CN110034571A - A kind of distributed energy storage addressing constant volume method considering renewable energy power output - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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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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- H02J3/382—
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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
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
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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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- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a kind of distributed energy storage addressing constant volume methods of consideration renewable energy power output.The technical solution adopted by the present invention: force data is gone out according to previous renewable energy, power output and the load data that conventional generator goes out force data and load data predicts second day；Analysis of history data provide the primary data of energy-accumulating power station according to actual needs；The optimal models for considering the distributed energy storage addressing constant volume of renewable energy power output are established, using the SOC of the position of energy-accumulating power station, capacity and each moment as optimized variable, to reduce storage energy operation cost as target, consider device model constraint and system operation constraint；According to model built, establishes and be based on windows operating system, the emulation platform of matlab environment, with genetic algorithm solving model.The present invention efficiently solves the problems, such as the optimal addressing constant volume of energy storage, and reduces storage energy operation cost.
Description
Technical field
The invention belongs to Power System Planning field, specifically a kind of distributed storage for considering renewable energy power output
It can addressing constant volume method.
Background technique
Energy storage involves great expense, and considers that the planning problem of energy storage addressing constant volume has become current research hotspot.Both at home and abroad
The existing Electric power network planning method for considering energy-storage system addressing constant volume, only considers the energy storage device of fixed capacity and power, ignores it
The operating cost that scale and addressing optimization may cause is undesirable.
Chinese Patent Application No. 201811339760.X discloses a kind of addressing of energy-storage system in distribution automation system
Method, with node voltage fluctuation, system loading fluctuation and the minimum target of energy storage system capacity, it is contemplated that energy storage accesses distribution
The security reliability of net, the analyses for considering energy storage cost/benefit not more, and to be imitative compared with mini system IEEE-14 node power distribution net
True verifying, cannot verify the system model very well.
Summary of the invention
To solve the above-mentioned problems of the prior art, the present invention provides a kind of distribution of consideration renewable energy power output
Energy storage addressing constant volume method, by establishing bi-level optimal model, i.e. outer layer location problem that energy storage is determined with branch definition method,
Internal layer determines memory capacity on energy storage optimum position and power, state-of-charge (SOC) and minimum assembly with improved adaptive GA-IAGA
This etc. effectively to solve the problems, such as the optimal addressing constant volume of energy storage, and reduces storage energy operation cost.
The purpose of the present invention is be achieved through the following technical solutions: a kind of distributed storage considering renewable energy power output
Energy addressing constant volume method comprising step:
1) force data is gone out according to previous renewable energy, conventional generator goes out force data and load data predicts second day
Power output and load data；
2) analysis of history data provide the primary data of energy-accumulating power station according to actual needs, i.e., initial SOC state, charge and discharge
Electric time, efficiency for charge-discharge and self-discharge rate；
3) optimal models for considering the distributed energy storage addressing constant volume of renewable energy power output are established, with the position of energy-accumulating power station
Set, capacity and the SOC at each moment are optimized variable, to reduce storage energy operation cost as target, consider energy-accumulating power station model about
Beam and system operation constraint；
4) it according to model built, establishes and is based on windows operating system, the emulation platform of matlab environment is calculated with heredity
Method solving model.
The present invention considers the distributed energy storage addressing constant volume method of renewable energy power output, compares more other determining energy storage
The method of addressing constant volume, the present invention consider other indexs such as the safety of electric system, stability, combine upper layer power grid
Tou power price is dissolved with indirect type power supply and is maximized, while considering to be full of with the investment cost of energy storage, system losses expense, energy storage
The minimum optimization aim of totle drilling cost of benefit has engineering practicability.
As the supplement of the above method, the operation characteristic of the energy-accumulating power station is as follows:
1) charging process
S (t)=(1- δ Δ t) S (t-1)+Pi c(t)Δtηc/ C,
2) discharge process
In formula, S (t) is the state-of-charge in t moment energy storage；S (t-1) is the state-of-charge in t moment energy storage；δ is certainly
Discharge rate；Pi c(t) and Pi dIt (t) is to be charged and discharged power respectively；ηcAnd ηdIt is to be charged and discharged efficiency respectively；C is energy storage electricity
The capacity stood；Δ t is time interval；
There are following relationships for the charge power and discharge power:
In formula, Pi(t) it is an exchange power between energy-accumulating power station and power grid, T is taken in same time interval
The maximum moment；For an individual energy-accumulating power station, the P of synchronizationi c(t) and Pi d(t) value mutual exclusion, has and only one
A is zero.
As the supplement of the above method, the objective functions of the optimal models includes three parts, i.e., energy storage investment cost,
System losses expense and energy storage charge and discharge profit, are shown below:
In formula, NbusIt is system node number；P is the unit capacity cost of energy-accumulating power station；Q (t) is the electricity in t moment
Valence；CiIt is the capacity of the energy-accumulating power station at i-node；ziIt is the binary variable in i-node, is shown below:
Indicate network loss, PIiIt (t) is net active injection power summation at t moment i-node, it is as follows
Shown in formula:
Wherein, Pi net(t) it is net active injection power at t moment i-node, it includes load, generator and can be again
Raw energy power output, but do not include exchange power Pi(t)；Vi(t) voltage on table i-node, YikIt is between i-node and j node
Line admittance, VikIt is the voltage between i-node and k node；* adjoint matrix is collectively referred to as with the matrix in bracket.
As the supplement of the above method, the constraint of optimal models is divided into two classes: first is that energy-accumulating power station model constrains, including storage
Energy system battery and distribution network system, to prevent energy-accumulating power station from overcharging the generation with over-discharge, state-of-charge SOC, energy storage are filled
Discharge power meets the restriction of upper and lower limit；Another kind of to be constrained to system operation constraint, i.e., system should meet in operation
Constraint, when this kind of constraint includes system operation each moment should meet power-balance constraint and within dispatching cycle just
Begin and the SOC of end time energy storage system storage battery should be consistent.
As the supplement of the above method, node power constraint is as follows:
In formula, PiAnd QiIt is the active and idle injecting power of i-node, V respectivelyiAnd VjIt is i-node and j node respectively
Voltage, θijIt is the phase difference of voltage between i-node and j node, GijAnd BijIt is the conductance of route between i-node and j node respectively
And susceptance.
As the supplement of the above method, node voltage amplitude and phase angle constraint are as follows:
In formula,Vi WithIt is node voltage V respectivelyiLower and upper limit,θi WithIt is node voltage phase angle theta respectivelyiUnder
Limit and the upper limit.
As the supplement of the above method, transmission capacity constraint is as follows:
Pij=ViVj(Gijcosθij+Bijsinθij)-Vi 2Gij,
In formula, PijIt is the Line Flow between i-node and j node,It is the maximum route biography between i-node and j node
Defeated capacity.
As the supplement of the above method, energy-accumulating power station and its power constraint are as follows:
In formula,Si WithIt is energy-accumulating power station SOC value S respectivelyiLower and upper limit；
In formula,WithIt is that the maximum of energy-accumulating power station is persistently charged and discharged power respectively.
As the supplement of the above method, the capacity C of energy-accumulating power stationiIt constrains as follows:
C≤Ci,
In formula,CIt is the minimum capacity of energy-accumulating power station,CGreater than zero.
As the supplement of the above method, genetic parameter is adaptively adjusted in searching process using genetic algorithm,
And constraint condition is handled using penalty function method, obtain the corresponding optimized variable of optimal models and target function value, i.e. energy storage electricity
The optimal location stood, capacity, corresponding SOC value, energy storage investment cost, system losses expense and energy storage charge and discharge profit it is total at
This.
The present invention has the advantage that and has the beneficial effect that
The present invention considers other indexs such as safety and stability of system, joined AC Ioad flow model.The present invention
The tou power price of upper layer power grid is combined, is dissolved and is maximized with indirect type power supply, while being considered with the investment cost of energy storage, system
The minimum optimization aim of totle drilling cost of cost of losses, energy storage charge and discharge profit.The present invention is by establishing bi-level optimal model, i.e., outer
Layer determines that the location problem of energy storage, internal layer determine that the storage on energy storage optimum position is held with improved adaptive GA-IAGA with branch definition method
Amount and power, state-of-charge (SOC) and minimum total cost etc., efficiently solve the problems, such as the optimal addressing constant volume of energy storage, and reduce
Storage energy operation cost.The present invention adaptively adjusts genetic parameter in searching process using self-adapted genetic algorithm, and
Constraint condition is handled using penalty function method, to improve the calculating speed and ability of searching optimum of algorithm.
Detailed description of the invention
Fig. 1 is the flow chart of distributed energy storage addressing constant volume method of the present invention.
Specific embodiment
It is carried out with reference to the accompanying drawings of the specification with specific embodiment technical solution in the embodiment of the present invention clear, complete
Site preparation description.
A kind of distributed energy storage addressing constant volume method of consideration renewable energy power output as shown in Figure 1, it includes as follows
Step:
By previous renewable energy (wind-powered electricity generation/photovoltaic/water power etc.) goes out force data, conventional generator goes out force data and load number
It is predicted that the data of second day power output and load.
According to the analysis of historical data, provides the initial SOC state of energy-accumulating power station (herein referring to electrochemical energy storage power station), fills
Discharge time, efficiency for charge-discharge, self-discharge rate.
The optimal models for solving to consider the distributed energy storage addressing constant volume of renewable energy power output are established, with energy-accumulating power station
The SOC of position, capacity and each moment is optimized variable, to reduce storage energy operation cost as target, considers energy-accumulating power station model
Constraint and system operation constraint；
It according to model built, establishes and is based on windows operating system, the emulation platform of matlab environment uses genetic algorithm
Solving model.
The operation characteristic of energy-accumulating power station, as shown in (1) formula, (2) formula:
1) charging process:
S (t)=(1- δ Δ t) S (t-1)+Pi c(t)Δtηc/C (1)
2) discharge process:
In formula, S (t) is the state-of-charge in t moment energy storage；δ is self-discharge rate；Pi c(t) and Pi dIt (t) is charging respectively
And discharge power；ηcAnd ηdIt is to be charged and discharged efficiency respectively；C is the capacity of energy-accumulating power station.
Here there are such relationships for charge power and discharge power, as shown in (3) formula:
In formula, P (t) is an exchange power between energy-accumulating power station and power grid.For an individual energy-accumulating power station,
The P of synchronizationi c(t) and Pi d(t) value is mutual exclusion, has and only one is zero.
Further, the objective function of optimal models of the invention includes three parts, i.e. energy storage investment cost, system network
Damage expense and energy storage charge and discharge profit, as shown in (4) formula:
In formula, NbusIt is system node number；P is the unit capacity cost of energy-accumulating power station；Q (t) is the electricity in t moment
Valence；CiIt is the capacity of the energy-accumulating power station at i-node；ziIt is the binary variable in i-node, as shown in formula (5):
The Section 2 of formula (4) is to indicate network loss, PIiIt (t) is net active injection power summation at t moment i-node, such as
Shown in formula (6):
Wherein, Pi net(t) it is net active injection power at t moment i-node, it includes load, generator and can be again
Raw energy power output, but do not include exchange power P (t).Vi(t) voltage on table i-node, YikIt is the line between i-node and j node
Road admittance, VikIt is the voltage between i-node and k node.
After optimal models are built up, need to carry out physical constraint to some primary variables of model, to ensure the effective of model
Property.
Further, the constraint of optimal models is divided into two classes: first is that energy-accumulating power station model constrains, including energy-storage system electric power storage
Pond and distribution network system, to prevent energy-accumulating power station from overcharging the generation with over-discharge, state-of-charge SOC, energy storage charge-discharge electric power etc.
Meet the restriction of upper and lower limit.It is another kind of to be constrained to system operation constraint, i.e. the constraint that should meet in operation of system,
Each moment should meet power-balance constraint and initial and whole within dispatching cycle when this kind of constraint includes system operation
Only the SOC of moment energy storage system storage battery should be consistent.
The specific constraint of above-mentioned optimal models is as follows:
1) node power constraint is as follows:
In formula, PiAnd QiIt is the active and idle injecting power of i-node, Vi and V respectivelyjIt is i-node and j node respectively
Voltage, θijIt is the phase difference of voltage between i-node and j node, GijAnd BijIt is the conductance of route between i-node and j node respectively
And susceptance.
2) node voltage amplitude and phase angle constraint are as follows:
In formula,Vi WithIt is the lower and upper limit of node voltage respectively,θ iWithIt is the lower limit of node voltage phase angle respectively
And the upper limit.
3) transmission capacity constraint is as follows:
Pij=ViVj(Gijcosθij+Bijsinθij)-Vi 2Gij (11)
In formula, PijIt is the Line Flow between i-node and j node, PijIt is the maximum route biography between i-node and j node
Defeated capacity.
4) energy-accumulating power station and its power constraint are as follows:
In formula,S iWithIt is the lower and upper limit of energy-accumulating power station SOC respectively.Usual battery energy storage power station takesS i=0.1~
0.2,In addition, in order to ensure energy-accumulating power station is starting Shi Nengyou charging and discharging state, take the initial value S (0) of SOC=
0.3~0.6.
In formula,WithIt is that the maximum of energy-accumulating power station is persistently charged and discharged power respectively.
5) capacity-constrained of energy-accumulating power station is as follows:
C≤Ci(16)
In formula,CIt is the minimum capacity of energy-accumulating power station,CGreater than zero.
Genetic parameter is adaptively adjusted in searching process using genetic algorithm, and is handled using penalty function method
Constraint condition, obtains the corresponding optimized variable of the optimal models and target function value, i.e., the optimal location of energy-accumulating power station, capacity,
Corresponding SOC value, energy storage investment cost, system losses expense and energy storage charge and discharge profit resulting cost.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of the technical program, should all be covered in the present invention
Scope of the claims in.
Claims (10)
1. a kind of distributed energy storage addressing constant volume method for considering renewable energy power output, which is characterized in that comprising steps of
1) force data is gone out according to previous renewable energy, the power output that conventional generator goes out force data and load data predicts second day
And load data；
2) analysis of history data provide the primary data of energy-accumulating power station according to actual needs, i.e., when initial SOC state, charge and discharge
Between, efficiency for charge-discharge and self-discharge rate；
3) establish consider renewable energy power output distributed energy storage addressing constant volume optimal models, with the position of energy-accumulating power station,
Capacity and the SOC at each moment are optimized variable, to reduce storage energy operation cost as target, consider the constraint of energy-accumulating power station model and
System operation constraint；
4) it according to model built, establishes and is based on windows operating system, the emulation platform of matlab environment is asked with genetic algorithm
Solve model.
2. distributed energy storage addressing constant volume method according to claim 1, which is characterized in that the operation of the energy-accumulating power station
Characteristic is as follows:
1) charging process
S (t)=(1- δ Δ t) S (t-1)+Pi c(t)Δtηc/ C,
2) discharge process
In formula, S (t) is the state-of-charge in t moment energy storage；S (t-1) is the state-of-charge in t moment energy storage；δ is self discharge
Rate；Pi c(t) and Pi dIt (t) is to be charged and discharged power respectively；ηcAnd ηdIt is to be charged and discharged efficiency respectively；C is energy-accumulating power station
Capacity；Δ t is time interval；
There are following relationships for the charge power and discharge power:
In formula, Pi(t) it is an exchange power between energy-accumulating power station and power grid, T is the maximum taken in same time interval
Moment；For an individual energy-accumulating power station, the P of synchronizationi c(t) and Pi d(t) value mutual exclusion, has and only one is zero.
3. distributed energy storage addressing constant volume method according to claim 2, which is characterized in that the target of the optimal models
Function includes three parts, i.e. energy storage investment cost, system losses expense and energy storage charge and discharge profit, is shown below:
In formula, NbusIt is system node number；P is the unit capacity cost of energy-accumulating power station；Q (t) is the electricity price in t moment；Ci
It is the capacity of the energy-accumulating power station at i-node；ziIt is the binary variable in i-node, is shown below:
Indicate network loss, PIi(t) it is net active injection power summation at t moment i-node, such as following formula institute
Show:
Wherein, Pi net(t) it is net active injection power at t moment i-node, it includes load, generator and renewable energy
Power output, but do not include exchange power Pi(t)；Vi(t) voltage on table i-node, YikIt is that route between i-node and j node is led
It receives, VikIt is the voltage between i-node and k node；* adjoint matrix is collectively referred to as with the matrix in bracket.
4. distributed energy storage addressing constant volume method according to claim 3, which is characterized in that the constraint of optimal models is divided into
Two classes: first is that energy-accumulating power station model constrains, including energy storage system storage battery and distribution network system, to prevent energy-accumulating power station from overcharging and
The generation of over-discharge, state-of-charge SOC, energy storage charge-discharge electric power meet the restriction of upper and lower limit；It is another kind of to be constrained to system
The constraint that operation constraint, i.e. system should meet in operation, each moment should expire when this kind of constraint is including system operation
The SOC of sufficient power-balance constraint and the initial and end time energy storage system storage battery within dispatching cycle should be consistent.
5. distributed energy storage addressing constant volume method according to claim 4, which is characterized in that node power constraint is as follows:
In formula, PiAnd QiIt is the active and idle injecting power of i-node, V respectivelyiAnd VjIt is the voltage of i-node and j node respectively,
θijIt is the phase difference of voltage between i-node and j node, GijAnd BijIt is the conductance and electricity of route between i-node and j node respectively
It receives.
6. distributed energy storage addressing constant volume method according to claim 4, which is characterized in that node voltage amplitude and phase angle
It constrains as follows:
In formula,Vi WithIt is node voltage V respectivelyiLower and upper limit,θi WithIt is node voltage phase angle theta respectivelyiLower limit and
The upper limit.
7. distributed energy storage addressing constant volume method according to claim 5, which is characterized in that transmission capacity constraint is as follows:
Pij=ViVj(Gijcosθij+Bijsinθij)-Vi 2Gij,
In formula, PijIt is the Line Flow between i-node and j node,It is the maximum line transmission appearance between i-node and j node
Amount.
8. distributed energy storage addressing constant volume method according to claim 4, which is characterized in that energy-accumulating power station and its power are about
Beam is as follows:
In formula,S iWithIt is energy-accumulating power station SOC value S respectivelyiUpper and lower bound；
In formula,WithIt is that the maximum of energy-accumulating power station is persistently charged and discharged power respectively.
9. distributed energy storage addressing constant volume method according to claim 4, which is characterized in that the capacity C of energy-accumulating power stationiAbout
Beam is as follows:
C≤Ci,
In formula,CIt is the minimum capacity of energy-accumulating power station,CGreater than zero.
10. -9 described in any item distributed energy storage addressing constant volume methods according to claim 1, which is characterized in that using heredity
Algorithm adaptively adjusts genetic parameter in searching process, and handles constraint condition using penalty function method, obtains most
The corresponding optimized variable of excellent model and target function value, the i.e. optimal location of energy-accumulating power station, capacity, corresponding SOC value, energy storage are thrown
Rate use, system losses expense and energy storage charge and discharge profit resulting cost.
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| CN111009912A (en) * | 2019-12-16 | 2020-04-14 | 南京工程学院 | Thermal power plant energy storage configuration system and strategy based on power distribution network scene |
| CN111539086A (en) * | 2020-04-21 | 2020-08-14 | 中国电力科学研究院有限公司 | Energy storage power station multi-point layout method and system |
| CN111859608A (en) * | 2020-05-29 | 2020-10-30 | 杭州电子科技大学 | Energy storage site selection and volume fixing optimization method considering scene of relieving electric power gap |
| CN112491072A (en) * | 2020-12-03 | 2021-03-12 | 国网四川省电力公司经济技术研究院 | Energy storage layout method and device for multi-terminal hybrid direct-current power transmission system |
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