CN108270244B - Method for scheduling new energy power system in multiple control domain operation modes - Google Patents
Method for scheduling new energy power system in multiple control domain operation modes Download PDFInfo
- Publication number
- CN108270244B CN108270244B CN201810076500.1A CN201810076500A CN108270244B CN 108270244 B CN108270244 B CN 108270244B CN 201810076500 A CN201810076500 A CN 201810076500A CN 108270244 B CN108270244 B CN 108270244B
- Authority
- CN
- China
- Prior art keywords
- energy
- domain
- output
- power system
- scheduling
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000004146 energy storage Methods 0.000 claims description 25
- 238000005338 heat storage Methods 0.000 claims description 24
- 239000011159 matrix material Substances 0.000 claims description 21
- 230000002159 abnormal effect Effects 0.000 claims description 12
- 230000017525 heat dissipation Effects 0.000 claims description 9
- 230000005611 electricity Effects 0.000 claims description 8
- 230000032683 aging Effects 0.000 claims description 6
- 239000002184 metal Substances 0.000 claims description 6
- 238000011160 research Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000005265 energy consumption Methods 0.000 claims description 3
- 239000012535 impurity Substances 0.000 claims description 3
- 108091026890 Coding region Proteins 0.000 claims description 2
- 230000001105 regulatory effect Effects 0.000 claims 1
- 238000010248 power generation Methods 0.000 abstract description 5
- 230000033228 biological regulation Effects 0.000 abstract description 4
- 238000005457 optimization Methods 0.000 description 4
- 241000197727 Euscorpius alpha Species 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012356 Product development Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a scheduling method of a new energy power system in multiple regulation and control domain operation modes, and belongs to the technical field of renewable energy power generation and new energy power grids. The method combines the operation state information of each operation domain of the power system, and comprehensively considers the effective demand and the energy scheduling feasibility of each operation domain, thereby completing the scheduling. The scheme realizes the multi-source cooperation of the multi-operation domain information of the electric power system, can adaptively select the optimal unit combination, and improves the operation stability of the electric power system under the multi-operation domain.
Description
Technical Field
The invention belongs to the technical field of renewable energy power generation and new energy power grids, and particularly relates to a scheduling method of a new energy power system in a multi-source multi-regulation-control-domain operation mode.
Background
Due to the diversification of renewable energy power generation and new energy forms and the rapid development of the trend of large-scale access to the power grid, the access of multiple energy forms brings huge challenges to the traditional scheduling method and scheduling strategy of the power grid. The uncertainty of new energy power generation requires that different scheduling methods be adopted when the power grid is in different operation modes.
When the power grid operates in a normal regulation and control domain, the total load requirement is more than 50% of the maximum total output of all the hydropower and hydroelectric generating units in the power grid, and the starting point of the power grid dispatching method is to optimize the capability of tracking the load and the new energy generating output fluctuation by the total output of the power supply. According to the requirements of a power system on the frequency, the waveform, the voltage and the change amplitude of the power grid, the traditional scheduling method only sorts the adjustment capacity and the adjustment speed of each unit in all hydropower and thermal power units with adjustment capacity according to the load and frequency change quantity, and then adjusts the load and the frequency of the system according to the sequence. However, the traditional scheduling strategy belongs to the multi-objective optimization scheduling problem of the power system. And (3) focusing on solving an analysis space of the multi-target optimization scheduling, and then determining a final implementation scheme of the multi-target scheduling of the power system by using a fuzzy decision method or a good-bad solution distance method with a processing mode not fine enough. In addition, the method neglects the effective requirement of the operation domain and the feasibility of energy scheduling, and the scheduling under the handling of the sudden working condition is difficult to effectively provide a final scheduling scheme, so that the scheduling uncertainty of a dispatcher is increased, and the uncertainty is important in the scientific field of scheduling decision and must be solved.
In the operation interval of the system abnormal regulation and control domain, the total load requirement is lower than 50% of the maximum total output of all the hydroelectric and hydroelectric generating sets in the power grid. The combined output of the system unit needs to be adjusted greatly, strategies of nuclear abandonment, wind abandonment and light abandonment are optimized when the system load is reduced to a certain degree, a scheduling plan is made, and a command of abandoning the source is sent out. In the operation region of the system emergency control region, the combined output of the system units is in an unadjustable emergency neighborhood state, and the heat storage and the electricity storage are in a saturated stage. The scheduling plan is required to be appointed to adjust the unit combination or reasonably abandon the source. However, at present, no literature report and no product development are available for the unit scheduling method under the three states of the power system.
Disclosure of Invention
Aiming at the blank, the invention provides a scheduling method of a new energy power system in multiple control domain operation modes. The method combines the operation state information of each operation domain of the power system, and comprehensively considers the effective demand and the energy scheduling feasibility of each operation domain, thereby completing the scheduling. The scheme realizes the multi-source cooperation of the multi-operation domain information of the electric power system, can adaptively select the optimal unit combination, and improves the operation stability of the electric power system under the multi-operation domain.
The invention is realized by the following specific technical scheme:
the energy system of the invention comprises: the system comprises a conventional generator set of a power system, an energy storage battery system and a heat storage system. The method for setting the power system operation domain comprises the following steps: normal domain, abnormal domain, urgent domain. Coding the energy system, the coding sequence A of the conventional generator set 1 -A n Energy storage battery system code sequence a 1 -a m Heat storage system code sequence alpha 1 -α i 。
Step 1: and establishing an F function of the output model of the conventional unit, wherein the corresponding independent data are different in different coding sequences. Here, a general formula is given.
The data to be collected comprises: coefficient of heat dissipation of the element C k The operation time length t, the number N of conventional generator sets and the output power of the ith generator set at the moment t
The aging coefficient mu of the metal of the generator set rotor (stator).
Step 2: and (4) establishing an electricity storage output model, wherein the output function is B. Different coding sequences correspond to different independent data. Here, a general formula is given.
The data to be collected comprises: coefficient of heat dissipation of the element C k Energy consumption coefficient M of battery and energy storage efficiency
Operation time t and number of electricity storage units N k The output power of the ith energy storage battery at the time t
Energy density coefficient alpha of energy storage battery k Humidity of air
Metal aging coefficient gamma, and total volume A of the research area space.
And step 3: to code as alpha 1 For example, a heat storage output model is established, and the output function is H. Different coding sequences correspond to different independent data, and a general formula is given.
The data to be collected comprises: coefficient of heat dissipation of the element C k Time of operation t, air density ρ, air humidity
Heat dissipation coefficient beta, temperature rise delta T and heat storage efficiency gamma H The running time t, the total volume A of the space of the research area,Impurity rate η of heat storage device k Average impedance Z of transmission cable and coefficient of thermal effect upsilon of cable h 。
Further, step 1 is to encode as A 1 By way of example, the conventional generator set of (1),
further, step 2 is to encode as a 1 For the example of the energy storage battery system of (1),
further, step 3 is to encode the code as alpha 1 For example, the thermal storage system of (a),
and 4, step 4: constructing a discrimination matrix kappa, wherein elements in the matrix represent the instantaneous output variation trend of each unit of the energy system
B. The H function represents a general output formula of a conventional generator set, an energy storage battery system and a heat storage system of the power system, and for the generator sets with different codes, the output condition of the generator set can be obtained only by taking actual data of the generator set. The total coding condition L of the energy system can be represented in a matrix form:
because the output functions F, B and H which are independently corresponding to each unit are all functions related to time, partial derivatives of the energy system L matrix to the time are made, and the partial derivatives are defined as zeta.
And substituting actual time (moment) to obtain the instantaneous output variation trend (slope) of each unit of the energy system, wherein the matrix is defined as a discrimination matrix and is represented by k:
and 5: method for scheduling operation domains of power system
For the division of each operation domain of the power system, the period which can ensure the stability of the power grid frequency only by the output adjustment of the water and thermal power generating units is initially defined as a normal domain, the dispatching and control process can be met by the output adjustment of the units in the normal domain, and the output adjustment capacity of the default unit is 50-100%; the output pressure of a conventional unit is minimized, and the unit enters an abnormal domain when the system frequency requirement cannot be met, and an energy storage device is used for scheduling and adjusting in the region; the load peak-valley time period is an emergency area, and nuclear abandoning, wind abandoning and light abandoning measures are considered in the area.
Step 5.1 scheduling method for normal operation domain of power system
For the sake of concisely characterizing the content of the present invention, the present embodiment sets the energy system as follows: conventional generator sets 4 groups, A 1 、A 2 、A 3 、A 4 (ii) a Energy storage battery system 4 groups, i.e. a 1 、a 2 、a 3 、a 4 (ii) a Heat storage systems of 3 groups, i.e. alpha 1 ，α 2 ，α 3 . The load of the power system is increased rapidly in a normal operation domain, and the power system has a large demand on the energy system. The unit scheduling satisfies the following relations:
|X * | max ＞0→1 *
|X * | max＞min＞0 ＞0→2 * (X＝A 1 * ...A n * ...a 1 * ...a m * ...α 1 * ...α i * )
|X * |＝0→0
1 * indicating a first priority of use, 2 * Indicating a second priority of use and 0 indicating no consideration of scheduling.
The decision matrix κ in this case is represented as follows:
if the element | a in the matrix k is determined 3 * |、|α 1 * If | is 0, the call unit under the normal operation domain is:
| A 1 * | a 1 * | |
| A 2 * | a 2 * | α 2 * |
| A 3 * | α 3 * | |
| A 4 * | a 4 * |
step 5.2 scheduling method for abnormal operation domain of power system
For the sake of concisely characterizing the content of the present invention, the present embodiment sets the energy system as follows: conventional generator sets 4 groups, A 1 、A 2 、A 3 、A 4 (ii) a Energy storage battery system 4 groups, i.e. a 1 、a 2 、a 3 、a 4 (ii) a Heat storage systems of 3 groups, i.e. alpha 1 ，α 2 ，α 3 . Electric power systemWhen the system is in an abnormal operation domain, the power system has a trade-off on the requirement of the energy system. The unit scheduling satisfies the following relations:
X * max ＞0→0
X * max＞min＞0 ＞0→0(X＝a 1 * ...a m * ...α 1 * ...α i * )
X * max＞min＞0 ＜0→1(X＝a 1 * ...a m * ...α 1 * ...α i * )
X * ＝0→1
in the scheduling scheme, 1 represents reservation, and 0 represents no consideration for use, so that the scheduling scheme only discards or reserves the electricity storage and heat storage systems.
The decision matrix κ in this case is represented as follows:
if the element a in the matrix k is determined 2 * 、a 3 * 、a 4 * The magnitudes are all positive; a is 1 * 0; and alpha is 1 * 、α 2 * The magnitude being positive, α 3 * <0, the unit running under the abnormal running domain is as follows:
| A 1 * | a 1 * | |
| A 2 * | ||
| A 3 * | α 3 * | |
| A 4 * |
step 5.3 electric power system emergency operation domain scheduling method
For the sake of concisely characterizing the content of the present invention, the present embodiment sets the energy system as follows: conventional generator sets 4 groups, A 1 、A 2 、A 3 、A 4 (ii) a Energy storage battery system 4 groups, i.e. a 1 、a 2 、a 3 、a 4 (ii) a Heat storage systems of 3 groups, i.e. alpha 1 ，α 2 ，α 3 . In an emergency operation domain, the power system needs to reduce the output of the energy system as much as possible. The unit scheduling satisfies the following relations:
in the scheduling scheme, 1 represents reservation, and 0 represents no consideration for use, so that the scheduling scheme only aims at the conventional units of the power system, and electricity storage and heat storage are completely abandoned.
The decision matrix κ in this case is represented as follows:
if the element in the matrix k is determined
The magnitude is less than 1; while
Then, in this example, the unit operating in the emergency operation domain is:
| A 1 * | ||
| A 2 * | ||
| A 3 * | ||
advantageous effects
The invention provides a scheduling method of a new energy power system under various regulation and control domain operation modes. The scheme combines the operation state information of each operation domain of the power system, and comprehensively considers the effective requirements and the energy scheduling feasibility of each operation domain, so that the scheduling of the new energy power system in various control domain operation modes can be completed quickly, concisely and effectively. The scheme realizes the multi-source cooperation of the multi-operation domain information of the electric power system, can adaptively select the optimal unit combination, improves the operation stability of the electric power system under the multi-operation domain, and has extremely high environmental adaptability and working energy efficiency.
Drawings
Fig. 1 is a flowchart of a control method of a new energy power system in a multi-source multi-regulation domain operation mode according to the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
An adjustable energy system group in a certain province and a certain region consists of 4 groups of conventional thermal power generating sets, 4 groups of energy storage battery sets and 4 groups of heat storage sets, and output set combination is judged under the abnormal operation region of the power system in the region.
The multi-source multi-domain scheduling method comprises the following steps:
step 1: collecting 4 groups of related parameters of a conventional unit; collecting 4 groups of related parameters of the energy storage battery unit; collecting 4 groups of heat storage unit related parameters;
in a conventional unit, the data to be collected includes: : heat dissipation coefficient of element C k Time length t of operation, number N of conventional generator sets, and output power of ith generator set at time t
The aging coefficient mu of the metal of the generator set rotor (stator).
TABLE 1 conventional Unit specific parameters
In the energy storage battery unit, need the data acquisition to include: coefficient of heat dissipation of the element C k The energy consumption coefficient M of the battery,
Efficiency of energy storage
Operation time t and number of electricity storage units N k The output power of the ith energy storage battery at the time t
Energy density coefficient alpha of energy storage battery k Humidity of air
Metal aging coefficient gamma, total volume of the study area space A.
TABLE 2 specific parameters of the energy storage battery set
In the heat-retaining unit, need the data acquisition to include: coefficient of heat dissipation of the element C k Time of operation t, air density ρ, air humidity
Heat dissipation coefficient beta, temperature rise delta T and heat storage efficiency gamma H Researching total volume A of area space and impurity rate eta of heat storage device k Average impedance Z of transmission cable and coefficient of thermal effect upsilon of cable h 。
TABLE 3 specific parameters of the heat storage unit
The general formula of the F function representing the conventional unit output model is shown as the following formula:
the general formula of the function B of the output model of the characteristic power storage unit is shown as the following formula:
the general formula of the H function representing the output model of the heat storage unit is shown as the following formula:
step 2: calculating the region discrimination matrix k
In this embodiment, with reference to step 1, the assignment of the decision matrix κ calculated according to the collected parameters is as follows:
and step 3: scheduling method for abnormal operation domain of power system
From step 5.2 of the present invention, it can be seen that the power system is in the abnormal operation domain, and the power system has a trade-off for the demand of the energy system. The unit scheduling satisfies the following relations:
X * max ＞0→0
X * max＞min＞0 ＞0→0(X＝a 1 * ...a m * ...α 1 * ...α i * )
X * max＞min＞0 ＜0→1(X＝a 1 * ...a m * ...α 1 * ...α i * )
X * ＝0→1
and (3) by combining assignment of the elements of the discrimination matrix kappa under the case, judging the adjustable unit under the abnormal domain as follows:
at present, no literature and products develop the unit scheduling strategy research under three states of the power system, and the performance of the method is compared with the performance of the existing power system optimization scheduling scheme. Since the optimal scheduling scheme of the power system usually needs to consider a plurality of targets, such as indexes of power generation cost, system grid loss, static voltage stability index, voltage deviation and the like, a great deal of work is also done in this respect by the predecessors, but almost all research is only limited to solving multi-target optimal scheduling, and the important problem of how to select the final scheduling scheme is not deeply researched. The scheduling of the new energy power system under various control domain operation modes is complicated in operation condition, the assessment indexes of a dispatcher are diversified, on the premise of processing the coordination problem of multiple sources of information of multiple operation domains of the power system, a multi-objective optimization algorithm is too complex and tedious, and long in resolving time, so that the final scheduling scheme is not beneficial to rapidly determining, the only power system scheduling scheme is far from meeting the requirements, a novel scheduling scheme is explored, the problem that the final scheduling solution is scientifically selected to solve the scheduling of the new energy power system under various control domain operation modes is solved, and the method has important practical significance.
It can be seen that the advantages of the present invention are: the scheme combines the operation state information of each operation domain of the new energy power system, comprehensively considers the effective demand and the energy scheduling feasibility of each operation domain, and realizes the multi-source cooperation of the multi-operation domain information of the power system. The optimal unit combination can be selected in a self-adaptive mode, so that a final scheduling scheme is determined, and the operation stability of the power system in multiple operation domains is improved.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.
Claims (1)
1. A scheduling method of a new energy power system in a multi-source multi-regulation domain operation mode is characterized in that the method combines operation state information of each operation domain of the power system, considers effective requirements and energy scheduling feasibility of each operation domain, and the energy system comprises the following steps: the system comprises a conventional generator set of a power system, an energy storage battery system and a heat storage system; the method for setting the power system operation domain comprises the following steps: normal domain, abnormal domain, emergency domain; coding the energy system, the coding sequence A of the conventional generator set 1 -A n Energy storage battery system code sequence a 1 -a m Heat storage system code sequence alpha 1 -α i ；
The method comprises the following steps:
step 1: establishing a F function of a conventional unit output model, wherein different coding sequences correspond to different independent data, and the data needing to be acquired comprises the following steps: coefficient of heat dissipation of the element C k Time length t of operation, number N of conventional generator sets, and output power of ith generator set at time t
The metal aging coefficient mu of the rotor/stator of the generator set,
step 2: establishing an electricity storage output model, wherein an output function is B, and data to be acquired comprises: coefficient of heat dissipation of the element C k Energy consumption coefficient M of battery, energy storage efficiency operation time length t, and number of electricity storage units N k The output power of the ith energy storage battery at the time t
Energy density coefficient alpha of energy storage battery k Humidity of air
The metal aging coefficient gamma, the total volume of the research area space A,
and step 3: establishing a heat storage output model, wherein an output function is H, and data to be acquired comprises the following steps: coefficient of heat dissipation of the element C k Time of operation t, air density ρ, air humidity
Heat dissipation coefficient beta, temperature rise delta T, heat storage efficiency gamma H Studying the total volume A of the area space and the impurity rate eta of the heat storage device k Average impedance Z of transmission cable and coefficient of thermal effect upsilon of cable h ，
And 4, step 4: constructing a discrimination matrix kappa, wherein elements in the matrix represent the instantaneous output variation trend of each unit of the energy system, a F, B, H function represents a conventional generator set of the power system, an energy storage battery system and a heat storage system output general formula, for the units with different codes, the actual data brought into the units can obtain the output condition of the units, and the total coding condition L of the energy system is represented by a matrix form:
because the output functions F, B and H which are independently corresponding to each unit are functions related to time, partial derivatives of the energy system L matrix to the time are made, and the partial derivatives are defined as zeta:
and substituting actual time to obtain the instantaneous output variation trend of each unit of the energy system, wherein the matrix is defined as a discrimination matrix and is represented by k:
and 5: scheduling methods of each operation domain of the power system;
in the step 5, the time period which can ensure the frequency stability of the power grid only by regulating the output of the thermal power generating unit is initially defined as a normal domain for dividing each operation domain of the power system; the output pressure of a conventional unit is minimized, and the unit enters an abnormal domain when the system frequency requirement cannot be met, and an energy storage device is used for scheduling and adjusting in the region; the load peak-valley period is an emergency area, and nuclear, wind and light abandoning measures are considered in the area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810076500.1A CN108270244B (en) | 2018-01-26 | 2018-01-26 | Method for scheduling new energy power system in multiple control domain operation modes |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810076500.1A CN108270244B (en) | 2018-01-26 | 2018-01-26 | Method for scheduling new energy power system in multiple control domain operation modes |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108270244A CN108270244A (en) | 2018-07-10 |
| CN108270244B true CN108270244B (en) | 2022-08-09 |
Family
ID=62776857
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810076500.1A Active CN108270244B (en) | 2018-01-26 | 2018-01-26 | Method for scheduling new energy power system in multiple control domain operation modes |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108270244B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110601190B (en) * | 2019-09-23 | 2023-06-02 | 国网辽宁省电力有限公司鞍山供电公司 | Regional power grid operation domain division method |
| CN114662798B (en) * | 2022-05-17 | 2022-09-06 | 浙江大学 | Scheduling method and device based on power grid economic operation domain and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103248056A (en) * | 2013-05-25 | 2013-08-14 | 南京南瑞集团公司 | Reactive voltage emergency control method in concentrated grid connecting area of wind power plant |
| CN105896616A (en) * | 2016-06-02 | 2016-08-24 | 清华大学 | Thermal power generation unit real-time dispatching requirement determination method considering maximum new energy utilization |
| CN107134810A (en) * | 2017-06-09 | 2017-09-05 | 燕山大学 | A kind of micro- energy net energy-storage system of self distributes method for solving rationally |
| CN107154644A (en) * | 2017-05-24 | 2017-09-12 | 国网辽宁省电力有限公司 | A kind of new energy adjusted based on generation frequency limit value is dissolved method |
| CN107204632A (en) * | 2017-07-15 | 2017-09-26 | 东北电力大学 | A kind of flexible load Optimization Scheduling for lifting wind electricity digestion |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9563215B2 (en) * | 2012-07-14 | 2017-02-07 | Causam Energy, Inc. | Method and apparatus for actively managing electric power supply for an electric power grid |
| US20140257584A1 (en) * | 2013-03-07 | 2014-09-11 | Kabushiki Kaisha Toshiba | Energy management system, energy management method, medium, and server |
-
2018
- 2018-01-26 CN CN201810076500.1A patent/CN108270244B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103248056A (en) * | 2013-05-25 | 2013-08-14 | 南京南瑞集团公司 | Reactive voltage emergency control method in concentrated grid connecting area of wind power plant |
| CN105896616A (en) * | 2016-06-02 | 2016-08-24 | 清华大学 | Thermal power generation unit real-time dispatching requirement determination method considering maximum new energy utilization |
| CN107154644A (en) * | 2017-05-24 | 2017-09-12 | 国网辽宁省电力有限公司 | A kind of new energy adjusted based on generation frequency limit value is dissolved method |
| CN107134810A (en) * | 2017-06-09 | 2017-09-05 | 燕山大学 | A kind of micro- energy net energy-storage system of self distributes method for solving rationally |
| CN107204632A (en) * | 2017-07-15 | 2017-09-26 | 东北电力大学 | A kind of flexible load Optimization Scheduling for lifting wind electricity digestion |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108270244A (en) | 2018-07-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109256799B (en) | New energy power system optimal scheduling method based on sample entropy | |
| CN101576055B (en) | Generation control method for wind electric power generation fields group capable of restraining 'crowding out effect' | |
| CN108695857B (en) | Automatic voltage control method, device and system for wind power plant | |
| CN112583017B (en) | Hybrid micro-grid energy distribution method and system considering energy storage operation constraint | |
| CN115640982B (en) | Pumped storage priority regulation-based day-ahead optimal scheduling method for multi-energy complementary system | |
| CN110957717A (en) | Multi-target day-ahead optimal scheduling method for multi-power-supply power system | |
| CN109474007B (en) | Energy internet scheduling method based on big data cloud technology | |
| CN112769156B (en) | Source network load storage coordinated operation method considering large-scale offshore wind power grid connection | |
| CN108270244B (en) | Method for scheduling new energy power system in multiple control domain operation modes | |
| CN108182487B (en) | Family energy data optimization method based on particle swarm optimization and Bendel decomposition | |
| Dzobo | Virtual power plant energy optimisation in smart grids | |
| CN112583051B (en) | Optimized scheduling model construction method of variable-speed pumped storage unit in regional power grid | |
| CN116402210A (en) | Multi-objective optimization method, system, equipment and medium for comprehensive energy system | |
| CN108448655B (en) | Passive power grid wide-area power generation control method and system | |
| CN104701870A (en) | Power system energy accumulation optimizing method | |
| CN105207255A (en) | Electric system peak regulation calculation method suitable for wind power output | |
| CN114676921A (en) | Method for calculating wind power receptibility of system by considering source load storage coordination optimization | |
| CN107528352A (en) | A kind of power distribution network active optimization method based on regenerative resource high permeability | |
| CN114723278A (en) | Community microgrid scheduling method and system considering photovoltaic energy storage | |
| CN111082442B (en) | Energy storage capacity optimal configuration method based on improved FPA | |
| CN113346487A (en) | Source-load matching method, system and equipment based on multi-source complementation | |
| Qian et al. | Optimization of Virtual Power Plant Considering comprehensive Energy Efficiency Planning Strategy | |
| CN107846044B (en) | Multi-source coordination scheduling method for improving power grid regulation abundance | |
| CN117526299B (en) | Active and reactive power coordination control system and method for micro-grid | |
| CN109301868B (en) | Intelligent dormancy control system and method for high-power modularized wind power converter |
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 |