two crucial constraints while the wind power is being integrated into grid. The first constraint associated with the battery capacity is to guarantee the battery ...
9th International Conference on Power Electronics-ECCE Asia June 1 - 5, 2015 / 63 Convention Center, Seoul, Korea
Power Dispatch Capability of Wind-Battery Hybrid Power System Cong-Long Nguyen1, Hong-Hee Lee2 1, 2
School of Electrical Engineering, University of Ulsan, Ulsan, Korea
Abstract--This paper focuses on determining the power dispatch capability of wind-battery hybrid power system (WBHPS). To cooperate with the transmission system operator (TSO) in the modern electric power market, the power dispatch capability of the WBHPS must be defined primarily at beginning of each dispatching time interval. Based on the wind power available and the battery capacity status, the power dispatch capability which means the power level of the WBHPS can dispatch to grid is determined. The proposed determination method aims to satisfy two crucial constraints while the wind power is being integrated into grid. The first constraint associated with the battery capacity is to guarantee the battery power lower than its rating. And, the second one is to keep the state of charge (SOC) of the battery within a safe range. In order to determine the maximum and minimum range of the power dispatch, the availability level of the wind power in each dispatching time interval is estimated based on the wind speed forecast. To evaluate the proposed determination method, we perform a numerical study using a 3-MW wind turbine generator with a real wind speed data measured on Jeju Island. Index Terms—Wind-battery hybrid power system (WBHPS), transmission system operator (TSO), power dispatch control, state of charge (SOC) control, wind power forecast.
I. INTRODUCTION Wind power source is a leading candidate in the renewable energy conversion systems, which has grown rapidly in recent years to resolve critical global problems such as the environmental pollution and the exhausted fossil energy [1]. However, the inherent intermittent characteristic of the wind is a main issue that prevents a wind power from penetrating into the electric power system. In [2], a grid frequency deviation caused by the wind power fluctuation was investigated to show that the wind power varying in min-range segment would result in a significant grid frequency deviation. The other severe impacts of the wind power fluctuation on the power system including the grid connection, power quality, and the system reliability are examined in [3]. Therefore, the intermittent characteristic of the wind power needs to be overcome before dispatching high wind power into the grid. There are several methods to mitigate the wind power fluctuation, which can be classified into two groups. The first one includes the power smoothing methods without using energy storage systems such as the pith angle control [4] and the wind generator rotor inertia regulation [5]. Even though these methods are low investment cost,
2015 KIPE
they do not ensure that wind turbines (WTs) capture the maximum available wind power [6]. In the second group, wind power is smoothened by using the energy storage systems such as the battery, supper-capacitor, superconducting magnetic energy storage, and flywheel. The application of such energy storages makes the renewable energy conversion system not only be dispatchable but also harness the maximum power available in wind [7]. As a result, the use of energy storages in wind system has been actively researched as a feasible solution to enable the high penetration wind power into the electric power market. Among the energy storage systems, the battery is widely used in the wind power system because of its firm-developed technology and the sufficient power and energy density [8]. Integrating the battery with wind farm (WF) results in a wind-battery hybrid power system (WBHPS), in which the system cost and the power control need to be taken into account. Related to the system cost concerned, optimization of the system operation is getting attractive in recent published literatures. The authors in [9] suggested using a zerophase low-pass filter in order to remove the phase-delay between the power dispatch and wind power, which results in the minimum battery capacity required. To control the dispatching power effectively, a new power dispatch control method is proposed based on the state of charge (SOC) of battery [10]. In addition, a two-timescale coordination control was presented in [11] to minimize the battery capacity while the power dispatch fluctuation is still limited within the allowable range. When the penetration level of wind power into electric power systems can be comparable with the conventional power sources such as hydroelectric and nuclear power plants, the transmission system operator (TSO) expects WFs can supply a constant power in each dispatching interval [12]. To commit such requirement, the constant power dispatch control method was introduced in [13] by means of averaging the available wind power. The other interesting method is based on the maximum and minimum level of the wind power to make the battery operate with full charge-discharge cycles, so that the battery lifetime can be prolonged significantly [14]. In order to effectively cooperate with the TSO, the power dispatch capability of the WBHPS, which means the maximum and minimum power dispatch level, must be defined primarily at the beginning of each dispatching time interval. Based on such levels, the TSO can command a suitable power dispatch to the WBHPS; this helps the WBHPS to be able to meet the command
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3.0
Power (MW)
2.5 2.0 1.5 1.0 0.5
0 0
t −κ ≤τ ≤ t
t −κ ≤τ ≤t
PWTR
,
0 0
600
700
100
200
300 400 Time (h)
500
600
700
(b) Fig. 1. Wind power fluctuation. (a) Output power of 3-MW WT in one month. (b) Wind power fluctuation level in 10-min time window.
Pw Pd
Generator
Pb ∗ b
P SOC
Pw Fig. 2.
Grid
Transformer
PCC
PCS
(1)
where, PWTR is the WT power rating. In Fig. 1(b), the wind power fluctuation in 10-min time window, i.e. ΔFw10 (t ) , is plotted. It can be recognized that in 10-min interval, the wind power can vary up to 90% of the WT power rating. With such a high wind power fluctuation, the integration of the wind farm into the grid would introduce several challenges such as grid interconnections and control, voltage and frequency regulation, stabilization of power, and low voltage fault ride through capability [15]-[17]. Recently, the maximum allowable variation of the power dispatch has been introduced in the grid code requirements to limit the fluctuation of the wind power dispatch [17]. Grid code requirements are actually the major driver for WT technology developments and instruct to management of the wind system. Usually, the allowable power dispatch variation is defined as the
500
30
BESS
ΔFwκ (t ) =
300 400 Time (h)
60
A. Wind Power Fluctuation The wind speed depends on natural and meteorological condition; therefore, the WT output power is essentially fluctuating. Fig. 1(a) shows the power response of a 3MW WT in one month. It is seen that the wind power can vary from the WT rating to zero for a short interval. In order to study the fluctuation level of a power Pw (t ) profile, the power fluctuation in κ − min time window is defined as:
MAX { Pw (τ )} − MIN { Pw (τ )}
200
(a)
II. WIND-BATTERY HYBRID POWER SYSTEM Although wind is clean energy source, its intermittent characteristic is the biggest problem to supply a stable power for grid. In order to overcome this problem, the wind power fluctuation needs to be investigated primarily.
100
90 10-Min Fluctuation (%)
successfully. Consequently, the determination of the power dispatch capability is an essential step to manage the WBHPS. In this paper, we propose a method how to determine the power dispatch capability of WBHPS. The power dispatch capability is determined based on the wind power availability and the battery capacity status. Two crucial constraints must be fulfilled fully while the wind power is being dispatched to grid. The first constraint related to the battery power rating is to guarantee the battery power lower than its rating, and the second one is to keep the state of charge (SOC) of the battery within a safe range. The wind power is forecasted to estimate the availability of the wind power in each dispatching time interval; therefore, the maximum and minimum range of the power dispatch can be defined. In order to demonstrate the proposed determination method, we show a numerical example using a 3-MW wind turbine generator with a real wind speed data measured on Jeju Island.
Pd∗
PMS Pdcap TSO
The wind-battery hybrid power system.
maximum power fluctuation in κ − min time window. However, in the modern electric market, the power dispatch must be not less fluctuating but also firmly constant in each dispatching time interval; and the power dispatch schedule is required to submit ahead in several hours to the TSO. In order to satisfy such requirements, utilization of battery is a feasible solution. B. Configuration of Wind-Battery Hybrid Power System Fig. 2 shows a schematic diagram of a battery energy storage system (BESS) applied to a WF to mitigate the wind power fluctuation. The BESS is connected to the point of common coupling (PCC) via a power conversion system (PCS) that must control a bidirectional power flow: When the storage is charged, its power is a positive value (i.e. Pb > 0 ), and vice versa. Under the assumption that power losses in the system are negligible, the
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WBHPS output power Pd dispatching to the grid can be calculated from BESS power Pb and the wind power Pw as (2)
The power management system (PMS) controls the WBHPS by determining a suitable power reference Pb∗ for the PCS to successfully commit the power dispatch command Pd∗ that is defined by the TSO of the electric power system. From (2) and with the assumption that the PCS is capable of tracking the power reference Pb∗ , the PMS can determine the power reference for the PCS as follows: Pb* = Pw − Pd* .
Pw ( t )
Pfu ( t )
2 .0
Pfl ( t )
Pf ( t )
1.5
1.0 t0 − 2Td
Fig. 3.
t0 − Td
t0
t0 + Td
Time (h)
The upper wind power range Pfu (t ) , the lower wind power
range Pfl (t ) , the forecasted wind power Pf (t ) , and the real wind power Pw (t ) .
(3)
Therefore, the power dispatch command Pd∗ is firmly associated with the WBHPS operation. In order to effectively manage the overall system, the PMS must submit the power dispatch capability to the TSO. Based on the information of the power dispatch capability, the TSO is aware of the WBHPS status, so that the TSO can order a power dispatch command properly. As shown in Fig. 2, Pdcap denotes the power dispatch capability that includes the minimum and maximum levels represented by Pdmin and Pdmax , respectively. When the PMS sends the power dispatch capability to the TSO, the TSO can order the power dispatch command arbitrarily, but it must satisfy the following constraint: Pdmin ≤ Pd* ≤ Pdmax .
2 .5
Power (MW)
Pd = Pw − Pb .
3 .0
(4)
In order to determine the power dispatch capability Pdcap including Pdmin and Pdmax , the PMS inquires the wind power and the SOC of battery information. III. DETERMINATION OF POWER DISPATCH CAPABILITY The constraint shown in (4) mentions that the power dispatch is bounded by a range because the power dispatch is completely associated with the system operation such as the battery status and the wind power availability. During the WBHPS dispatching power to grid, the wind power is usually forecasted to estimate the wind power availability in next dispatching time interval. So far, there have been several wind power forecast methods introduced to improve the accuracy. However, the forecast error is inevitable, so that it is necessary to analyze the forecast error before determination of the power dispatch capability. A. Determination of Wind Power Range Several advanced forecasting techniques to predict wind speed accurately have been developed in recent years [18]. However, error in these forecast data is inevitable and highly dependent on the time-horizon of the forecast. The authors in [19] summarized that when the forecast horizon is less than a half-day, the probability
of error in the wind speed forecast can be described in the form of a normal distribution with a constant mean μ and a standard deviation σ . Therefore, the real wind speed is bounded by the upper limit v uf (t ) and the lower limit v lf (t ) from the forecasted data v f (t ) , expressed as
follows:
v uf (t ) = v f (t ) + ( μ + Aσ )Vr ,
(5)
v lf (t ) = v f (t ) − ( μ + Aσ )Vr ,
(6)
where Vr is the rated wind speed and A is a constant specifying the forecast certainty level, e.g. the probability that the real wind speed is within the upper and lower limit is 68.269% and 99.73% if A = 1 and A = 3 , respectively. The relationship between wind speed v(t ) and the WT output power p ( t ) is Av 3 (t ) , in which A is a coefficient defined through the WT specifications [20]. As a result, the wind power range can be determined from the wind speed forecast by using the upper and lower limits of the wind speed defined in (5) and (6). Based on the WT power curve, the upper and lower ranges of the wind power forecast (i.e. Pfl (t ) and Pfu (t ) ) can be computed consequently. Fig. 3 shows an example of the wind power range in case of 99.73% forecast confidence level, zero mean error, and 3% standard deviation level. We can recognize that the real wind power is rather different to the forecasted value, but it is mostly bounded by the upper and lower forecast power ranges. B. Power Dispatch Capability of Wind-Battery Hybrid Power System The power dispatch capability depends on the wind power availability and the battery capacity status. Based on the upper and lower wind power ranges, the wind power availability can be estimated. The other technical consideration during the WBHPS operation is to ensure the battery below its power rating and to keep its SOC within a safe range. Therefore, the determination of the
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power dispatch capability means to find the maximum and minimum levels of the power dispatch which are defined to satisfy such constraints. The first constraint is to keep battery power not over the battery power rating Pbrat at any time, which is expressed as: (7)
{P (t)} + P
rat b
(8)
Pdmin1 = MAX Pfu (t ) − Pbrat
{
(9)
Pdmax1 = MIN
t0 ≤t ≤t0 +Td
t0 ≤t ≤t0 +Td
l f
}
Fig. 4 demonstrates the power dispatch capability determined from (8) and (9). At the time t1 , the lower range of wind power is minimal, so that the maximum level of the power dispatch is defined at this moment by using (8). And, the minimum level of the power dispatch is defined by (9) at time t2 when the upper range of wind power is maximum. The second constraint is to guarantee the SOC within a safe range, i.e.,
SOCL ≤ SOC (t ) ≤ SOCU
Pd =
Ew − {SOC (t0 + Td ) − SOC0 } Ebrat Td
lower limit E lf
Fig. 4.
and the upper limit E uf that are
computed based on the wind power range Pfl (t ) and Pfu (t ) . From (11), the maximum and minimum levels of
the power dispatch can be defined to meet the second constraint as follows:
Pbrat
P fl ( t )
Time
t 0 + Td
t2
Illustration of determining the power dispatch capability.
3.0
2.0
1.0
0 0
Fig. 5.
1
2
3
4
5
7 6 Time (h)
10
9
8
11
12
Wind power profile and wind power forecast range.
Pdmax 2
=
Pdmin 2 =
E lf − (SOCL − SOC0 ) Ebrat Td E uf − (SOCU − SOC0 ) Ebrat Td
,
(12)
.
(13)
To ensure the power dispatch capability satisfy both constraints, the maximum and minimum levels of power dispatch are defined as:
(
)
Pdmax = MIN Pdmax1 , Pdmax 2 ,
(
(11)
where Ebrat is the battery energy rating, and Ew is the wind power energy in the dispatching interval from t0 to t0 + Td . The wind power energy is bounded by the
Pf ( t )
t 0 t1
(10)
where SOCL and SOCU denote the upper and lower allowable limits of SOC, respectively. At the beginning of the next dispatching interval i.e., at t0 , the SOC is given by SOC0 . Using the energy conservation law, the constant power dispatch can be expressed as follows:
Pbrat
Pdmin1
Power (MW)
At the beginning of the next dispatching interval, which is denoted by t0 , the wind power range in one dispatching interval Td is estimated by using the process discussed in Section III.A. Based on the upper and lower ranges of the wind power and the relationship between the power dispatch and battery power defined in (2), the maximum and minimum level of the power dispatch can be determined to satisfy the first constraint as follows:
P fu ( t )
Wind Power
Pb (t ) ≤ Pbrat .
Pdmax1
)
Pdmin = MAX 0, Pdmin1 , Pdmin 2 .
(14) (15)
IV. CASE STUDY In order to demonstrate the proposed power dispatch capability determination method, we investigate a 3-MW WT model [20] using MATLAB/Simulink with a real wind speed data measured on Jeju Island on 15th March 2013. The wind speed is sampled in 10-s period, and the dispatching time interval is set at 1h (i.e., Td=1h). The optimal battery capacity for the WBHPS is determined by a method presented in [21]; the battery power rating and energy rating defined in this paper are Pbrat = 2.4 MW and Ebrat = 1.6 MWh , respectively.
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TABLE I DETERMINATION OF POWER DISPATCH CAPACITY (MW)
1
2
3
4
5
6
SOC
0.5
0.61
0.478
0.48
0.44
0.66
Pdmax1
2.309
2.244
2.142
2.128
2.303
min1 d
-0.37
-0.67
-0.53
-0.84
max 2 d
0.882
0.945
0.68
min 2 d
P
0.654
0.57
Pdmax
0.882
min d
P P
P
Pd
8
9
10
11
12
0.55
0.53
0.54
0.47
0.67
0.57
2.796
2.604
2.340
2.978
2.596
2.750
2.506
-0.1
0.4
0.314
-0.27
-0.34
0.449
0.489
0.366
0.613
0.935
1.87
1.31
0.973
0.934
1.53
2.11
1.47
0.189
0
0.6
1.56
1.14
0.69
0.56
1.27
1.68
1.16
0.945
0.68
0.613
0.935
1.87
1.31
0.973
0.934
1.53
2.11
1.47
0.654
0.57
0.189
0
0.6
1.56
1.14
0.69
0.56
1.27
1.68
1.16
0.74
0.854
0.613
0.525
0.644
1.83
1.24
0.857
0.903
1.284
1.98
1.32
3.0
Power (MW)
2.0
1.0
0
0
2
4
6
8
10
12 14 Time (h)
16
18
20
22
24
16
18
20
22
24
(a)
Battery Power (MW)
2.0
1.0
0
−1.0 0
2
4
6
8
10
12 14 Time (h)
(b) 1.0
SOC
value drawn from a standard uniform distribution to the wind power data, the real wind power is defined. Fig. 5 shows the wind power profile as well as the upper and lower wind power ranges in the first 12 hours of a day. We can see that the estimated wind power and the real values are quite different, but are bounded by the specified range. Based on the wind power profile and the power forecast range, the power dispatch capability is determined. Table I shows the data obtained from the proposed power dispatch capability decision method, in which the SOC value represents the initial SOC of the battery in each dispatching interval. The power dispatch capability in 12 dispatching intervals is determined by keeping two constraints. In the first constraint, Pdmin1 and Pdmax1 ensure that the battery power is lower than its power rating. Additionally, Pdmin 2 and Pdmax 2 involve the SOC control, which ensures that the SOC is in the safe range: 20% to 100%. Finally, the maximum and minimum levels of power dispatch are defined based on (14) and (15). For example, at the first dispatching interval, the battery SOC is initially set at 50%, and the maximum and minimum levels of the power dispatch to satisfy the first constraint are Pdmax1 = 2.309 MW and Pdmin1 = −0.37 MW . To meet the second constraint, Pdmax 2 = 0.882 MW and
0.8
Pdmin 2 = 0.654 MW . Therefore, from (14) and (15), the
0.6
power dispatch capability is
0.4 0.2 0
Time (h) 7
2
4
6
8
10
12 14 Time (h)
16
18
20
22
24
(c) Fig. 6. Wind power, power dispatch capability, and battery power response. (a) Wind power, maximum and minimum power dispatch levels, and power dispatch. (b) Battery power response. (c) SOC of BESS.
We estimate the wind power by using the given wind power data. By adding an error that is a pseudorandom
Pdmax = 0.882 MW and
Pdmin = 0.654 MW . Based on the power dispatch capability, the TSO can command the power dispatch to the PMS in a suitable manner and Pd = 0.74 MW . Fig. 6 shows the system performance including the maximum and minimum power dispatch levels, the power dispatch to grid, the battery power response, and the SOC of the battery in one day. We can see that the battery power is always kept below the 2.4 MW battery power rating. In addition, the SOC is controlled successfully within the expected range, which verifies the effectiveness of the proposed determination method.
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V. CONCLUSIONS This paper presented a method to determine the power dispatch capability of WBHPS. With the obtained power dispatch capability, the TSO is able to give a suitable power command to the PMS so that the WBHPS can operate reliably. The proposed method ensures the following crucial requirements: The battery should operate below its power rating and within the safe SOC range. In order to evaluate the effectiveness of the proposed method, we performed a numerical study using a 3-MW wind turbine generator with real wind speed data measured on Jeju Island. From the case study, it is proved that the power dispatch in each dispatching interval was constant and that the SOC was always kept in a safe range by using the proposed power dispatch capability decision method. ACKNOWLEDGMENT This work was supported by a National Research Foundation of Korea grant funded by the Korean Government under Grant 2013R1A2A2A01016398. REFERENCES [1] Renewables 2014 Global Status Report [Online]. www.ren21.net/ren21activities/globalstatusreport.aspx. [2] J. Lin, Y. Z. Sun, P. Sorensen, G. J. Li, and W. Z. Gao, "Method for assessing grid frequency deviation due to wind power fluctuation based on “Time-frequency transformation”," IEEE Trans. Sustain., vol.3, no.1, pp. 65–73, Jan. 2012. [3] A. Abrantes, "Overview of power quality aspects in wind generation," in Proc. North American Power Symposium (NAPS), pp. 1–6, Sept. 2012. [4] E. Muljadi and C. Butterfield, “Pitch-controlled variablespeed wind turbine generation,” IEEE Trans. Ind. Appl., vol. 37, no. 1, pp. 240–246, Feb. 2001. [5] A. Abedini, G. Mandic, and A. Nasiri, “Wind power smoothing using rotor inertia aimed at reducing grid susceptibility, ” Int. Journal of Power Electronics, vol. 1, no. 2, pp. 227–247, Feb. 2008. [6] A. M. Howlader, N. Urasaki, A. Yona, T. Senjyu, and A. Y. Saber, “A review of output power smoothing methods for wind energy conversion systems,” Renewable and Sustainable Energy Reviews, vol. 26, pp. 135–146, Oct. 2013. [7] C. L. Nguyen and H. H. Lee, "Optimization of power dispatch to minimize battery storage capacity in wind farm," in Proc. Energy Conversion Congress and Exposition (ECCE), 2014 IEEE, pp. 420–427, Sep. 2014. [8] A. M. Howlader, Y. Izumi, A. Uehara, N. Urasaki, T. Senjyu, and A. Y. Saber, “A robust controller based frequency control approach using the wind-battery coordination strategy in a small power system,” Int. J. Elec. Power & Energy Sys., vol. 58, pp. 190–198, Jun. 2014. [9] C. L. Nguyen and H. H. Lee, “Optimization of wind power dispatch to minimize energy storage system capacity,” Journal of Electrical Engineering & Technology, vol. 9, no. 3, pp. 1080–1088, May 2014. [10] K. Yoshimoto, T. Nanahara, and G. Koshimizu, “New control method for regulating state-of-charge of a battery in hybrid wind power/battery energy storage system,” in Proc. Power Systems Conf. and Expo., IEEE 2006, pp. 1244–1251, Nov. 2006.
[11] Q. Jiang and H. Wang, "Two-time-scale coordination control for a battery energy storage system to mitigate wind power fluctuations," IEEE Trans. Energy Convers., vol. 28, no. 1, pp. 52–61, Mar. 2013. [12] H. Borhan, M. A. Rotea, and D. Viassolo “Optimizationbased power management of a wind farm with battery storage,” Wind Energy, vol. 16, no. 8, pp. 1197–1211, Sept. 2012. [13] S. Teleke, M. E. Baran, S. Bhattacharya, and A. Q. Huang, “Optimal control of battery energy storage for wind farm dispatching,” IEEE Trans. Energy Convers., vol. 25, no. 3, pp. 787–794, Sep. 2010. [14] Q. Li, S. S. Choi, Y. Yuan, and D. L. Yao, “On the determination of battery energy storage capacity and shortterm power dispatch of a wind farm,” IEEE Trans. Sustain. Energy, vol. 2, no. 2, pp. 148–158, Apr. 2011. [15] C. L. Nguyen, H. H. Lee, and T. W. Chun, "Cost optimized battery capacity and short-term power dispatch control for wind farm," IEEE Trans. Ind. Appl., vol. 51, no. 1, pp. 595–606, Jan/Feb 2015. [16] M. M. Chowdhury, M. E. Haque, M. Aktarujjaman, M. Negnevitsky, and A. Gargoom, "Grid integration impacts and energy storage systems for wind energy applications A review," in Proc. Power and Energy Society General Meeting, 2011 IEEE, pp. 1–8, Jul. 2011. [17] M. Tsili and S. Papathanassiou, "A review of grid code technical requirements for wind farms," IET Renew. Power Generation, vol. 3, no. 3, pp. 308–332, Sep. 2009. [18] G. Sideratos and N. D. Hatziargyriou, “An advanced statistical method for wind power forecasting,” IEEE Trans. Power Syst., vol.22, no.1, pp. 258–265, Feb. 2007. [19] G. Giebel, R. Brownsword, and G. Kariniotakis, “The state of the art in short-term prediction of wind power: A literature overview,” [Online]. http://anemos.cma.fr (deliverable D1.1 of ANEMOS project). [20] V112-3.0 MW [Online]. http://creativeenergyalternati ves.com/wind/Vestas_V_112_web_100309.pdf. [21] X. Y. Wang, D. V. Mahinda, and S. S. Choi, “Determination of battery storage capacity in energy buffer for wind farm,” IEEE Trans. Energy Convers., vol. 23, no. 3, pp. 868–878, Sep. 2008.
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