Maintenance schedule optimization method for electric power system including large-scale wind power

Patent metadata

Field	Value
Publication number	US-10037502-B2
Application number	US-201414648249-A
Country	US
Kind code	B2
Filing date	Apr 2, 2014
Priority date	May 3, 2013
Publication date	Jul 31, 2018
Grant date	Jul 31, 2018

Abstract

The present invention discloses a maintenance schedule optimization method for an electric power system including large-scale wind power to build objective function to make it get optimal results, then constrain the objective function, constraint conditions include: constrain according to maintenance natural environment; constrain according to maintenance time of wind power plant; constrain according to maintenance site of wind power plant; constrain according to system reliability at wind power plant; constrain according to maintenance continuity of generator unit. Under the above constraint conditions, the electric power system is modeled with heuristic algorithm according to the above objective function. Through modeling the electric power system including large-scale wind power, optimize maintenance time of wind turbines in electric power system through modeling under a certain constraint conditions to maintain the wind power equipment properly while ensuring maximum efficiency of wind power generation at the same time.

First claim

The invention claimed is:

1. A method of conducting maintenance to an electric power system with a large-scale wind power plant based on an optimized maintenance schedule that ensures maximum efficiency of wind power generation, the method consisting of the steps of:
building an objective function by using a computer processor;
modelling the electric power system with heuristic algorithm according to the objective function by using the computer processor and received input information on number of wind turbines, geographical distribution, conditions about manpower and material resources, and historical year round power output data of the wind power plant;
outputting the optimized maintenance schedule to an output device; and
conducting maintenance according to the optimized maintenance schedule;
wherein the objective function is:
C Km =C m −L m·max −R Rm −R Sm ,

minimum wind curtailment electric quantity of wind power in a common year is:

min
⁢

{

E
WMQ

|

min
⁢

{

1
12

⁢

∑

m
=
1

12

⁢

⁢

[

C
Km

-

1
12

⁢

∑

m
=
1

12

⁢

C
Km

]

2

}

}

;

wherein: C Km is spare capacity of the electric power system of the m month in a common year; C m is available generating capacity of the electric power system after deducting unit maintenance capacity of the m month in common year; L m·max is the maximum load of the electric power system on maximum load day of the m month in the common year; R Rm and R Sm refer to spinning reserve capacity and shutdown reserve capacity of the electric power system of the m month in common year; E WMQ is unused electric quantity of wind turbine maintenance;
in order to obtain optimal results, constrains conditions are imposed to the objective function, the constraint conditions comprise: constrain conditions according to maintenance natural environment; constrain according to maintenance time of the wind power plant; constrain conditions according to maintenance site of the wind power plant; constrain conditions according to system reliability at the wind power plant; constrain conditions according to maintenance continuity of generator units;
under the above constraint conditions, modelling the electric power system with heuristic algorithm according to the objective function;
wherein the constraint conditions on maintenance time for wind power plant are:

30
⁢

∑

m
=
0

11

⁢

⁢

n
Mim

=

N
WRi

⁢

D
Wi

;

wherein, the constraint on maintenance site of the wind power plant refers to:
n WRim ≤ n Mi ≤N Wim ;

the constraint conditions on reliability of the wind power system refers to:
R m ≥R min

the constraint conditions on maintenance continuity of generator units refers that the units, maintenance time of which exceeds one month should be maintained in next successive month;
in the above formula, “i” represents the wind power plant and N WRim is number of the units in the wind power plant “i” that have to be maintained in a common year; N Wim is count of machine of the wind power plant “i” of the m month in a common year; n Mi and n WRim refer to the constrained number of units arranged to be maintained and number of the actually maintained units of the wind power plant “i” in the m month; D Wi is the average days needed to maintain each generator unit of the wind power plant “i” of the m month in a common year; Rm is the spinning reserve capacity of the electric power system of the m month in a common year; Rmin is the minimum requirement for spinning reserve capacity of the electric power system in a common year; and
wherein the modeling steps are as follows:

step 1: dividing all wind turbines into N batches for maintenance according to the number of the wind turbines, geographical distribution and conditions about manpower and material resources at the wind power plant;
step 2: drawing a curve on output assurance rate of the wind power plant in each month all the year round according to output historical data all the year round of the wind power plant;
step 3: calculating generating capacity of the wind power plant in each month all the year round according to confidence level of wind power output of the wind power plant;
step 4: finding the month for minimum generating capacity and the month for second minimum genera

Assignees

• State Grid Corp China
• State Grid Gansu Electric Power Corp
• Gansu Electric Power Corporation Wind Power Tech Center
• State Grid Gansu Eletric Power Corp

Inventors

• Wang Ningbo
• Ma Ming
• Ma Yanhong
• Liu Guangtu
• Zhao Long
• Zhou Qiang
• Wang Dingmei
• Lu Liang
• Zhang Jianmei
• Lv Qingquan

Classifications

• F05B2270/20

  to optimise the performance of a machine · CPC title

• F03D80/50

  Maintenance or repair · CPC title

• Y02E10/72

  Wind turbines with rotation axis in wind direction · CPC title

• G06Q10/06312 Primary

  Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling · CPC title

• G06Q50/06

  Energy or water supply · CPC title