CN110768264A - Hybrid energy storage power distribution method for improving wind power dispatching reliability - Google Patents
Hybrid energy storage power distribution method for improving wind power dispatching reliability Download PDFInfo
- Publication number
- CN110768264A CN110768264A CN201911165452.4A CN201911165452A CN110768264A CN 110768264 A CN110768264 A CN 110768264A CN 201911165452 A CN201911165452 A CN 201911165452A CN 110768264 A CN110768264 A CN 110768264A
- Authority
- CN
- China
- Prior art keywords
- power
- data
- energy storage
- wind power
- 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.)
- Granted
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
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
-
- 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
- Y02E40/30—Reactive power compensation
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a wind power energy storage technology, in particular to a hybrid energy storage power distribution method for improving wind power scheduling reliability. The invention relates to the design of power forecast data, the configuration of hybrid energy storage capacity and the specific numerical value giving of power distribution, and the wind power grid-connected power dispatching plan forecast accuracy is improved by combining software and hardware means. And by combining offline design and online control, the actual forecast deviation needs dynamic signal analysis and then is distributed to a super capacitor and a battery, and the expected reliable scheduling control target is realized by means of the throughput of the hybrid energy storage energy. Through the handling behavior of the energy storage system, the large wind power fluctuation is stabilized, and meanwhile the accuracy of wind power grid-connected forecasting is improved. The scientific and reasonable hybrid energy storage dynamic power distribution can ensure that the forecast power deviation is effectively compensated, provides powerful guarantee for the performance of an energy storage system in the aspect of wind power forecast reliability, and improves the schedulability of wind power grid-connected power generation.
Description
Technical Field
The invention relates to a wind power energy storage technology, in particular to a hybrid energy storage power distribution method for improving wind power scheduling reliability.
Background
The grid-connected capacity of the wind power plant is increased, so that the grid breakdown is easily caused by insufficient peak regulation capacity. The high-quality wind power forecast is beneficial to bringing wind power into a power grid dispatching plan better, the proportion of wind power generation in the power grid is improved while the balance of the supply and demand of the power grid is maintained, the requirement of the rotating reserve capacity of the power grid is reduced, and the penalty problem is avoided, so that the economical and reliable operation of the power grid is ensured.
Although the prediction deviation of the existing power prediction model of the wind power plant is reduced on the whole, the prediction absolute error is correspondingly increased along with the increase of the capacity of a single fan, the fluctuation of the wind power generation power is obviously enhanced, and the scheduling balance of a power grid is directly influenced. Therefore, optimizing wind power generation grid-connected power forecasting cannot simply depend on improvement of a prediction model, and hardware adjusting means are needed. The forecasting method combining the intelligent combined power forecasting model and the energy storage system can restrain deviation of grid-connected power forecasting, improve forecasting confidence coefficient, stabilize wind power output power fluctuation and reduce negative influence of wind power access on a power grid.
The randomness and the fluctuation of wind power cause that the requirements on an energy storage system are very complex. Energy storage techniques are diverse and have different characteristics, and the difference results in application in different occasions. The existing literature research shows that the battery with almost no single medium for energy storage of the wind power grid-connected system meets the complete performance, so that the complementary hybrid energy storage technology is gradually developed in the wind power system, and the effectiveness of the complementary hybrid energy storage technology in relieving the intermittent application of wind power mainly depends on the cost and the technology.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the hybrid energy storage power distribution method which can solve the problem that the dispatching balance of a power grid is influenced due to the fluctuation of the wind power generation power and can improve the dispatching reliability of the wind power.
In order to achieve the purpose, the invention adopts the following technical scheme:
a hybrid energy storage power distribution method for improving wind power dispatching reliability comprises the following steps:
step 1, forecasting by weather forecast and field historical operation data, obtaining wind power data through wavelet packet frequency analysis and offline smoothing processing, and using the wind power data as wind power grid-connected scheduling data;
step 3, comparing power data obtained after wavelet packet smoothing is carried out on the power prediction historical data with field historical operation data to obtain power deviation data, and carrying out offline integral statistics and Kalman filtering frequency analysis to obtain the total capacity and power of the required hybrid energy storage, wherein the total capacity of the hybrid energy storage comprises the capacities of a super capacitor and a battery, and the power of the hybrid energy storage comprises high-frequency part power and low-frequency part power;
and 5, respectively taking the super capacitor and the battery as a first converter and a second converter, and connecting the super capacitor and the battery to a wind power grid-connected power generation end together.
As a preferred technical scheme of the invention: the specific steps of the step 1 are as follows: step 1.1, forecasting by weather forecast and site historical operation data to obtain wind power forecasting data; step 1.2, wind power data are obtained by performing wavelet packet frequency analysis and offline smoothing on wind power prediction data; and step 1.3, taking the wind power data as wind power grid-connected scheduling data.
As a preferred technical scheme of the invention: the specific steps of the step 2 are as follows: step 2.1, dynamically monitoring the actual wind speed by using wind measuring equipment, and obtaining corresponding actual power according to the actual wind speed; step 2.2, comparing the actual power with wind power grid-connected scheduling data to obtain forecast deviation data; and 2.3, performing wavelet online frequency analysis processing on the forecast deviation data to obtain a high-frequency power control signal and a low-frequency power control signal.
As a preferred technical scheme of the invention: the specific steps of the step 3 are as follows: step 3.1, performing wavelet packet smoothing processing on the power prediction historical data to obtain power data; step 3.2, comparing the power data with the field historical operation data to obtain power deviation data; step 3.3, performing offline integral statistics on the power deviation data to obtain the total capacity of the required hybrid energy storage; step 3.4, analyzing the power deviation data by Kalman filtering frequency to obtain power of hybrid energy storage, wherein the power of the hybrid energy storage comprises high-frequency part power and low-frequency part power, the maximum throughput power of the high-frequency part power is used as the power grade configured by the super capacitor, and the maximum throughput power of the low-frequency part power is used as the power grade configured by the battery; and 3.5, obtaining the capacity requirement of the super capacitor through the high-frequency part power integration, and obtaining the capacity requirement of the battery through the low-frequency part power integration.
Compared with the prior art, the hybrid energy storage power distribution method for improving the wind power dispatching reliability has the following technical effects:
(1) the forecasting accuracy of the power dispatching plan influences the participation of owners in the electric power market transaction, and the competitiveness is improved; the reasonable configuration of the energy storage system and the effective exertion of the power distribution scheduling strategy are beneficial to the short-term forecast of wind power calibration; the power dispatching department can adjust the dispatching plan in time in advance according to the power plan forecast given by the majority of owners so as to ensure the quality of electric energy, reduce the reserve capacity and reduce the operation cost of the power system.
(2) The scientific and reasonable hybrid energy storage dynamic power distribution can ensure that the forecast power deviation is effectively compensated, provides powerful guarantee for the performance of an energy storage system in the aspect of wind power forecast reliability, and improves the schedulability of wind power grid-connected power generation.
(3) The power distribution is used for carrying out throughput energy control on the hybrid energy storage, so that the wind power forecasting accuracy is improved.
(4) By combining the working characteristics of the super capacitor and the battery and according to the frequency analysis result of the power deviation signal, the functions of the super capacitor and the battery can be fully exerted, the utilization efficiency is increased, the frequent charging and discharging actions of the battery are avoided, and the service life of the battery is effectively prolonged.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a power distribution control diagram of the present invention;
FIG. 3 is a wind power forecast total deviation power distribution diagram of the invention requiring hybrid energy storage consumption at any time;
FIG. 4 is a graph of the power distribution of the present invention after frequency decomposition requiring battery uptake;
FIG. 5 is a graph of the power distribution of the present invention after frequency decomposition requiring super capacitor absorption.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, a hybrid energy storage power allocation method for improving wind power scheduling reliability includes the following steps:
step 1, forecasting by weather forecast and field historical operation data, obtaining wind power data through wavelet packet frequency analysis and offline smoothing processing, and using the wind power data as wind power grid-connected scheduling data; step 2, dynamically monitoring the actual wind speed to obtain corresponding actual power, comparing the actual power with wind power grid-connected scheduling data, and performing wavelet online frequency analysis on forecast deviation data to obtain a high-frequency power control signal and a low-frequency power control signal; step 3, comparing power data obtained after wavelet packet smoothing is carried out on the power prediction historical data with field historical operation data to obtain power deviation data, and carrying out offline integral statistics and Kalman filtering frequency analysis to obtain the total capacity and power of the required hybrid energy storage, wherein the total capacity of the hybrid energy storage comprises the capacities of a super capacitor and a battery, and the power of the hybrid energy storage comprises high-frequency part power and low-frequency part power; step 4, the high-frequency power control signal is used as a power control signal of the super capacitor to control energy transmission power to the super capacitor, and the low-frequency power control signal is used as a power control signal of the battery to control energy transmission power to the battery; and 5, respectively taking the super capacitor and the battery as a first converter and a second converter, and connecting the super capacitor and the battery to a wind power grid-connected power generation end together.
The specific steps of step 1 are as follows: step 1.1, forecasting by weather forecast and site historical operation data to obtain wind power forecasting data; step 1.2, wind power data are obtained by performing wavelet packet frequency analysis and offline smoothing on wind power prediction data; and step 1.3, taking the wind power data as wind power grid-connected scheduling data.
The specific steps of step 2 are as follows: step 2.1, dynamically monitoring the actual wind speed by using wind measuring equipment, and obtaining corresponding actual power according to the actual wind speed; step 2.2, comparing the actual power with wind power grid-connected scheduling data to obtain forecast deviation data; and 2.3, performing wavelet online frequency analysis processing on the forecast deviation data to obtain a high-frequency power control signal and a low-frequency power control signal.
The specific steps of step 3 are as follows: step 3.1, performing wavelet packet smoothing processing on the power prediction historical data to obtain power data; step 3.2, comparing the power data with the field historical operation data to obtain power deviation data; step 3.3, performing offline integral statistics on the power deviation data to obtain the total capacity of the required hybrid energy storage; step 3.4, analyzing the power deviation data by Kalman filtering frequency to obtain power of hybrid energy storage, wherein the power of the hybrid energy storage comprises high-frequency part power and low-frequency part power, the maximum throughput power of the high-frequency part power is used as the power grade configured by the super capacitor, and the maximum throughput power of the low-frequency part power is used as the power grade configured by the battery; and 3.5, obtaining the capacity requirement of the super capacitor through the high-frequency part power integration, and obtaining the capacity requirement of the battery through the low-frequency part power integration.
The battery can be a lithium battery. As shown in fig. 2, a power distribution control diagram of a hybrid energy storage power distribution method for improving the wind power scheduling reliability, where PM is forecast power data, PW is actual power acquisition data, a deviation between PM and PW is a total power of hybrid battery power control, that is, a PHESS in the diagram, and the deviation value is subjected to frequency division by a wavelet packet transform algorithm, a high-frequency portion is used as a drive switch Pg control signal of a super capacitor to form energy transmission power Psg for the super capacitor, and the super capacitor outputs power PC to a grid-connected terminal; the low-frequency part is used as a control signal of a drive switch Psd of the lithium battery to form energy transmission power PB to the lithium battery, the power PC is output to a grid-connected end, and POUT is the total actual output power of mixed energy storage. SOCc and SOCb in the figure are actual SOC feedback values of the super capacitor and the lithium battery, respectively.
As shown in fig. 3, a wind power forecast total deviation power distribution diagram of hybrid energy storage consumption required at any time period according to the invention; FIG. 4 is a graph showing the power distribution required to be absorbed by the battery after frequency decomposition according to the present invention; FIG. 5 is a graph showing the power distribution of the present invention after frequency decomposition and requiring super-capacitor absorption. The ordinate in fig. 3 to 5 is power, the abscissa is the sampling point, and the sampling period is 15 minutes.
As can be seen from fig. 3 to 5, the calculated power of the hybrid energy storage system has a fast change speed and a wide fluctuation range. The lithium battery has low response speed, the super capacitor has small energy storage capacity, and the requirements of the output capacity and the power of the energy storage system are hardly met by the lithium battery and the super capacitor. The low-frequency component obtained by dividing the energy storage calculation power by adopting the wavelet transform algorithm and the low-frequency component sent to the lithium battery and the super capacitor by the high-frequency component are much smoother compared with the calculation power of an energy storage system, and the high-frequency component sent to the super capacitor is smaller. Thus, two energy storage performance characteristics are considered, and the advantages of the energy storage device can be made up and exerted to the greatest extent.
The invention relates to the design of power forecast data, the configuration of hybrid energy storage capacity and the specific numerical value giving of power distribution, and the wind power grid-connected power dispatching plan forecast accuracy is improved by combining software and hardware means. And by combining offline design and online control, the actual forecast deviation needs dynamic signal analysis and then is distributed to a super capacitor and a battery, and the expected reliable scheduling control target is realized by means of the throughput of the hybrid energy storage energy.
According to the method, by means of third-party hardware, offline analysis and online control are combined, large wind power fluctuation is stabilized through the handling behavior of the energy storage system, and meanwhile the accuracy of wind power grid-connected forecasting is improved. The scientific and reasonable hybrid energy storage dynamic power distribution can ensure that the forecast power deviation is effectively compensated, provides powerful guarantee for the performance of an energy storage system in the aspect of wind power forecast reliability, and improves the schedulability of wind power grid-connected power generation.
The output power of wind power generation is influenced by natural wind power to present a great fluctuation characteristic, the deviation of the forecast power data and the actual power data of the existing wind power station is large, and if the wind power station is directly connected to the grid for power generation, the continuous balance of power supply and power consumption of a dispatching system is broken, so that a power grid dispatching system is required to provide large reserve capacity wind. The grid-connected point is connected to the energy storage system, and through the handling behavior of the energy storage system, the scientific and reasonable hybrid energy storage dynamic power distribution can stabilize large wind power fluctuation, effective implementation of forecast power deviation compensation is ensured, the reliability of wind power grid-connected forecast is improved, and powerful support is provided in the aspect of wind power schedulability.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention, and are not intended to limit the scope of the present invention, and any person skilled in the art should understand that equivalent changes and modifications made without departing from the concept and principle of the present invention should fall within the protection scope of the present invention.
Claims (4)
1. A hybrid energy storage power distribution method for improving wind power dispatching reliability is characterized by comprising the following steps:
step 1, forecasting by weather forecast and field historical operation data, obtaining wind power data through wavelet packet frequency analysis and offline smoothing processing, and using the wind power data as wind power grid-connected scheduling data;
step 2, dynamically monitoring the actual wind speed to obtain corresponding actual power, comparing the actual power with wind power grid-connected scheduling data, and performing wavelet online frequency analysis on forecast deviation data to obtain a high-frequency power control signal and a low-frequency power control signal;
step 3, comparing power data obtained after wavelet packet smoothing is carried out on the power prediction historical data with field historical operation data to obtain power deviation data, and carrying out offline integral statistics and Kalman filtering frequency analysis to obtain the total capacity and power of the required hybrid energy storage, wherein the total capacity of the hybrid energy storage comprises the capacities of a super capacitor and a battery, and the power of the hybrid energy storage comprises high-frequency part power and low-frequency part power;
step 4, the high-frequency power control signal is used as a power control signal of the super capacitor to control energy transmission power to the super capacitor, and the low-frequency power control signal is used as a power control signal of the battery to control energy transmission power to the battery;
and 5, respectively taking the super capacitor and the battery as a first converter and a second converter, and connecting the super capacitor and the battery to a wind power grid-connected power generation end together.
2. The hybrid energy storage power distribution method for improving the wind power dispatching reliability according to claim 1, characterized in that the specific steps of step 1 are as follows:
step 1.1, forecasting by weather forecast and site historical operation data to obtain wind power forecasting data;
step 1.2, wind power data are obtained by performing wavelet packet frequency analysis and offline smoothing on wind power prediction data;
and step 1.3, taking the wind power data as wind power grid-connected scheduling data.
3. The hybrid energy storage power distribution method for improving the wind power dispatching reliability according to claim 1, wherein the specific steps of the step 2 are as follows:
step 2.1, dynamically monitoring the actual wind speed by using wind measuring equipment, and obtaining corresponding actual power according to the actual wind speed;
step 2.2, comparing the actual power with wind power grid-connected scheduling data to obtain forecast deviation data;
and 2.3, performing wavelet online frequency analysis processing on the forecast deviation data to obtain a high-frequency power control signal and a low-frequency power control signal.
4. The hybrid energy storage power distribution method for improving the wind power dispatching reliability according to claim 1, wherein the specific steps of the step 3 are as follows:
step 3.1, performing wavelet packet smoothing processing on the power prediction historical data to obtain power data;
step 3.2, comparing the power data with the field historical operation data to obtain power deviation data;
step 3.3, performing offline integral statistics on the power deviation data to obtain the total capacity of the required hybrid energy storage;
step 3.4, analyzing the power deviation data by Kalman filtering frequency to obtain power of hybrid energy storage, wherein the power of the hybrid energy storage comprises high-frequency part power and low-frequency part power, the maximum throughput power of the high-frequency part power is used as the power grade configured by the super capacitor, and the maximum throughput power of the low-frequency part power is used as the power grade configured by the battery;
and 3.5, obtaining the capacity requirement of the super capacitor through the high-frequency part power integration, and obtaining the capacity requirement of the battery through the low-frequency part power integration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911165452.4A CN110768264B (en) | 2019-11-25 | 2019-11-25 | Hybrid energy storage power distribution method for improving wind power dispatching reliability |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911165452.4A CN110768264B (en) | 2019-11-25 | 2019-11-25 | Hybrid energy storage power distribution method for improving wind power dispatching reliability |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110768264A true CN110768264A (en) | 2020-02-07 |
| CN110768264B CN110768264B (en) | 2020-12-08 |
Family
ID=69339321
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911165452.4A Active CN110768264B (en) | 2019-11-25 | 2019-11-25 | Hybrid energy storage power distribution method for improving wind power dispatching reliability |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110768264B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111864798A (en) * | 2020-07-23 | 2020-10-30 | 南通大学 | Energy storage energy throughput implementation method in wind power integration forecast deviation real-time compensation |
| CN111932400A (en) * | 2020-08-06 | 2020-11-13 | 南通大学 | Wind/storage integrated power scheduling plan optimization implementation method |
| CN112398151A (en) * | 2020-11-30 | 2021-02-23 | 广东电网有限责任公司电力调度控制中心 | Offshore wind power energy storage configuration method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103580046A (en) * | 2013-11-27 | 2014-02-12 | 上海空间电源研究所 | Compound energy storage proportioning method for active distribution network |
| CN104361405A (en) * | 2014-10-28 | 2015-02-18 | 广东电网有限责任公司电力科学研究院 | Micro grid energy storage device design method based on capacity limit value constrain |
| US20170329319A1 (en) * | 2016-05-10 | 2017-11-16 | Conectric, Llc | Method and system for adaptively switching prediction strategies optimizing time-variant energy consumption of built environment |
| CN108365627A (en) * | 2018-03-09 | 2018-08-03 | 沈阳工程学院 | A kind of wind storage isolated network power supply system control method for coordinating based on flexible coordinating factor |
| US20190148943A1 (en) * | 2017-11-14 | 2019-05-16 | Inventus Holdings, Llc | Battery energy storage system integrated with electrical generation site |
-
2019
- 2019-11-25 CN CN201911165452.4A patent/CN110768264B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103580046A (en) * | 2013-11-27 | 2014-02-12 | 上海空间电源研究所 | Compound energy storage proportioning method for active distribution network |
| CN104361405A (en) * | 2014-10-28 | 2015-02-18 | 广东电网有限责任公司电力科学研究院 | Micro grid energy storage device design method based on capacity limit value constrain |
| US20170329319A1 (en) * | 2016-05-10 | 2017-11-16 | Conectric, Llc | Method and system for adaptively switching prediction strategies optimizing time-variant energy consumption of built environment |
| US20190148943A1 (en) * | 2017-11-14 | 2019-05-16 | Inventus Holdings, Llc | Battery energy storage system integrated with electrical generation site |
| CN108365627A (en) * | 2018-03-09 | 2018-08-03 | 沈阳工程学院 | A kind of wind storage isolated network power supply system control method for coordinating based on flexible coordinating factor |
Non-Patent Citations (1)
| Title |
|---|
| 丁明等: "面向风电平抑的混合储能系统容量配置方法", 《太阳能学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111864798A (en) * | 2020-07-23 | 2020-10-30 | 南通大学 | Energy storage energy throughput implementation method in wind power integration forecast deviation real-time compensation |
| CN111932400A (en) * | 2020-08-06 | 2020-11-13 | 南通大学 | Wind/storage integrated power scheduling plan optimization implementation method |
| CN111932400B (en) * | 2020-08-06 | 2021-04-30 | 南通大学 | Wind/storage integrated power scheduling plan optimization implementation method |
| CN112398151A (en) * | 2020-11-30 | 2021-02-23 | 广东电网有限责任公司电力调度控制中心 | Offshore wind power energy storage configuration method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110768264B (en) | 2020-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110768264B (en) | Hybrid energy storage power distribution method for improving wind power dispatching reliability | |
| Li et al. | Analysis of battery lifetime extension in a SMES-battery hybrid energy storage system using a novel battery lifetime model | |
| CN105260797B (en) | Planning control method for micro-grid energy storage power station | |
| CN104734166A (en) | Hybrid energy storage system and wind power generation power smooth control method | |
| CN104362658A (en) | Energy type and power type hybrid energy storage coordination control method | |
| CN104104107B (en) | The model predictive control method of wind power fluctuation is stabilized with hybrid energy-storing | |
| CN110783959B (en) | New forms of energy power generation system's steady state control system | |
| CN105244920B (en) | Consider the energy-storage system multi objective control method and its system of cell health state | |
| CN112736973A (en) | Battery energy storage capacity configuration method and system for stabilizing output fluctuation of wind power and photovoltaic power station | |
| CN111030150A (en) | Hybrid energy storage capacity determination method for reliable load power supply of micro-grid system | |
| CN105680771A (en) | Wind-solar complementary generation system and control method | |
| CN111245096A (en) | Distributed photovoltaic data acquisition terminal, system and data processing method | |
| CN114580180A (en) | Hybrid energy storage capacity configuration method based on self-adaptive analog digital VMD algorithm | |
| CN115733180A (en) | Hybrid energy storage system frequency modulation control method and system based on self-adaptive analog mode number VMD algorithm | |
| CN117394403B (en) | Big data analysis-based intelligent control system for charging and discharging of energy storage power supply | |
| CN111932400A (en) | Wind/storage integrated power scheduling plan optimization implementation method | |
| CN114696352A (en) | Operation control method for battery energy storage system in new energy power distribution network | |
| CN103560533A (en) | Method and system for causing energy storage power station to smooth wind and photovoltaic power generation fluctuation based on change rate | |
| CN115065075B (en) | Energy storage station optimal scheduling method, system and storage medium in wind storage cluster | |
| CN116054214A (en) | Household photovoltaic energy storage system based on intelligent regulation and control | |
| Meng et al. | Research on the Automatic Control Method of Energy Storage for Wind Power Consumption | |
| CN113364019B (en) | Method and system for participating in electric power peak shaving by vehicle-mounted energy storage resources | |
| CN115207918A (en) | Real-time stable control device suitable for new forms of energy grid-connected system | |
| Zhu et al. | Capacity optimization configuration of wind farm energy storage system based on life cycle economic analysis | |
| CN112838605A (en) | Energy storage power station coordination control and energy management and operation method based on multi-agent particle swarm optimization |
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 |