CN106451508B  Distributed hybrid energy storage system configuration and charging and discharging method and device  Google Patents
Distributed hybrid energy storage system configuration and charging and discharging method and device Download PDFInfo
 Publication number
 CN106451508B CN106451508B CN201610893391.3A CN201610893391A CN106451508B CN 106451508 B CN106451508 B CN 106451508B CN 201610893391 A CN201610893391 A CN 201610893391A CN 106451508 B CN106451508 B CN 106451508B
 Authority
 CN
 China
 Prior art keywords
 power
 super capacitor
 lithium battery
 energy
 bat
 Prior art date
 Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
 Active
Links
 238000004146 energy storage Methods 0.000 title claims abstract description 81
 238000007599 discharging Methods 0.000 title claims abstract description 29
 239000003990 capacitor Substances 0.000 claims abstract description 158
 WHXSMMKQMYFTQSUHFFFAOYSAN lithium Chemical compound data:image/svg+xml;base64,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 data:image/svg+xml;base64,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 [Li] WHXSMMKQMYFTQSUHFFFAOYSAN 0.000 claims abstract description 154
 229910052744 lithium Inorganic materials 0.000 claims abstract description 154
 238000005457 optimization Methods 0.000 claims abstract description 50
 238000010248 power generation Methods 0.000 claims abstract description 39
 280000867207 Lambda companies 0.000 claims description 7
 238000004220 aggregation Methods 0.000 claims description 6
 230000002776 aggregation Effects 0.000 claims description 6
 239000002245 particles Substances 0.000 claims description 6
 238000004422 calculation algorithm Methods 0.000 claims description 5
 238000010521 absorption reactions Methods 0.000 claims description 3
 238000006243 chemical reactions Methods 0.000 claims description 3
 238000010276 construction Methods 0.000 claims description 3
 230000001276 controlling effects Effects 0.000 claims description 3
 239000002699 waste materials Substances 0.000 claims description 3
 238000010586 diagrams Methods 0.000 description 9
 239000006185 dispersions Substances 0.000 description 8
 238000000034 methods Methods 0.000 description 8
 238000004590 computer program Methods 0.000 description 7
 230000000087 stabilizing Effects 0.000 description 7
 281000056277 Storage Technology, Corp. companies 0.000 description 3
 230000000875 corresponding Effects 0.000 description 3
 238000003860 storage Methods 0.000 description 3
 238000004364 calculation methods Methods 0.000 description 2
 239000000969 carriers Substances 0.000 description 2
 239000000203 mixtures Substances 0.000 description 2
 230000004048 modification Effects 0.000 description 2
 238000006011 modification reactions Methods 0.000 description 2
 230000002035 prolonged Effects 0.000 description 2
 280000156839 Program Products companies 0.000 description 1
 230000001133 acceleration Effects 0.000 description 1
 239000002253 acids Substances 0.000 description 1
 230000004931 aggregating Effects 0.000 description 1
 230000000694 effects Effects 0.000 description 1
 238000005516 engineering processes Methods 0.000 description 1
 238000004519 manufacturing process Methods 0.000 description 1
 230000003287 optical Effects 0.000 description 1
 230000001131 transforming 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
 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

 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
 H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
 H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
 H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices

 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]
Abstract
A distributed hybrid energy storage system configuration method, a charging and discharging method and a device are provided, wherein the distributed hybrid energy storage system configuration method comprises the following steps: acquiring power consumption information required by a load in unit time; acquiring power generation information of a power generation system in unit time; respectively acquiring electric information provided by a lithium battery and a super capacitor in unit time; establishing a multiobjective optimization model for the acquired required power utilization information and power generation information and the power information provided by the lithium battery and the super capacitor in unit time based on an optimization objective; and obtaining the optimized capacities of the lithium battery and the super capacitor according to the multiobjective optimization model. Therefore, the configuration of the distributed hybrid energy storage system is optimized, the economic benefit is improved, and the economic cost is saved.
Description
Technical Field
The invention relates to the technical field of energy storage, in particular to a distributed hybrid energy storage system configuration and charging and discharging method and device.
Background
The energy storage technology is more and more widely applied to the fields of new energy, smart power grids, energysaving technology, electric vehicles and the like, plays an important role in largescale renewable energy grid connection and distributed power generation and upgrading and transformation of traditional power grids, and is the most effective technical means for solving the problems of high grid connection difficulty and low power grid acceptance caused by intermittence and randomness existing in photovoltaic and wind power generation and adjusting the quality of electric energy.
The energy storage technology is applied to the fields of new energy, smart power grids, electric vehicles and the like, and mainly utilizes an energy storage carrier to temporarily store electric energy and release the electric energy at required time. Because the battery energy storage and super capacitor energy storage technologies are both widely applied and relatively mature energy storage carriers at present, in the application, the energy storage system mainly aims at meeting the energy requirement, but the single energy storage system is limited by the performance of the energy storage element when in use, for example, a lithium battery is limited by the number of charging cycles and the provided power, and a great deal of cost is generated when the technical requirement is met; the super capacitor can be regarded as a permanent device due to the characteristics of the super capacitor, but the capacity of the super capacitor is too small to meet the system requirement. Therefore, the configuration method of the energy storage system is one of the key technologies for solving the application of the energy storage technology, and is the key factor for determining the cost and the economy of the energy storage system.
Lithium batteries are widely used in energy storage systems due to their advantages of high energy density, high charging and discharging efficiency, and the like. In the lithium battery capacity calculation process, consumption of the battery in other aspects except for providing load support in the working process needs to be considered, the capacity of the required battery needs to be accurately calculated in order to analyze the consumption conveniently, modeling needs to be carried out according to the lithium battery composition principle, and an equivalent circuit model mainly comprises an internal resistance model, a resistancecapacitance model, a davinin model and the like. Lithium batteries have high energy density and high chargedischarge efficiency, but are limited by the number of charge cycles and the power supplied. The energy storage system needs to continuously absorb energy or release energy in the process of stabilizing power fluctuation of new energy power generation or other power utilization systems. The energy storage unit inevitably needs to be charged and discharged continuously, and the current for charging and discharging in a short time is relatively large.
Under the frequent working condition of the energy storage system, how to prolong the service life of the lithium battery and avoid frequent charging and discharging becomes the problem to be solved urgently, and in addition, how to optimize the configuration of the distributed hybrid energy storage system also becomes the problem to be solved urgently.
Disclosure of Invention
The first technical problem to be solved by the invention is how to optimize the configuration of the distributed hybrid energy storage system.
In addition, the second technical problem to be solved by the present invention is how to prolong the service life of the lithium battery to avoid frequent charging and discharging.
To solve the first technical problem, according to a first aspect, an embodiment of the present invention discloses a method for configuring a distributed hybrid energy storage system, including:
acquiring power consumption information required by a load in unit time; acquiring power generation information of a power generation system in unit time; respectively acquiring electric information provided by a lithium battery and a super capacitor in unit time; establishing a multiobjective optimization model based on the acquired electricity information and electricity generation information required by the optimization objective pair and the electricity information provided by the lithium battery and the super capacitor in unit time; and obtaining the optimized capacities of the lithium battery and the super capacitor according to the multiobjective optimization model.
Alternatively,linearly weighting and aggregating a plurality of subobjective functions into a single objective function, introducing constraint conditions to establish the following multiobjective optimization model:x＝[W_{bat},W_{uc},P_{bat},P_{uc}]^{T}wherein f (x) is the final objective function after aggregation; f. of_{1}(x) A cost subtargeting function; f. of_{2}(x) A subtarget function for power supply reliability and energy surplus; f. of_{3}(x) A supply and demand balancing subobjective function; x is a configuration variable, including lithium battery energy, super capacitor energy, lithium battery power and super capacitor power; w_{bat}Energy of the lithium battery; w_{uc}Is the super capacitor energy; p_{bat}The power of the lithium battery; p_{uc}Is the super capacitor power; x is a constraint condition of variable X, including the state of charge S of the lithium battery_{OC}Constraint condition, super capacitor terminal voltage V_{OC}Constraint conditions and maximum power constraint conditions; lambda [ alpha ]_{1}、λ_{2}、λ_{3}Respectively, the weights of the subtargeting functions.
Optionally, the optimized capacities for representing the lithium battery and the super capacitor according to the multiobjective optimization model are obtained by adopting a particle swarm algorithm and iterating for K times to obtain an optimal configuration variable, wherein K is a positive integer.
According to a second aspect, an embodiment of the present invention discloses a distributed hybrid energy storage system configuration device, including:
the first acquisition module is used for acquiring the power consumption information required by the load in unit time; the second acquisition module is used for acquiring the power generation information of the power generation system in unit time; the third acquisition module is used for respectively acquiring the electric information provided by the lithium battery and the super capacitor in unit time; a modeling module for modeling the model of the model,
the system comprises a multiobjective optimization model, a multiobjective optimization model and a control module, wherein the multiobjective optimization model is used for establishing a multiobjective optimization model for the acquired required power utilization information and power generation information and the power information provided by a lithium battery and a super capacitor in unit time based on an optimization objective; and the optimization module is used for obtaining optimized capacities for representing the lithium battery and the super capacitor according to the multiobjective optimization model.
Optionally, buildingThe model module linearly weights and aggregates a plurality of subobjective functions into a single objective function, introduces constraint conditions and establishes the following multiobjective optimization model:x＝[W_{bat},W_{uc},P_{bat},P_{uc}]^{T}wherein f (x) is the final objective function after aggregation; f. of_{1}(x) A cost subtargeting function; f. of_{2}(x) A subtarget function for power supply reliability and energy surplus; f. of_{3}(x) A supply and demand balancing subobjective function; x is a configuration variable, including lithium battery energy, super capacitor energy, lithium battery power and super capacitor power; w_{bat}Energy of the lithium battery; w_{uc}Is the super capacitor energy; p_{bat}The power of the lithium battery; p_{uc}Is the super capacitor power; x is a constraint condition of variable X, including the state of charge S of the lithium battery_{OC}Constraint condition, super capacitor terminal voltage V_{OC}Constraint conditions and maximum power constraint conditions; lambda [ alpha ]_{1}、λ_{2}、λ_{3}Respectively, the weights of the subtargeting functions.
To solve the second technical problem, according to a third aspect, an embodiment of the present invention discloses a charging and discharging method for a distributed hybrid energy storage system, including:
acquiring the sending power of a distributed hybrid energy storage system, the load power of a load and the percentage of the residual electric quantity of a super capacitor; and determining the working state of the distributed hybrid energy storage system according to the sending power, the load power and the percentage of the residual electric quantity.
Optionally, when the sending power is greater than the load power and the percentage of the remaining power is less than a first preset value, the super capacitor independently charges the power; when the sending power is greater than the load power and the percentage of the remaining electric quantity is greater than a first preset value, the super capacitor and the lithium battery are charged together; when the sending power is smaller than the load power and the percentage of the residual electric quantity is larger than a second preset value, the super capacitor independently discharges electricity; and when the sending power is smaller than the load power and the percentage of the residual electric quantity is smaller than a second preset value, the super capacitor and the lithium battery are used for discharging together.
Optionally, when the super capacitor and the lithium battery are charged together, the super capacitor absorbs the highfrequency power component, and the lithium battery absorbs the lowfrequency power component; when the super capacitor and the lithium battery are used for discharging together, the lithium battery compensates lowfrequency power fluctuation, and the super capacitor compensates highfrequency power fluctuation.
According to a fourth aspect, an embodiment of the present invention discloses a charging and discharging device for a distributed hybrid energy storage system, including:
the system parameter acquisition module is used for acquiring the sending power of the distributed hybrid energy storage system, the load power of a load and the percentage of the residual electric quantity of the super capacitor; and the state determination module is used for determining the working state of the distributed hybrid energy storage system according to the output power, the load power and the percentage of the residual electric quantity.
Optionally, the state determination module is configured to: when the sending power is greater than the load power and the percentage of the residual electric quantity is less than a first preset value, independently charging by the super capacitor; when the sending power is greater than the load power and the percentage of the remaining electric quantity is greater than a first preset value, the super capacitor and the lithium battery are charged together; when the sending power is smaller than the load power and the percentage of the residual electric quantity is larger than a second preset value, the super capacitor independently discharges electricity; and when the sending power is smaller than the load power and the percentage of the residual electric quantity is smaller than a second preset value, the super capacitor and the lithium battery are used for discharging together.
The technical scheme of the invention has the following advantages:
according to the configuration method and the configuration device for the distributed hybrid energy storage system, the multiobjective optimization model is established based on the optimization objective according to the required electricity utilization information, the electricity generation information and the electricity information provided by the lithium battery and the super capacitor in unit time, and the capacities of the lithium battery and the super capacitor corresponding to the multiobjective optimization model are obtained, so that the configuration of the distributed hybrid energy storage system is optimized, the economic benefit is improved, and the economic cost is saved.
According to the charging and discharging method and device for the distributed hybrid energy storage system, the working state of the distributed hybrid energy storage system is determined according to the sending power, the load power and the percentage of the residual electric quantity, so that the service life of a lithium battery is prolonged under the condition that the energy storage system works frequently, and frequent charging and discharging are avoided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for configuring a distributed hybrid energy storage system according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a process for solving the target model according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a distributed hybrid energy storage system configuration apparatus according to an embodiment of the present invention;
fig. 4 is a flowchart of a charging and discharging method for a distributed hybrid energy storage system according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
To optimize the configuration of the distributed hybrid energy storage system, referring to fig. 1, a flowchart of the configuration method is shown, where the configuration method includes the following steps:
and step S100, acquiring the electricity consumption information required by the load in unit time. In a specific embodiment, the power consumption information required by the load per unit time may include energy required by the load and power required by the load. In particular, the load daily load usage data, i.e. the load daily profile, may be included in a unit time of day.
Step S200, acquiring the power generation information of the power generation system per unit time. Specifically, the time unit of day may include new energy (e.g., photovoltaic power generation) information and other power generation information, i.e., photovoltaic power generation daily curves and other power generation daily curves.
And step S300, respectively acquiring the electric information provided by the lithium battery and the super capacitor in unit time. In a specific embodiment, the electrical information of the lithium battery includes energy and power provided by the lithium battery; the electrical information of the super capacitor includes the energy and power provided by the super capacitor. Specifically, the unit time of day may include energy and power of a lithium battery and energy and power of a super capacitor.
And S400, establishing a multiobjective optimization model based on the acquired required power utilization information, the acquired power generation information and the acquired power information provided by the lithium battery and the super capacitor in unit time. In this embodiment, the optimization objectives may be the lowest economic cost, power supply reliability and energy surplus, optimal supply and demand matching, and the like. Specifically, a target optimization model is established, for example, with an economic target and energy and power constraints.
And S500, obtaining the optimized capacities of the lithium battery and the super capacitor according to the multiobjective optimization model. After the target optimization model is established, the capacities of the lithium battery and the super capacitor can be obtained by solving the multitarget optimization model, so that the capacities of the lithium battery and the super capacitor are optimally configured.
In an alternative embodiment, in the step S500, a particle swarm algorithm may be used to obtain the optimized parameters.
In an alternative embodiment, in performing step S400, the target optimization model may be established using the following formula:
x＝[W_{bat},W_{uc},P_{bat},P_{uc}]^{T}
wherein f (x) is the final objective function after aggregation; f. of_{1}(x) A cost subtargeting function; f. of_{2}(x) A subtarget function for power supply reliability and energy surplus; f. of_{3}(x) A supply and demand balancing subobjective function; x is a configuration variable, including lithium battery energy, super capacitor energy, lithium battery power and super capacitor power; w_{bat}Energy of the lithium battery; w_{uc}Is the super capacitor energy; p_{bat}The power of the lithium battery; p_{uc}Is the super capacitor power; x is a constraint condition of variable X, including the state of charge S of the lithium battery_{OC}Constraint condition, super capacitor terminal voltage V_{OC}Constraint conditions and maximum power constraint conditions; lambda [ alpha ]_{1}、λ_{2}、λ_{3}Respectively, the weights of the subtargeting functions.
To better model, in alternative embodiments, typical photovoltaic power generation curves, load curves and other new energy power generation curves per unit time (e.g., day) may be divided into N parts, each of which is formed into an array { P }_{PV}(1),P_{PV}(2),...,P_{PV}(N)}、{P_{L}(1),P_{L}(2),...,P_{L}(N)}、{P_{WG}(1),P_{WG}(2),...,P_{WG}(N) are respectively a load instantaneous power array, a fan instantaneous power array and a photovoltaic instantaneous power array. It is assumed that the high frequency part of the required power is distributed to the super capacitor and the low frequency part to the lithium battery by using a low pass filter by the control strategy. In a specific embodiment:
wherein, P_{bat}(i)、P_{uc}(i) Respectively giving instantaneous output power for the lithium battery and the super capacitor at the ith moment; t is_{r}Is the low pass filter time constant; p_{L}(i) For instantaneous power consumption of the load at moment i, P_{PV}(i) For photovoltaic instantaneous power generation at moment i, P_{WG}(i) And the instantaneous generated power of other new energy at the ith moment.
In particular embodiments, the objective optimization model may include a plurality of subobjective functions:
1) cost subtargeting function
The hybrid energy storage cost comprises initial construction cost and later maintenance cost, and the energy and power of the lithium battery and the super capacitor are comprehensively considered, and the following formula is shown:
W_{uc}、W_{bat}supercapacitors and supercapacitors respectively configured for system needsEnergy of a lithium battery; p_{uc}、P_{bat}Power of super capacitor and lithium battery respectively required to be configured η_{1}、η_{2}The efficiencies of energy conversion of the super capacitor and the lithium battery are respectively; c. C_{e1}、c_{e2}Respectively the unit energy prices of the super capacitor and the lithium battery; c. C_{m1}、c_{m2}The maintenance cost of the unit power of the super capacitor and the lithium battery is respectively;
2) power supply reliability and energy surplus subtarget function
The electric energy in the microgrid is not connected to the grid, so that if the generating power is greater than the load power plus the stored energy absorption power, power waste is caused, and if the generating power plus the stored energy discharge power is less than the load power, the system is unreliable. The supply reliability and energy surplus subtargets are thus represented by the sum of the squares of the configured lithium battery power and the power difference allocated by the actual control strategy, as shown in the following equation.
P_{bat}(i) Lithium battery instantaneous power P distributed for ith moment control strategy_{uc}(i) Super capacitor instantaneous power, P, allocated for the ith time control strategy_{N,bat}Rated power, P, for lithium battery configurations_{N,uc}Rated power configured for the super capacitor.
3) Supply and demand balance subtarget function
The hybrid energy storage system needs to ensure that the power variation provided or absorbed by the lithium battery and the super capacitor is as small as possible to ensure the stability of the system, so that the power variance provided by the lithium battery and the super capacitor is taken as a target:
P_{N,bat}、P_{N,uc}respectively configuring rated power for the lithium battery and the super capacitor; p_{bat}(i)、P_{uc}(i) Respectively controlling the power of the lithium battery and the power of the super capacitor distributed by the strategy at the moment i; delta P_{avg,bat}、ΔP_{avg,uc}The average power deviation of the lithium battery and the average power deviation of the super capacitor are respectively.
In a specific embodiment, when there are multiple subgoal functions, the weights of the goal functions need to be configured, and the importance of the subgoal functions is weighed. In particular, a dispersion ranking method may be used to determine the weight of each subtargeting function. The difference between the different capacity configurations and the optimal configuration is called dispersion, as shown in the following formula.
m is the target number; f. of_{i} ^{j}＝f_{i}(x_{j}) And measuring the objective function value for different values of the energy storage capacity. The larger the dispersion, the larger the difference of the energy storage configuration from the optimal solution.
The steps of determining the weight of each subtargeting function by the dispersion sorting method are as follows:
1) m subtargets are set, and each subtarget f is respectively solved_{i}(x) Is recorded as x_{i}。
2) Substituting the optimal solution obtained by each objective function into different subobjective functions to obtain corresponding objective function value f_{i} ^{j}。
3) Calculating different optimal solutions x_{i}When, the dispersion of each subtarget function is recordedAs shown in equation (7). Since the target value is compared with the singletarget optimal solution target value, the dispersion is not negative.
4) Calculating the average dispersion of the ith subtarget:
due to the fact thatTherefore, the mean deviation is obtained as m1.
5) Calculating a weight coefficient:
6) since the dispersion is all nonnegative, the weight coefficients calculated through the above process are all positive, and then the weight coefficients are normalized
Wherein n is the number of subtarget functions, lambda_{i}Is a weight coefficient of a subobjective function, andand reconstructing the objective function by the subtargets in a weighted average sum mode.
In an alternative embodiment, the constraints may be established as follows:
1) state of charge S of lithium battery_{OC}Constraint conditions
Limiting the lithium battery S in order to prevent damage to the life of the lithium battery and the life of the supercapacitor_{OC}Upper and lower limits.
S_{OC,min}≤S_{OC,i}≤S_{OC,max}(11)
Wherein S is_{OC,min}The lower limit of the charge state of the lithium battery is generally 2030 percent; s_{OC,max}The upper limit of the state of charge of the lithium battery is generally 80100%.
2) Terminal voltage V of super capacitor_{OC}Constraint conditions
To prevent the lifetime of the supercapacitor from being reduced, the terminal voltage V of the supercapacitor is limited_{OC}Upper and lower limits.
V_{OC,min}≤V_{OC,i}≤V_{OC,max}(12)
Wherein, V_{OC,min}Generally 10% 20% of V_{OC,max}Generally 90100 percent.
3) Maximum power requirement constraint condition
There may be important loads in the system that need to be guaranteed not to be powered down, and therefore there is a minimum power requirement, while at the same time ensuring that the maximum power does not exceed the maximum power limit of the respective converter
Wherein, P_{N,bat}、P_{N,uc}Rated power of the lithium battery and the super capacitor respectively; p_{min}Is the minimum power requirement; p_{bat,max}、P_{uc,max}The maximum power limits of the lithium battery and the super capacitor are respectively.
In the embodiment, the optimized capacities for representing the lithium battery and the super capacitor are obtained according to the multiobjective optimization model, and the optimal configuration variable is obtained after K iterations by adopting a particle swarm algorithm, wherein K is a positive integer. Specifically, the objective function is solved by using a particle swarm optimization PSO, please refer to fig. 2, which is a flowchart for solving the objective function in this embodiment, and first, a vector in a model is explicitly optimized, where x is [ W ═ W_{bat},W_{uc},P_{bat},P_{uc}]^{T}. The optimization process of the hybrid energy storage capacity comprises the following steps:
in step S10, a capacity configuration is initialized. Capacity configuration initialization is performed first. N groups of capacity configuration x by randomly generating lithium batteries and super capacitors_{i}＝[W_{bat,i},W_{uc,i},P_{bat,i},P_{uc,i}]^{T},i＝1,2,...,N。
In step S20, the Ngroup capacity allocation is evaluated. The method comprises the following steps that N groups of lithium batteries and super capacitors are configured, typical wind power and load data are compensated under control strategy formulas (2) and (3), if all described constraints cannot be met in realtime control, the configuration is invalid, and the configuration needs to be regenerated and evaluated; if the described constraints can be met, then the respective objective function values for the N sets of capacity configurations are calculated.
And step S30, updating the respective historical optimal configuration and the total optimal configuration of the N groups. After the N groups of configurations are evaluated, the N groups of configurations are compared, and the N groups of capacity configurations are found out and are compared with respective historical values to obtain optimal values; and simultaneously finding the optimal value in the N groups of capacity configurations and comparing the optimal value with the historical total optimal value. And finally obtaining N groups of respective historical optimal solutions and a total optimal solution.
Step S40, the next set of capacity configurations is updated. And updating the next group of capacity configurations according to a position formula and a speed formula in the particle swarm algorithm.
x_{i}(t)＝x_{i}(t1)+v_{i}(t1),t＝1,2,...,K (21)
v_{i}(t)＝wx_{i}(t1)+c_{1}r_{1}(x_{pbest}x_{i})+c_{2}r_{2}(x_{gbest}x_{i}),t＝1,2,...,K (22)
Wherein t represents the number of iterations and also represents the capacity configuration updated for t times; x is the number of_{i}(t) the configuration results of the lithium battery and the super capacitor after the tth updating;
v_{i}(t) is the difference between the solution at the tth time and the previous solution. x is the number of_{pbest}Until the t iteration, the current individual optimal solution is obtained; vx_{gbest}Until the t iteration, the current overall optimal solution is obtained; w is the inertia weight, c_{1}And c_{2}As an acceleration factor, r_{1}And r_{2}Is a random number between 0 and 1.
Step S50, determine whether the number of iterations reaches K. And finally judging whether the iteration frequency reaches a value K, wherein K is the iteration frequency determined after the precision and the calculation complexity of the final solution are balanced.
If not, go back to step S30 to continue execution; and if the K is reached, stopping searching, and outputting the optimal solution of the optimal configuration of the lithium battery and the super capacitor.
x_{op}＝[W_{bat,op},W_{uc,op},P_{bat,op},P_{uc,op}]^{T}(23)
Wherein x is_{op}For an optimal solution after K iterations, W_{bat,op}The energy optimal value of the lithium battery in the optimal solution is obtained; w_{uc,op}The energy optimal value of the super capacitor in the optimal solution is obtained; p_{bat,op}Optimally configuring the power of the lithium battery in the optimal solution; p_{uc,op}And the power of the super capacitor in the optimal solution is optimally configured.
The embodiment also discloses a distributed hybrid energy storage system configuration device, please refer to fig. 3, the configuration device includes: a first acquisition module 100, a second acquisition module 200, a third acquisition module 300, a modeling module 400, and an optimization module 500, wherein:
the first obtaining module 100 is configured to obtain power consumption information required by a load in unit time; the second obtaining module 200 is configured to obtain power generation information of the power generation system per unit time; the third obtaining module 300 is configured to obtain electrical information provided by the lithium battery and the super capacitor in unit time respectively; the modeling module 400 is used for establishing a multiobjective optimization model for the acquired required power utilization information, power generation information and power information provided by the lithium battery and the super capacitor in unit time based on an optimization objective; the optimization module 500 is configured to obtain optimized capacities of the lithium battery and the super capacitor according to the multiobjective optimization model.
In an optional embodiment, the modeling module linearly weights and aggregates a plurality of subobjective functions into a single objective function, introduces a constraint condition and establishes the following multiobjective optimization model:
x＝[W_{bat},W_{uc},P_{bat},P_{uc}]^{T}
wherein f (x) is the final objective function after aggregation; f. of_{1}(x) A cost subtargeting function; f. of_{2}(x) A subtarget function for power supply reliability and energy surplus; f. of_{3}(x) A supply and demand balancing subobjective function; x is a configuration variable, including lithium battery energy, super capacitor energy, lithium battery power and super capacitor power; w_{bat}Energy of the lithium battery; w_{uc}Is the super capacitor energy; p_{bat}The power of the lithium battery; p_{uc}Is the super capacitor power; x is a constraint condition of variable X, including the state of charge S of the lithium battery_{OC}Constraint condition, super capacitor terminal voltage V_{OC}Constraint conditions and maximum power constraint conditions; lambda [ alpha ]_{1}、λ_{2}、λ_{3}Respectively, the weights of the subtargeting functions.
According to the configuration method and the configuration device for the distributed hybrid energy storage system, the target optimization model is established based on the preset configuration target according to the required power utilization information, the power generation information and the provided power information, and the optimization parameters corresponding to the target optimization model and used for representing the capacities of the lithium battery and the super capacitor are obtained, so that the configuration of the distributed hybrid energy storage system is optimized, the economic benefit is improved, and the economic cost is saved.
In order to prolong the service life of a lithium battery and avoid frequent charging and discharging, the embodiment discloses a charging and discharging method of a distributed hybrid energy storage system, wherein a distributed hybrid system is configured by adopting the configuration method of the embodiment, and referring to fig. 4, the charging and discharging method includes the following steps:
and step S1, acquiring parameters of the distributed hybrid energy storage system. Specifically, the sending power P of the distributed hybrid energy storage system, the load power P1 of the load and the remaining capacity percentage SOC of the super capacitor are obtained.
And step S2, determining the working state of the distributed hybrid energy storage system. In this embodiment, the working state of the distributed hybrid energy storage system is determined according to the output power P, the load power P1 and the percentage SOC of the remaining power. In a specific embodiment:
(1) when the emitted power P is greater than the load power P1 and the remaining capacity percentage SOC is less than a first preset value (e.g., 40%), the super capacitor is independently charged.
(2) When the output power P is greater than the load power P1 and the percentage SOC of the remaining charge is greater than a first preset value (e.g., 40%), the super capacitor and the lithium battery are charged together.
(3) When the emitted power P is less than the load power P1 and the remaining capacity percentage SOC is greater than a second preset value (e.g., 70%), the super capacitor independently discharges the electricity.
(4) When the output power P is smaller than the load power P1 and the remaining capacity percentage SOC is smaller than a second preset value (for example, 70%), the super capacitor and the lithium battery are used for discharging together.
In a preferred embodiment, when the super capacitor and the lithium battery are charged together, the super capacitor absorbs a highfrequency power component and the lithium battery absorbs a lowfrequency power component while stabilizing power fluctuation; when the super capacitor and the lithium battery discharge together, the super capacitor and the lithium battery bear power fluctuation together, the lithium battery compensates lowfrequency power fluctuation, and the super capacitor compensates highfrequency power fluctuation. It should be noted that leadacid batteries may also be used as the lithium batteries in the present embodiment.
It should be noted that, in this embodiment, stabilizing fluctuation of the power generation system mainly refers to stabilizing fluctuation of a new energy power generation system, such as a wind power generation system and a photovoltaic power generation system. When the hybrid energy storage system is used for stabilizing the photovoltaic power, the ideal stabilizing effect is that the power generated by the photovoltaic after the hybrid energy storage stabilization is consistent with the power expected to be output, namely, the power difference value between the photovoltaic power and the power expected to be output, namely, the highfrequency fluctuation part and the lowfrequency fluctuation part in the total power of the energy storage system are respectively absorbed and stabilized by the lithium battery and the super capacitor. However, in the actual operation process, due to the limited energy storage capacity, the limitation of charge and discharge power and other factors, an ideal stabilizing effect is difficult to realize, and the optimal optimization effect is achieved by determining reasonable energy storage capacity.
This embodiment has still disclosed a distributed hybrid energy storage system chargedischarge device, and wherein, distributed hybrid system adopts the device configuration that the abovementioned embodiment is disclosed to form, and this chargedischarge device includes: the system comprises a system parameter acquisition module and a state determination module, wherein the system parameter acquisition module is used for acquiring the emitted power P of the distributed hybrid energy storage system, the load power P1 of a load and the residual capacity percentage SOC of a super capacitor; the state determination module is used for determining the working state of the distributed hybrid energy storage system according to the emitted power P, the load power P1 and the remaining capacity percentage SOC.
In a particular embodiment, the state determination module is to: when the sending power P is greater than the load power P1 and the remaining capacity percentage SOC is less than a first preset value, independently charging by the super capacitor; when the sending power P is greater than the load power P1 and the percentage SOC of the remaining electric quantity is greater than a first preset value, the super capacitor and the lithium battery are charged together; when the sending power P is smaller than the load power P1 and the percentage SOC of the remaining capacity is larger than a second preset value, the super capacitor independently discharges; and when the sending power P is smaller than the load power P1 and the remaining capacity percentage SOC is smaller than a second preset value, the super capacitor and the lithium battery are used for discharging together.
According to the charging and discharging method and device for the distributed hybrid energy storage system, the working state of the distributed hybrid energy storage system is determined according to the sending power, the load power and the percentage of the residual electric quantity, so that the service life of a lithium battery is prolonged under the frequent working condition of the energy storage system, and frequent charging and discharging are avoided.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computerusable storage media (including, but not limited to, disk storage, CDROM, optical storage, and the like) having computerusable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computerreadable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computerreadable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (4)
1. A charging and discharging method of a distributed hybrid energy storage system is characterized by comprising the following steps:
configuring the distributed hybrid energy storage system;
acquiring the sending power (P) of the distributed hybrid energy storage system, the load power (P1) of a load and the residual capacity percentage (SOC) of a super capacitor;
determining an operating state of the distributed hybrid energy storage system from the emitted power (P), the load power (P1) and the percentage of charge remaining (SOC);
when the emitted power (P) is greater than the load power (P1) and the percentage of remaining charge (SOC) is less than a first preset value, independently charging by the super capacitor;
when the emitted power (P) is greater than the load power (P1) and the percentage of remaining charge (SOC) is greater than a first preset value, the super capacitor and the lithium battery are charged together;
when the emitted power (P) is less than the load power (P1) and the percentage of remaining charge (SOC) is greater than a second preset value, independently discharging by the super capacitor;
when the emitted power (P) is less than the load power (P1) and the percentage of remaining charge (SOC) is less than a second preset value, the super capacitor and the lithium battery are used for discharging together;
the configuring the distributed hybrid energy storage system comprises:
acquiring power consumption information required by a load in unit time;
acquiring power generation information of a power generation system in unit time;
respectively acquiring electric information provided by a lithium battery and a super capacitor in unit time;
establishing a multiobjective optimization model for the acquired required power utilization information, the acquired power generation information and the power information provided by the lithium battery and the super capacitor in unit time based on an optimization objective;
obtaining optimized capacities of the lithium battery and the super capacitor according to the multiobjective optimization model;
wherein, the multiobjective optimization model is as follows:
x＝[W_{bat},W_{uc},P_{bat},P_{uc}]^{T}
(x) is the final objective function after aggregation; f. of_{1}(x) A cost subtargeting function; f. of_{2}(x) A subtarget function for power supply reliability and energy surplus; f. of_{3}(x) A supply and demand balancing subobjective function; x is a configuration variable, including lithium battery energy, super capacitor energy, lithium battery power and super capacitor power; w_{bat}Energy of the lithium battery; w_{uc}Is the super capacitor energy; p_{bat}The power of the lithium battery; p_{uc}Is the super capacitor power; x is a constraint condition of variable X, including the state of charge S of the lithium battery_{OC}Constraint condition, super capacitor terminal voltage V_{OC}Constraint conditions and maximum power constraint conditions; lambda [ alpha ]_{1}、λ_{2}、λ_{3}Are subtarget functions respectivelyThe weight of (c);
wherein, 1) the cost subtargeting function
The hybrid energy storage cost comprises initial construction cost and later maintenance cost, and the energy and power of the lithium battery and the super capacitor are comprehensively considered, and the following formula is shown:
W_{uc}、W_{bat}respectively configuring the energy of a super capacitor and the energy of a lithium battery for the system; p_{uc}、P_{bat}Power of super capacitor and lithium battery respectively required to be configured η_{1}、η_{2}The efficiencies of energy conversion of the super capacitor and the lithium battery are respectively; c. C_{e1}、c_{e2}Respectively the unit energy prices of the super capacitor and the lithium battery; c. C_{m1}、c_{m2}The maintenance cost of the unit power of the super capacitor and the lithium battery is respectively;
2) power supply reliability and energy surplus subtarget function
The electric energy in the microgrid is not connected to the grid, so that if the generating power is greater than the load power plus the stored energy absorption power, power waste is caused, and if the generating power plus the stored energy discharge power is less than the load power, the system is unreliable, so that the subtargets of power supply reliability and energy surplus are represented by the square sum of the configured lithium battery power and the power difference distributed by the actual control strategy, as shown in the following formula;
P_{bat}(i) lithium battery instantaneous power P distributed for ith moment control strategy_{uc}(i) Super capacitor instantaneous power, P, allocated for the ith time control strategy_{N,bat}Rated power, P, for lithium battery configurations_{N,uc}Rated power configured for the super capacitor;
3) supply and demand balance subtarget function
The hybrid energy storage system needs to ensure that the power variation provided or absorbed by the lithium battery and the super capacitor is as small as possible to ensure the stability of the system, so that the power variance provided by the lithium battery and the super capacitor is taken as a target:
P_{N,bat}、P_{N,uc}respectively configuring rated power for the lithium battery and the super capacitor; p_{bat}(i)、P_{uc}(i) Respectively controlling the power of the lithium battery and the power of the super capacitor distributed by the strategy at the moment i; delta P_{avg,bat}、ΔP_{avg,uc}The average power deviation of the lithium battery and the super capacitor is respectively, and N is a typical photovoltaic power generation curve, a load curve and other new energy power generation curve parts in unit time.
2. The charging and discharging method of the distributed hybrid energy storage system according to claim 1,
when the super capacitor and the lithium battery are charged together, the super capacitor absorbs highfrequency power components, and the lithium battery absorbs lowfrequency power components;
when the super capacitor and the lithium battery discharge together, the lithium battery compensates lowfrequency power fluctuation, and the super capacitor compensates highfrequency power fluctuation.
3. The method for configuring the distributed hybrid energy storage system according to claim 1, wherein the optimized capacities for representing the lithium battery and the super capacitor obtained according to the multiobjective optimization model are obtained by performing iteration for K times through a particle swarm algorithm, and K is a positive integer.
4. The utility model provides a distributing type mixes energy storage system chargedischarge device which characterized in that, chargedischarge device includes:
the configuration module is used for configuring the distributed hybrid energy storage system;
the system parameter acquisition module is used for acquiring the sending power (P) of the distributed hybrid energy storage system, the load power (P1) of a load and the residual capacity percentage (SOC) of a super capacitor;
a state determination module for determining an operating state of the distributed hybrid energy storage system from the emitted power (P), the load power (P1) and the percentage of remaining charge (SOC);
when the emitted power (P) is greater than the load power (P1) and the percentage of remaining charge (SOC) is less than a first preset value, independently charging by the super capacitor;
when the emitted power (P) is greater than the load power (P1) and the percentage of remaining charge (SOC) is greater than a first preset value, the super capacitor and the lithium battery are charged together;
when the emitted power (P) is less than the load power (P1) and the percentage of remaining charge (SOC) is greater than a second preset value, independently discharging by the super capacitor;
when the emitted power (P) is less than the load power (P1) and the percentage of remaining charge (SOC) is less than a second preset value, the super capacitor and the lithium battery are used for discharging together;
the configuration module includes:
the first acquisition module is used for acquiring the power consumption information required by the load in unit time;
the second acquisition module is used for acquiring the power generation information of the power generation system in unit time;
the third acquisition module is used for respectively acquiring the electric information provided by the lithium battery and the super capacitor in unit time;
the modeling module is used for establishing a multiobjective optimization model for the acquired required power utilization information and power generation information and the power information provided by the lithium battery and the super capacitor in unit time based on an optimization objective;
the optimization module is used for obtaining optimized capacities of the lithium battery and the super capacitor according to the multiobjective optimization model;
wherein, the multiobjective optimization model is as follows:
x＝[W_{bat},W_{uc},P_{bat},P_{uc}]^{T}
(x) is the final objective function after aggregation; f. of_{1}(x) A cost subtargeting function; f. of_{2}(x) A subtarget function for power supply reliability and energy surplus; f. of_{3}(x) A supply and demand balancing subobjective function; x is a configuration variable, including lithium battery energy, super capacitor energy, lithium battery power and super capacitor power; w_{bat}Energy of the lithium battery; w_{uc}Is the super capacitor energy; p_{bat}The power of the lithium battery; p_{uc}Is the super capacitor power; x is a constraint condition of variable X, including the state of charge S of the lithium battery_{OC}Constraint condition, super capacitor terminal voltage V_{OC}Constraint conditions and maximum power constraint conditions; lambda [ alpha ]_{1}、λ_{2}、λ_{3}Weights of the subtargeting functions are respectively;
wherein, 1) the cost subtargeting function
The hybrid energy storage cost comprises initial construction cost and later maintenance cost, and the energy and power of the lithium battery and the super capacitor are comprehensively considered, and the following formula is shown:
W_{uc}、W_{bat}respectively configuring the energy of a super capacitor and the energy of a lithium battery for the system; p_{uc}、P_{bat}Power of super capacitor and lithium battery respectively required to be configured η_{1}、η_{2}The efficiencies of energy conversion of the super capacitor and the lithium battery are respectively; c. C_{e1}、c_{e2}Respectively the unit energy prices of the super capacitor and the lithium battery; c. C_{m1}、c_{m2}The maintenance cost of the unit power of the super capacitor and the lithium battery is respectively;
2) power supply reliability and energy surplus subtarget function
The electric energy in the microgrid is not connected to the grid, so that if the generated power is greater than the load power plus the stored energy absorption power, power waste is caused, and if the generated power plus the stored energy discharge power is less than the load power, the system is unreliable, so that the subtargets of power supply reliability and energy surplus are represented by the square sum of the configured lithium battery power and the power difference distributed by the actual control strategy, as shown in the following formula:
P_{bat}(i) lithium battery instantaneous power P distributed for ith moment control strategy_{uc}(i) Super capacitor instantaneous power, P, allocated for the ith time control strategy_{N,bat}Rated power, P, for lithium battery configurations_{N,uc}Rated power configured for the super capacitor;
3) supply and demand balance subtarget function
The hybrid energy storage system needs to ensure that the power variation provided or absorbed by the lithium battery and the super capacitor is as small as possible to ensure the stability of the system, so that the power variance provided by the lithium battery and the super capacitor is taken as a target:
P_{N,bat}、P_{N,uc}respectively configuring rated power for the lithium battery and the super capacitor; p_{bat}(i)、P_{uc}(i) Respectively controlling the power of the lithium battery and the power of the super capacitor distributed by the strategy at the moment i; delta P_{avg,bat}、ΔP_{avg,uc}The average power deviation of the lithium battery and the super capacitor is respectively, and N is a typical photovoltaic power generation curve, a load curve and other new energy power generation curve parts in unit time.
Priority Applications (1)
Application Number  Priority Date  Filing Date  Title 

CN201610893391.3A CN106451508B (en)  20161013  20161013  Distributed hybrid energy storage system configuration and charging and discharging method and device 
Applications Claiming Priority (1)
Application Number  Priority Date  Filing Date  Title 

CN201610893391.3A CN106451508B (en)  20161013  20161013  Distributed hybrid energy storage system configuration and charging and discharging method and device 
Publications (2)
Publication Number  Publication Date 

CN106451508A CN106451508A (en)  20170222 
CN106451508B true CN106451508B (en)  20200204 
Family
ID=58173682
Family Applications (1)
Application Number  Title  Priority Date  Filing Date 

CN201610893391.3A Active CN106451508B (en)  20161013  20161013  Distributed hybrid energy storage system configuration and charging and discharging method and device 
Country Status (1)
Country  Link 

CN (1)  CN106451508B (en) 
Families Citing this family (3)
Publication number  Priority date  Publication date  Assignee  Title 

CN107248025A (en) *  20170522  20171013  东南大学  A kind of Demand Side Response control method based on both sides of supply and demand electricity ratio at times 
CN108448652A (en) *  20180406  20180824  刘玉华  A kind of new energy and power grid cooperated power supply method and its calibration equipment 
CN110350559A (en) *  20190701  20191018  山东省科学院自动化研究所  It provides multiple forms of energy to complement each other mixed energy storage system voltage hierarchy system and energy management method 
Citations (3)
Publication number  Priority date  Publication date  Assignee  Title 

CN102545261A (en) *  20120116  20120704  沈阳工程学院  Microgrid experiment system 
CN102931683A (en) *  20121102  20130213  浙江工业大学  Windsolar direct current microgrid gridconnection control method based on substation typical daily load curve 
CN105140942A (en) *  20151009  20151209  国家电网公司  Hybrid energy storage optimal power allocation method with stateofcharge deviation being taken into consideration 
Family Cites Families (3)
Publication number  Priority date  Publication date  Assignee  Title 

CN103078340B (en) *  20121224  20150429  天津大学  Mixed energy storing capacity optimization method for optimizing microgrid call wire power 
US20160233679A1 (en) *  20131018  20160811  State Grid Corporation Of China  A method and system for control of smoothing the energy storage in wind phtovolatic power fluctuation based on changing rate 
CN205385293U (en) *  20160219  20160713  安徽工程大学  Little electric wire netting energy storage system of new forms of energy 

2016
 20161013 CN CN201610893391.3A patent/CN106451508B/en active Active
Patent Citations (3)
Publication number  Priority date  Publication date  Assignee  Title 

CN102545261A (en) *  20120116  20120704  沈阳工程学院  Microgrid experiment system 
CN102931683A (en) *  20121102  20130213  浙江工业大学  Windsolar direct current microgrid gridconnection control method based on substation typical daily load curve 
CN105140942A (en) *  20151009  20151209  国家电网公司  Hybrid energy storage optimal power allocation method with stateofcharge deviation being taken into consideration 
NonPatent Citations (1)
Title 

微电网复合储能多目标优化配置方法及评价指标;谭兴国 等;《电力系统自动化》;20140425;第38卷(第8期);714 * 
Also Published As
Publication number  Publication date 

CN106451508A (en)  20170222 
Similar Documents
Publication  Publication Date  Title 

Chong et al.  An optimal control strategy for standalone PV system with BatterySupercapacitor Hybrid Energy Storage System  
US10601239B2 (en)  Systems and methods for series battery charging  
Liu et al.  Sizing a hybrid energy storage system for maintaining power balance of an isolated system with high penetration of wind generation  
Krieger et al.  A comparison of leadacid and lithiumbased battery behavior and capacity fade in offgrid renewable charging applications  
Li et al.  Analysis of battery lifetime extension in a SMESbattery hybrid energy storage system using a novel battery lifetime model  
Cabrane et al.  Analysis and evaluation of batterysupercapacitor hybrid energy storage system for photovoltaic installation  
KR101725701B1 (en)  Secondary cell system having plurality of cells, and method for distributing charge/discharge electric power or current  
CN104935045B (en)  Battery pack equalization method for energy storage system adopting nickelseries storage batteries  
US9627907B2 (en)  Storage battery control device, storage battery control method, program, electricity storage system, and power supply system  
US9488977B2 (en)  Power storage system having modularized BMS connection structure and method for controlling the system  
Liu et al.  Search for an optimal fivestep charging pattern for Liion batteries using consecutive orthogonal arrays  
Jiang et al.  A battery energy storage system duallayer control strategy for mitigating wind farm fluctuations  
CN104037793B (en)  A kind of energystorage units capacity collocation method being applied to active distribution network  
JP6168564B2 (en)  Method and power generation system for supplying electrical energy from a distributed energy source  
US8655524B2 (en)  Power supply system, vehicle provided with the same and control method of power supply system  
CN105098807B (en)  Complementary optimal control method in energystorage system between multiple hybrid accumulators  
CN102084570B (en)  Power storage device for electric power generation system and method for operating the power storage device  
JP5408410B2 (en)  How to determine the aging status of a battery  
Jing et al.  Dynamic power allocation of batterysupercapacitor hybrid energy storage for standalone PV microgrid applications  
Li et al.  Analysis of a new design of the hybrid energy storage system used in the residential mCHP systems  
US9278622B2 (en)  Vehicle battery management unit having cell balancer based on capacity differences of battery cells  
CN106651026B (en)  Multitime scale microgrid energy management optimization scheduling method  
Jiang et al.  Research on power sharing strategy of hybrid energy storage system in photovoltaic power station based on multiobjective optimisation  
CN106779291B (en)  Intelligent power utilization park demand response strategy  
CN103918120B (en)  Lead accumulator system 
Legal Events
Date  Code  Title  Description 

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