,maxIt indicates on the energy storage power of k-th of energy storage device
Limit；
The constraint of (3.2.4) energy storage energy balance:；
In summary listed objective function and constraint condition, the addressing constant volume Model for Multi-Objective Optimization of energy storage device are as follows:
Step (4), solves above-mentioned multi-objective optimization question, is divided into two steps:
(4.1) multiple target is converted into single goal, is carried out in two steps:
(4.1.1) is standardized using deviation to each sub-goal functional value normalized, the steps include:
Step1 is within the scope of feasible solution, to each single-object problem, finds out the solution and target function value of formula (1), table respectively
It is shown as fi,min。
minfi(x) (i=1,2 ..., m) (1)
Wherein, independent variable x indicates the node of distribution network, fi(x) i-th of objective optimization function is indicated.
Step2 is within the scope of feasible solution, to each single-object problem, finds out the solution and target function value of formula (2), table respectively
It is shown as fi,max。
maxfi(x) (i=1,2 ..., m) (2)
Step3 is normalized using transfer function:
(4.1.2) weigthed sums approach polymerize multiple objective function:
Wherein, m indicate objective function quantity, value 3, X be the node set for meeting constraint condition, i.e. constraint set, table
Show the feasible solution range for meeting constraint condition.λiMeetλ is obtained with analytic hierarchy process (AHP)iValue.
(4.2) single-goal function that step 4.1 obtains is solved using the PSO algorithm (particle swarm algorithm) of linear weight,
Obtain single-goal function optimal solution, the i.e. optimal addressing of energy storage device.
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| CN111191820A (en) * | 2019-12-17 | 2020-05-22 | 国网浙江省电力有限公司 | Site selection and volume fixing optimization planning method for energy storage device in comprehensive energy system |
| CN111509744A (en) * | 2020-04-21 | 2020-08-07 | 中国电力科学研究院有限公司 | Energy storage multifunctional application layout method and system |
| CN112165112A (en) * | 2020-09-23 | 2021-01-01 | 广东电网有限责任公司肇庆供电局 | Distributed energy storage system planning method for solving low voltage of distribution network |
| CN114498740A (en) * | 2022-01-26 | 2022-05-13 | 国网河南省电力公司电力科学研究院 | Energy storage configuration optimization method for normal and fault voltage fluctuation suppression of photovoltaic access power grid |
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