CN112952877B - Hybrid energy storage power capacity configuration method considering characteristics of different types of batteries - Google Patents
Hybrid energy storage power capacity configuration method considering characteristics of different types of batteries Download PDFInfo
- Publication number
- CN112952877B CN112952877B CN202110234575.XA CN202110234575A CN112952877B CN 112952877 B CN112952877 B CN 112952877B CN 202110234575 A CN202110234575 A CN 202110234575A CN 112952877 B CN112952877 B CN 112952877B
- Authority
- CN
- China
- Prior art keywords
- power
- energy storage
- battery
- storage system
- capacity
- 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
Images
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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF 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
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/10—Flexible AC transmission systems [FACTS]
-
- 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 an energy storage comprehensive preparation method considering the characteristics of different types of batteries, which comprises the following steps: step 1: acquiring specific parameters of a lithium battery and a flow battery in a battery energy storage system; step 2: establishing a battery energy storage system SOC state model; and 3, step 3: firstly, processing wind power data, wherein the processed data is expected output of a wind farm after the power of the wind farm is stabilized by a battery energy storage system, then establishing an index, and finally formulating a corresponding energy storage configuration scheme according to the set index; and 4, step 4: and (4) according to the energy storage configuration scheme in the step (3), configuring the lithium battery and the flow battery by using an immune algorithm, and obtaining the most economical and reasonable energy storage configuration scheme while meeting the technical requirements.
Description
Technical Field
The invention relates to the field of power systems, in particular to a hybrid energy storage power capacity configuration method considering characteristics of different types of batteries.
Background
Recently, the permeability of the new energy station in the power grid is increased due to the continuous consumption of the traditional fossil energy and the relevant policies of the country. However, the output condition of the new energy is limited by the characteristics of the new energy, and is often limited by various factors and conditions, such as the sunshine time of a photovoltaic power station and the wind power of a wind power station. In addition, weather conditions including temperature and humidity have great influence on the output of the new energy station.
The existing common method is to add an energy storage device to solve the problems of unstable output of new energy and the like, a common energy storage configuration scheme is that a pumped storage power station is matched with the output of the new energy power station, the output of the new energy power station is ensured to be stable in a proper range on a large-scale time scale, a storage battery and a super capacitor are matched with the output of the new energy power station, and the compensation power and the compensation capacity are calculated after the cutoff frequency is determined through spectrum analysis so as to meet the requirements of the new energy power station.
Some targets achieved by existing energy storage configuration only aim at a certain specific problem faced by a new energy station, the function is single, and the power and capacity problems can be solved according to a hybrid energy storage configuration scheme after spectrum analysis, for example, a hybrid energy storage scheme of a storage battery and a super capacitor, and a hybrid energy storage configuration scheme of pumped storage and a battery, but due to the problems of small capacity of the super capacitor and the like, the pumped storage site selection requirement is high, and the self limiting conditions of energy storage facilities are met, so that if the multi-target energy storage configuration scheme is to be completed, and when the hybrid configuration is distributed, the selection of cut-off frequency is complex, more engineering practical experience is needed to be reasonably selected according to the local requirements on electric energy quality, and the universality is not high. The method selects proper technology to get rid of space and environment and overcome the technical defects of the method, and the practicability of the energy storage configuration scheme in engineering can be greatly improved.
Disclosure of Invention
In view of the defects in the prior art, the present invention aims to provide a hybrid energy storage power capacity configuration method considering different battery characteristics.
In order to achieve the purpose, the invention adopts the technical scheme that:
a hybrid energy storage power capacity configuration method considering characteristics of different types of batteries comprises the following steps:
step 1: acquiring specific parameters of a lithium battery and a flow battery in a battery energy storage system;
step 2: establishing a battery energy storage system SOC state model;
and step 3: firstly, processing wind power data, wherein the processed data is expected output of a wind farm after the power of the wind farm is stabilized by a battery energy storage system, then establishing indexes, and finally formulating a corresponding energy storage configuration scheme according to the set indexes;
and 4, step 4: and (4) according to the energy storage configuration scheme in the step (3), configuring the lithium battery and the flow battery by using an immune algorithm, and obtaining the most economical and reasonable energy storage configuration scheme while meeting the technical requirements.
On the basis of the scheme, in the step 1, the specific parameters of the lithium battery comprise: maximum depth SOC of charge and discharge capacity Li-ion max SOC with minimum depth of charge-discharge capacity Li-ion min And charge-discharge efficiency eta Li-ion Annual operation and maintenance cost coefficient K of unit power of stored energy O-Li-ion Annual operation and maintenance cost coefficient K of unit capacity of stored energy M-Li-ion Annual construction cost coefficient of unit power of stored energy C P-Li-ion Annual construction cost coefficient of unit capacity of stored energy C E-Li-ion Self discharge rate alpha Li-ion And life a 1 (ii) a The specific parameters of the flow battery comprise: maximum depth SOC of charge and discharge capacity VRB max SOC with minimum depth of charge-discharge capacity VRB min And charge-discharge efficiency eta VRB Annual operation and maintenance cost coefficient K of unit power of stored energy O-VRB Annual operation and maintenance cost coefficient K of unit capacity of stored energy M-VRB Annual construction cost coefficient of unit power of stored energy C P-VRB Annual construction cost coefficient of unit capacity of stored energy C E-VRB Self discharge rate alpha VRB And life a 2 。
On the basis of the scheme, the specific steps of the step 2 are as follows:
when the battery energy storage system is in a charging state, the SOC state model is specifically as shown in formula (1):
when the battery energy storage system is in a discharge state, the SOC state model is specifically as shown in formula (2):
in the above formula, SOC (t) is the current load state of the battery, and the value range is [0,1 ]]，P charge (t) and P discharge (t) is the charging power and the discharging power of the battery energy storage system respectively, Δ t is the charging and discharging time interval, η is the energy conversion efficiency, E is the total energy of the battery energy storage system,and alpha is the self-discharge rate of the current battery.
On the basis of the scheme, the specific steps of the step 3 are as follows:
acquiring wind power data, processing the wind power data, taking short-time average output of wind power as expected output, wherein the specific time is T, the value of T is 1800s, and T = M Δ T, and then in the period of time, the expected output of the wind field of the wind power plant after the power of the battery energy storage system is stabilized is P ref Specifically, as shown in formula (3):
in the formula, t 1 ＝t 0 +(k-1)T，t 2 ＝t 1 +(M-1)△t，t 0 For the initial time, Δ T is the time interval, where 1s is taken, M is the number of time intervals in T, where 1800, P is taken wind (T) is the real-time power output by the wind field, k is the number of the starting time intervals of the time period taken in one day when T is 1800s, and k belongs to [1,48 ]]；
The indicators include: the effectiveness of power stabilization, the upper and lower limits of capacity stabilization power, peak clipping rate and stability,
(1) Power stabilizing effectiveness η p-effect Specifically, as shown in formula (4):
in the formula (I), the compound is shown in the specification,
which represents the sum of time when the actual output meets the desired output after power compensation of the battery energy storage system is added,
represents the total time within the entire settling time period;
(2) L for upper bound of capacity-stabilized power high To show that the lower bound of the capacity-stabilized power is L low To represent;
(3) Peak clipping ratio lambda PeakCutting Specifically, as shown in formula (5):
in the formula, P ref-rc (t) is the final output power of the wind field after peak clipping and valley filling of the battery energy storage system and then the wind field is merged into the power grid, max and min are respectively a maximum function and a minimum function, and the function is to obtain P wind (t) and P ref-rc (t) maximum and minimum values over the entire time period;
(4) Stability lambda Steady Specifically, as shown in formula (6):
in the formula, E bess-discharge The amount of charging power that can be accommodated by the battery energy storage system, E total-discharge The total required charging capacity under the given wind power data;
the energy storage configuration scheme comprises the following specific steps:
(1) Writing a program in which input power suppression effectiveness eta is p-effect The reference value of (2), the reference value being determined according to the actual situation,
(2) Carrying out the iteration of the steady power compensation according to the formula (4), calculating the compensation power,
(3) If eta passrate ≤η p-effect Then go to (4), otherwise go back to (2), where η passrate A value representing the effectiveness of power leveling in the actual calculation process,
(4) Input capacity stabilizing power upper bound L high L for lower bound of capacity-stabilized power low Peak reduction ratio lambda PeakCutting And stability lambda Steady ，
(5) According to the formulas (5) and (6), the power and capacity iteration during peak clipping and valley filling is carried out, the capacity and the power required by peak clipping and valley filling are calculated,
(6) If gamma is less than or equal to lambda PeakCutting ,β≥λ Steady Then the power P required for stabilizing the wind power stage is output rep Capacity E rep Power P required for peak clipping and valley filling PC And capacity E PC Otherwise, returning to (5), wherein gamma and beta represent the peak clipping rate and stability calculated under the corresponding power compensation and capacity compensation respectively.
On the basis of the scheme, the specific steps of the step 4 are as follows:
firstly, introducing the average use cost LCUS of the stored energy Y Specifically, as shown in formula (7):
wherein Y denotes a predetermined age, Y =0,1,2, \ 8230;, Y,
refers to the initial construction cost of the modified battery energy storage system,
the subsequent operation and maintenance cost of the simplified battery energy storage system is pointed out,
refers to the energy released by the battery energy storage system within a specified service life;
initial construction cost of battery energy storage system initial The calculation formula of (a) is as follows:
cost initial ＝C P P ESS +C E E ESS (8)
in the formula: p ESS 、E ESS The power and the capacity of the battery energy storage system are respectively; c P 、C E Unit investment of power and capacity of the battery energy storage system respectively;
the equal-year-number coefficient C (r, n) is expressed as:
in the formula: r is a reference discount rate; n is the operating time limit of the battery energy storage system;
correcting the initial construction cost of the battery energy storage system to obtain the corrected initial construction cost of the battery energy storage system
Comprises the following steps:
subsequent operation and maintenance cost of battery energy storage system
The calculation formula of (2) is as follows:
cost operating ＝K O P ESS +K M Q ESS (11)
in the formula: k is O Annual operation and maintenance cost coefficient of unit power of the battery energy storage system; k M Annual operation and maintenance cost coefficient of unit capacity of the battery energy storage system; q ESS The annual energy production of the battery energy storage system;
when K is O And K M When the subsequent operation and maintenance cost of the battery energy storage system is not easy to determine, the subsequent operation and maintenance cost of the battery energy storage system is generally estimated according to a certain proportion of the initial construction cost of the battery energy storage system, and therefore the subsequent operation and maintenance cost of the simplified battery energy storage system is obtained
Specifically, as shown in formula (12):
in the formula: mu is the operation and maintenance cost coefficient of the battery energy storage system;
writing a program in a placeIn the process of programming, inputting the specific parameters of the lithium battery and the specific parameters of the flow battery in the step 1, and then determining an objective function minLCUS Y ，
The constraint conditions are specifically as follows:
(1) Capacity limitation
In the formula, P Li-ion,ref And P VRB,ref Rated power of lithium battery and flow battery respectively, E Li-ion And E VRB The capacities of the lithium battery and the flow battery are respectively;
(2) Power limitation
In the formula, P min Below which the power storage configuration cannot achieve its intended effect, is the lowest limit of power;
(3) Charge-discharge depth limitation
0.2≤SOC Li-ion ≤0.8 (15)
0.05≤SOC VRB ≤0.95 (16)
In the formula, SOC Li-ion Is the charge-discharge depth, SOC, of the lithium battery VRB The charging depth of the flow battery is set;
adopting immune algorithm to carry out optimization, respectively calculating the capacity and power of the lithium battery and the flow battery, and if the capacity and power of the lithium battery and the flow battery meet the LCUS Y (k * )-LCUS Y (k * If-1) is less than or equal to epsilon, the power P of the lithium battery is output Li-ion And capacity E Li-ion Power P of the flow battery VRB And capacity E VRB Wherein, LCUS Y (k * ) And LCUS Y (k * -1) represents the calculated average cost of energy storage during the optimization process, epsilon is the acceptable error range of the setting, k * And k * -1 represents the number of times in the optimization process.
The technical scheme of the invention has the following beneficial effects:
(1) According to the energy storage configuration scheme provided by the invention, the cutoff frequency does not need to be selected according to experience, and the great energy storage stabilizing effect and expected error caused by poor selection of the cutoff frequency are not worried about.
(2) The energy storage configuration scheme provided by the invention does not only pay attention to economy but does not consider actual engineering technical indexes, and is an economic optimal solution sought on a series of conditions meeting the technical indexes.
(3) The cost measurement index provided by the invention has no limit on the type of energy storage technology, can compare the economy of each energy storage scheme from multiple angles, is convenient and effective, and is easy to realize.
Drawings
The invention has the following drawings:
fig. 1 shows a new energy station and battery energy storage system combined output system.
Fig. 2 is a simplified circuit diagram of the battery.
Fig. 3 is a flow chart of an energy storage configuration scheme under technical index limitation.
Fig. 4 is an economic energy storage configuration allocation flow diagram.
Fig. 5 wind power force diagram.
Fig. 6 is a power-stabilizing effect diagram.
FIG. 7 is a graph showing the peak reduction effect.
FIG. 8 is a graph of stability change.
FIG. 9 is a flow chart of an immunization algorithm.
Figure 10 energy storage scheme cost comparison graph.
Fig. 11 is a graph of the effect of the output of the lithium battery.
Fig. 12 is a graph of the effects of flow battery output.
Detailed Description
The present invention is described in further detail below with reference to figures 1-12.
In order to make engineering application more convenient and not need to select proper cut-off frequency, a hybrid energy storage power capacity configuration method considering different battery characteristics is provided. The hybrid energy storage refers to the mixing between different battery types, and is different from the mixing energy storage between a common storage battery and other types.
The energy storage configuration between the batteries fully considers the basic characteristics of the batteries, exerts the characteristics and advantages of the power type battery and the capacity type battery, simultaneously leads the power fluctuation suppression and the peak clipping and valley filling to belong to the tasks of different batteries because of the characteristics of the batteries, and avoids the selection of cut-off frequency without low-pass filtering. The connection schematic diagram of the new energy station and the battery energy storage system is shown in the attached figure 1.
The invention provides a hybrid energy storage power capacity configuration method considering characteristics of different types of batteries, aiming at the current situation that the corresponding technical index configuration is ambiguous in the existing energy storage capacity configuration method of a new energy station. The method comprises the following steps:
step 1: acquiring specific parameters of a lithium battery and a flow battery in a battery energy storage system;
and 2, step: establishing a battery energy storage system SOC state model;
and step 3: firstly, processing wind power data, wherein the processed data is expected output of a wind farm after the power of the wind farm is stabilized by a battery energy storage system, then establishing indexes, and finally formulating a corresponding energy storage configuration scheme according to the set indexes;
and 4, step 4: and (4) according to the energy storage configuration scheme in the step (3), configuring the lithium battery and the flow battery by using an immune algorithm, and obtaining the most economical and reasonable energy storage configuration scheme while meeting the technical requirements.
Step 1, concrete parameters of lithium battery and flow battery
The step is to collect several parameters most relevant to the monitoring of the battery energy storage system, and the economic judgment index of the subsequent energy storage configuration is relevant to the parameters.
Table 1 table of parameters required for configuration process
Note: the operation life and the charge and discharge depth provided in the table have a relatively large relationship with the operation environment, and the most suitable charge and discharge depth under the ideal condition is provided by the manufacturer under the general condition.
Fig. 2 is a simplified circuit diagram of a typical battery, and it can be seen from fig. 2 that since all the battery energy storage systems adopted at this time are batteries, the specific batteries may have differences in efficiency and the like, but the basic formula principles are completely the same. According to the attached figure 2, the following charge state formula can be obtained (for the convenience of distinguishing a charge state from a discharge state subsequently and for unifying the charge state and the discharge state with the working state of the battery energy storage system, the power during charging is considered to be negative, the power during discharging is considered to be positive, after the power is stabilized, the expected output of a wind field is consistent in a short time, the time can be used for substituting the time for the power integration over the time, and the limitation of the SOC state mainly aims at the application part of peak clipping and valley filling in the battery energy storage system.)
When the battery energy storage system is in a charging state, the SOC state model is specifically as shown in formula (1):
when the battery energy storage system is in a discharge state, the SOC state model is specifically as shown in formula (2):
in the above formula, SOC (t) is the current load state of the battery, and the value range is [0,1 ]]，P charge (t) and P discharge (t) is the charging and discharging power of the battery energy storage system respectively, delta t is the charging and discharging time interval, eta is the energy conversion efficiency, E is the total energy of the battery energy storage system, and alpha is the self-discharging rate of the current battery.
Step 3, wind power data processing, index establishment and energy storage configuration
Acquiring wind power data, and processing the wind power data toThe short-time average output of the wind power is expected output, wherein the specific time is T, the value of T is 1800s, and T = M Δ T, and then the expected output of the wind field after the power of the battery energy storage system of the wind power plant is stabilized in the period of time is P ref Specifically, as shown in formula (3):
in the formula t 1 ＝t 0 +(k-1)T，t 2 ＝t 1 +(M-1)△t，t 0 T =0.5h, Δ T =1s, M is the number of time intervals within T, here 1800, P, is chosen for the initial moment wind (T) is the real-time power output by the wind field, k is the number of the starting time intervals of the time period taken in one day when T is 1800s, and k belongs to [1,48 ]]。
First we need to define specific indices of several quantities before making the standard. These indexes are just taken as constraints on energy storage technical conditions, and energy storage configuration optimization is meaningful only when the conditions are met.
(1) Power-stabilizing effectiveness (%)
In the above, the wind field power after the power of the battery energy storage system is stabilized is expected, but in an actual situation, due to sudden rise or sudden fall of the wind speed in a certain time period, the output of the fan is greatly floated, the power generated by the fan greatly deviates from an expected calculated value, the fan can be completely compensated, the compensation power is overlarge, the fan is idle in most of other time, and the economic benefit brought by the stabilized wind field power is not enough to cover and construct the compensation equipment with high power, which is not favorable for the consideration of the economic aspect. At the moment, a value is set, and the wind power in the day basically meets the requirement in one day under certain power compensation, so that the economic cost can be greatly reduced, and the power stabilizing effectiveness eta p-effect Specifically, as shown in formula (4).
In the formula (I), the compound is shown in the specification,
represents the sum of the time when the actual output meets the desired output after power compensation of the battery energy storage system is added,
representing the total time within the entire settling time period.
(2) Upper and lower bounds of capacity-stabilized power
The power stabilizing effectiveness defined by the user is that after the power stabilizing is finished, the output of a fan theoretically has little difference with an expected calculated value, but when the grid is connected, the power fluctuates greatly in a small time length, so that the user needs to establish the standard, the upper limit and the lower limit of the power fluctuation are higher than the upper limit, the user considers that the generated power is larger than the expected output, namely the battery energy storage system starts to charge at the moment, the corresponding wind power is smaller than the lower limit, namely the battery energy storage system starts to discharge at the moment, and therefore a stable interval of the wind power plant is maintained. This index plays a very crucial role in sizing the capacity allocation, belonging to the precondition. Here we use the upper bound of power as L high Is shown by L for lower bound low To indicate.
(3) Peak clipping ratio (%)
After upper and lower bounds of capacity stabilizing power are defined, in order to conduct configuration analysis on capacity, power needed by the capacity needs to be configured, the power is used for peak clipping and valley filling of wind field energy, even after the upper and lower bounds of the capacity stabilizing power are defined, all conditions cannot meet requirements due to the limitation of a battery energy storage system, another index is provided, the peak clipping rate (%) is used for judging whether capacity is compensated or not, if capacity compensation is set, the ratio of peak-valley difference power to peak power and the difference before capacity compensation are conducted, the peak clipping rate and the peak clipping rate lambda can be defined as PeakCutting Specifically, as shown in equation (5).
In the formula, P ref-rc (t) is the final output power of the wind field finally merged into the power grid after peak clipping and valley filling of the battery energy storage system, max and min are respectively a maximum function and a minimum function, and the function is to obtain P wind (t) and P ref-rc (t) maximum and minimum values over the entire time period.
(4) Stability (%)
After a wind field and a battery energy storage system are jointly output, when the energy of the battery energy storage system is excessive, the electric quantity can be stabilized by abandoning wind, but if the wind quantity is insufficient, no method is provided except for the battery energy storage system, and the stability is defined as the reduction degree of the probability of the energy shortage of the battery energy storage system after energy storage configuration and the stability lambda Steady Specifically, as shown in equation (6).
In the formula, E bess-discharge The amount of charging power that can be accommodated by the battery energy storage system, E total-discharge For the total required charging capacity under the given wind power data
The power and capacity of the battery energy storage system are configured according to the above indexes, and fig. 3 is a detailed flow of energy storage configuration in this step. The method comprises the following specific steps:
(1) Writing a program in which input power suppression validity η p-effect The reference value of (2), the reference value being determined according to the actual situation,
(2) Carrying out the iteration of the steady power compensation according to the formula (4), calculating the compensation power,
(3) If eta passrate ≤η p-effect Then go to (4), otherwise go back to (2), where η passrate A value representing the effectiveness of power leveling in the actual calculation process,
(4) Input capacity stabilizing powerUpper bound L high L for lower bound of capacity-stabilized power low Peak reduction ratio lambda PeakCutting And stability lambda Steady ，
(5) According to formulas (5) and (6), the power and capacity iteration during peak clipping and valley filling is carried out, the capacity and the power required by peak clipping and valley filling are calculated,
(6) If gamma is less than or equal to lambda PeakCutting ,β≥λ Steady Then the power P required for stabilizing the wind power stage is output rep Capacity E rep Power P needed for peak clipping and valley filling PC And capacity E PC Otherwise, returning to (5), wherein gamma and beta represent the peak clipping rate and stability calculated under the corresponding power compensation and capacity compensation respectively.
Step 4, determining algorithm and explicitly distributing
After the energy storage configuration of the technical indexes is determined, energy storage allocation can be performed on the power and the capacity of the two batteries according to the characteristics of the batteries. The optimal allocation scheme of the stored energy is obtained according to the algorithm and the limiting conditions as shown in figure 4.
Firstly, an objective function configured by a scheme is determined, all requirements on technical indexes are already finished in the previous steps, good application effects on stabilizing wind power, peak clipping and valley filling are achieved, economy is only taken as the objective function, and a concept called energy Storage average use Cost (LCUS) is introduced.
Average energy storage use cost LCUS Y Specifically, as shown in formula (7):
y means the specified age, Y =0,1,2, \8230;, Y,
This model discusses the energy released by the battery energy storage system during the service period, and the amount of discharge refers to the energy delivered to the customer grid at the connection point. This value may not be as simple as the nominal energy value of the battery energy storage system multiplied by the number of cycles it has been subjected to. For example, if the power rating of a battery energy storage system is 10MWh at 3 hours, pass LCUS Y The cost of a 1 hour discharge cycle is calculated, the extra losses of the battery energy storage system due to the use of faster charge and discharge rates must be taken into account.
Such LCUS Y The calculation formula (2) can be applied in three practical ways:
1. the costs between selecting different energy storage technologies, whether they come from different technologies, designs or manufacturers, may be compared.
2. The cost of a particular energy storage technology or product in different application scenarios may be compared.
3. Helping to clarify how certain modes of operation affect the owner's overall investment costs.
The specific calculation formula of the variable in the formula is
Initial construction cost of battery energy storage system initial
The initial construction cost refers to a fixed capital invested once in the initial construction period of the battery energy storage system engineering, is usually used for purchasing main equipment and the like, and the calculation formula is
cost initial ＝C P P ESS +C E E ESS (8)
In the formula: p is ESS 、E ESS The power and the capacity of the battery energy storage system are respectively; c P 、C E Unit investment for power and capacity of the battery energy storage system respectively.
The equal-annual-number coefficient C (r, n) is expressed as
In the formula: r is a reference discount rate; and n is the operating period (service life) of the battery energy storage system, and year.
Correcting the initial construction cost of the battery energy storage system by considering the time value of capital to obtain the corrected initial construction cost of the battery energy storage system
Comprises the following steps:
subsequent operation and maintenance cost of battery energy storage system operating
The subsequent operation and maintenance cost refers to the fund dynamically invested for ensuring the normal operation of the battery energy storage system in the service life, and generally includes the expenses of the battery energy storage system such as test, installation, loss, outage, manpower, overhaul and maintenance, and the calculation formula is as follows by taking the year as a unit:
cost operating ＝K O P ESS +K M Q ESS (11)
in the formula: k O Operating and maintaining cost coefficient for unit power year of the battery energy storage system; k M Annual operation and maintenance cost coefficient of unit capacity of the battery energy storage system; q ESS The annual energy production of the battery energy storage system.
When K is O And K M When the subsequent operation and maintenance cost of the battery energy storage system is not easy to determine, the subsequent operation and maintenance cost of the battery energy storage system is generally approximately estimated according to a certain proportion of the initial construction cost of the battery energy storage system, and therefore the subsequent operation and maintenance cost of the simplified battery energy storage system is obtained
Specifically, as shown in formula (12):
in the formula: mu is the operation and maintenance cost coefficient of the battery energy storage system.
The lithium battery and the flow battery respectively have the initial investment cost and the subsequent operation and maintenance cost, but the formulas of the lithium battery and the flow battery are completely the same as the formulas of the lithium battery and the flow battery, so the description is omitted.
Writing a program, inputting the specific parameters of the lithium battery and the specific parameters of the flow battery in the step 1 in the written program, and determining an objective function minLCUS Y Meanwhile, because the lithium battery and the flow battery have advantages in power and capacity, and the advantages in capacity and power are respectively utilized, an optimal solution is ensured to meet the requirement of the objective function under the limit of a certain range.
Constraint conditions are as follows:
(1) Capacity limitation
In both lithium batteries and flow batteries, at least one of the lithium batteries and flow batteries plays a role in stabilizing power according to the final energy storage configuration effect, and the functions require the minimum discharge time to be 0.25h, so that:
in the formula, P Li-ion,ref And P VRB,ref Rated power of lithium battery and flow battery respectively, E Li-ion And E VRB The capacities of the lithium battery and the flow battery are respectively.
(2) Power limitation
In addition, when the lithium battery and the flow battery are used for storing and releasing the peak clipping and valley filling, certain power also needs to be ensured, otherwise, the stored energy of the corresponding power configuration cannot meet the use of the self-configured capacity, so that the configured capacity is idle, and the final energy storage configuration cannot meet the technical requirements.
In the formula, P min Below this power storage configuration, the desired effect cannot be achieved for the lowest limit of power.
(3) Charge-discharge depth limitation
Here, since the life of the lithium battery itself is reduced with the increase of the charging and discharging depth, the charging and discharging depth superior to the case of ensuring the life of the lithium battery obtained by the selection experiment is as follows:
0.2≤SOC Li-ion ≤0.8 (15)
the flow battery is a reactor reaction due to the structure of the flow battery, and the charging and discharging depth has little influence on the service life of the flow battery, so the charging and discharging constraints are as follows:
0.05≤SOC VRB ≤0.95 (16)
here, the reason why the charging and discharging depth of the flow battery does not reach the maximum value theoretically without impairing the life is that there is a possibility that an accident may occur, and therefore, a certain margin is set to cope with a possible problem.
Variables appearing in fig. 3: eta passrate And gamma and beta represent peak clipping rates and stabilities calculated under corresponding power compensation and capacity compensation respectively.
Variables appearing in fig. 4: LCUSY (k) * ) And LCUS Y (k * -1) represents the calculated average cost of energy storage usage, k, during the optimization process * And k * -1 represents the number of times in the optimization process.
Preferred embodiments for specific applications:
the values of the battery parameters adopted in the actual engineering are shown in table 2. (Table 2 shows the specific battery parameters in an ideal state, and the actual calendar parameters are needed to be analyzed and tested according to the local environmental factors in order to obtain more accurate calendar parameters)
TABLE 2 detailed parameters of the batteries
And (3) acquiring the parameters in the step (1) according to the parameters given in the table 2 to obtain all the basic parameters used in the subsequent process. Then, an SOC state model of the battery energy storage system is established, and then wind power data is obtained and processed, where the processed data is expected output of a wind farm after the power of the battery energy storage system is stabilized, as shown in fig. 5 (the maximum output of the wind farm is 400 MW).
An index is then established.
First, as the power stabilizing effectiveness increases (equation (4)), the compensation power also increases, as shown in fig. 6. Here we set the power leveling effectiveness to 95%.
Next is a simulation of the peak clipping and valley filling sections, where we set the upper bound L of the capacity-stabilized power high Lower bound of power L for 70MW and capacity leveling low The peak clipping rate is calculated as shown in formula (5) and is 30MW (the upper and lower bounds of the capacity stabilizing power fluctuate by 20MW according to the average output power in one year).
Fig. 7 shows the relationship of the peak clipping rate rising with the capacity compensation, and it can be seen that the peak clipping rate variation is close to a linear piecewise function. Fig. 8 shows a specific curve of the stability of the battery energy storage system with the capacity.
The above simulation is based on the established target standard, and we can determine that the final compensated effective power is 68MW in total and the effective capacity is 134.4MW · h, based on the calculated energy storage capacity configuration meeting the requirement in a pure technical sense. However, the same available compensation power and capacity are configured, and the costs of different configuration strategies are different, so as to find the optimal cost configuration strategy, step 4 is performed.
The detailed configuration flow chart of the lithium battery and the flow battery is calculated according to an immune algorithm. The immune algorithm is used because the immune algorithm has stronger global search capability and simultaneously has the advantages of robustness and rapidity.
The 4 unknown variables involved in the optimization process are respectively the lithium battery power P Li-ion Capacity E of lithium cell Li-ion Power of flow battery P VRB Capacity of flow cell E VRB 。
After the immune algorithm, the algorithm flow is shown in fig. 9 (after the energy conversion rate and the limiting condition of the lithium battery and the flow battery are considered), the obtained optimal solution is P Li-ion ＝45MW，E Li-ion ＝21.067MW·h，P VRB ＝23MW，E VRB ＝195.497MW·h。
Table 3 energy storage scheme configuration comparison
Fig. 10 shows the average cost of energy storage usage for 3 schemes, and we can finally conclude that the hybrid energy storage configuration scheme is favorable for reducing economic cost without sacrificing technical reliability in combination with the above analysis. In the energy storage configuration, the selection of the cut-off frequency and other standards is not involved, so that the scheme greatly improves the practicability of the actual engineering, and can provide a certain reference value for the energy storage configuration of the new energy station.
Fig. 11-12 are graphs of the effect of the output of the lithium battery and the flow battery, respectively, fig. 11 is a graph of the effect of the output of the lithium battery in stabilizing the wind power during operation, and fig. 12 is a graph of the final effect of the flow battery in wind field peak clipping and valley filling.
The technical key points and points to be protected of the invention are as follows:
the energy storage configuration is carried out according to the characteristics of each battery, so that the selection of cut-off frequency can be avoided, the characteristics of batteries of different types can be fully exerted, and the established optimization calculation model is optimized in the iterative operation and optimization process.
Those not described in detail in this specification are within the skill of the art.
Claims (4)
1. A hybrid energy storage power capacity configuration method considering characteristics of different types of batteries is characterized by comprising the following steps:
step 1: acquiring specific parameters of a lithium battery and a flow battery in a battery energy storage system;
step 2: establishing a battery energy storage system SOC state model;
and step 3: firstly, processing wind power data, wherein the processed data is expected output of a wind farm after the power of the wind farm is stabilized by a battery energy storage system, then establishing indexes, and finally formulating a corresponding energy storage configuration scheme according to the set indexes;
and 4, step 4: according to the energy storage configuration scheme in the step 3, an immune algorithm is used for configuring the lithium battery and the flow battery, and the most economical and reasonable energy storage configuration scheme is obtained while the technical requirements are met;
the specific steps of the step 3 are as follows:
acquiring wind power data, processing the wind power data, taking short-time average output of wind power as expected output, wherein the specific time is T, the value of T is 1800s, and T = M Δ T, and then in the period of time, the expected output of the wind field of the wind power plant after the power of the battery energy storage system is stabilized is P ref Specifically, as shown in formula (3):
in the formula, t 1 ＝t 0 +(k-1)T，t 2 ＝t 1 +(M-1)△t，t 0 For the initial time, Δ T is the time interval, where 1s is taken, M is the number of time intervals in T, where 1800, P is taken wind (T) is the real-time power output by the wind field, k is the number of the starting time intervals of the time period taken in one day when T is 1800s, and k belongs to [1,48 ]]；
The indexes include: power stabilizing effectiveness, capacity stabilizing power upper and lower bounds, peak clipping rate and stability,
(1) Power-stabilizing effectiveness eta p-effect Specifically, as shown in formula (4):
in the formula (I), the compound is shown in the specification,
represents the sum of the time when the actual output meets the desired output after power compensation of the battery energy storage system is added,
represents the total time within the entire settling time period;
(2) L for upper bound of capacity-stabilized power high To show that the lower bound of the capacity-stabilized power is L low To represent;
(3) Peak clipping ratio lambda PeakCutting Specifically, as shown in formula (5):
in the formula, P ref-rc (t) is the final output power of the wind field after peak clipping and valley filling of the battery energy storage system and then the wind field is merged into the power grid, max and min are respectively a maximum function and a minimum function, and the function is to obtain P wind (t) and P ref-rc (t) maximum and minimum values over the entire time period;
(4) Stability lambda Steady Specifically, as shown in formula (6):
in the formula, E bess-discharge For the amount of charging power that the battery energy storage system can accommodate, E total-discharge The total required charging capacity under the given wind power data;
the energy storage configuration scheme comprises the following specific steps:
(1) Writing a program in which input power suppression effectiveness eta is p-effect The reference value of (2), the reference value being determined according to the actual situation,
(2) Carrying out the iteration of the steady power compensation according to the formula (4), calculating the compensation power,
(3) If eta passrate ≤η p-effect Then go to (4), otherwise go back to (2), where η passrate A value representing the effectiveness of power leveling in the actual calculation process,
(4) Input capacity stabilizing power upper bound L high L for lower bound of capacity-stabilized power low Peak reduction ratio lambda PeakCutting And stability lambda Steady ，
(5) According to formulas (5) and (6), the power and capacity iteration during peak clipping and valley filling is carried out, the capacity and the power required by peak clipping and valley filling are calculated,
(6) If gamma is less than or equal to lambda PeakCutting ,β≥λ Steady Then the power P required for stabilizing the wind power stage is output rep Capacity E rep Power P needed for peak clipping and valley filling PC And capacity E PC Otherwise, returning to (5), wherein gamma and beta represent the peak clipping rate and stability calculated under the corresponding power compensation and capacity compensation respectively.
2. The method for configuring the capacity of the hybrid energy storage power considering the characteristics of the different types of batteries according to claim 1, wherein in the step 1, the specific parameters of the lithium battery comprise: maximum depth SOC of charge and discharge capacity Li-ionmax And minimum depth SOC of charge-discharge capacity Li-ionmin And charge-discharge efficiency eta Li-ion Annual operation and maintenance cost coefficient K of unit power of stored energy O-Li-ion Annual operation and maintenance cost coefficient K of unit capacity of stored energy M-Li-ion Annual construction cost coefficient of unit power of stored energy C P-Li-ion Annual construction cost coefficient of unit capacity of stored energy C E-Li-ion Self-discharge rate alpha Li-ion And life a 1 (ii) a Specific parameters of the flow battery include: maximum depth SOC of charge and discharge capacity VRBmax SOC with minimum depth of charge-discharge capacity VRBmin And charge-discharge efficiency eta VRB Annual operation and maintenance cost coefficient K of unit power of stored energy O-VRB Energy storageAnnual operation and maintenance cost coefficient K of unit capacity M-VRB Annual construction cost coefficient of unit power of stored energy C P-VRB Annual construction cost coefficient of unit capacity of stored energy C E-VRB Self-discharge rate alpha VRB And life a 2 。
3. The method for configuring the capacity of the hybrid energy storage power considering the characteristics of the different types of batteries according to claim 2, wherein the step 2 comprises the following specific steps:
when the battery energy storage system is in a charging state, the SOC state model is specifically as shown in formula (1):
when the battery energy storage system is in a discharge state, the SOC state model is specifically as shown in formula (2):
in the above formula, SOC (t) is the current load state of the battery, and the value range is [0,1]，P charge (t) and P discharge (t) is the charging power and the discharging power of the battery energy storage system respectively, delta t is the charging and discharging time interval, eta is the energy conversion efficiency, E is the total energy of the battery energy storage system, and alpha is the self-discharging rate of the current battery.
4. The method for configuring capacity of hybrid energy storage power considering characteristics of heterogeneous batteries according to claim 3, wherein the specific steps of the step 4 are as follows:
firstly, introducing the average use cost LCUS of the stored energy Y Specifically, as shown in formula (7):
wherein Y denotes a predetermined age, Y =0,1,2, \ 8230;, Y,
referring to the initial construction cost of the modified battery energy storage system,
the subsequent operation and maintenance cost of the simplified battery energy storage system is pointed out,
refers to the energy released by the battery energy storage system within a specified service life;
initial construction cost of battery energy storage system initial The calculation formula of (a) is as follows:
cost initial ＝C P P ESS +C E E ESS (8)
in the formula: p ESS 、E ESS The power and the capacity of the battery energy storage system are respectively; c P 、C E Unit investment of power and capacity of the battery energy storage system respectively;
the annual-valued coefficient C (r, n) is expressed as:
in the formula: r is a reference discount rate; n is the running time limit of the battery energy storage system;
correcting the initial construction cost of the battery energy storage system to obtain the corrected initial construction cost of the battery energy storage system
Comprises the following steps:
battery with a battery cellSubsequent operation and maintenance cost of energy storage system operating The calculation formula of (2) is as follows:
cost operating ＝K O P ESS +K M Q ESS (11)
in the formula: k O Annual operation and maintenance cost coefficient of unit power of the battery energy storage system; k M Annual operation and maintenance cost coefficient of unit capacity of the battery energy storage system; q ESS The annual energy production of the battery energy storage system is realized;
when K is O And K M When the subsequent operation and maintenance cost of the battery energy storage system is not easy to determine, the subsequent operation and maintenance cost of the battery energy storage system is generally estimated according to a certain proportion of the initial construction cost of the battery energy storage system, and therefore the subsequent operation and maintenance cost of the simplified battery energy storage system is obtained
Specifically, as shown in formula (12):
in the formula: mu is the operation and maintenance cost coefficient of the battery energy storage system;
writing a program, inputting the specific parameters of the lithium battery and the specific parameters of the flow battery in the step 1 in the written program, and then determining an objective function minLCUS Y ，
The constraint conditions are specifically as follows:
(1) Capacity limitation
In the formula, P Li-ion,ref And P VRB,ref Rated power of lithium battery and flow battery respectively, E Li-ion And E VRB The capacities of the lithium battery and the flow battery are respectively;
(2) Power limitation
In the formula, P min Below which the power storage configuration cannot achieve its intended effect, is the lowest limit of power;
(3) Depth limit for charging and discharging
0.2≤SOC Li-ion ≤0.8 (15)
0.05≤SOC VRB ≤0.95 (16)
In the formula, SOC Li-ion Is the charge-discharge depth, SOC, of the lithium battery VRB The charging depth of the flow battery is set;
adopting immune algorithm to carry out optimization, respectively calculating the capacity and power of the lithium battery and the flow battery, and if the capacity and power of the lithium battery and the flow battery meet the LCUS Y (k * )-LCUS Y (k * If-1) is less than or equal to epsilon, the power P of the lithium battery is output Li-ion And capacity E Li-ion Power P of the flow battery VRB And capacity E VRB Wherein, LCUS Y (k * ) And LCUS Y (k * -1) represents the calculated average cost of energy storage usage during the optimization process, epsilon is the acceptable error range of the setting, k * And k * -1 represents the number of times in the optimization process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110234575.XA CN112952877B (en) | 2021-03-03 | 2021-03-03 | Hybrid energy storage power capacity configuration method considering characteristics of different types of batteries |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110234575.XA CN112952877B (en) | 2021-03-03 | 2021-03-03 | Hybrid energy storage power capacity configuration method considering characteristics of different types of batteries |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112952877A CN112952877A (en) | 2021-06-11 |
| CN112952877B true CN112952877B (en) | 2022-10-14 |
Family
ID=76247371
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110234575.XA Active CN112952877B (en) | 2021-03-03 | 2021-03-03 | Hybrid energy storage power capacity configuration method considering characteristics of different types of batteries |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112952877B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114243678B (en) * | 2021-11-04 | 2023-07-18 | 山东电力工程咨询院有限公司 | Comprehensive energy storage configuration scheme generation method and system for photovoltaic power station |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103779869A (en) * | 2014-02-24 | 2014-05-07 | 国家电网公司 | Energy storage station capacity optimizing calculation method considering dynamic adjustment of electrically charged state |
| CN106972516A (en) * | 2017-04-24 | 2017-07-21 | 国家电网公司 | A kind of polymorphic type energy storage Multistage Control method suitable for microgrid |
| WO2017161785A1 (en) * | 2016-03-23 | 2017-09-28 | 严利容 | Method for controlling stable photovoltaic power output based on energy storage running state |
| CN110460075A (en) * | 2019-08-21 | 2019-11-15 | 国网河南省电力公司电力科学研究院 | A kind of hybrid energy-storing for stabilizing power grid peak-valley difference goes out force control method and system |
| CN111724064A (en) * | 2020-06-20 | 2020-09-29 | 国网福建省电力有限公司 | Energy-storage-containing power distribution network planning method based on improved immune algorithm |
-
2021
- 2021-03-03 CN CN202110234575.XA patent/CN112952877B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103779869A (en) * | 2014-02-24 | 2014-05-07 | 国家电网公司 | Energy storage station capacity optimizing calculation method considering dynamic adjustment of electrically charged state |
| WO2017161785A1 (en) * | 2016-03-23 | 2017-09-28 | 严利容 | Method for controlling stable photovoltaic power output based on energy storage running state |
| CN106972516A (en) * | 2017-04-24 | 2017-07-21 | 国家电网公司 | A kind of polymorphic type energy storage Multistage Control method suitable for microgrid |
| CN110460075A (en) * | 2019-08-21 | 2019-11-15 | 国网河南省电力公司电力科学研究院 | A kind of hybrid energy-storing for stabilizing power grid peak-valley difference goes out force control method and system |
| CN111724064A (en) * | 2020-06-20 | 2020-09-29 | 国网福建省电力有限公司 | Energy-storage-containing power distribution network planning method based on improved immune algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 王晓东 等.基于双回路SOC调节的风电场功率平滑控制策略.《电气技术》.2017,(第11期), * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112952877A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108667052B (en) | Multi-type energy storage system planning configuration method and system for virtual power plant optimized operation | |
| CN110676870B (en) | Hybrid energy storage capacity configuration method suitable for wind power grid connection | |
| CN109038560B (en) | Power distribution network distributed energy storage economy evaluation method and system based on operation strategy | |
| CN112086975B (en) | Optimal scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulation | |
| CN111628558B (en) | System and method for optimizing energy management and capacity configuration of hybrid energy storage system | |
| CN112952877B (en) | Hybrid energy storage power capacity configuration method considering characteristics of different types of batteries | |
| CN110661250B (en) | Reliability evaluation method and system for wind-solar energy storage and power generation power transmission system | |
| CN114006442A (en) | Battery energy storage power station energy management method considering charge state consistency | |
| CN113488995B (en) | Shared energy storage capacity optimal configuration method and device based on energy storage cost | |
| CN112928769B (en) | Photovoltaic hybrid energy storage control method capable of compensating prediction error and stabilizing fluctuation | |
| CN114123280A (en) | Battery energy storage power station energy management method considering system efficiency | |
| CN109004642B (en) | Distribution network distributed energy storage evaluation method for stabilizing power fluctuation of distributed power supply | |
| CN113313351A (en) | Electric-gas-thermal system flexibility evaluation method considering multi-energy coupling influence | |
| CN112968515A (en) | Energy management strategy and system for emergency power supply of fuel cell | |
| CN111817329A (en) | Optimal operation method and device for photovoltaic power station | |
| CN117154770A (en) | Super-capacitor-based electricity-hydrogen hybrid energy storage capacity optimal configuration method | |
| CN111680816A (en) | Energy storage system operation method and system for providing multiple services | |
| CN115347590A (en) | Hybrid energy storage microgrid optimization control method based on reversible solid oxide battery | |
| CN115189423A (en) | Multi-energy coordination optimization scheduling method and device for wind-fire storage system | |
| CN114118579B (en) | New energy station energy storage configuration planning method and device and computer equipment | |
| CN108988370A (en) | The capacity determining methods of energy storage device, equipment and storage medium in electric system | |
| CN114418453A (en) | Micro-grid multi-time scale energy management system based on electric power market | |
| CN114498690A (en) | Multi-element composite energy storage optimal configuration method supporting large-scale renewable energy consumption | |
| CN115706416A (en) | Capacity optimization configuration method for grid-connected light storage micro-grid battery energy storage system | |
| CN112883566A (en) | Photovoltaic producer and consumer energy modeling method and system based on virtual battery model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |