CN104092231A - Method for optimal configuration of independent micro grid mixed energy storage capacity - Google Patents
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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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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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
Abstract
The invention relates to a method for optimal configuration of independent micro grid mixed energy storage capacity. According to a mixed energy storage system composed of a storage battery and a supercapacitor, with one-time investment of the mixed energy storage system serving as an objective function, the capacity of the mixed energy storage system is comprehensively considered and determined from the aspects of the energy supply capability and the power supply capability of the energy storage system, a particle swarm optimization algorithm is adopted to solve a calculation example, and an optimal solution which is the least investment of the mixed energy storage system on the premise of meeting system reliability is output finally. The correctness of a mixed energy storage capacity optimization model is verified, and the method reduces investment cost of an energy storage device and improves utilization efficiency of renewable resources.
Description
Technical field
The present invention relates to a kind of microgrid capacity configuration technology, particularly the mixed stored energy capacitance Optimal Configuration Method of a kind of independent micro-grid.
Background technology
In micro-electrical network, the generating situation of wind-force, photovoltaic distributed power supply is subject to the restriction of weather condition, there is unsteadiness and uncontrollability, add load and also have certain randomness, the equilibrium of supply and demand that is therefore difficult to accomplish electric weight in micro-electrical network of independent operating.By configuring certain energy-storage units, can effectively improve the power supply reliability of micro-electrical network, realize the equilibrium of supply and demand of energy.At present, the price of various energy storage devices is all more expensive, therefore, in the time of configuration energy-storage system, makes every effort to reach distributed power source, load, the energy-storage system best of breed on capacity, thereby the comparatively economic micro-mains supply integrity problem of solution, the stability of raising system operation.
At present, to the capacity configuration problem of mixed energy-storage system in micro-electrical network, relevant scholar both domestic and external is studied it, and has obtained the achievement of some theory and practice aspects.As Sun Yaojie, Kang Yunlong etc. have set up the Mathematical Modeling of battery capacity optimization constraint planning, considering, on the constraints of accumulator cell charging and discharging and the basis of distributed power source stochastic behaviour, battery capacity optimization problem to be summed up as to Chance Constrained Programs.Liu Jiantao, once Jiangxi life etc. are for independent photovoltaic hybrid power system, cost of investment using mixed energy storage system is minimum as target function, taking the running technology index of independent photovoltaic generating system as constraint, based on typical case's day meteorological data, the capacity configuration optimizing method based on simplex method is proposed.
Summary of the invention
The present invention be directed to the problem that is difficult to the equilibrium of supply and demand of accomplishing electric weight in micro-electrical network of independent operating, the mixed stored energy capacitance Optimal Configuration Method of a kind of independent micro-grid has been proposed, the micro-electrical network of wind light mutual complementing for independent operating carries out distributing rationally of stored energy capacitance, considering on the basis of generator unit, energy-storage units part throttle characteristics, set up to mix the target function that energy-storage system cost of investment is minimum, energy constraint and power constraint have been proposed, and example is solved with particle swarm optimization algorithm, prove the correctness of this capacity configuration optimizing method.
Technical scheme of the present invention is: a kind of independent micro-grid mixes stored energy capacitance Optimal Configuration Method, specifically comprises the steps:
1) set micro-electric network source and comprise wind power generation unit, photovoltaic generation unit, energy-storage system, wherein energy-storage system is the mixed energy-storage system of storage battery and ultracapacitor composition;
2), to micro-electric network source modeling, calculate each power supply energy output and load power consumption according to historical data;
3) target function that capacity is optimized: taking the one-time investment minimum of energy-storage system as target function;
In formula:
n 1 ,
n 2 be respectively the number of storage battery, ultracapacitor;
m bat for the unit price of storage battery;
m uc for the unit price of ultracapacitor;
4) energy-storage system constraint:
A: generating surplus constraint, establish the
imoon electric weight surplus maximum, 12>=
i>=1, electric weight surplus is used
e (i)represent, the electric weight surplus of average every day is
e (i)/d, wherein
dfor of that month number of days, the total capacity that energy-storage system can store is less than
d 1 e (i)/d, wherein
d 1 for energy-storage system recovery time,
；
B: generation deficiency constraint, establish the
jmonth electric weight breach maximum, 12>=
j>=1) a month electric weight breach is used
e (j)represent, the electric weight breach of average every day is
e (j)/d, the total capacity of energy-storage system is greater than
d 2 e (j)/d, wherein
d 2 for the self-supporting time of energy-storage system,
；
C: power constraint, in the time not having distributed power source to exert oneself, the power output capacity of energy-storage system should be greater than peak load, the power stage of batteries
p bat , the power stage of ultracapacitor
p uc ,
When there is great fluctuation process in load, the variation of energy-storage system energy strain burden,
In formula:
tfor the impact load duration;
p lmax for impact load power;
5) target function and constraints are converted into the optimization method that specifically can solve;
6) use particle swarm optimization algorithm to solve optimization method;
7) finally exporting optimal solution is in the investment-saving that meets under system reliability prerequisite mixed energy-storage system.
Beneficial effect of the present invention is: independent micro-grid of the present invention mixes stored energy capacitance Optimal Configuration Method, for the mixed energy-storage system of storage battery and ultracapacitor composition, using the one-time investment minimum of mixed energy storage system as target function, consider from Power supply ability and power supply ability two aspects of energy-storage system the capacity of determining mixed energy storage system.Adopt particle swarm optimization algorithm to solve example, by sample calculation analysis, proved the correctness of the mixed stored energy capacitance Optimized model that the present invention proposes, the cost of investment of having saved energy storage device, has increased the utilization ratio of renewable resource.
Brief description of the drawings
Fig. 1 is the structure chart of the independent micro-electrical network of the present invention;
Fig. 2 is the flow chart that capacity of the present invention is distributed rationally;
Fig. 3 is the moon energy output of wind turbine generator of the present invention;
Fig. 4 is the moon energy output of photovoltaic cell of the present invention.
Embodiment
In the time that wind power generation unit and photovoltaic unit are not enough to supply with the required electric weight of load, energy-storage system is being undertaken the important task of undersupply power; In the time that energy output exists surplus, energy-storage system absorbs the power of surplus.The surplus of micro-grid generation amount and deficiency are to determine the foundation of energy storage system capacity.Equally, in the time of suddenly pulsation of load, also provide shock absorbing power by energy-storage system, thus percussion power when load great fluctuation process and during this period of time in power consumption be also one of key factor of definite stored energy capacitance.Having considered on the basis of generator unit, energy-storage units part throttle characteristics, set up to mix the target function that energy-storage system cost of investment is minimum, propose energy constraint and power constraint, and example has been solved with particle swarm optimization algorithm.
One, micro-power network modeling:
Typical independent micro-grid is made up of wind power generation unit, photovoltaic generation unit, energy-storage system, load and corresponding control unit, and the micro-configuration of power network of independence that the present invention sets up as shown in Figure 1.Control unit is mainly DC/DC and AC/DC, realizes distributed power source, energy-storage units, load be connected with DC bus by these power inverters.The model of distributed electrical source model and energy-storage units is as follows:
1, wind power generation unit modeling
Wind turbine generator power output
p wt with actual wind speed
vrelation can be described below:
（1）
In formula:
for the rated output of blower fan;
for incision wind speed;
for cut-out wind speed;
for rated wind speed.
2, photovoltaic cell modeling
The power output of photovoltaic generation unit and intensity of illumination, ambient temperature is closely related.The real-time power output of photovoltaic generation unit
p pv can be with following function representation:
（2）
In formula:
for STC(standard test condition) peak power output of lower photovoltaic generation unit;
for actual solar irradiation intensity (W/m
2);
for solar irradiation intensity (W/m under standard test condition
2);
for temperature power coefficient (%/DEG C);
for battery temperature;
for reference temperature.
3, storage battery modeling
For monomer capacity be
c bat (Ah), rated voltage is
u bat (V), cascade number is
n bat the electric energy total amount that stores in theory of batteries
e bat (in the present invention, the unit of electric energy is kWh unless otherwise noted, and power unit is kW) is:
（3）
The depth of discharge of supposing storage battery is λ (1> λ >0), the electric energy that batteries can be emitted in each cycle charge discharge electric process
e bat_f for:
（4）
The electric energy needing in charging process
e bat_ch for:
（5）
In formula:
ηfor the charge efficiency of storage battery.
Under normal circumstances, storage battery is regarded as to constant voltage work, operating current is controlled at 0.1
c bat , therefore the power output capacity of above-mentioned batteries is:
（6）
4, ultracapacitor modeling
When ultracapacitor discharges and recharges, it is equivalent to ideal capacitance.The electric capacity of note monomer ultracapacitor is
c uc (F), rated voltage is
u uc (V), the electric weight of ultracapacitor storage
e uc for:
（7）
In actual application, the operating voltage of super capacitor has a scope
u ucmin ~ U ucmax , for cascade quantity be
n uc bank of super capacitors, the electric energy that can be system in each charge and discharge cycles provides is:
（8）
Be the ultracapacitor of γ (1> γ >0) for charge efficiency, the electric energy that ultracapacitor needs in charging process is:
（9）
The power output of above-mentioned bank of super capacitors is:
（10）
In formula:
p uc_max for capacitor group peak power output;
i uc_max for the operating current upper limit of monolithic capacitor.
Two, mixed energy storage system capacity optimization aim function and constraints:
1, the target function that capacity is optimized
The most important target of configuration of energy-storage system is the requirement that meets system power supply reliability, be the bigger the better in theory, but the larger one-time investment of the capacity of energy-storage units is also just larger.At present, energy-storage system occupies larger ratio in the micro-electrical network total cost of independent operating, and taking micro-electrical network of independent operating as example, storage battery accounts for 20% ~ 25% of whole micro-electric grid investment cost.The present invention is taking the one-time investment minimum of energy-storage system as target function.
（11）
In formula:
n 1 ,
n 2 be respectively the number of storage battery, ultracapacitor;
m bat for the unit price of storage battery;
m uc for the unit price of ultracapacitor.
2, the constraint equation that capacity is optimized
Energy-storage system plays very important effect in micro-electrical network, in the time that wind and solar hybrid generating system generating is sufficient, unnecessary power storage in storage battery and ultracapacitor; In the time of wind-light complementary system generation deficiency, energy-storage system can send power and make up the electric weight vacancy of system rapidly.In principle, the capacity of energy-storage system should be not less than annual maximum electric weight breach continuously.Therefore the present invention adopts month a concept for statistics electric weight, utilizes the month of electric weight surplus maximum in a year and plans the total capacity of energy-storage system maximum month of electric weight vacancy, can fully avoid like this waste or the deficiency of stored energy capacitance.In view of storage battery and ultracapacitor charge-discharge characteristic and advantage separately, storage battery is as topmost energy storage device, and ultracapacitor is as auxiliary unit, when to load power supply relatively stably, consideration be mainly the power supply capacity of storage battery; And in the time there is impact load, consideration be mainly the high-power charging and discharging capabilities of ultracapacitor.The present invention, from the total capacity that retrains energy-storage system over two months of electric weight surplus, quantity of electric charge decrement maximum, retrains the power of energy-storage system from the handling capacity of impact load.
(1) generating surplus constraint
In the time that generating has surplus, should allow energy-storage units that unnecessary electric weight is stored.And the capacity of energy-storage travelling wave tube should be less than the electric weight of average surplus, avoid too much configuration energy storage.If the
i(12>=
i>=1) month electric weight surplus maximum, electric weight surplus is used
e (i)represent.The electric weight surplus of average every day is
e (i)/d, wherein
dfor of that month number of days.For fear of the waste of energy, the total capacity that energy-storage system can store should be less than
d 1 e (i)/d, wherein
d 1 for energy-storage system recovery time.Expression formula is as follows:
（12）
(2) generation deficiency constraint
In the time of generation deficiency, energy-storage units makes up electric weight breach.If the
j(12>=
j>=1) month electric weight breach maximum, a month electric weight breach is used
e (j)represent, the electric weight breach of average every day is
e (j)/d.In order to ensure that (wind speed is little or unglazed) energy-storage system can provide enough electric energy to load under bad weather, the total capacity of energy-storage system should be greater than
d 2 e (j)/d, wherein
d 2 for the self-supporting time of energy-storage system.Expression formula is as follows:
（13）
(3) power constraint
In storage battery-super capacitor energy storage system, storage battery bears as formant the work that most electric weight stores and supplies, and according to the characteristic of ultracapacitor, ultracapacitor is as auxiliary unit, in there is great fluctuation process in load, utilize the characteristic of ultracapacitor fast charging and discharging, give full play to the advantage of the high-power handling capacity of ultracapacitor.Consider that under limiting case, in the time not having distributed power source to exert oneself, the power output capacity of energy-storage system should be greater than peak load, shown in 14,15.
（14）
（15）
In formula:
tfor the impact load duration;
p lmax for impact load power.
Three, the capacity based on particle group optimizing method is distributed rationally:
1. particle swarm optimization algorithm
Particle cluster algorithm is a kind of population random optimization method that the simulation based on birds social action grows up.The motion process of PSO algorithm is as follows: the population scale of supposition population is
n,
rin dimension space, the position phasor representation of particle is
x i =[
x i1
,
x i2
...
x ir ], particle
ispeed definition be the distance that in iteration, particle moves, speed phasor representation is
v i =[
v i1
,
v i2
...,
v ir ].
p i represent particle
ithe desired positions experiencing,
p g represent the desired positions that in population, all particles experience.The every renewal of particle once, is just calculated fitness value one time, and comprehensively relatively ideal adaptation degree value and colony's fitness value upgrade
p i with
p g , finally converge to optimal solution or the approximate solution of problem.In PSO algorithm each particle in iterative process according to following two formulas speed and the position to particle upgrade:
（16）
（17）
In formula:
ifor evolutionary generation;
for inertia weight;
c 1,
c 2for accelerated factor, be also referred to as perception factor and the social factor;
rand() be the random number between [0,1].
As shown in Figure 2, its concrete steps are capacity configuration flow chart based on particle swarm optimization algorithm:
1) according to the energy output of historical data Computation distribution formula generating and load power consumption;
2) whether verification electric quantity balancing principle is set up, and can be micro-electrical network configuration energy-storage system if set up; If be false, this micro-electrical network does not possess the condition of configuration energy-storage system;
3) target function and constraints are converted into the optimization method that specifically can solve;
4) use particle swarm optimization algorithm to solve the optimization method in step 3);
5) finally exporting optimal solution is in the investment-saving that meets under system reliability prerequisite mixed energy-storage system.
Four, sample calculation analysis:
1, example system
Application the inventive method is that the micro-electrical network of certain wind light mutual complementing is example, and mixed energy storage system capacity is distributed rationally.The design parameter of example is as follows:
1) the rated power 75kW of wind power generation, the specified generated output 25kW of photovoltaic unit.The monthly energy output of wind power generation and photovoltaic generation respectively as shown in Figure 3, Figure 4.
2) load day power consumption 850kWh/d, average power 35kW, load there will be the pulsation of 30s every day, produces very large peak power when pulsation, is 5 times of its rated power.
3) basic parameter of cell batteries and ultracapacitor is as shown in table 1.
Table 1
2, sample calculation analysis and discussion
First whether verification electric quantity balancing principle is set up, and the moon statistics electric weight of distributed power source and load is as shown in table 2.The annual energy output of distributed power source is 313381.23kWh, and load Urban Annual Electrical Power Consumption amount is 310250kWh, and energy output is slightly larger than power consumption, meets system requirements.The maximum surplus of electric weight appears at December as shown in Table 2, is total to surplus 1366.52kWh in month, and the electric weight of average surplus every day is 45.55 kWh; The maximum vacancy that electric weight is supplied with appears at May, and the electric weight breach in May is 1174.52kWh, and the electric weight breach of average every day is 39.16kWh.For ensureing the power supply of system stability, energy-storage system total capacity should be not less than the electric weight breach in 5 days Mays; In order to make to avoid the waste of stored energy capacitance, the gross energy of energy-storage system is not more than the surplus electric weight in 5 days December simultaneously; Load peak power is 175kW, considers that under limiting case (calm, unglazed photograph), the power output capacity of energy-storage system should be greater than load peak power.
Table 2
Comprehensively above-mentioned, in wushu (3) ~ (10) substitution constraints, and the design parameter of substitution storage battery and ultracapacitor, the constraints of capacity optimization arranges and is:
（18）
Target function and constraints are converted into the concrete optimization method solving, and the relevant parameter of substitution storage battery and ultracapacitor obtains formula (19), uses particle swarm optimization algorithm to solve:
（19）
In formula:
p (x, M)for energy-storage system one-time investment cost, be the fitness function of particle swarm optimization algorithm;
n (1)for storage battery number;
n (2)for ultracapacitor number;
mfor penalty factor,
mfor the positive number much larger than 1,
.
Population population scale is set
sizepop=100, accelerated factor
c 1 =c 2 =1.4955, weight coefficient
ω=1.Obtain through 200 iteration the result that capacity optimizes as shown in table 3.
Table 3
If use merely as shown in Table 3 storage battery as energy-storage units, in order to meet the power supply to load in there is impact load, must be equipped with a large amount of storage batterys, Financial cost is very high.Use after hybrid energy-storing, give full play to the high-power handling capacity of ultracapacitor, reduced the quantity of storage battery, saved Financial cost.
Claims (1)
1. the mixed stored energy capacitance Optimal Configuration Method of independent micro-grid, is characterized in that, specifically comprises the steps:
1) set micro-electric network source and comprise wind power generation unit, photovoltaic generation unit, energy-storage system, wherein energy-storage system is the mixed energy-storage system of storage battery and ultracapacitor composition;
2), to micro-electric network source modeling, calculate each power supply energy output and load power consumption according to historical data;
3) target function that capacity is optimized: taking the one-time investment minimum of energy-storage system as target function;
In formula:
n 1 ,
n 2 be respectively the number of storage battery, ultracapacitor;
m bat for the unit price of storage battery;
m uc for the unit price of ultracapacitor;
4) energy-storage system constraint:
A: generating surplus constraint, establish the
imoon electric weight surplus maximum, 12>=
i>=1, electric weight surplus is used
e (i)represent, the electric weight surplus of average every day is
e (i)/d, wherein
dfor of that month number of days, the total capacity that energy-storage system can store is less than
d 1 e (i)/d, wherein
d 1 for energy-storage system recovery time,
；
B: generation deficiency constraint, establish the
jmonth electric weight breach maximum, 12>=
j>=1) a month electric weight breach is used
e (j)represent, the electric weight breach of average every day is
e (j)/d, the total capacity of energy-storage system is greater than
d 2 e (j)/d, wherein
d 2 for the self-supporting time of energy-storage system,
；
C: power constraint, in the time not having distributed power source to exert oneself, the power output capacity of energy-storage system should be greater than peak load, the power stage of batteries
p bat , the power stage of ultracapacitor
p uc ,
When there is great fluctuation process in load, the variation of energy-storage system energy strain burden,
In formula:
tfor the impact load duration;
p lmax for impact load power;
5) target function and constraints are converted into the optimization method that specifically can solve;
6) use particle swarm optimization algorithm to solve optimization method;
7) finally exporting optimal solution is in the investment-saving that meets under system reliability prerequisite mixed energy-storage system.
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Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104361405A (en) * | 2014-10-28 | 2015-02-18 | 广东电网有限责任公司电力科学研究院 | Micro grid energy storage device design method based on capacity limit value constrain |
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| CN105186570A (en) * | 2015-10-19 | 2015-12-23 | 国网北京市电力公司 | Micro power grid power supply control method and device |
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| CN109428344A (en) * | 2017-09-01 | 2019-03-05 | 国家电网公司 | More generator investment planning method and apparatus containing wind power plant |
| CN111049246A (en) * | 2019-12-25 | 2020-04-21 | 浙江合众新能源汽车有限公司 | Capacity configuration method for hybrid energy storage system |
| CN111082442A (en) * | 2019-12-06 | 2020-04-28 | 昆明理工大学 | Energy storage capacity optimal configuration method based on improved FPA |
| CN111222699A (en) * | 2020-01-07 | 2020-06-02 | 沈阳工业大学 | Multi-energy system capacity optimization method based on hydrogen-heat hybrid energy storage device |
| CN116760029A (en) * | 2023-08-15 | 2023-09-15 | 国网浙江省电力有限公司宁波供电公司 | Rural roof photovoltaic power generation and supply method, system, computing equipment and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130099581A1 (en) * | 2010-06-21 | 2013-04-25 | National University Of Singapore | Energy Storage System |
-
2014
- 2014-06-27 CN CN201410293628.5A patent/CN104092231A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130099581A1 (en) * | 2010-06-21 | 2013-04-25 | National University Of Singapore | Energy Storage System |
Non-Patent Citations (1)
| Title |
|---|
| 钟永等: ""风光互补微电网中混合储能系统的容量优化研究"", 《华东电力》 * |
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| CN105186570A (en) * | 2015-10-19 | 2015-12-23 | 国网北京市电力公司 | Micro power grid power supply control method and device |
| CN105552965A (en) * | 2016-02-18 | 2016-05-04 | 中国电力科学研究院 | Chance constraint planning based optimal configuration method of distributed energy source |
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| CN105871302A (en) * | 2016-04-26 | 2016-08-17 | 华北电力科学研究院有限责任公司 | Method and device for determining capacity of new energy delivery channel |
| CN106022627A (en) * | 2016-05-27 | 2016-10-12 | 国家电网公司 | Energy storage application value evaluation method and system based on data processing |
| CN105896582A (en) * | 2016-06-16 | 2016-08-24 | 南京工程学院 | Micro-grid energy storage capacity optimization configuration method |
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| CN109428344A (en) * | 2017-09-01 | 2019-03-05 | 国家电网公司 | More generator investment planning method and apparatus containing wind power plant |
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| CN107834601A (en) * | 2017-11-17 | 2018-03-23 | 燕山大学 | A kind of independent micro-grid system capacity configuration optimizing method for considering flexible load |
| CN111082442A (en) * | 2019-12-06 | 2020-04-28 | 昆明理工大学 | Energy storage capacity optimal configuration method based on improved FPA |
| CN111082442B (en) * | 2019-12-06 | 2022-10-18 | 昆明理工大学 | Energy storage capacity optimal configuration method based on improved FPA |
| CN111049246A (en) * | 2019-12-25 | 2020-04-21 | 浙江合众新能源汽车有限公司 | Capacity configuration method for hybrid energy storage system |
| CN111222699A (en) * | 2020-01-07 | 2020-06-02 | 沈阳工业大学 | Multi-energy system capacity optimization method based on hydrogen-heat hybrid energy storage device |
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