# A Wind Power and Load Prediction Based Frequency Control Approach for Wind-Diesel-Battery Hybrid Power System

Authors: Chao Peng; Zhenzhen Zhang; Jia Wu
Journal: Mathematical Problems in Engineering
 (2015)

---

## Abstract

A frequency control approach based on wind power and load power prediction information is proposed for wind-diesel-battery hybrid power system (WDBHPS). To maintain the frequency stability by wind power and diesel generation as much as possible, a fuzzy control theory based wind and diesel power control module is designed according to wind power and load prediction information. To compensate frequency fluctuation in real time and enhance system disturbance rejection ability, a battery energy storage system real-time control module is designed based on ADRC (active disturbance rejection control). The simulation experiment results demonstrate that the proposed approach has a better disturbance rejection ability and frequency control performance compared with the traditional droop control approach.


---

## Body

## 1. Introduction

Remote Area Power Supply (RAPS) systems which are used to supply power for rural and remote areas, including island, could not rely on the main grid supply system. The power supply of such areas is always provided with diesel power generation, due to its merits of low installation cost, high reliability, and simple operation. The major drawbacks of this type of power generation is finite fuel, low utilization efficiency, high transportation cost, environmental pollution, and so on [1].Recently, more and more RAPS systems use renewable power generation, such as wind power, to overcome the draws of diesel power generation. However, the active power output of wind power generation is random and fluctuant, which will seriously affect the stability of RAPS [2]. To solve this problem, wind-diesel-battery hybrid power system (WDBHPS) is introduced, which have become a popular power generation system used in RAPS. It could utilize wind power generation to reduce the use of diesel generators and environmental pollution and utilize battery energy storage system (BESS) to compensate the wind power fluctuation.Frequency stability reflects the active power balance between the supply and demand, which is important factor in operation security and stability of the wind-diesel-battery hybrid power system. The fluctuation of wind and load would cause system frequency deviation and fluctuation. Thus, frequency control problem is main problem which hinder the development and utilization of wind-diesel-storage hybrid system in practice.Currently, more and more scholars focus on the research on the frequency control of wind-diesel hybrid power system. Droop control based frequency control approach is the mostly used in wind-diesel hybrid power system in practice [3]. The traditional droop control use a fixed droop coefficient to increase or decrease the active power output of power sources when load demand changes. It could not avoid large deviation of frequency or voltage amplitude for its fixed droop coefficient. Therefore, many scholars studied the enhanced droop control approach. To reduce magnitude of frequency change, an adaptive droop control approach which could adjust the droop coefficient according to the change of active power and load was proposed in [4]. To eliminate the static error of frequency, an integral controller is introduced into frequency droop control in [5]. But its parameter selection is troublesome. In [6], a centralized frequency control strategy is presented, which calculates allocated active power of wind power turbine, diesel generator, and discharging/charging power of BESS according to the deviation of frequency. In [7], a proportional-integral (PI) control based frequency control approach was presented to maintain the balance between power generation and load demand. In [8], a fuzzy logic based supervisory frequency control approach was proposed to maintain the frequency stability of wind-diesel hybrid power system.Most above frequency control approaches only use BESS to compensate the frequency deviation caused by wind power and load fluctuation and maintain power-load balance by diesel generator. Very few of them consider regulating the active power output of wind turbine to reduce frequency fluctuation. Meanwhile, they did not utilize prediction information of wind power and load to stabilize WDBHPS’s frequency. The developments of wind power prediction technology and load prediction technology for microgrid, such as wavelet transformed based wind power prediction approach [9], grey model based wind power prediction model [10], artificial neural network based load prediction approach [11], and bilevel prediction strategy based load prediction approach [12], make their application become possible. If the prediction information could be taken into account in frequency control, the active power output of wind turbine and diesel generator could be regulated to reduce the frequency deviation caused by wind power and load fluctuation.In this paper, a wind power and load prediction based frequency control approach is proposed for WDBHPS. In this approach, a fuzzy control based wind and diesel power control module is used to reduce frequency deviation by wind turbine and diesel generator according to the wind power and load prediction information. To overcome the draws of traditional droop control, such as difficulty of droop coefficient adjusting, static error of frequency control, and poor disturbance rejection ability, an ADRC based real-time control module of BESS is used to compensate the real-time frequency fluctuation.


## 2. Wind Power and Load Prediction Based Frequency Control Approach

Wind-diesel-battery hybrid power system is shown in Figure1. As seen in Figure 1, this system consists of a wind turbine, diesel generator, BESS, AC/AC transformers, and DC/AC convert. The output power of wind turbine and diesel generator and charging/discharging power of BESS are controlled to keep power-load balance and frequency stable in the system.Figure 1
Wind-diesel-battery hybrid power system.The control scheme of the proposed control approach is shown in Figure2, where P
w
r
e
f and P
w are the reference of active power output and actual active power output of wind turbine, respectively, P
d
r
e
f and P
d are the reference of active power output and actual active power output of diesel generator, respectively, and P
b
r
e
f and P
b are the reference of charging/discharging power and actual charging/discharging power of BESS, respectively. P
L is the actual load of system, and Δ
f is the system frequency deviation.Figure 2
Control structure of wind power and load prediction based frequency control.As seen in Figure2, the proposed frequency control system mainly consists of two modules: wind-diesel power control module and BESS real-time control module. The wind-diesel power control module regulates the active power output of wind turbine and diesel generator according to wind power and load prediction information. It could regulate the active power output of wind turbine and diesel generator to track the predicted load as close as possible, so as to reduce the frequency deviation to be as small as possible. The wind-diesel power control module is designed based on fuzzy control, which could choose different rules based on wind power and load prediction information. An active disturbance rejection control (ADRC) based BESS real-time control module is designed to compensate the frequency fluctuation in real time, which could use the extended state observer to estimate the disturbance and compensate the real-time frequency fluctuation caused by wind power and load fluctuation.


## 3. Wind-Diesel Power Control Module

The scheme of wind-diesel power control module is shown in Figure3. It consists of three parts: interpolation module, power control, and correction module.Figure 3
The scheme of wind-diesel control module.


### 3.1. Interpolation Module

The interpolation module determines the predictive value of wind power and load between each two adjacent prediction periods for power control, when prediction period is unmatched with control period. The interpolation module utilizes linear interpolation method to calculate and smooth predictive value at control period. Assumes that the prediction period isT whose unit is s (seconds), the predictive value is y
0 at t
0, and the predictive value is y
1 at t
0
+
T. The linear interpolation formula is as follows:(1)
y
t
=
a
t
+
b
,where t
0
≤
t
≤
t
0
+
T. Replace t and y in (1) by values of (t
0
,
y
0) and (t
0
+
T
,
y
1)​​, and coefficients a and b can be obtained:(2)
a
=
y
1
-
y
0
T
,
b
=
T
+
t
0
y
0
-
t
0
y
1
T
.


### 3.2. Power Calculation Module

The design objective of power calculation module is to calculate reference power for wind turbine and diesel generation, which could keep the system power balance between generating power and demanding load under the premise of using wind energy as much as possible. Its control strategy is designed as follows:(1)
WhenP
L
_
p
r
e
<
P
w
_
p
r
e, that is, prediction value of load is less than the prediction value of wind power, the load fluctuation would be compensated by wind turbine alone and reference power of diesel generator could be zero. The control of the power value is shown as follows:(3)
P
w
_
ref
=
P
L
_
p
r
e
,
P
d
_
r
e
f
=
0
.
(2)
WhenP
L
_
p
r
e
<
P
w
_
p
r
e, that is, prediction value of load is equal to or greater than the prediction value of wind power, the reference power of wind power could be set to the prediction value of wind power to maximize the use of wind power and the frequency fluctuation would be compensated by diesel generator. The calculated reference power values for wind turbine and diesel generator are shown as follows:(4)
P
w
_
r
e
f
=
P
w
_
p
r
e
,
P
d
_
r
e
f
=
P
L
_
p
r
e
-
P
w
_
r
e
f
.


### 3.3. Correction Module

Correction module adjusts the reference power calculated by power calculation module according to the restraints of active power output change of wind turbine and diesel generator. When wind speed fluctuates sharply, the active power output of wind turbine would fluctuate sharply if wind turbine tracks the calculated reference power in (4) directly. Although diesel generator can be controlled to output any value between zero and the rated power, the rate of change of its power has its constraint.The objective of correction module is reducing the change of active power output of wind turbine, the operation of diesel generator. The correction module is designed based on fuzzy control, which utilizes the wind power and load prediction information to adjust the active power output of wind turbine and diesel generator.The fuzzy logic controller for correction module uses two single inputs and two-dimensional output of the structure. Its structure is shown in Figure4.Figure 4
Control structure of correction module.The input variables to the fuzzy logic controller are as follows.The first input variable is the error between reference power and actual active power output of wind turbineΔ
P
w, that is, P
w
_
r
e
f
-
P
w. The second input variable is the error between reference power and actual active power output of diesel generator Δ
P
d.The output variable is adjusting powerΔ
P which is used to adjust the reference power of wind turbine and diesel generator calculated in power calculation module:(5)
P
w
ref
=
P
w
_
r
e
f
+
Δ
P
,
P
d
ref
=
P
d
_
r
e
f
+
Δ
P
.Define language variable ofΔ
P
w as E
1, language variable of Δ
P
d as E
2, language variable of Δ
P as U, respectively. The fuzzy set of input variables and output variable is NB, NM, NS, ZO, PS, PM, PB, which means that the change of power is negative big, negative middle, negative small, zero, positive small, positive middle, positive big.The membership function of the two inputs and output of the fuzzy logic controller adopts the triangle membership function and are shown in Figures5, 6, and 7. Table 1 shows the fuzzy control rule. The weighted average method is used for defuzzification.Table 1
The rule of fuzzy controller.
U
E
1
NB
NM
NS
ZO
PS
PM
PB
E
2
NB
ZO
PS
PS
PM
PM
PB
PB
NM
ZO
ZO
PS
PS
PM
PM
PB
NS
NS
ZO
ZO
PS
PS
PM
PM
ZO
NS
NS
ZO
ZO
ZO
PS
PS
PS
NM
NM
NS
NS
ZO
ZO
PS
PM
NB
NM
NM
NS
NS
ZO
ZO
PB
NB
NB
NM
NM
NS
NS
ZOFigure 5
Membership function of the inputE
1.Figure 6
Membership function of the inputE
2.Figure 7
Membership function of the outputU.


## 3.1. Interpolation Module

The interpolation module determines the predictive value of wind power and load between each two adjacent prediction periods for power control, when prediction period is unmatched with control period. The interpolation module utilizes linear interpolation method to calculate and smooth predictive value at control period. Assumes that the prediction period isT whose unit is s (seconds), the predictive value is y
0 at t
0, and the predictive value is y
1 at t
0
+
T. The linear interpolation formula is as follows:(1)
y
t
=
a
t
+
b
,where t
0
≤
t
≤
t
0
+
T. Replace t and y in (1) by values of (t
0
,
y
0) and (t
0
+
T
,
y
1)​​, and coefficients a and b can be obtained:(2)
a
=
y
1
-
y
0
T
,
b
=
T
+
t
0
y
0
-
t
0
y
1
T
.


## 3.2. Power Calculation Module

The design objective of power calculation module is to calculate reference power for wind turbine and diesel generation, which could keep the system power balance between generating power and demanding load under the premise of using wind energy as much as possible. Its control strategy is designed as follows:(1)
WhenP
L
_
p
r
e
<
P
w
_
p
r
e, that is, prediction value of load is less than the prediction value of wind power, the load fluctuation would be compensated by wind turbine alone and reference power of diesel generator could be zero. The control of the power value is shown as follows:(3)
P
w
_
ref
=
P
L
_
p
r
e
,
P
d
_
r
e
f
=
0
.
(2)
WhenP
L
_
p
r
e
<
P
w
_
p
r
e, that is, prediction value of load is equal to or greater than the prediction value of wind power, the reference power of wind power could be set to the prediction value of wind power to maximize the use of wind power and the frequency fluctuation would be compensated by diesel generator. The calculated reference power values for wind turbine and diesel generator are shown as follows:(4)
P
w
_
r
e
f
=
P
w
_
p
r
e
,
P
d
_
r
e
f
=
P
L
_
p
r
e
-
P
w
_
r
e
f
.


## 3.3. Correction Module

Correction module adjusts the reference power calculated by power calculation module according to the restraints of active power output change of wind turbine and diesel generator. When wind speed fluctuates sharply, the active power output of wind turbine would fluctuate sharply if wind turbine tracks the calculated reference power in (4) directly. Although diesel generator can be controlled to output any value between zero and the rated power, the rate of change of its power has its constraint.The objective of correction module is reducing the change of active power output of wind turbine, the operation of diesel generator. The correction module is designed based on fuzzy control, which utilizes the wind power and load prediction information to adjust the active power output of wind turbine and diesel generator.The fuzzy logic controller for correction module uses two single inputs and two-dimensional output of the structure. Its structure is shown in Figure4.Figure 4
Control structure of correction module.The input variables to the fuzzy logic controller are as follows.The first input variable is the error between reference power and actual active power output of wind turbineΔ
P
w, that is, P
w
_
r
e
f
-
P
w. The second input variable is the error between reference power and actual active power output of diesel generator Δ
P
d.The output variable is adjusting powerΔ
P which is used to adjust the reference power of wind turbine and diesel generator calculated in power calculation module:(5)
P
w
ref
=
P
w
_
r
e
f
+
Δ
P
,
P
d
ref
=
P
d
_
r
e
f
+
Δ
P
.Define language variable ofΔ
P
w as E
1, language variable of Δ
P
d as E
2, language variable of Δ
P as U, respectively. The fuzzy set of input variables and output variable is NB, NM, NS, ZO, PS, PM, PB, which means that the change of power is negative big, negative middle, negative small, zero, positive small, positive middle, positive big.The membership function of the two inputs and output of the fuzzy logic controller adopts the triangle membership function and are shown in Figures5, 6, and 7. Table 1 shows the fuzzy control rule. The weighted average method is used for defuzzification.Table 1
The rule of fuzzy controller.
U
E
1
NB
NM
NS
ZO
PS
PM
PB
E
2
NB
ZO
PS
PS
PM
PM
PB
PB
NM
ZO
ZO
PS
PS
PM
PM
PB
NS
NS
ZO
ZO
PS
PS
PM
PM
ZO
NS
NS
ZO
ZO
ZO
PS
PS
PS
NM
NM
NS
NS
ZO
ZO
PS
PM
NB
NM
NM
NS
NS
ZO
ZO
PB
NB
NB
NM
NM
NS
NS
ZOFigure 5
Membership function of the inputE
1.Figure 6
Membership function of the inputE
2.Figure 7
Membership function of the outputU.


## 4. Real-Time BESS Frequency Control Module

The design objective of the real-time BESS frequency module is to compensate the frequency fluctuations caused by real-time wind power and load fluctuation and maintain the system frequency in rated frequency range to meet the active power-load balance.Figure8 shows the control structure of the real-time BESS frequency control module based on ADRC controller, where Δ
f
r
e
f is reference frequency error, which is always given as zero, that is, the real-time frequency error between rated frequency and actual frequency, Δ
P
f is adjusting charging or discharging power of BESS, and Δ
f is the actual frequency deviation of system. P
L
-
P
w
-
P
d is the actual power deviation between load and power generated by wind turbine and diesel. It could be seen as disturbance to system frequency.Figure 8
Control structure of BESS based on ADRC.In Figure8, BESS could be seen as a first-order lag loop [13]. Therefore, the whole controlled object can be seen as a concatenation of two first-order loops. A second-order ADRC controller is used for real-time frequency BEES control.Figure9 shows the control structure of the second-order ADRC controller, where G
1 is the TD (Tracking Difference), G
2 is the ESO (extended state observer), G
3 is NLSEF (Nonlinear State Error Feedback), and G
0 is the controlled object.Figure 9
Control structure of second-order ADRC.(a) Tracking Difference
G
1. The math model of TD is designed as follows:(6)
v
1
˙
=
v
2
,
v
2
˙
=
fst
⁡
v
1
,
v
2
,
r
,
h
,where function fst
⁡
(
v
1
,
v
2
,
r
,
h
) is determined by following equation:(7)
δ
=
r
·
h
,
δ
0
=
δ
·
h
,
y
=
v
1
-
Δ
f
r
e
f
+
h
·
v
2
,
a
0
=
δ
2
+
8
r
y
,
a
=
x
2
+
y
h
y
≤
δ
0
x
2
+
a
0
-
δ
2
sign
⁡
y
y
>
δ
0
,
fst
⁡
v
1
,
v
2
,
r
,
h
=
-
r
·
a
δ
a
≤
δ
-
r
·
sign
⁡
a
a
>
δ
,where r is the speed factor, h is the filter factor, sign
⁡ is a sign function, and r and h are adjustable parameters of TD.(b) Extended State Observer
G
2. The math model of ESO is designed as follows:(8)
ε
=
z
1
-
y
,
z
1
˙
=
z
2
-
β
01
·
ε
,
z
2
˙
=
z
3
-
β
02
·
fal
⁡
ε
,
α
1
,
δ
1
+
b
0
·
u
,
z
3
˙
=
-
β
03
·
fal
⁡
ε
,
α
2
,
δ
1
,where function fal
⁡
(
ε
,
α
,
δ
) is given as follows:(9)
fal
⁡
ε
,
α
,
δ
=
ε
α
sign
⁡
ε
ε
>
δ
≥
0
ε
δ
1
-
α
ε
≤
δ
.In (8), by choosing appropriate {
α
1
,
α
2
,
δ
1
,
β
01
,
β
02
,
β
03
}, z
1, z
2 could be used to estimate the controlled variable y and its differential. z
3 could be used to estimate the disturbance.(c) Nonlinear State Error Feedback
G
3. The math model of NLSEF is designed as follows:(10)
e
1
=
v
1
-
z
1
,
e
2
=
v
2
-
z
2
,
u
0
=
k
1
·
fal
⁡
e
1
,
α
3
,
δ
2
+
k
2
fal
⁡
e
2
,
α
4
,
δ
2
,
u
=
u
0
-
z
3
b
0
,where function fal
⁡
(
) is showed in (9), z
3 is the extended state variable, and k
1, k
2, α
3, α
4, and δ
2 are the adjusting parameters of NLSEF.(d) Control Object
G
0. The structure of controlled object G
0 is shown in Figure 10, whose control variable is u
=
Δ
P
f, output variable is y
=
Δ
f, and disturbance is P
L
-
P
w
-
P
d.Figure 10
Control structure from control variable to output.The transfer function of control objectG
0 could be written as follows: (11)
G
s
=
-
1
T
b
s
M
s
+
D
.The corresponding state space expression ofG
0 could be written as follows:(12)
x
1
˙
=
1
M
-
D
x
1
+
x
2
,
x
2
˙
=
-
1
T
b
u
,where x
=
[
x
1
,
x
2
]
=
[
Δ
f
,
P
b
] is the state variable, y
=
x
1 is the output, and u
=
Δ
P
f is the control variable.


## 5. Simulation and Analysis

In this section, the simulation experiment of frequency control of WPBHPS will be conducted to demonstrate the effectiveness of the proposed approach. A wind-diesel-battery hybrid power system [14], which consists of a 750 kW wind turbine, a 350 kW diesel generator, and a 300 kW BESS with 50 kW·h capacity is used in the simulation experiments.For comparison, two different frequency control approaches will be used in the simulation experiment: (a) the proposed control approach and (b) the control approach which control the active power output of wind turbine by maximum power point tracking (MPPT) control approach and compensate frequency deviation by diesel generator and BESS only.A load prediction method based on artificial neural network [11] is used to predict the load. The actual load and predictive load curve is shown in Figure 11.Figure 11
Actual load and predictive load.The actual wind speed data of wind turbines is shown in Figure12.Figure 12
Actual wind speed.A grey model based wind power prediction method [15] is used to predict the available active power of wind turbine. The predictive power of the wind turbine is shown in Figure 13.Figure 13
Predictive power of wind turbine.The results of frequency control simulation experiments are shown in Figure14. By using control approach (b), the maximum and minimum frequency deviations are 1.2411 Hz and 0.4772 Hz, respectively. The maximum frequency deviation of control approach (a) is 0.4103 Hz, which is 33.5% of that of control approach (b). The minimum frequency deviation of control approach (a) is 0.2920 Hz, which is 61.2% of that of control approach (b). It is obvious that the frequency control performance of control approach (a) is better than approach (b).Figure 14
Frequency fluctuation of system.The active power output of wind turbine and diesel generator are shown in Figures15 and 16. As seen in them, the active power output of wind turbine and diesel generator by using control approach (a) is smoother than that by using control approach (b).Figure 15
Active power output of wind turbine.Figure 16
Active power output of diesel generator.The charging/discharging power curve and SOC of BESS are shown in Figures17 and 18. As seen in Figure 16, the charging and discharging power of BESS are much smaller by using control approach (a) compared with control approach (b). As seen in Figure 17, the initial SOC of BESS is 0.5. By using control approach (a), the SOC of BESS is around 0.5. By using control approach (b), the SOC of BESS is far below 0.5.Figure 17
Charging/discharging power of BESS.Figure 18
SOC of BESS.


## 6. Conclusions

This paper proposed a load and wind power prediction based frequency control approach for wind-diesel-battery hybrid power system. In this control approach, a wind-diesel power control module is designed to regulate the active power output of wind turbine and diesel generator according to the prediction information of load and wind power, which not only reduces frequency deviation caused by fluctuation of load and wind power but also maximizes use of wind power and reduces operation of diesel generator. A real-time BESS frequency control module based on ADRC controller is used to compensate the disturbance caused by load and wind power fluctuation in real time. The simulation results demonstrate that the proposed approach has a better disturbance rejection ability and frequency control performance compared with the traditional control approach without prediction information and traditional droop control approach.



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*Source: 715435-2015-11-26.xml*