CN107968444B - New energy cluster coordination optimization control method - Google Patents

New energy cluster coordination optimization control method Download PDF

Info

Publication number
CN107968444B
CN107968444B CN201711383775.1A CN201711383775A CN107968444B CN 107968444 B CN107968444 B CN 107968444B CN 201711383775 A CN201711383775 A CN 201711383775A CN 107968444 B CN107968444 B CN 107968444B
Authority
CN
China
Prior art keywords
power
generating set
value
input
output
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
Application number
CN201711383775.1A
Other languages
Chinese (zh)
Other versions
CN107968444A (en
Inventor
罗恩博
陆海
杨洋
李浩涛
陈世游
苏适
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Electric Power Research Institute of Yunnan Power Grid Co Ltd
Original Assignee
Electric Power Research Institute of Yunnan Power Grid Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Electric Power Research Institute of Yunnan Power Grid Co Ltd filed Critical Electric Power Research Institute of Yunnan Power Grid Co Ltd
Priority to CN201711383775.1A priority Critical patent/CN107968444B/en
Publication of CN107968444A publication Critical patent/CN107968444A/en
Application granted granted Critical
Publication of CN107968444B publication Critical patent/CN107968444B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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]
    • H02J3/383—
    • H02J3/386—
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Eletrric Generators (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The application provides a new energy cluster coordination optimization control method, which specifically comprises the following steps: according to the difference between a power value slightly lower than the power transmission line and the predicted power of the fan and the photovoltaic power generation at the next moment, processing according to the hydropower and installed capacity distribution strategy to obtain the given power values of the thermal power generating unit and the hydroelectric power generating unit; the difference value is made between the actual output values of the photovoltaic and the fan at the current moment and the value of the current moment predicted at the previous moment, and the difference value is introduced into a control system in a feedforward mode; aiming at the characteristics of large inertia, large delay and uncertain model of a main steam temperature control system of a thermal power plant, complexity, nonlinearity, non-minimum phase and the like of a hydroelectric generating set system, fuzzy control is introduced; fourthly, according to the steps, a control model is established to seek a transfer function value in an optimal feedforward link so as to overcome the defect of large inertia of the regulation and education of hydroelectric power and fire power, so that the transmission power is as large as possible, and the channel utilization rate is improved.

Description

New energy cluster coordination optimization control method
Technical Field
The disclosure relates to the technical field of new energy, in particular to a new energy cluster coordination optimization control method.
Background
When wind, light, water and fire are used for combined power generation, the new energy source has high volatility, and due to the fact that the response speed of active power of a thermal power generating unit and a hydroelectric power generating unit is poor, an active power curve cannot be made to be close to the power transmission online value as much as possible, transmission power is low, and further the channel utilization rate is low.
Disclosure of Invention
The embodiment of the invention provides a new energy cluster coordination optimization control method, which aims to solve the problem of low channel utilization rate in the prior art.
The invention provides a new energy cluster coordination optimization control method, which comprises the following steps:
respectively calculating a given power value WP of a hydroelectric generating set and a given power value WT of a thermal generating set according to a total power prediction value r and a target power value m of the fan and the photovoltaic at the next moment t;
calculating the difference value delta P between the actual output values of the photovoltaic and the fan at the current moment and the predicted output values of the photovoltaic and the fan at the moment at the previous momentiAnd according to said difference and transfer function Gr1(s) and Gr2(S) respectively obtaining the corresponding relation between the input quantity V1 of the hydroelectric generating set controller in the energy cluster control system and the actual power U of the hydroelectric generating set, and obtaining the input quantity V3 of the thermal generating set controller in the energy cluster control system and the actual power U of the thermal generating setTThe corresponding relationship of (a);
according to the input quantity V1 of the hydroelectric generating set controller, the derivative value V2 of the input quantity V1 of the hydroelectric generating set controller, the input quantity V3 of the thermal generating set controller and the derivative value V4 of the input quantity V3 of the thermal generating set controller, the actual generating power U of the hydroelectric generating set and the actual generating power U of the thermal generating set are used as input valuesTEstablishing a controlled object model as an output value;
establishing a fuzzy relation between the input quantity and the output quantity of the controller according to the controlled object model, and obtaining the actual power U of the hydroelectric generating set and the actual power U of the thermal generating set by using the fuzzy relationTAnd a transfer function Gr1(s) and Gr2(S)。
Preferably, the calculating the given power value WP of the hydroelectric power unit and the given power value WT of the thermal power unit respectively according to the total power prediction value r and the target power value m of the wind turbine and the photovoltaic at the next time t includes:
setting 95% of the upper limit value of the section transmission power as a target power value m;
according to the formula
Figure GDA0001588421760000011
And formula
Figure GDA0001588421760000012
Calculating to obtain hydropowerThe power value setting method comprises the following steps of setting a power value WP of a power unit and setting a power value WT of a thermal power unit, wherein n1 and n2 are the installed capacity of water and the installed capacity of thermal power respectively.
Preferably, the corresponding relation between the input quantity V1 of the hydroelectric generating set controller and the actual generating power U of the hydroelectric generating set is V1＝(WP-U)-ΔPiGr1(S), input quantity V3 of thermal power unit controller and actual power U of thermal power unitTHas a corresponding relationship of V3＝(WT-uT)-ΔPiGr2(S)。
Preferably, the controlled object model is:
Figure GDA0001588421760000013
wherein G is11A transfer function with V1 as input and U as output; g12A transfer function with V2 as input and U as output; g13A transfer function with V3 as input and U as output; g14A transfer function with V4 as input and U as output; g21Using V1 as input, uTA transfer function that is an output; g22Using V2 as input, uTA transfer function that is an output; g23Using V3 as input, uTA transfer function that is an output; g24Using V4 as input, uTIs the transfer function of the output.
Preferably, the fuzzy relation between the input quantity and the output quantity of the controller is as follows:
Figure GDA0001588421760000021
wherein k is 1,2,3,4, j is 1,2,3, 4;
obtaining the actual power U of the hydroelectric generating set and the actual power U of the thermal generating set by utilizing the fuzzy relationTThe calculation formulas of (A) and (B) are respectively as follows:
Figure GDA0001588421760000022
the beneficial effect of this application is as follows:
the new energy cluster coordination optimization control strategy provided by the application is characterized in that wind, light, water and fire are combined to optimize and coordinate power generation, so that a section transmission power curve is as close as possible to a target slightly lower than a power transmission line, and the channel utilization rate is improved. The specific method comprises the following steps: according to the difference between a power value slightly lower than the power transmission line and the predicted power of the fan and the photovoltaic power generation at the next moment, processing according to the hydropower and installed capacity distribution strategy to obtain the given power values of the thermal power generating unit and the hydroelectric power generating unit; the difference value is made between the actual output values of the photovoltaic and the fan at the current moment and the value of the current moment predicted at the previous moment, and the difference value is introduced into a control system in a feedforward mode; aiming at the characteristics of large inertia, large delay and uncertain model of a main steam temperature control system of a thermal power plant, complexity, nonlinearity, non-minimum phase and the like of a hydroelectric generating set system, fuzzy control is introduced; fourthly, according to the steps, a control model is established to seek a transfer function value in an optimal feedforward link so as to overcome the defect of large inertia of the regulation and education of hydroelectric power and fire power, so that the transmission power is as large as possible, and the channel utilization rate is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic block diagram of policy control for coordination optimization control of a new energy cluster according to an embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When wind, light, water and fire are used for combined power generation, the new energy source has high volatility, and due to the fact that the response speed of active power of a thermal power generating unit and a hydroelectric power generating unit is poor, an active power curve cannot be made to be close to the power transmission online value as much as possible, transmission power is low, and further the channel utilization rate is low. According to the new energy cluster coordination optimization control strategy, a control instruction is given in advance by using a method of time-sharing prediction of fan and photovoltaic output and introduction of prediction deviation dynamic feedforward, an optimal prediction deviation dynamic feedforward transfer function value is selected, wind-light-water-fire optimization coordination control is achieved, and the conveying capacity of a new energy limited section is improved.
Referring to fig. 1, a schematic block diagram of policy control for coordination and optimization control of a new energy cluster according to an embodiment of the present application is shown. As shown in FIG. 1 above, where Δ PiDifference between predicted value and actual value of fan and photovoltaic output, Gr1(s) and Gr2And (S) introducing the difference value between the actual output and the predicted output into a control link, adding feed-forward control, and improving the dynamic control effect. The essence here is to seek the optimum Gr1(s) and Gr2And (S) the transfer function achieves the purpose of improving the channel utilization rate. The new energy cluster coordination optimization control strategy provided by the application is based on a time-sharing prediction and dynamic feedforward method, can improve the new energy transmission capacity, and specifically seeks the optimal control effect through the following steps:
firstly, on the premise of ensuring the stable operation of a power grid, the utilization rate of a section power transmission channel needs to be fully considered, so that a power value m slightly lower than the upper limit of section transmission power is selected, and the value of m is 95% of the upper limit of the section transmission power; and then, making a difference between the total power prediction value r of the fan and the photovoltaic at the next moment t and a target power value m, and finally processing the m-r value according to a hydropower and installed capacity distribution strategy to obtain a given power value of the thermal power generating unit and the hydroelectric power generating unit, wherein the specific calculation formula is as follows:
Figure GDA0001588421760000031
in the formula (1) n1And n2Respectively representing the installed capacity of water and the installed capacity of thermal power; WP is a hydropower output given value obtained according to a hydropower and thermal power installed capacity distribution strategy; WT is a thermal power output given value obtained according to a hydropower and thermal power installed capacity distribution strategy.
Secondly, the predicted values of the photovoltaic and the fan are deviated from the actual output values, so that the difference value between the actual output values of the photovoltaic and the fan at the current moment and the value predicted at the moment is obtained to obtain delta PiIntroduction of a transfer function Gr1(s) and Gr2And (S) improving the output rapidity of the hydroelectric power unit and the thermal power unit through feedforward compensation control. We assume the transfer function Gr1(s) and Gr2(S) is processed according to a constant value, and the specific expression is as follows:
Figure GDA0001588421760000032
in the second formula, U is the actual power of the hydroelectric generating set, UTFor actual power generation of the thermal power generating unit, V1 is the input quantity of a hydroelectric generating unit controller in the energy cluster control system, and V3 is the input quantity of the thermal power generating unit controller in the energy cluster control system.
And thirdly, establishing a controlled object model by using the V1 and V3 values and corresponding derivative values V2 and V4 determined in the schemes of the first step and the second step as input values of the model and using the actual power of the hydroelectric generating set and the actual power of the thermal generating set as output values.
Figure GDA0001588421760000033
In the formula III, G11A transfer function with V1 as input and U as output; g12With the V2 as an input,u is the transfer function of the output; g13A transfer function with V3 as input and U as output; g14A transfer function with V4 as input and U as output; g21Using V1 as input, uTA transfer function that is an output; g22Using V2 as input, uTA transfer function that is an output; g23Using V3 as input, uTA transfer function that is an output; g24Using V4 as input, uTIs the transfer function of the output.
And fourthly, aiming at the characteristics of large inertia, large delay and uncertain model of a main steam temperature control system of the thermal power plant, complexity, nonlinearity, non-minimum phase and the like of a hydroelectric generating set system, the conventional PID control is difficult to obtain a good control effect. Fuzzy control is introduced, the defect that the traditional PID parameters are fixed according to the deviation and the size of the deviation change value is overcome, and the control effect is more consistent with the real control rule of the controlled object.
The controlled objects studied are independent and not coupled with each other, so the energy cluster control system can be treated as two fuzzy systems with two inputs and single output, the input of the fuzzy controller is set as V1, V2, V3 and V4, and the output is U and UTThe fuzzy relationship of a Multiple Input Single Output (MISO) controller is defined as:
Figure GDA0001588421760000041
in the formula: l is the number of fuzzy rules; u is the output of the control system; dimension of R dimR ═ d1×d2×d3×d4×du，d1～d4Are each V1～V4The discourse domain quantization level of (a); duThe number of levels is quantized for the domain of the output u, so the output of the controlled quantity is represented as:
Figure GDA0001588421760000042
is provided with
Figure GDA0001588421760000043
Wherein the content of the first and second substances,
Figure GDA0001588421760000044
for two-dimensional fuzzy relation, only (d)1+d2+d3+d4)duAnd (4) each element. Under certain approximate conditions, available
Figure GDA00015884217600000412
Instead of a-operation representation, i.e.
Figure GDA0001588421760000045
Wherein the fuzzy relationship is defined as:
Figure GDA0001588421760000046
Figure GDA0001588421760000047
wherein k is 1,2,3, 4.
The above equation represents a relational matrix algorithm for the sub-controllers that make up the MIMO fuzzifier. The multiple input multiple output system MIMO is formed by the MISO multiple input single output system, and the output expression is as follows:
Figure GDA0001588421760000048
Figure GDA0001588421760000049
wherein the fuzzy relation is:
Figure GDA00015884217600000410
wherein k is 1,2,3,4, j is 1,2,3, 4.
According to the conclusion, the key of designing the unit load control is to determine the fuzzy relation R11，R21，R31，R41，R12，R22，R32，R42. After the fuzzy relation is determined, the output of the fuzzy controller can be obtained according to the seven expression and the eight expression. According to the conclusion, the key point of designing the unit set load control is to determine the fuzzy relation.
The method of determining the fuzzy relation will be described by taking R11 as an example. Herein R11 is the fuzzy relation of the input variable V1 (the deviation of the given load from the output load, i.e. the power deviation) to the controlled output U, as follows:
step 1: the power deviation variable V1(Pe) is divided into 7 levels { -3, -2, -1,0,1,2,3}, so that the power deviation domain V1 { -3, -2, -1,0,1,2,3 }. The 5 linguistic values a1, a2, A3, a4, a5 are taken, and PB, PS, O, NS, nb.ai (i ═ 1,2,3,4,5) are fuzzy sets on the domain of discourse V1, respectively.
Step 2: the power deviation variable V1(Pe) is fuzzified, the fuzzy set A is generally determined according to the actual condition of the control system and the expert experienceiThe membership degree of (A) is shown in Table 1, wherein the coefficients a 1-a 35 are values between 0 and 1.
Please refer to table 1: fuzzy set AiMembership function table
Figure GDA00015884217600000411
TABLE 1
Step 2: a fuzzy control rule is determined. When a power offset variable V1 is obtained (P e), U should be adjusted. The U is classified into 9 levels { -4, -3, -2, -1,0,1,2,3,4} according to the actual conditions of the control system and expert experience, so that the domain of discourse V1 { -4, -3, -2, -1,0,1,2,3,4 }. 5 language values B1, B2, B3, B4 and B5 are taken, and the meanings are PB, PS, O, NS and NB respectively. Bi (i ═ 1,2,3,4,5) is the fuzzy set on domain U. The membership functions are determined according to the actual conditions of the control system and expert experience, and the values of b 1-b 45 in the table are between 0 and 1 as shown in the table 2.
Please refer to table 2: fuzzy set BiMembership function table
Figure GDA0001588421760000051
TABLE 2
Determining a fuzzy control rule according to the experience and expert opinions of field operators as follows:
if V1＝NB,then U＝PB；or if V1＝NS,then U＝PS；or if V1＝O,then U＝O；
or if V1＝PS,then U＝NS；or if V1＝PB,then U＝NB.
please refer to table 3, which shows a fuzzy control rule table
Figure GDA0001588421760000052
TABLE 3
Multiple conditional statements from V1The fuzzy relation to U is:
R11＝A5×B1×A4×B2×A3×B3×A2×B4×A1×B5
by the above method, R is obtained21，R31，R41，R12，R22，R32，R42. After the fuzzy relation is determined, the output of the fuzzy controller can be obtained according to the seven expression and the eight expression. Thereby completing the design of the whole controller.
A new energy cluster coordination optimization control strategy is characterized in that wind, light, water and fire are combined to optimize and coordinate power generation, so that a section transmission power curve is as close as possible to a target slightly lower than a power transmission line, and the channel utilization rate is improved. Firstly, according to the difference between a power value slightly lower than the power transmission line and the predicted power of a fan and photovoltaic power generation at the next moment, processing according to the hydropower and installed capacity distribution strategy to obtain the given power values of a thermal power generating unit and a hydroelectric power generating unit; secondly, a difference value is made between the actual output values of the photovoltaic and the fan at the current moment and the value of the current moment predicted at the previous moment, and the difference value is introduced into the control system in a feedforward mode; thirdly, aiming at the characteristics of large inertia, large delay and uncertain model of a main steam temperature control system of the thermal power plant, complexity, nonlinearity, non-minimum phase and the like of a hydroelectric generating set system, fuzzy control is introduced; fourthly, according to the steps, a control model is established to seek a transfer function value in an optimal feedforward link so as to overcome the defect of large inertia of the regulation and education of hydroelectric power and fire power, so that the transmission power is as large as possible, and the channel utilization rate is improved.

Claims (4)

1. A new energy cluster coordination optimization control method is characterized by comprising the following steps:
respectively calculating a given power value WP of a hydroelectric generating set and a given power value WT of a thermal generating set according to a total power prediction value r and a target power value m of the fan and the photovoltaic at the next moment t;
calculating the difference value delta P between the actual output values of the photovoltaic and the fan at the current moment and the predicted output values of the photovoltaic and the fan at the moment at the previous momentiAnd according to said difference and transfer function Gr1(s) and Gr2(S) respectively obtaining the corresponding relation between the input quantity V1 of the hydroelectric generating set controller in the energy cluster control system and the actual power U of the hydroelectric generating set, and obtaining the input quantity V3 of the thermal generating set controller in the energy cluster control system and the actual power U of the thermal generating setTThe corresponding relationship of (a);
according to the input quantity V1 of the hydroelectric generating set controller, the derivative value V2 of the input quantity V1 of the hydroelectric generating set controller, the input quantity V3 of the thermal generating set controller and the derivative value V4 of the input quantity V3 of the thermal generating set controller, the actual generating power U of the hydroelectric generating set and the actual generating power U of the thermal generating set are used as input valuesTEstablishing a controlled object model as an output value;
establishing a fuzzy relation between the input quantity and the output quantity of the controller according to the controlled object model, and obtaining the actual power U of the hydroelectric generating set and the actual power U of the thermal generating set by using the fuzzy relationTAnd a transfer function Gr1(s) and Gr2(S)。
2. The method according to claim 1, wherein the calculating the given power value WP of the hydroelectric power unit and the given power value WT of the thermal power unit according to the total power prediction value r and the target power value m of the wind turbine and the photovoltaic power at the next time t respectively comprises:
setting 95% of the upper limit value of the section transmission power as a target power value m;
according to the formula
Figure FDA0002902182520000011
And formula
Figure FDA0002902182520000012
And calculating to obtain a given power value WP of the hydroelectric generating set and a given power value WT of the thermal generating set, wherein n1 and n2 are the installed capacity of water and the installed capacity of thermal power respectively.
3. The method according to claim 1, wherein the input V1 of the hydroelectric generating set controller corresponds to the actual generating power U of the hydroelectric generating set in a relationship V1＝(WP-U)-ΔPiGr1(S), the input quantity V3 of the thermal power unit controller and the actual power u of the thermal power unitTHas a corresponding relationship of V3＝(WT-uT)-ΔPiGr2(S)。
4. The method of claim 1, wherein the controlled object model is:
Figure FDA0002902182520000013
wherein G is11A transfer function with V1 as input and U as output; g12A transfer function with V2 as input and U as output; g13A transfer function with V3 as input and U as output; g14A transfer function with V4 as input and U as output; g21Using V1 as input, uTA transfer function that is an output; g22Using V2 as input, uTFor transmission of outputA transfer function; g23Using V3 as input, uTA transfer function that is an output; g24Using V4 as input, uTIs the transfer function of the output.
CN201711383775.1A 2017-12-20 2017-12-20 New energy cluster coordination optimization control method Active CN107968444B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711383775.1A CN107968444B (en) 2017-12-20 2017-12-20 New energy cluster coordination optimization control method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711383775.1A CN107968444B (en) 2017-12-20 2017-12-20 New energy cluster coordination optimization control method

Publications (2)

Publication Number Publication Date
CN107968444A CN107968444A (en) 2018-04-27
CN107968444B true CN107968444B (en) 2021-03-02

Family

ID=61995765

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711383775.1A Active CN107968444B (en) 2017-12-20 2017-12-20 New energy cluster coordination optimization control method

Country Status (1)

Country Link
CN (1) CN107968444B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114428456B (en) * 2020-10-29 2024-05-14 北京国电智深控制技术有限公司 Control method and device for thermal power generating unit control system

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103066626A (en) * 2012-10-31 2013-04-24 中国电力科学研究院 Multi-source coordinating day-ahead generation scheduling method
CN103825301A (en) * 2014-03-14 2014-05-28 云南电力试验研究院（集团）有限公司电力研究院 Modeling method of uniform interface for accessing distributed energy sources into active power distribution system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10732042B2 (en) * 2016-10-13 2020-08-04 California Institute Of Technology Geostationary earth orbit (GEO) earth multispectral mapper (GEMM)

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103066626A (en) * 2012-10-31 2013-04-24 中国电力科学研究院 Multi-source coordinating day-ahead generation scheduling method
CN103825301A (en) * 2014-03-14 2014-05-28 云南电力试验研究院（集团）有限公司电力研究院 Modeling method of uniform interface for accessing distributed energy sources into active power distribution system

Also Published As

Publication number Publication date
CN107968444A (en) 2018-04-27

Similar Documents

Publication Publication Date Title
Mohanty et al. Differential evolution algorithm based automatic generation control for interconnected power systems with non-linearity
Panda et al. Hybrid BFOA–PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems
Elsisi Optimal design of non‐fragile PID controller
Barisal et al. Improved PSO based automatic generation control of multi-source nonlinear power systems interconnected by AC/DC links
Zhou et al. Robust control for a class of uncertain nonlinear systems: adaptive fuzzy approach based on backstepping
Chaturvedi et al. Adaptive polar fuzzy logic based load frequency controller
CN110601222B (en) Fractional order load frequency control method based on Levy moth algorithm
CN105425612B (en) A kind of method for optimizing of Adaptive System of Water-Turbine Engine control parameter
CN107045289A (en) A kind of nonlinear neural network optimization PID control method of electric furnace temperature
CN107515598A (en) Fired power generating unit distributed and coordinated control system based on multi-parameter dynamic matrix control
CN103345153B (en) General purpose networked predicative fuzzy control method
CN107908106B (en) Double reheat power generation sets reheat steam temperature concentrates Prediction Control system from depression of order multiloop
CN115313403A (en) Real-time voltage regulation and control method based on deep reinforcement learning algorithm
CN113471989A (en) Intelligent micro-grid secondary frequency control method based on wolf optimization method
Ren et al. Feedforward feedback pitch control for wind turbine based on feedback linearization with sliding mode and fuzzy PID algorithm
CN107968444B (en) New energy cluster coordination optimization control method
CN113690926B (en) Method for optimizing control parameter setting of single-phase inverter by improving firework algorithm
Hu et al. Model predictive control for constrained systems with uncertain state‐delays
Wan et al. Optimization of AP1000 power control system setpoints using genetic algorithm
Cao et al. Load frequency regulation of multi-area power systems with communication delay via cascaded improved ADRC
Pandey Optimization of PID controller parameters for speed control of DC motor using firefly and fminsearch algorithms
Andic et al. A novel Sea Horse Optimizer based load frequency controller for two-area power system with PV and thermal units
Xie et al. The design and simulation of fuzzy PID parameter self-tuning controller
Tian et al. A self-tuning fuzzy controller for networked control system
CN113741169A (en) Control method and system of potentiostat

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