CN114024325B - Energy storage capacity configuration method based on big data analysis under electric energy substitution - Google Patents
Energy storage capacity configuration method based on big data analysis under electric energy substitution Download PDFInfo
- Publication number
- CN114024325B CN114024325B CN202111287249.1A CN202111287249A CN114024325B CN 114024325 B CN114024325 B CN 114024325B CN 202111287249 A CN202111287249 A CN 202111287249A CN 114024325 B CN114024325 B CN 114024325B
- Authority
- CN
- China
- Prior art keywords
- calculating
- annual
- power generation
- energy storage
- wind
- 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
-
- 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- 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
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- 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]
-
- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
-
- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- 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
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Photovoltaic Devices (AREA)
Abstract
An energy storage capacity configuration method based on big data analysis under the replacement of electric energy belongs to the technical field of electric energy utilization efficiency and comprehensive energy. The invention aims to rapidly obtain the energy storage capacity configuration method based on big data analysis under the replacement of electric energy with corresponding capacity value through an improved safety stock algorithm under the condition of possessing big data of user electricity consumption. The method comprises the following steps: the method comprises the steps of inputting electric energy to replace wind power generation and photovoltaic power generation installed quantity and wind power and photovoltaic power generator parameters of a target area after completion, inputting annual load, annual wind speed, annual illumination intensity and annual air temperature hour data of users in the target area, calculating annual wind power and photovoltaic power generation hour data, calculating daily average electricity shortage quantity, calculating fluctuation period and annual fluctuation quantity, calculating energy storage power station capacity corresponding to certain fluctuation according to an improved safety inventory algorithm, and selecting the maximum energy storage power station capacity corresponding to all period fluctuation. The invention greatly improves the calculation speed and reduces the calculation difficulty.
Description
Technical Field
The invention belongs to the technical field of electricity utilization efficiency and comprehensive energy.
Background
In partial areas, particularly in northeast China, regional micro-grids are formed due to remote pastures, sparse population and the like, and the micro-grids construct distributed wind power, photovoltaic equipment, self-provided diesel engines and other equipment to form relatively independent micro-grids, so that the connection with a large power grid is weak. With the continuous promotion of the progress of electric energy replacement, all original parts of coal-fired and fuel-fired units are replaced. The micro-grid is characterized in that most of self-used fuel oil units are eliminated or stored and managed, and energy storage equipment is used to be matched with original distributed wind power and photovoltaic equipment. Therefore, the reasonable configuration of the energy storage capacity becomes an important way for guaranteeing the production and life of users after the replacement of electric energy.
The configuration method is characterized in that the optimal value meeting the given constraint condition is calculated as the configuration quantity by means of software simulation and the like from the power utilization level of a user and the power generation capacity of power generation equipment. In practice, the electricity consumption in these areas is small, and the installation and maintenance price of the current energy storage device is too high, so that the power supply reliability is completely met, and economic waste is caused.
Disclosure of Invention
The invention aims to rapidly obtain the energy storage capacity configuration method based on big data analysis under the replacement of electric energy with corresponding capacity value through an improved safety stock algorithm under the condition of possessing big data of user electricity consumption.
The method comprises the following steps:
step 1: inputting electric energy to replace wind power generation, photovoltaic power generation installed quantity and wind power and photovoltaic power generator parameters of the completed target area;
step 11: wind power generation and photovoltaic power generation installed quantity S of input target area W ，S pv ；
Step 12: inputting the wind power and photovoltaic generator power generation parameters f of a target area W (v w ),f pv (E,temp)；
Step 2: inputting the annual load, annual wind speed, annual illumination intensity and annual air temperature hour data of the user in the target area;
step 21: inputting annual load p of user in target area L(i)(j) Wherein (i) (j) represents the ith and the jth times;
step 22: inputting annual wind velocity v of target region w(i)(j) Intensity of annual light E (i)(j) Temperature temp. of year (i)(j) Data;
step 3: and (3) according to the wind power generation installation quantity and the photovoltaic power generation installation quantity input in the step (1). Calculating annual wind power and photovoltaic power generation hour data;
step 31: calculating annual wind power hours data, wherein:
P w(i)(j) ＝f W (v w(i)(j) ) (1)
step 32: calculating annual photovoltaic power generation hour data, wherein:
P pv(i)(j) ＝f pv (E (i)(j) ,temp (i)(j) ) (2)；
step 4: calculating daily average electricity shortage quantity;
step 41: calculating daily average power generation
Wherein:
step 42: calculating daily average power consumption
Wherein:
step 43: calculating daily average electricity shortage
Wherein:
T 1h ＝1h(8)；
step 5: calculating the fluctuation period and the annual fluctuation quantity;
step 51: calculating a fluctuation period Δt, wherein:
step 52: calculating the annual fluctuation quantity n which is the occurrence number of delta T in one year;
step 6: calculating the capacity of the energy storage power station corresponding to certain fluctuation according to an improved safety stock algorithm;
step 61: the wind-light load fluctuation after recalculation is calculated on the basis of that the wind-light power generation at the ith and the jth moment is higher than the average wind-light power generation level at the same moment and the load at the ith and the jth moment is lower than the average load at the same moment is not considered, wherein delta P (i)(j) ：
Step 62: calculating corresponding safety stock capacity SS according to the improved safety stock formula 1 Wherein:
step 63: calculating corresponding batch inventory capacity SS according to a batch inventory formula 2 Wherein:
step 64: calculating the capacity SS of the energy storage power station corresponding to a certain fluctuation according to the results of the steps 62 and 63, wherein:
step 7: selecting the corresponding maximum energy storage power station capacity in all period fluctuation;
step 71: calculating the maximum value of all the energy storage power station capacities SS as a final result:
according to the invention, under the condition of grasping the electricity consumption big data of the user, the improved safety stock formula does not need to use auxiliary calculation tools such as a solver and the like, so that the calculation speed is greatly improved, and the calculation difficulty is reduced. Meanwhile, different energy storage planning values under different power supply safety levels can be rapidly given.
Drawings
FIG. 1 is a process flow diagram of the present invention;
FIG. 2 is an annual wind velocity graph;
FIG. 3 is an annual light map;
FIG. 4 is an annual load diagram;
FIG. 5 is a graph of photovoltaic panel cell temperature;
FIG. 6 is a graph of battery month utilization with two typical energy storage power station capacity configurations;
fig. 7 is a graph of simulation results versus theoretical results.
Detailed Description
The invention is used for configuring the energy storage capacity in the micro-clean power grid by deriving a safety stock formula in the traditional stock management. In the application process, the storage means of the energy storage power station for the most electric energy is similar to a warehouse in the traditional sense, so that the energy storage capacity of the energy storage power station is taken as an inventory; in the whole process of generating, using and storing electric energy, the electric energy is used as a circulation main body, so that the electric energy is used as commodity; wind power and photovoltaic power generation serve as the only electric energy production means in the whole system, correspondingly serve as stock supplement means in a safety stock theory, and loads serve as electric energy consumption means, correspondingly serve as stock consumption means. In safety stock theory, the service level represents the probability of an out-of-stock occurrence. According to the theory, the safety factor is set up in relation to the normal distribution, and accords with the standard normal distribution N (0, 1). Therefore, the use of power supply as service content becomes a safety factor for the safety and reliability of power supply.
The invention aims at a capacity configuration method of an energy storage power station after electric energy substitution based on electricity consumption big data. Under the condition of possessing large data of user electricity consumption, the corresponding capacity value is obtained rapidly through an improved safety stock algorithm.
The invention is used for configuring the energy storage capacity in the micro-clean power grid by deriving a safety stock formula in the traditional stock management. The similarity characteristics of the energy storage and the inventory are utilized, and the difference between the energy storage and the inventory is solved by improving a formula, namely, the early-stage concept in the inventory management does not exist in the energy storage equipment.
The improved safety stock formula does not need to use auxiliary calculation tools such as a solver and the like, so that the calculation speed is greatly improved, and the calculation difficulty is reduced. Meanwhile, different energy storage planning values under different power supply safety levels can be rapidly given.
The method comprises the following steps:
step 1: inputting electric energy to replace wind power generation, photovoltaic power generation installed quantity and wind power and photovoltaic power generator parameters of the completed target area;
step 11: wind power generation and photovoltaic power generation installed quantity S of input target area W ，S pv ；
Step 12: inputting the wind power and photovoltaic generator power generation parameters f of a target area W (v w ),f pv (E,temp)。
Step 2: inputting the annual load, annual wind speed, annual illumination intensity and annual air temperature hour data of the user in the target area;
step 21: inputting annual load p of user in target area L(i)(j) Wherein (i) (j) represents the ith and the jth times;
step 22: inputting annual wind velocity v of target region w(i)(j) Intensity of annual light E (i)(j) Temperature temp. of year (i)(j) Data.
Step 3: and (3) according to the wind power generation installation quantity and the photovoltaic power generation installation quantity input in the step (1). Calculating annual wind power and photovoltaic power generation hour data;
step 31: calculating annual wind power hours data, wherein:
P w(i)(j) ＝f W (v w(i)(j) ) (1)
step 32: calculating annual photovoltaic power generation hour data, wherein:
P pv(i)(j) ＝f pv (E (i)(j) ,temp (i)(j) ) (2)。
step 4: calculating daily average electricity shortage quantity;
step 41: calculating daily average power generation
Wherein:
step 42: calculating daily average power consumption
Wherein:
step 43: calculating daily average electricity shortage
Wherein:
T 1h ＝1h (8)。
step 5: calculating the fluctuation period and the annual fluctuation quantity;
step 51: calculating a fluctuation period Δt, wherein:
step 52: the annual fluctuation number n, n being the number of occurrences of Δt in one year, is calculated.
Step 6: calculating the capacity of the energy storage power station corresponding to certain fluctuation according to an improved safety stock algorithm;
step 61: the wind-light load fluctuation after recalculation is calculated on the basis of that the wind-light power generation at the ith and the jth moment is higher than the average wind-light power generation level at the same moment and the load at the ith and the jth moment is lower than the average load at the same moment is not considered, wherein delta P (i)(j) ：
Step 62: calculating corresponding safety stock capacity SS according to the improved safety stock formula 1 Wherein:
step 63: calculating corresponding batch inventory capacity SS according to a batch inventory formula 2 Wherein:
step 64: calculating the capacity SS of the energy storage power station corresponding to a certain fluctuation according to the results of the steps 62 and 63, wherein:
step 7: selecting the corresponding maximum energy storage power station capacity in all period fluctuation;
step 71: calculating the maximum value of all the energy storage power station capacities SS as a final result:
simulation calculation example
A remote pasture area of a certain place (122.7 DEG E,45.0 DEG N) in the north of China is simulated, the area has rich wind power resources and sufficient sunlight, and has good foundation for building wind power and photovoltaic power stations, wherein the average monthly wind speed and the average hourly illumination intensity are respectively shown in tables 1 and 2. The data used herein is based on historical wind speed and illumination area profiles. The wind speed, the illumination condition and the load data are shown in figures 1,2 and 3, and the operation temperature of the simulated photovoltaic panel is shown in figure 4. The isolated clean energy power system is provided with 1.5MW photovoltaic power generation and 0.45MW wind power generation. Wherein the single machine capacity of the selected wind driven generator is 10kW, the rated wind speed is 11m/s, the cut-in wind speed is 3m/s, the cut-out wind speed is 20m/s, alpha=17, H=25 m, H ref =10m. The working temperature of the photovoltaic power generation equipment is 25 ℃, the reference temperature is 25 ℃, the temperature coefficient of power is-0.48%, the illuminance under the standard condition is 1kW/m < 2 >, and the maximum power under the standard test condition is 1kW/m < 2 >. Taking the single battery capacity of the energy storage power station of 1.2kWh, the initial electric quantity of 100 percent and the charge-discharge efficiency eta c 、η d The self-discharge rate delta is 1%, the rated output power is 0.12kW, and the wind power, photovoltaic power generation and energy storage models are as follows.
Wind power generation model:
wherein:
P r the rated power of the fan; v ci Is the cut-in wind speed; v r Is the rated wind speed; v co To cut out wind speed.
Photovoltaic power generation model:
P pv ＝P STC G AC [1+k(T C -T r )]/G STC (15)
wherein: g AC Is the illumination intensity; p (P) STC Maximum power under standard test conditions; g STC The illumination intensity under standard test conditions; k is a temperature power coefficient; t (T) C Is the actual working temperature of the solar photovoltaic panel; t (T) r Is the reference temperature.
And (3) an energy storage power station charge-discharge model:
and (3) charging:
S sOC (t)＝(1-δ)S SOC (t-1)+P c Δtη c /E C (16)
the discharging process comprises the following steps:
S SOc (t)＝(1-δ)S SOC (t-1)-P d Δt/E C η d (17)
wherein: s is S SOC (t) is the residual capacity of the battery of the energy storage power station at the moment t; delta is the self-discharge rate of the energy storage power station battery; η (eta) c ，η d The charging and discharging efficiencies of the energy storage power station battery are respectively; p (P) c ，P d Respectively charging and discharging power of the energy storage power station battery; e (E) C And the rated capacity of the battery of the energy storage power station.
Table 2 average wind speed per month
| Month of month | Wind speed (m/s) | Month of month | Wind speed (m/s) |
| -month of the year | 8.81 | August of August | 5.61 |
| February month | 8.40 | September (September) | 6.65 |
| March (March) | 8.26 | October (October) | 7.62 |
| April (April) | 8.56 | October month | 8.34 |
| July of five months | 7.79 | December month | 8.78 |
| June (June) | 6.02 | Average of | 7.51 |
| July of Ju | 5.42 |
TABLE 3 average hourly illumination intensity on day 3
And calculating the capacity of the energy storage power station under the condition of complete power load guarantee by using a traditional method, and simultaneously calculating the battery utilization rate under different capacities of the energy storage power station. As shown in table 4, the calculation results of the conventional method and the battery month utilization of the method at 100% power supply safety level are simultaneously selected as shown in fig. 6.
Table 4 battery utilization table for different battery capacity planning energy storage power station
The capacity configuration results of the energy storage power station based on the safety stock theory are shown in the following table 5, and the simulation results and the theoretical results are shown in fig. 7.
TABLE 5 calculation capacities of corresponding energy storage power stations at different power supply safety levels and simulation results thereof
| Safety level of power supply (%) | Energy storage power station capacity (kwh) | Simulation results (%) | Error (%) |
| 100 | 3364.70 | 99.64 | -0.36 |
| 99.99 | 3356.81 | 99.62 | -0.37 |
| 99.87 | 3293.73 | 99.51 | -0.36 |
| 99.2 | 2820.60 | 98.05 | -1.15 |
| 99 | 2765.40 | 97.79 | -1.21 |
| 98 | 2544.61 | 96.53 | -1.47 |
| 97 | 2410.55 | 95.52 | -1.48 |
| 96 | 2308.04 | 94.62 | -1.38 |
| 95 | 2229.17 | 93.79 | -1.21 |
| 90 | 1945.31 | 89.60 | -0.4 |
From table 5, fig. 7 shows that the maximum error between the theoretical power supply safety level and the simulation result is 1.48%, the average error is 0.93%, and the power supply safety level is 99.2% -95% and has larger error, but the result is basically within an acceptable range, and the consistency is considered to be better. According to the analysis of the results, the method provided by the invention has certain accuracy, and the results are better than the traditional methods.
Claims (1)
1. The energy storage capacity configuration method based on big data analysis under the replacement of electric energy is characterized by comprising the following steps of:
step 1: inputting electric energy to replace wind power generation, photovoltaic power generation installed quantity and wind power and photovoltaic power generator parameters of the completed target area;
step 11: wind power generation and photovoltaic power generation installed quantity S of input target area W ，S pv ；
Step 12: inputting the wind power and photovoltaic generator power generation parameters f of a target area W (v w ),f pv (E,temp)；
Step 2: inputting the annual load, annual wind speed, annual illumination intensity and annual air temperature hour data of the user in the target area;
step 21: inputting annual load p of user in target area L(i)(j) Wherein (i) (j) represents the ith and the jth times;
step 22: inputting annual wind velocity v of target region w(i)(j) Intensity of annual light E (i)(j) Temperature temp. of year (i)(j) Data;
step 3: according to the wind power generation installation quantity and the photovoltaic power generation installation quantity input in the step 1; calculating annual wind power and photovoltaic power generation hour data;
step 31: calculating annual wind power hours data, wherein:
P w(i)(j) ＝f W (v w(i)(j) ) (1)
step 32: calculating annual photovoltaic power generation hour data, wherein:
P pv(i)(j) ＝f pv (E (i)(j) ,temp (i)(j) ) (2)；
step 4: calculating daily average electricity shortage quantity;
step 41: calculating daily average power generation
Wherein:
step 42: calculating daily average power consumption
Wherein:
step 43: calculating daily average power shortageMeasuring amount
Wherein:
T 1h ＝1h (8)；
step 5: calculating the fluctuation period and the annual fluctuation quantity;
step 51: calculating a fluctuation period Δt, wherein:
step 52: calculating the annual fluctuation quantity n which is the occurrence number of delta T in one year;
step 6: calculating the capacity of the energy storage power station corresponding to certain fluctuation according to an improved safety stock algorithm;
step 61: the wind-light load fluctuation after recalculation is calculated on the basis of that the wind-light power generation at the ith and the jth moment is higher than the average wind-light power generation level at the same moment and the load at the ith and the jth moment is lower than the average load at the same moment is not considered, wherein delta P (i)(j) ：
Step 62: calculating corresponding safety stock capacity SS according to the improved safety stock formula 1 Wherein:
step 63: calculating corresponding batch inventory capacity SS according to a batch inventory formula 2 Wherein:
step 64: calculating the capacity SS of the energy storage power station corresponding to a certain fluctuation according to the results of the steps 62 and 63, wherein:
step 7: selecting the corresponding maximum energy storage power station capacity in all period fluctuation;
step 71: calculating the maximum value of all the energy storage power station capacities SS as a final result:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111287249.1A CN114024325B (en) | 2021-11-02 | 2021-11-02 | Energy storage capacity configuration method based on big data analysis under electric energy substitution |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111287249.1A CN114024325B (en) | 2021-11-02 | 2021-11-02 | Energy storage capacity configuration method based on big data analysis under electric energy substitution |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114024325A CN114024325A (en) | 2022-02-08 |
| CN114024325B true CN114024325B (en) | 2023-05-23 |
Family
ID=80059717
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111287249.1A Active CN114024325B (en) | 2021-11-02 | 2021-11-02 | Energy storage capacity configuration method based on big data analysis under electric energy substitution |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114024325B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103904696A (en) * | 2014-03-08 | 2014-07-02 | 哈尔滨工程大学 | Capacity allocation optimization method for island/offshore platform independent new energy resource micro-grid |
| WO2015054878A1 (en) * | 2013-10-18 | 2015-04-23 | 中国电力科学研究院 | Change rate-based method and system for controlling energy storage power station in smoothing wind/light fluctuations |
| CN105071444A (en) * | 2015-07-16 | 2015-11-18 | 北京交通大学 | Capacity configuration method used for grid-connected wind and light storage system |
| CN106410824A (en) * | 2016-10-08 | 2017-02-15 | 重庆大学 | Community micro-grid energy storage capacity optimization and configuration method considering temperature control device |
-
2021
- 2021-11-02 CN CN202111287249.1A patent/CN114024325B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015054878A1 (en) * | 2013-10-18 | 2015-04-23 | 中国电力科学研究院 | Change rate-based method and system for controlling energy storage power station in smoothing wind/light fluctuations |
| CN103904696A (en) * | 2014-03-08 | 2014-07-02 | 哈尔滨工程大学 | Capacity allocation optimization method for island/offshore platform independent new energy resource micro-grid |
| CN105071444A (en) * | 2015-07-16 | 2015-11-18 | 北京交通大学 | Capacity configuration method used for grid-connected wind and light storage system |
| CN106410824A (en) * | 2016-10-08 | 2017-02-15 | 重庆大学 | Community micro-grid energy storage capacity optimization and configuration method considering temperature control device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114024325A (en) | 2022-02-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102623989B (en) | Method for optimization and configuration of intermittent distributed generation (DG) | |
| CN110084465B (en) | Wind power generation system cost/power supply reliability evaluation method based on energy storage | |
| CN112600209A (en) | Multi-objective capacity optimization configuration method for island independent micro-grid containing tidal current energy | |
| CN112366684A (en) | Island micro-grid system | |
| CN110994606A (en) | Multi-energy power supply capacity configuration method based on complex adaptive system theory | |
| CN115186791A (en) | Electricity-hydrogen thermal coupling multi-energy complementary micro-grid optimization configuration method considering hydrogen transaction | |
| CN112865097A (en) | Power supply ratio optimization method based on wind, light, fire and storage integrated base income | |
| CN115995822A (en) | Optimization method for distributed energy access micro-grid | |
| Zhang et al. | Optimized scheduling model for isolated microgrid of wind-photovoltaic-thermal-energy storage system with demand response | |
| CN112653195B (en) | Method for configuring robust optimization capacity of grid-connected micro-grid | |
| CN114024325B (en) | Energy storage capacity configuration method based on big data analysis under electric energy substitution | |
| Han et al. | Analysis of economic operation model for virtual power plants considering the uncertainties of renewable energy power generation | |
| CN116739141A (en) | Independent micro-grid optimal configuration method considering demand response | |
| CN116207760A (en) | Source network charge storage capacity configuration method | |
| Wang et al. | Optimal capacity allocation of wind-light-water multi-energy complementary capacity based on improved multi-objective optimization algorithm | |
| Sadullaev et al. | Micro-grid based power supply of remote consumers located away from the centralized power grid | |
| CN114172148B (en) | Black start capacity configuration method based on cooperative game | |
| Heo et al. | Energy resource capacity optimization for zero-energy housing complex | |
| Julianto et al. | Confronting the Duck Curve Problem Using Dynamic Economic Emission Dispatch with CAES. | |
| Luo et al. | The joint operation strategy of energy storage power station and photovoltaic power station based on typical output scenarios | |
| CN112087041A (en) | Photovoltaic full-electric kitchen and energy management optimization system | |
| CN111786419A (en) | Regional power grid energy configuration system | |
| Xing et al. | An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II | |
| Liu et al. | Optimal Configuration of Energy Storage Capacity on PV-Storage-Charging Integrated Charging Station | |
| Dai et al. | Optimal day-ahead operation strategy of an electricity-hydrogen integrated energy system considering extra-day forecast information |
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 |