CN112769149A - Hybrid wind-solar micro-grid rapid frequency response distributed coordination control method and system - Google Patents
Hybrid wind-solar micro-grid rapid frequency response distributed coordination control method and system Download PDFInfo
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- 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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Abstract
The utility model provides a quick frequency response distributed coordination control method and system for a series-parallel wind-solar micro-grid, which obtains the operation state data of each distributed energy; obtaining total optimal power input according to a model predictive control algorithm by using the obtained running state data; distributing total optimal power input on all distributed energy sources in the microgrid through the maximum rated power and the charging state at a given time; the present disclosure significantly improves the inertia of an ac microgrid with multiple distributed converter interfaces for wind or solar photovoltaic power, reducing the frequency variations of the system when the power drawn by the load changes suddenly.
Description
Technical Field
The disclosure relates to the technical field of micro-grid frequency response control, in particular to a hybrid wind-solar micro-grid rapid frequency response distributed coordination control method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In order to reduce the environmental impact of greenhouse gases causing global warming, the demand for renewable energy power is increasing. 40% of the total atmospheric carbon dioxide emissions are generated by electricity generation worldwide. Thus, the power industry's energy needs to move from non-renewable energy sources (e.g., coal and natural gas) to more sustainable energy sources (e.g., solar, wind, hydro, hydrogen, etc.). However, with the prior art, this change to the form of energy comes at the cost of lower power quality and weaker grids. For example, large rotating high inertia centralized power generation systems are employed in conventional power systems, while high level renewable energy power generation systems are associated with distributed power sources (primarily static).
For the outstanding problem of frequency control in low-inertia alternating-current micro-grid, the existing solutions can be divided into two types, namely: conventional sag-based methods and synchronous machine inertia simulations. The droop method uses battery storage control to improve the frequency response of the power system. Synchronous machine inertia simulation, also known as Virtual Synchronous Generator (VSG), utilizes adaptive inertia and damping coefficients to improve the frequency performance of a microgrid or power system when the load changes suddenly.
The inventors have found that conventional droop-based methods have a high rate of frequency change during rapid load changes, a problem that can lead to power system instability. And these methods have a constant droop coefficient and cannot be modified during dynamic frequencies, resulting in slower return of the frequency to nominal after a deviation occurs.
The synchronous machine inertia simulation shows better performance compared to the conventional droop method. However, there is currently no solution for how to coordinate the multiple VSG power converters distributed across the various wind power plants/solar photovoltaic generators within the microgrid so that they effectively provide sufficient inertia to reduce frequency interference. This feature is necessary, however, for example, in a microgrid with multiple wind or solar powered DERs, it is desirable to enable the wind/solar photovoltaic system of each DER to contribute to the frequency stability of the bus bars, thereby eliminating interference more quickly and enhancing the stability of the microgrid system.
Meanwhile, the inertia simulation method of the synchronous motor has the following problems to be solved: which storage devices and power converters are required for each DER, how to locally control the storage devices and power converters of each DER, and how to cooperatively control the storage devices and power converters of all connected DERs in the microgrid when control algorithms and necessary hardware are applied to achieve control objectives, in order not to affect the service life and efficiency of the devices.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a quick frequency response distributed coordination control method and system for a series-parallel wind-solar micro-grid, which obviously improve the inertia of a wind energy or solar photovoltaic power supply alternating-current micro-grid with a plurality of distributed converter interfaces and reduce the frequency change of the system when the power drawn by a load changes suddenly.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
the first aspect of the disclosure provides a hybrid wind-solar micro-grid rapid frequency response distributed coordination control method.
A quick frequency response distributed coordination control method for a series-parallel wind-solar micro-grid comprises the following steps:
acquiring running state data of each distributed energy source;
obtaining total optimal power input according to a model predictive control algorithm by using the obtained running state data;
the total optimal power input is distributed over all distributed energy sources in the microgrid over a maximum rated power and state of charge at a given time.
The second aspect of the disclosure provides a hybrid wind-solar micro-grid rapid frequency response distributed coordination control system.
A quick frequency response distributed coordination control system of a series-parallel wind-solar micro-grid comprises:
a data acquisition module configured to: acquiring running state data of each distributed energy source;
an optimal total power input acquisition module configured to: obtaining total optimal power input according to a model predictive control algorithm by using the obtained running state data;
a distributed energy optimal power input acquisition module configured to: the total optimal power input is distributed over all distributed energy sources in the microgrid over a maximum rated power and state of charge at a given time.
A third aspect of the present disclosure provides a computer-readable storage medium, on which a program is stored, which when executed by a processor, implements the steps in the hybrid wind-solar micro-grid fast frequency response distributed coordination control method according to the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, where the processor executes the program to implement the steps in the hybrid wind-solar-microgrid fast frequency response distributed coordination control method according to the first aspect of the present disclosure.
Compared with the prior art, the beneficial effect of this disclosure is:
1. the method, system, medium or electronic device provided by the present disclosure significantly improves the inertia of an ac microgrid with multiple distributed converter interfaces for wind or solar photovoltaic power, and reduces the frequency variations of the system when the power drawn by the load suddenly changes.
2. The present disclosure provides methods, systems, media, or electronic devices that maximize the use of energy distributed throughout the microgrid, allowing it to work in concert to ensure frequency control of the microgrid, and thus, as the number of DER increases, they may also be accommodated for continued operation.
3. The method, system, medium, or electronic device provided by the present disclosure uses only the communication of each DER and its direct neighbors, uses less communication bandwidth, and works well in the presence of communication channel delays and interference.
4. The present disclosure provides methods, systems, media, or electronic devices that provide power input by using a super capacitor overlay, accommodate large frequency deviations from nominal values, and extend the battery life of the storage system.
4. The present disclosure provides methods, systems, media, or electronic devices that securely maintain a rate of frequency change within a range that ensures stability of a microgrid system.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a block diagram of an ac microgrid having N distributed energy sources (DER) provided in embodiment 1 of the present disclosure.
Fig. 2 is a network physical layout of N DERs provided in embodiment 1 of the present disclosure.
FIG. 3 is a flowchart of the method operations provided in embodiment 1 of the present disclosure
Fig. 4 is an internal view of each DER provided in embodiment 1 of the present disclosure, including a wind power plant (with MPPT converter), a photovoltaic panel (with DC-DC boost converter), a bi-directional DC-DC converter, a battery (B1), a super capacitor (C1), an inverter, and an LC filter.
Fig. 5 is a schematic diagram of a control method of an inverter provided in embodiment 1 of the present disclosure.
Fig. 6 is a schematic diagram of VSG-droop control of the inverter provided in embodiment 1 of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
the large electric pump and the air conditioner are started, so that the load of the alternating-current micro-grid is suddenly changed, and the frequency of the micro-grid is reduced. Therefore, power electronic converters are required to regulate the frequency return to the nominal value. As shown in fig. 1, several distributed energy resources DER in the microgrid will be controlled1、DER2，···，DERN。
The method provided by the embodiment is distributed cooperative control, as shown in fig. 2, each DER is represented as a node 1, 2, …, i, … N, and is performed in two steps, as shown in fig. 3:
step 1: total optimum power input Δ P is calculated using Model Predictive Control (MPC) from all storage systems required to control frequency and rate of change of frequencyct。
Step 2: the total power input required is distributed over all N DER in the microgrid over a maximum rated power and state of charge at a given time.
Specifically, the method comprises the following steps:
in the step 1:
for the objective function G, the optimal required total power control input is calculated using model predictive control:
wherein the microgrid system inertia
Δ f is the deviation of the frequency,
rate of Change of frequency (ROOF), Δ PctIs the total optimum power, mu, required by all DER's in the microgrid to regulate the frequencyf，μdfAnd muuRespectively, the tuning weights for frequency, ROCOF, and input power.
And is affected by the following factors: frequency deviation, rate of change of frequency (ROCOF) limit and power limit constraints.
The instantaneous control input needs to meet both the regulatory requirements and the physical constraints of the DER.
Equation (1) gives the optimum total power value in the microgrid for all power converters to maintain the frequency and rate of change of frequency within the upper and lower limits.
In the step 2:
determining each DERi(i.e. P)ci) Such that
Each DERiThe contribution of (c) follows two rules:
(1) each DER will allocate power at its maximum rating;
(2) each DER will distribute power according to its current state of charge;
these two rules are embodied by capacity cooperative control ( equations 2a and 2b) and state of charge cooperative control (equations 3a and 3b), respectively.
Capacity coordination control provides for each DER for the next time step
The charging state cooperatively controls to provide the energy storage coefficient
Using these two parameters, each DER can be determinediPower input contribution of
Where k represents the digital sampling instant and t represents time (a continuous variable).
Power input in VSG inverter control (e.g., FIG. 6)
For controlling the inverters shown in fig. 4 and 5, in fig. 4, a battery B1MPC ofbatThe controller ensures that the battery is only loaded with power Δ PLIs providing energy, and the supercapacitor controller MPCu/cEnsuring super-capacitor to regulate load power Δ PLThe larger variation provides power in a manner that extends battery life and reduces overall cost of the microgrid system.
The capacity cooperative control specifically comprises the following steps:
the following capacity control equation, one for each DERiIs proportional to its rated capacity, and thus a DER with a larger rated power can provide more power for adjusting the frequency offset.
Wherein the content of the first and second substances,
γi,γiis a weighting coefficient proportional to the maximum storage capacity installed, obeys
I.e. the sum of the input powers at all sampling times equals the total initial input power, Px_maxRepresents DERxWhere x ∈ [ i, j ] is set]。
Charge state coordination control
The state of charge cooperative control is described by equations (3a) and (3b), each DERiRequiring only charge state information of neighboring DERThe average charge level of all N-DERs in the range of the microgrid system is obtained, which makes it require only a low bandwidth and works well even in the event of communication failure or delay.
The system average state of charge (SoC) is based on observer design:
where τ denotes an integration parameter, NiIs the set of all neighbors of node i, aijIs the (i, j) th element of the adjacency matrix, and defines the cost-based storage participation coefficient as:
that is, when DER is appliedi(SoCi) Is greater than the average state of charge observed at node i, DERiA power input is provided, otherwise it does not provide any power. Furthermore, DERiThe higher the charge level of (c), the greater its contribution to the power input required to achieve the control target.
The contribution of each DERI is given by equation (4):
wherein eta isiIs the charge-discharge efficiency, Δ P, of the battery and supercapacitor storageiIs each DERiPower input contribution of betaiIs DERiThe stored charge coefficient of (c).
Example 2:
the embodiment 2 of the present disclosure provides a hybrid wind-solar micro-grid fast frequency response distributed coordination control system, including:
a data acquisition module configured to: acquiring running state data of each distributed energy source;
an optimal total power input acquisition module configured to: obtaining total optimal power input according to a model predictive control algorithm by using the obtained running state data;
a distributed energy optimal power input acquisition module configured to: the total optimal power input is distributed over all distributed energy sources in the microgrid over a maximum rated power and state of charge at a given time.
The working method of the system is the same as the hybrid wind-solar micro-grid rapid frequency response distributed coordination control method provided in embodiment 1, and details are not repeated here.
Example 3:
the embodiment 3 of the present disclosure provides a computer-readable storage medium, on which a program is stored, where the program, when executed by a processor, implements the steps in the method for fast frequency response distributed coordination control of a hybrid wind-solar micro-grid according to the embodiment 1 of the present disclosure, where the steps are:
acquiring running state data of each distributed energy source;
obtaining total optimal power input according to a model predictive control algorithm by using the obtained running state data;
the total optimal power input is distributed over all distributed energy sources in the microgrid over a maximum rated power and state of charge at a given time.
The detailed steps are the same as those of the hybrid wind-solar micro-grid rapid frequency response distributed coordination control method provided in embodiment 1, and are not described herein again.
Example 4:
the embodiment 4 of the present disclosure provides an electronic device, which includes a memory, a processor, and a program stored in the memory and executable on the processor, where the processor implements the steps in the hybrid wind-solar-microgrid fast frequency response distributed coordination control method according to the embodiment 1 of the present disclosure when executing the program, where the steps are:
acquiring running state data of each distributed energy source;
obtaining total optimal power input according to a model predictive control algorithm by using the obtained running state data;
the total optimal power input is distributed over all distributed energy sources in the microgrid over a maximum rated power and state of charge at a given time.
The detailed steps are the same as those of the hybrid wind-solar micro-grid rapid frequency response distributed coordination control method provided in embodiment 1, and are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A quick frequency response distributed coordination control method for a series-parallel wind-solar micro-grid is characterized by comprising the following steps: the method comprises the following steps:
acquiring running state data of each distributed energy source;
obtaining total optimal power input according to a model predictive control algorithm by using the obtained running state data;
the total optimal power input is distributed over all distributed energy sources in the microgrid over a maximum rated power and state of charge at a given time.
2. The fast frequency response distributed coordination control method of the series-parallel wind-solar micro-grid according to claim 1, characterized in that:
the total optimum power input that maintains the frequency and frequency rate of change within the upper and lower limits is derived from a model predictive control algorithm.
3. The fast frequency response distributed coordination control method of the series-parallel wind-solar micro-grid according to claim 1, characterized in that:
and calculating the power input contribution of each distributed energy source in the next time step by using capacity cooperative control, calculating an energy storage coefficient by using charge state cooperative control, and obtaining the final power input contribution of each distributed energy source by combining the charge and discharge efficiency stored by the battery and the super capacitor.
4. The fast frequency response distributed coordination control method of the series-parallel wind-solar micro-grid according to claim 3, characterized in that:
the capacity is coordinated such that the input power contribution of each distributed energy source is proportional to its rated capacity.
5. The fast frequency response distributed coordination control method of the series-parallel wind-solar micro-grid according to claim 3, characterized in that:
by utilizing charge state cooperative control, each distributed energy source only needs charge state information of adjacent distributed energy sources, and the average charge level of all distributed energy sources in the range of the micro-grid system is obtained.
6. The fast frequency response distributed coordination control method of the series-parallel wind-solar micro-grid according to claim 5, characterized in that:
the system average state of charge is based on an observer design, which provides power input when the state of charge of a distributed energy source is greater than the average state of charge observed at a node, otherwise it does not provide any power.
7. The fast frequency response distributed coordination control method of the series-parallel wind-solar micro-grid according to claim 3, characterized in that:
the final power input contribution of each distributed energy source is: pci＝ΔPiβi/ηiWherein ηiCharge-discharge efficiency, Δ P, for battery and supercapacitor storageiFor the power input contribution of the ith distributed energy source, β, based on a capacity co-control approachiIs the stored charge factor at the ith distributed energy source.
8. A quick frequency response distributed coordination control system of a series-parallel wind-solar micro-grid is characterized in that: the method comprises the following steps:
a data acquisition module configured to: acquiring running state data of each distributed energy source;
an optimal total power input acquisition module configured to: obtaining total optimal power input according to a model predictive control algorithm by using the obtained running state data;
a distributed energy optimal power input acquisition module configured to: the total optimal power input is distributed over all distributed energy sources in the microgrid over a maximum rated power and state of charge at a given time.
9. A computer readable storage medium having a program stored thereon, wherein the program when executed by a processor implements the steps in the hybrid wind-solar micro-grid fast frequency response distributed coordination control method according to any of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the steps of the hybrid wind-solar-microgrid fast frequency response distributed coordination control method according to any one of claims 1 to 7 when executing the program.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114465288A (en) * | 2022-01-28 | 2022-05-10 | 山东大学 | Interconnected micro-grid inertia control method and system |
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| CN111027807B (en) * | 2019-11-12 | 2024-02-06 | 国网河北省电力有限公司经济技术研究院 | Distributed power generation site selection and volume determination method based on tide linearization |
| CN115833102B (en) * | 2022-12-08 | 2023-08-25 | 南方电网数字电网研究院有限公司 | Wind power plant frequency quick response control method based on model predictive control |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102842904A (en) * | 2012-07-30 | 2012-12-26 | 东南大学 | Micro-grid collaborative frequency control method based on power shortage prediction and distribution |
| CN104167750A (en) * | 2014-08-18 | 2014-11-26 | 国家电网公司 | Distribution network peak load shifting distributed energy storage power distribution and coordination control method |
| CN107196294A (en) * | 2017-06-16 | 2017-09-22 | 国网江苏省电力公司电力科学研究院 | Micro-capacitance sensor Multiple Time Scales adaptive energy dispatching method under the net load interaction pattern of source |
| CN107516887A (en) * | 2017-08-04 | 2017-12-26 | 华中科技大学 | A kind of distributed DC micro-capacitance sensor composite energy storage control method |
| US10298016B1 (en) * | 2016-02-05 | 2019-05-21 | National Technology & Engineering Solutions Of Sandia, Llc | Systems, methods and computer program products for electric grid control |
| CN110429615A (en) * | 2019-07-15 | 2019-11-08 | 内蒙古电力(集团)有限责任公司电力调度控制分公司 | Wind storage exchange micro-capacitance sensor automated power balance control method and system |
| CN111881616A (en) * | 2020-07-02 | 2020-11-03 | 国网河北省电力有限公司经济技术研究院 | Operation optimization method of comprehensive energy system based on multi-subject game |
| CN111900721A (en) * | 2020-06-28 | 2020-11-06 | 湖南大学 | Smart power grid frequency control method based on wind-water cooperative power generation mode |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6155140B2 (en) * | 2013-08-14 | 2017-06-28 | 有限会社グリテックスインターナショナルリミテッド | Photosensitive panel drive device for tracking sunlight |
| CN110808616B (en) * | 2019-10-14 | 2023-04-07 | 广东工业大学 | Micro-grid frequency control method based on power shortage distribution |
| CN112803505B (en) * | 2021-02-05 | 2023-02-03 | 山东大学 | Distributed voltage source converter cooperative control method and alternating current-direct current parallel-serial micro-grid |
-
2021
- 2021-01-21 CN CN202110082360.0A patent/CN112769149B/en active Active
- 2021-02-07 WO PCT/CN2021/075815 patent/WO2022156014A1/en active Application Filing
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102842904A (en) * | 2012-07-30 | 2012-12-26 | 东南大学 | Micro-grid collaborative frequency control method based on power shortage prediction and distribution |
| CN104167750A (en) * | 2014-08-18 | 2014-11-26 | 国家电网公司 | Distribution network peak load shifting distributed energy storage power distribution and coordination control method |
| US10298016B1 (en) * | 2016-02-05 | 2019-05-21 | National Technology & Engineering Solutions Of Sandia, Llc | Systems, methods and computer program products for electric grid control |
| CN107196294A (en) * | 2017-06-16 | 2017-09-22 | 国网江苏省电力公司电力科学研究院 | Micro-capacitance sensor Multiple Time Scales adaptive energy dispatching method under the net load interaction pattern of source |
| CN107516887A (en) * | 2017-08-04 | 2017-12-26 | 华中科技大学 | A kind of distributed DC micro-capacitance sensor composite energy storage control method |
| CN110429615A (en) * | 2019-07-15 | 2019-11-08 | 内蒙古电力(集团)有限责任公司电力调度控制分公司 | Wind storage exchange micro-capacitance sensor automated power balance control method and system |
| CN111900721A (en) * | 2020-06-28 | 2020-11-06 | 湖南大学 | Smart power grid frequency control method based on wind-water cooperative power generation mode |
| CN111881616A (en) * | 2020-07-02 | 2020-11-03 | 国网河北省电力有限公司经济技术研究院 | Operation optimization method of comprehensive energy system based on multi-subject game |
Non-Patent Citations (1)
| Title |
|---|
| 孟建辉等: "改善微电网频率稳定性的分布式逆变电源控制策略", 《电工技术学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114465288A (en) * | 2022-01-28 | 2022-05-10 | 山东大学 | Interconnected micro-grid inertia control method and system |
| CN114465288B (en) * | 2022-01-28 | 2023-05-16 | 山东大学 | Interconnected micro-grid inertia control method and system |
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