CN111884266A - Gas turbine intraday rolling unit combination optimization method - Google Patents

Gas turbine intraday rolling unit combination optimization method Download PDF

Info

Publication number
CN111884266A
CN111884266A CN202010633574.8A CN202010633574A CN111884266A CN 111884266 A CN111884266 A CN 111884266A CN 202010633574 A CN202010633574 A CN 202010633574A CN 111884266 A CN111884266 A CN 111884266A
Authority
CN
China
Prior art keywords
gas turbine
period
rolling unit
day
power
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.)
Pending
Application number
CN202010633574.8A
Other languages
Chinese (zh)
Inventor
周亦洲
卫志农
孙国强
臧海祥
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.)
Hohai University HHU
Original Assignee
Hohai University HHU
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 Hohai University HHU filed Critical Hohai University HHU
Priority to CN202010633574.8A priority Critical patent/CN111884266A/en
Publication of CN111884266A publication Critical patent/CN111884266A/en
Pending legal-status Critical Current

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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • 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
    • 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
    • H02J3/50—Controlling the sharing of the out-of-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]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

Abstract

The invention discloses a gas turbine intraday rolling unit combination optimization method, which comprises the following steps: predicting the renewable energy output after the current time period; establishing and solving a gas turbine rolling unit in-day combination model to obtain the gas turbine rolling unit in-day combination decision and output power at the current and later time periods; and repeating the steps until the optimization time period is finished. The invention considers the rolling adjustment of the daily scheduling to the combination decision of the gas turbine set, and can give full play to the flexibility of the gas turbine and the tracking capability of the gas turbine to the fluctuation of the renewable energy sources, thereby effectively coping with the severe fluctuation condition of the renewable energy sources.

Description

Gas turbine intraday rolling unit combination optimization method
Technical Field
The invention belongs to the field of power system scheduling, and particularly relates to a gas turbine unit combination optimization method.
Background
With the rapid development, economic globalization and accelerated industrialization of society, the energy demand is greatly increased. According to the energy demand of the current human society development, non-renewable energy sources such as fossil energy and the like are gradually difficult to support the sustainable development of the human society. The development of clean energy to gradually replace the traditional fossil energy becomes a way to be favorable. In recent years, renewable energy is widely connected to the grid. According to the prediction of International Energy Outlook published by the Energy information management agency in 2019, the renewable Energy power generation increment is at the first level (the average annual growth rate is up to 3.6%) in 2020 and 2050 years, and the renewable Energy power generation is used for replacing coal-fired power generation to become the primary power generation Energy in 2025 years.
However, the randomness and intermittency of renewable energy output presents a significant challenge to the safe and stable operation of power systems. The gas turbine is used as a quick response unit, and can well stabilize the output fluctuation of renewable energy. At present, the research on the combination of the gas turbine units generally refers to the traditional coal-fired unit, namely the combination scheduling of the gas turbine units before the day is considered. The scheduling strategy fixes the unit combination decision of the gas turbine in the day ahead, and considers that the unit combination decision remains unchanged in the day. However, the flexibility of the gas turbine is far greater than that of the traditional coal-fired unit, and the unit combination decision of fixing the gas turbine in scheduling in the day is difficult to fully exert the flexibility and the quick response capability of the gas turbine. With the increasing permeability of renewable energy sources, this strategy is not enough to cope with the severe fluctuations of renewable energy sources.
Disclosure of Invention
In order to solve the technical problems of the background technology, the invention provides a method for optimizing the combination of a rolling unit in a gas turbine day.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
a combined optimization method for a rolling unit in a gas turbine day comprises the following steps:
(1) predicting the renewable energy output after the current time period;
(2) establishing and solving a gas turbine rolling unit in-day combination model to obtain the gas turbine rolling unit in-day combination decision and output power at the current and later time periods;
(3) and (3) repeating the steps (1) to (2) until the optimization period is finished.
Further, in the step (1), a time series method, an artificial neural network or a support vector machine is adopted to predict the renewable energy output after the period c, wherein c is the current period.
Further, the specific process of step (2) is as follows:
(2-1) establishing an objective function of a rolling unit combination model in the day of the gas turbine:
Figure BDA0002566844200000021
in the above formula, the first and second carbon atoms are,
Figure BDA0002566844200000022
respectively, the start, stop, fixing and unit power generation costs of the gas turbine e; unit combined variable ue,c、ve,c、xe,cRespectively indicating whether the gas turbine e is started, stopped and operated at the current time interval, if so, setting 1, and otherwise, setting 0;
Figure BDA0002566844200000023
the output power of the gas turbine e in the current time period; unit combined variable ue,t、ve,t、xe,tRespectively indicating whether the gas turbine e is started, stopped and operated in the time period t;
Figure BDA0002566844200000024
output power of gas turbine e for time period t; t is an optimization time period;
(2-2) establishing constraint conditions of a gas turbine rolling unit in-day combination model:
a) gas turbine restraint:
xe,t-xe,t-1＝ue,t-ve,t
xe,τ≥ue,t
1-xe,τ≥ve,t
Figure BDA0002566844200000031
Figure BDA0002566844200000032
Figure BDA0002566844200000033
in the above formula, the first and second carbon atoms are,
Figure BDA0002566844200000034
maximum and minimum output power of the gas turbine e, respectively;
Figure BDA0002566844200000035
the maximum upward and downward climbing rates of the gas turbine e are respectively;
Figure BDA0002566844200000036
the output power of the gas turbine e for a period t-1; unit combined variable xe,t-1Indicating whether the gas turbine e works in the period of t-1, if so, setting 1, otherwise, setting 0; b) power flow constraint of the power distribution network:
Figure BDA0002566844200000037
Figure BDA0002566844200000038
Vj,t＝Vi,t-(Pij,trij+Qij,txij)/V0
in the above formula, the first and second carbon atoms are,
Figure BDA0002566844200000039
respectively, the active power output power and the reactive power output power of a node j in a period t, wherein,
Figure BDA00025668442000000310
comprising a node j gas turbineOutput power and renewable energy output of (2); pij,t、Qij,tRespectively the active power and the reactive power of the branch circuits i-j in the t period;
Figure BDA00025668442000000311
all branch sets with head end node j; pjk,t、Qjk,tRespectively the active power and the reactive power of the branch j-k in the time period t;
Figure BDA00025668442000000312
respectively the active load and the reactive load of a node j in the period t; vi,t、Vj,tThe voltage amplitudes of the nodes i and j in the period t are respectively; r isij、xijRespectively the resistance and reactance of the branch circuits i-j; v0Is a voltage reference value;
and (2-3) solving the gas turbine rolling unit combination model in the day by adopting modeling software to obtain the gas turbine unit combination decision and output power at the period c and later.
Further, only the unit combination decision and the output power in the period c are applied to the actual scheduling, and the unit combination decision and the output power in the period c after the actual scheduling need to be adjusted in real time according to the latest prediction information.
Further, the modeling software for solving the rolling unit combination model in the gas turbine day comprises CPLEX modeling software and GAMS modeling software.
Further, in step (3), the optimization time window is shifted backward by one period, i.e., c ═ c + 1; and repeatedly predicting the output of the renewable energy sources after the period c and solving the combined model of the rolling unit in the gas turbine day until the optimization period is finished.
Adopt the beneficial effect that above-mentioned technical scheme brought:
the method and the device roll and predict the output of the renewable energy sources in the scheduling in the day, continuously optimize the unit combination decision and the output power of the gas turbine, realize the real-time adjustment of the unit combination decision of the gas turbine in the day and the real-time tracking of the output of the renewable energy sources, and thus effectively cope with the severe fluctuation of the renewable energy sources.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic diagram of a power distribution network testing system of an IEEE33 node in an embodiment;
FIG. 3 is a schematic diagram of predicted output before the day of wind power generation, predicted output rolling in the day, and actual output data in the embodiment.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
The invention designs a gas turbine intraday rolling unit combination optimization method, as shown in figure 1.
An IEEE33 node power distribution network test system is adopted as an embodiment, and a schematic diagram is shown in figure 2. The renewable energy source unit considered is a wind turbine. The gas turbine GT1, the gas turbine GT2 and the wind turbine WT are respectively connected to the distribution network nodes 17, 32 and 21. The gas turbine parameters are shown in table 1. The scheduling period is 1 day and is divided into 24 periods. The wind power output is predicted by adopting a time series method, and the predicted output before the wind power day, the predicted output rolling in the day and the actual output are shown in fig. 3.
TABLE 1 gas turbine parameters
Figure BDA0002566844200000051
Solving a gas turbine day-ahead unit combination model and a day-in rolling unit combination model by using GAMS software to obtain a gas turbine unit combination decision xe,t(on state) is shown in table 2. It can be seen that in the intra-day rolling unit combined scheduling strategy, the start-up time period of the gas turbine GT1 is more, because intra-day prediction is more accurate, the scheduling strategy increases the start-up time period of the GT1 with high maximum power generation capacity to cope with the situation that the real-time wind power output is lower, i.e. to realize real-time tracking of the wind power output.
TABLE 2 gas turbine unit combination decision comparison
Figure BDA0002566844200000052
Figure BDA0002566844200000061
The total tangential load of the day-ahead unit combination model and the day-in rolling unit combination model is shown in table 3. It can be seen that in the unit combination scheduling strategy before the day, because the unit combination decision remains unchanged in the day, the fluctuation of the wind power output cannot be tracked in real time by the gas turbine, and when the actual wind power output is low, a severe load shedding situation is caused. And the combined dispatching strategy of the rolling unit in the day can well stabilize the fluctuation of wind power output by adjusting the combined decision of the gas turbine unit, thereby effectively avoiding the load shedding condition.
TABLE 3 comparison of objective function values for different methods
Day-ahead unit combination scheduling Combined scheduling of rolling unit in day
Load shedding amount/(MW) 5.708 0
The embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the scope of the present invention.

Claims (6)

1. A combined optimization method for a rolling unit in a gas turbine day is characterized by comprising the following steps:
(1) predicting the renewable energy output after the current time period;
(2) establishing and solving a gas turbine rolling unit in-day combination model to obtain the gas turbine rolling unit in-day combination decision and output power at the current and later time periods;
(3) and (3) repeating the steps (1) to (2) until the optimization period is finished.
2. The gas turbine intraday rolling unit combination optimization method according to claim 1, wherein in the step (1), the renewable energy output after the c period is predicted by using a time series method, an artificial neural network or a support vector machine, wherein c is the current period.
3. The gas turbine intraday rolling unit combination optimization method according to claim 2, wherein the specific process of the step (2) is as follows:
(2-1) establishing an objective function of a rolling unit combination model in the day of the gas turbine:
Figure FDA0002566844190000011
in the above formula, the first and second carbon atoms are,
Figure FDA0002566844190000012
respectively, the start, stop, fixing and unit power generation costs of the gas turbine e; unit combined variable ue,c、ve,c、xe,cRespectively indicating whether the gas turbine e is started, stopped and operated at the current time interval, if so, setting 1, and otherwise, setting 0;
Figure FDA0002566844190000013
the output power of the gas turbine e in the current time period; unit combined variable ue,t、ve,t、xe,tRespectively indicating whether the gas turbine e is started, stopped and operated in the time period t;
Figure FDA0002566844190000014
output power of gas turbine e for time period t; t is an optimization time period;
(2-2) establishing constraint conditions of a gas turbine rolling unit in-day combination model:
a) gas turbine restraint:
xe,t-xe,t-1＝ue,t-ve,t
xe,τ≥ue,t
1-xe,τ≥ve,t
Figure FDA0002566844190000021
Figure FDA0002566844190000022
Figure FDA0002566844190000023
in the above formula, the first and second carbon atoms are,
Figure FDA0002566844190000024
maximum and minimum output power of the gas turbine e, respectively;
Figure FDA0002566844190000025
the maximum upward and downward climbing rates of the gas turbine e are respectively;
Figure FDA0002566844190000026
the output power of the gas turbine e for a period t-1; unit combined variable xe,t-1Indicating whether the gas turbine e works in the period of t-1, if so, setting 1, otherwise, setting 0; b) power flow constraint of the power distribution network:
Figure FDA0002566844190000027
Figure FDA0002566844190000028
Vj,t＝Vi,t-(Pij,trij+Qij,txij)/V0
in the above formula, the first and second carbon atoms are,
Figure FDA0002566844190000029
respectively, the active power output power and the reactive power output power of a node j in a period t, wherein,
Figure FDA00025668441900000210
the output power and the renewable energy output of the gas turbine comprising the node j are included; pij,t、Qij,tRespectively the active power and the reactive power of the branch circuits i-j in the t period;
Figure FDA00025668441900000211
all branch sets with head end node j; pjk,t、Qjk,tRespectively the active power and the reactive power of the branch j-k in the time period t;
Figure FDA00025668441900000212
respectively the active load and the reactive load of a node j in the period t; vi,t、Vj,tThe voltage amplitudes of the nodes i and j in the period t are respectively; r isij、xijRespectively the resistance and reactance of the branch circuits i-j; v0Is a voltage reference value;
and (2-3) solving the gas turbine rolling unit combination model in the day by adopting modeling software to obtain the gas turbine unit combination decision and output power at the period c and later.
4. The gas turbine intraday rolling unit combination optimization method according to claim 3, wherein only the unit combination decision and output power of the period c are applied to actual scheduling, and the unit combination decision and output power of the period after c need to be adjusted in real time according to the latest prediction information.
5. The gas turbine intraday rolling unit combination optimization method of claim 3, wherein the modeling software for solving the gas turbine intraday rolling unit combination model includes CPLEX modeling software and GAMS modeling software.
6. The gas turbine intraday rolling train combination optimization method according to claim 2, wherein in step (3), the optimization time window is shifted backward by a time period, that is, c + 1; and repeatedly predicting the output of the renewable energy sources after the period c and solving the combined model of the rolling unit in the gas turbine day until the optimization period is finished.
CN202010633574.8A 2020-07-02 2020-07-02 Gas turbine intraday rolling unit combination optimization method Pending CN111884266A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010633574.8A CN111884266A (en) 2020-07-02 2020-07-02 Gas turbine intraday rolling unit combination optimization method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010633574.8A CN111884266A (en) 2020-07-02 2020-07-02 Gas turbine intraday rolling unit combination optimization method

Publications (1)

Publication Number Publication Date
CN111884266A true CN111884266A (en) 2020-11-03

Family

ID=73151625

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010633574.8A Pending CN111884266A (en) 2020-07-02 2020-07-02 Gas turbine intraday rolling unit combination optimization method

Country Status (1)

Country Link
CN (1) CN111884266A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114781866A (en) * 2022-04-21 2022-07-22 河海大学 Comprehensive energy system robust intraday rolling scheduling method based on data driving

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2434127A2 (en) * 2010-09-20 2012-03-28 General Electric Company Methods and systems for modeling turbine operation
CN104835080A (en) * 2015-04-30 2015-08-12 华南理工大学 Modeling method for micro-grid intraday scheduling plan mixed integer programming model
EP3026510A1 (en) * 2014-11-26 2016-06-01 General Electric Company Methods and systems for enhancing control of power plant generating units
CN106682808A (en) * 2016-09-20 2017-05-17 北京恒泰实达科技股份有限公司 Online rolling optimization scheduling model
CN110417006A (en) * 2019-07-24 2019-11-05 三峡大学 Consider the integrated energy system Multiple Time Scales energy dispatching method of multipotency collaboration optimization

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2434127A2 (en) * 2010-09-20 2012-03-28 General Electric Company Methods and systems for modeling turbine operation
EP3026510A1 (en) * 2014-11-26 2016-06-01 General Electric Company Methods and systems for enhancing control of power plant generating units
CN104835080A (en) * 2015-04-30 2015-08-12 华南理工大学 Modeling method for micro-grid intraday scheduling plan mixed integer programming model
CN106682808A (en) * 2016-09-20 2017-05-17 北京恒泰实达科技股份有限公司 Online rolling optimization scheduling model
CN110417006A (en) * 2019-07-24 2019-11-05 三峡大学 Consider the integrated energy system Multiple Time Scales energy dispatching method of multipotency collaboration optimization

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
梅建春等: "电―气互联综合能源系统多时间尺度动态优化调度", 《电力系统自动化》 *
肖浩等: "基于模型预测控制的微电网多时间尺度协调优化调度", 《电力系统自动化》 *
黄弦超等: "考虑机组灵活性的独立微网日前日内协调优化调度", 《电力自动化设备》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114781866A (en) * 2022-04-21 2022-07-22 河海大学 Comprehensive energy system robust intraday rolling scheduling method based on data driving
CN114781866B (en) * 2022-04-21 2023-10-31 河海大学 Robust intra-day rolling scheduling method for comprehensive energy system based on data driving

Similar Documents

Publication Publication Date Title
CN110071505B (en) Power transmission network extension and energy storage configuration combined planning method with large-scale wind power access
CN109508499B (en) Multi-period multi-scene distributed power supply optimal access position and capacity research method
CN107332234B (en) Active power distribution network multi-fault restoration method considering renewable energy source intermittency
CN103683326B (en) A kind of regional power grid wind-powered electricity generation multiple spot accesses the computational methods of best receiving ability
CN106786799B (en) Power stepped power generation plan optimization method for direct current connecting line
CN107944757A (en) Electric power interacted system regenerative resource digestion capability analysis and assessment method
CN107240933B (en) Wind-fire coordinated rolling scheduling method considering wind power characteristics
CN109767078B (en) Multi-type power supply maintenance arrangement method based on mixed integer programming
CN111985686B (en) Power distribution network distribution robust optimization scheduling method based on probability prediction
CN112186744B (en) Power supply recovery method suitable for power distribution network with distributed power supply and application
CN106447218A (en) Reliability evaluation method of pumped storage system based on multiple wind power plants
CN110783927B (en) Multi-time scale AC/DC power distribution network scheduling method and device
CN116402210A (en) Multi-objective optimization method, system, equipment and medium for comprehensive energy system
CN114123313B (en) Method for simulating new energy power system digestion by time sequence production
CN111884266A (en) Gas turbine intraday rolling unit combination optimization method
CN105956713A (en) New energy annual/monthly electric quantity plan making method
CN107453366B (en) UPFC-containing multi-target optimal power flow calculation method considering wind power decision risk
CN113489003B (en) Source network coordination planning method considering wind-light-water integrated complementary operation
CN105048491A (en) Multi-stage wind power accepted range calculating method based on unit combination and economic dispatching
CN111835005B (en) Method and device for determining power grid transmission capacity of new energy base and related equipment
CN110717694B (en) Energy storage configuration random decision method and device based on new energy consumption expected value
Cheng et al. A planning scenario clustering method based on monte-carlo simulation
CN113708371B (en) Method for calculating maximum transmission capacity of wind power plant output line
CN109447456A (en) Meter and the wind-electricity integration Power System Planning method of short term need response
Liu et al. Bi-level collaborative expansion planning for power grid and plants considering operational flexibility

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
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20201103