Coordination Between Short-Term and Real-Time Scheduling
Incorporating Wind Power
Kui Wang, Buhan Zhang, Jiajun Zhai, Wen Shao,
Xiaoshan Wu and Chengxiong Mao
State Key Laboratory of Advanced Electromagnetic Engineering and Technology
Huazhong University of Science and Technology, Wuhan, China
wangkui_
Keywords: short-term scheduling, real-term scheduling, coordination scheduling, wind power.
Abstract. Coordination strategies between short-term (e.g., weekly and daily scheduling ) and real-time scheduling in wind power integrated system are disscusd. To cope with the uncertainty of wind powe
r and load demands, weekly and daily rolling schedulings are applied. According to the latest updated prediction results of wind power and load demands, weekly rolling scheduling is applied to revi unit commitment and fuel allocation in remaining hours in a week. Daily rolling scheduling is applied to revi generation scheduling in remaining time in a day. A modified IEEE 118-bus system is applied to test the propod approach. Introduction
Generation scheduling are generally divided into long-term [1], mid-term [2], daily[3,4] and real-time [5] schedulings in time scales. The coordination among different time scales schedulings is very important, and effective coordination can ensure that the whole scheduling can be implemented smoothly. The coordination between long-term and short-term scheduling are discusd in references [6], and a predetermined minimum rerve energy was ud as the coordination index. If the rerve energy was less than the minimum value, the long-term scheduling would be adjusted. In reference [7], the daily unit commitment was implemented with daily energies constraints, obtained from the long-term scheduling.
However, there're few articles concerning the coordination between short-term and real-term scheduling, especially incorporating wind power. In weekly scheduling, the horizon of the study is one week with increments of an hour, mainly focusing on unit commitment and fuel allocation. The st
udy of the daily scheduling is one day with increments of 10min, mainly solving economic dispatch problem. Real-time scheduling is ud to adjust unit power in the nest 10min.The characteristics (e.g., randomness, volati-lity and unpredictable) of wind increa difficulty and uncertainty and lead to great difference between higher and lower time scales schedulings. To solve the above problems, we adopt rolling schedulings, including weekly and daily rolling schedulings. According to the latest updated prediction results of wind power and load demands, rolling schedulings are carried out to revi the results of the original schedulings. Multi-time scales generation schedulings
A. Weekly scheduling
Weekly scheduling mainly focus on unit commitment and fuel allocation in hours. The objective function consists of fuel cost )(⋅Gi F and start-up cost Git S .
∑∑==−−+=
week G
T t N i Git t i it Git Gi it week S u u P F
u F 11
)1(]
)1()([min (1)
where t is the index of time in weeks, and i is the index of thermal units. week T equals 168h in one
week. it u and Git P reprent the commitment state and the output power of unit i at time t respectively. 1=it u if unit i is running at time t el 0=it u .
Constraints of weekly scheduling are listed as follows. a) System power balance
t P P
P u Dt N j Wjt
N i Git it W江西习俗
G ∀=+∑∑== 1
1
(2)
where j and W N is the index and the number of wind farms respectively. Wjt P is the predicted output power of the wind farm j at time t , and Dt P is the predicted system load demands at time t . b) Thermal unit generation limits
t i P u P P u Gi it Git Gi it ∀∀≤≤ max min (3) where min Gi P and max Gi P are minimum and the maximum power of unit i . c) Thermal unit ramp rate limits
i u t Gi Git i d P P ξξ≤−≤−−)1( (4)
where i
u d /ξ reprent ramp down/up limits of unit i . d) Thermal unit minimum up/down time limits
0)()()1()1(≥−∗−−−on
i on t i it t i T T u u and 0)()()1()1(≥−∗−−−off i off t i t i it T T u u (5) where off on it T / reprent on/off time of unit i at time t , and off on i T / reprent minimum up/down time of unit i .
e) Weekly energy constraints
i
E T P
u E w
压岁钱英文
Gi w
T t Git it w Gi week
∀≤∆≤
∑= max 1
min
(6)
where w Gi E min and w
Gi E max reprent minimum and maximum weekly energy of unit i . w T ∆equals an hour. f) Up rerve constraints铜陵是几线城市
t
Dt N i Git Gi it
i u
it US P d P P
u u G
+∗≥−∑=%)]( , min[1象鼻龟
max
ξ (7)
g) Down rerve constraints
t Dt N i Gi Git
it i
d it DS P d P P u u G
+∗≥−∑=%)]( , min[1
min ξ
(8)卡纸手工制作大全
where ττDS US /reprent up/down spinning rerve requirements caud by wind power, and can be calculated by the cond model [3]. %d is spinning rerve requirements ratio of load demands. B. Daily scheduling
According to the unit commitment an fuel allocation results, obtained from weekly scheduling, daily s
cheduling is ud to deal with economic dispatch in one day with increments of 10min. The objective is to minimize the total generation costs of thermal units.
∑∑===
day G
T N i Gi Gi i
day P F u F 11)(min τ
ττ
τ (9)
where it i u u =τ, if t ∈τ. day T equals 144 in one day.
Daily scheduling should consider energy constraints obtained from weekly scheduling.
i E T P u E d
Gi d
T Gi i d Gi day
∀≤∆≤
∑= max 1
联想u盘启动min
τ
ττ (10)
where d Gi E min and d
Gi E max reprent minimum and maximum daily energy of unit i . d T ∆equals 10min. C. Real-time scheduling
According to daily scheduling, real-time scheduling is ud to adjust unit power in the nest 10min. The objective is to minimize the total adjusting costs of thermal units.
∑=∆+∆+=
G
N i Gi i Gi i Gi i i real P a P b P
a u F 1
2
])2[()(min τττ
ττ (11)
where i a and i b are consumption characteristic coefficients of unit i , and τGi P ∆ is the adjusting
power of unit i at time τ.
Similarly to weekly scheduling, daily and real-time schedulings must also satisfy a ries of constraints, such as system power balance, unit generation limits, unit ramp rate limits and up and down rerve constraints.
Coordination strategiess
Bad on the unit commitment states and fuel allocation results of weekly scheduling, daily scheduling mainly focus on economic dispatch in smaller increments (e.g., 10min). Real-time scheduling is applied to adjust unit power in next 10min bad on the results of daily scheduling. The daily energy minimum and maximum limits can be calculated as follows [7].
∑
=
=
week
T
t
Git
w
Gi
d
Gi
d
Gi
E
E
E
E
1
min
min
and
∑
=
=
week
T
t
Git
w
Gi
倒排计划
d
Gi
d
Gi
E
E
E
E
1
max
max
where
d
T
Gi
i
d
Gi
T
P
u
E
day
∆
=∑
=1
τ
τ
τ
(12)
The wind power and load prediction is more precily in shorter term than longer term, so it's necessary to revi the results of longer term scheduling. According to the latest updated prediction results of wind power and load demands, weekly rolling scheduling is applied to revi unit commitment and fuel allocation in remaining hours in a week. Similarly, daily rolling scheduling is applied to revi generation scheduling of remaining time in a day.
Solution methods
The weekly scheduling is divided into external subproblem deciding unit commitment states, and inner subproblem solving economic dispatch. External subproblem is solved by discrete particle swa
rm optimization algorithm, and inner subproblem is solved by Lagrangian multiplier method bad on the equal consumption increment principle. Daily and real-time scheduling can be solved by improved continuous particle swarm optimization effectively.
Ca study
A modified IEEE 118-bus system [1] is applied to test the propod model. Thermal units on 36, 69 and 77 bus are replaced by three wind farms with the capacity of 250MW, 180MW and 100MW repectively. Hourly wind power of the first week in winter is depicted as Fig. 1, and 10min wind power in the first day of the above mentioned week is depicted as Fig. 2.
one week farms in one day
The planned output energy of thermal units in the first week in the winter is shown in Fig. 3. It can be en that the lower unit generation cost is, the bigger unit planned energy will be. The planned output energy of unit 27 and 28, who generation costs are lower, are 38831.19MWh and 37286.47MWh. Bad on the results of weekly scheduling, the planned output energy of thermal unit in the first day is shown in Fig. 4.
Short-term load and wind power prediction results at some 10min, ud in daily scheduling are 3222.6732MW and 205.349MW respectively in a 10min. And ultra short-term prediction results of them, ud in real-time scheduling, are 3500MW and 180MW respectively in the above mentioned interval. The adjusting power of thermal units is shown in Fig. 5. It can be en that the lower unit adjusting cost is, the bigger unit adjusting power will be. Due to ramp limits, units 5, 10, 11, 20, 21, week. Fig. 4. Units output energy in a day. Fig. 5. Units adjusting power in
real-time. Conclusions
Weekly scheduling mainly focus on unit commitment and fuel allocation, and daily scheduling can
be treated as economic dispatch problem bad on the results of weekly scheduling. At last, real-time scheduling is applied to adjust output power of thermal units. In order to handle the future uncertainty, weekly and daily schedulings are implemented at regular intervals bad on the lastest updated load demands and wind power. The ca study shows the effectiveness of the coordination strategies of short-term and real-time scheduling. Acknowledgements
This work was supported by National High Technology Rearch and Development Program of China(863 Program) (2011AA05A101), National Basic Rearch Program of China (2009CB219702)
and National Natural Science Foundation of China (50837003). References
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Renewable and Sustainable Energy II
10.4028/www.scientific/AMR.512-515
Coordination between Short-Term and Real-Time Scheduling Incorporating Wind Power
10.4028/www.scientific/AMR.512-515.700
