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compensate a short-term wind power fluctuation, whereas the battery plays as a long-term one to dispatch a stable power into grid. In order to allocate optimally ...
IECON2015-Yokohama November 9-12, 2015

An Optimal Hybrid Supercapacitor and Battery Energy Storage System in Wind Power Application Cong-Long Nguyen

Hong-Hee Lee

School of Electrical Engineering University of Ulsan Ulsan, Korea [email protected]

School of Electrical Engineering University of Ulsan Ulsan, Korea [email protected]

Abstract—In this paper, we optimize a hybrid energy storage system that includes a supercapacitor and a battery storage system for economical dispatching of the intermittent wind power source. In the optimal hybrid energy storage system, based on the technical merits of each storage, the supercapacitor is used to compensate a short-term wind power fluctuation, whereas the battery plays as a long-term one to dispatch a stable power into grid. In order to allocate optimally the power flow between two storages, the supercapacitor power is effectively determined based on a zero-phase low-pass filter, and the battery power commits an average dispatching method to stabilize the power dispatch. To obtain the optimal capacity in hybrid energy storage system (HESS), the smoothing time constant of the zero-phase low-pass filter in each dispatching interval is optimized by using the particle swarm optimization (PSO) method. In order to evaluate the proposed optimal system, a numerical example is performed using a 3-MW wind turbine generator with a real wind speed data. Keywords—Hybrid energy storage system (HESS); wind energy conversion system; power dispatch; transmision system operator; particle swarm optimization (PSO).

I.

INTRODUCTION

Renewable energy has become a feasible alternative to solve the global issues concerned on the energy security and the environmental degradation. Among the renewable sources, amount of energy extracted from the wind has gained most rapidly due to the lower investment cost and the mature technology on manufacturing high-power wind turbines (WTs) [1]. As reported in [2], electricity generation from wind grew exponentially year by year; from 159 GW in 2009 to 318 GW in 2013. However, similar to other renewable resources such as solar and tide, wind energy is uncertain and uncontrollable; this intermittent characteristic raises the technical difficulty to guarantee the quality, stability, and reliability of the electric power grid [3]. Recently, combination of energy storages systems (ESSs) with the wind energy conversion system has been actively researched to deliver a stable power to the grid. In [4], for example, the authors proposed the use of battery energy storage systems (BESSs) incorporated with the wind farm (WF) to dispatch a firm power for each dispatching interval. In real projects, typical examples including the 254-MWh NAS battery installed for stabilizing a 51-MW WF presented in [5] and a 2-MWh Li-ion battery built in a 4.5-MW WF described in [6] demonstrate that the use of ESSs is a feasible solution.

978-1-4799-1762-4/15/$31.00 ©2015 IEEE

In near future, the penetration level of wind power into electric power systems is expected to replace the conventional power stations such as thermo-electric, hydroelectric and nuclear power plants. When this scenario occurs, the WF operator submits its power dispatch schedule in several hours ahead to the transmission system operator (TSO) in order to supply a firm power to the grid as planned. To satisfy this requirement, the use of ESSs is a considerable solution. Thus, the roles of ESSs in wind energy conversion systems are not only to mitigate wind power fluctuations but also to commit power dispatch schedules. Based on typical characteristics of ESS, combination of two different ESSs, in which one must be a high power density storage and another should have a large energy capacity, has been proposed to effectively dispatch wind power into the electric power grid [8]-[10]. Due to wind speed variation in a full range of the WT operation, the harnessed wind power is fluctuated with numerous time scales, which requires ESSs to be both high power response and large energy capacity. However, no single ESS technology can handle these requirements because of one ESS owning either high power density or large energy capacity [11]. The ESS holding a high power density (so-called a short-term storage device), which is able to compensate wind power fluctuations in a short time horizon, possibly includes superconductor magnetic energy storage SMES, flywheel energy storage (FES), and supercapacitor storage system (SCSS). Among these shortterm storages, SCSS is deemed to be the most effective option due to its low cost and less environment impact [12]. Combination of a SCSS with a long-term storage forms a hybrid ESS (HESS) in which the SCSS compensates the highly fluctuating wind power components whereas another aims to achieve a firm dispatch power schedule. This combination helps the long-term storage device to prevent the quick charged and discharged states, so as to prolong its lifespan. The long-term storage system, including compressed air energy storage (CAES), pumped-hydro storage (PHS), and battery energy storage system (BESS), provides a large charge and discharge energy capacity, and the BESS is more suitable for wind application. Therefore, in this investigation, the HESS is composed of a SCSS and a BESS in order to stabilize the intermittent wind power. In this paper, we optimize the HESS to economically dispatch the wind power source. In order to allocate optimally the power flow between two storages, the SCSS power is determined based on a zero-phase low-pass filter (ZLF), and

003010

the BESS commits an average dispatching method to stabilize the power delivered to grid. To determine the optimal HESS capacity, the smoothing time constant of the ZLF in each dispatching interval is optimized by the particle swarm optimization (PSO) method. To evaluate the proposed optimized system, we perform a numerical study using a real wind speed data with 3-MW wind turbine generator.

Generator

PMS Ps∗

HYBRID ENERGY STORAGE SYSTEM

In order to dispatch a firm power Pd to grid, a power management system (PMS) decides the suitable storage power reference for each corresponding PCS. A. Definition of Required Storages Capacity The storage capacity, which is normally specified in term of energy rating and power rating, is defined based on the storage power flow. From the storage power flow, the energy injected or drawn from the storages up to time t can be calculated as t

t

0

0

Eb ( t ) = ηb ³ Pb (τ )dτ ; and E s ( t ) = η s ³ Ps (τ )dτ ;

(2)

­°η ch when Pb (t ) > 0 , ηb = ® b dis °¯1 ηb when Pb (t ) ≤ 0

(3)

­°η ch when Ps (t ) > 0 , ηs = ® s dis °¯1 η s when Ps ( t ) ≤ 0

(4)

where

and

In (3), η bch and η bdis denote the charge and discharge efficiency of the BESS, respectively. In (4), η sch and η sdis represent the charge and discharge efficiency of the SCSS, respectively. The following equations define power rating and energy rating of each storage device: Pbrat = Pb (t )



= MAX | Pb (t ) | ,

(5)

Psrat = Ps (t )



= MAX | Ps (t ) | ,

(6)

Ebrat =

Esrat

=

0 ≤t ≤T

0 ≤ t ≤T

MAX { Eb (t )} − MIN {Eb (t )} 0≤ t ≤T

0 ≤ t ≤T

Db MAX { Es (t )} − MIN { Es (t )} 0≤ t ≤T

0≤ t ≤T

Ds

,

.

PCS

Grid

Transformer

Pb∗ Pb

PCC

PCS

Fig. 1. Integration of hybrid energy storage system into wind energy conversion system.

technical specifications of storages must be considered. The total system cost (TSC) of storages includes both power converters in PCSs and capital investments, which is expressed as follows:

(

) (

)

TSC = Cbp × Pbrat + Cbe × Ebrat + Csp × Psrat + Cse × Esrat ,

(9)

where Cbp and C sp are the cost per kW of battery and supercapacitor, whereas Cbe and C se are the cost per kWh of battery and supercapacitor, respectively. Based on the storages capacity definition in (2)-(4), the capacity of each storage system is firmly associated with its power flow. Therefore, the system optimization objective is to optimize the power flow of the storage devices. In other words, the HESS operates under the optimal power flow condition resulting in the minimal system cost. III.

POWER FLOW ALLOCATION CONTROL FOR HESS

Fig. 2 shows schematically the proposed power flow allocation control for the HESS. The SCSS power is determined based on the optimal ZLF whose smoothing time constant is optimized at initial time of each dispatching interval. The SCSS compensating the fast fluctuating wind power components makes the filtered power Pf to be low variation. Subsequently, the BESS power is defined by an average dispatching algorithm on the basis of the filtered power Pf to deliver a constant power Pd to the grid. According to the current development of PCS, the SCSS and BESS are reasonably assumed to successfully handle its power reference (i.e. Ps = Ps∗ and Pb = Pb∗ ); the power dispatch and the filtered wind power are equal to each reference, respectively: Pd = Pd∗ and Pf = Pf∗ .

(7)

Pw Pd

(8)

B. Storages Cost and Technical Considerations To optimize the operation of HESS in WF, cost and

Ps

BESS

Fig. 1 shows a schematic diagram of the HESS integrated with the WF to stabilize the wind power variation. The SCSS and BESS are connected to the point of common coupling (PCC) via power conversion systems (PCSs) that need to handle bidirectional power flows. In addition, when the storages are charged, their powers are denoted by positive values (i.e. Pb > 0 and Ps > 0 ) and vice versa. Under assumption that the system is lossless, the storages output power is identical to the power delivered to PCC. Consequently, the power dispatch can be expressed as follows: (1) Pd = Pw − Ps − Pb .

Pd

SCSS

II.

Pw

Optimal ZLF

Pb

BESS

Pf∗

Pb∗

Ps∗

Pd∗

SCSS

Ps

Average Algorithm

Fig. 2. The proposed power allocation control scheme for the HESS.

003011

Pf

A. Determination of SCSS Power Flow The SCSS takes the role of compensating the fast fluctuating wind power components, so that its power is determined based on the ZLF. In the conventional method, a FLF leads to a requirement of high storage capacity because of the long phase delay problem [7], [9]. In this paper, we develop an optimal ZLF to eliminate the phase delay issue so as to minimize the storage capacity. The ZLF means that in the pass-band region the phase and the magnitude should be zero degrees and decibels (dB), respectively. Meanwhile, in the stop-band region, the magnitudes should be as small as possible. To design the ZLF with a reduced number of coefficients, a symmetrical forwardreverse digital finite impulse response filter is applied, which is defined as follows Κ

H ( z ) = α0 + ¦ α k ( z k + z − k ) .

1.0

Pf Using FLF

Pw

Power (pu)

0.8 0.6

Pf Using ZLF

0.4 0.2

0 0

1

2

3

4

7 5 6 Time (Td )

8

9

10

N

(a)

(10)

k =1

Its frequency response is derived as Κ

H ( e jωTs ) = α0 + 2¦ α k cos( kωTs ) ,

(11)

k =1

Κ

Pf (t ) = α0 Pw (t ) + ¦ αk [ Pw (t − kTs ) + Pw (t + kTs )] .

(12)

(b) Fig. 3. The power flow of the SCSS respecting to the proposed ZLF and the conventional FLF. (a) Wind power and the filtered power. (b) SCSS power. 1 .0

Power (pu)

where K is length of the filter coefficient and Ts is the system sampling time. The frequency response is clearly real when all coefficients α k are real, which leads to a zero-delay time response for the filter. The filter coefficients α k are dependent on the smoothing time constant Tc , and they are computed by a simple process introduced in [7]. Using the FLF, wind power is filtered as k =1

0 0

To achieve such filtered power, the SCSS needs to provide a power defined as follows: Κ

Ps (t ) = [1 − α0 ] Pw (t ) + ¦ α k [ Pw (t − kTs ) + Pw (t + kTs )] .

(13)

k =1

In order to illustrate the SCSS power determination by using the ZLF, Fig. 3 shows the wind power, the filtered power, and the SCSS power by means of the ZLF and FLF. Because of the delay phase in FLF, the filtered power Pf by FLF is delayed compared with the wind power. Consequently, the SCSS power required by conventional FLF is significantly higher than that of the proposed ZLF method. B. Dispatch Power and BESS Power As shown in Fig. 2, the power dispatch and the BESS power are defined based on the filtered power Pf that is low variation; the BESS handles only the slowly fluctuating power component. This feature of the proposed coordination control is able to prolong the BESS operational lifetime. In the modern electric power market, the power dispatch of all generation systems must be constant in each dispatching interval. One of the most effective ways to achieve this requirement is to define the power dispatch by means of averaging the wind power. In the nth dispatching interval, the power dispatch is defined as follows: Pd (t ) =

1 Td

( n +1)Td

³

Pf (τ )dτ

(14)

0 .5

Pf ( t )

1

2

Pd ( t )

3

4

5 7 6 Time (Td )

8

9

10

11

12

Fig. 4. The dispatch power defined by the average dispatching algorithm.

Fig. 4 illustrates the average dispatching algorithm, in which the power dispatch is constant for each dispatching interval. By subtracting the dispatch power from the filtered power, the BESS power is defined as: (15) Pb (t ) = Pf (t ) − Pd (t ) It is seen that the filtered power is low variation, so the BESS power is much less fluctuating level than that of the wind power. IV. OPTIMIZATION OF HESS CAPACITY Based on the proposed power allocation algorithm presented in previous section, the capacity of storages depends on the smoothing time constant Tc of ZLF. With a longer Tc , the SCSS needs to charge and discharge a higher power, and the BESS power is thereby smaller. Meanwhile, when Tc is small, the SCSS compensates a low power and the BESS power is high. As a result, the smoothing time constant takes the key role to optimize the HESS capacity. At beginning of a new dispatching interval, we optimize the smoothing time constant Tc by using the PSO method. The objective function of the optimization model is expressed as

nTd

003012

MIN

Tc ∈( 2Ts , +∞ )

TSC(Tc ) .

(16)

and (n −1)Td ≤ t < nTd . Note that T = NTd . Step 2: At beginning of the nth interval, the PSO method is executed to search the optimal smoothing time constant. Subsequently, the SCSS power, the dispatch power, and the BESS power in the nth interval, which are respectively denoted n n n by Ps (t ) , Pd (t ) , and Pb (t ) , are computed from (12)-(15). Next, the rating of HESS power and HESS energy in the nth max max max interval presented by Ps [n] , Pb [n] , Es [n] , and

Ebmax [n] are defined in (5)-(8). Step 3: After determining the required HESS capacity in each dispatching interval, the largest value among these data is defined as the HESS capacity rating that is capable of compensating successfully the wind power variation as follows: (17) Psrat = MAX Psmax [ n]

{

}

{

}

Esrat = MAX Esmax [ n] 1≤ n ≤ N

(18)

n = 1; N = T / Td ; Psrat = 0; E srat = 0; Pbrat = 0; Ebrat = 0; n>N

Yes

END

No

1≤ n ≤ N

Psrat = MAX { Psrat , Psmax [n]}

} } [ n]}

Pbrat = MAX Pbrat , Pbmax [ n]

E srat = MAX E srat , E smax [n] n = n +1

}

(20)

V. NUMERICAL EXAMPLE To evaluate the proposed optimization system, we provide a numerical example using the MATLAB software. The wind speed profiles for the year of 2013 measured on Jeju Island, South Korea, with a 12-s sampling time (i.e., T = 1 year and Ts = 12 s) were converted into the power data using a 3-MW PMSG-WT model [13]. In addition, the dispatching time interval is assumed to be set to 1 hour by the TSO (i.e., Td = 1 h). Because of the low cost and firmly developed technology, we utilized the Lead-Acid battery combining with the supercapacitor to build the HESS. The system cost is determined based on the following prices [14]: Cbp = 300 $/kW, Cbe = 200 $/kWh, Csp = 125 $/kW, and Cse = 1000 $/kWh. For easy understanding, we only consider wind power within the first day of the year. In Fig. 6, the wind power Pw (t ) , the filter power Pf (t ) , and the dispatch power Pd (t ) in the sampled day are shown. It can be recognized that the dispatch power is constant in each dispatching interval, although the wind power is highly fluctuating. The filtered power is much less varying compared with the wind power, which is used to allocate the BESS power and SCSS power suitably. In order to obtain the optimal allocation power flow for the HESS, the optimization strategy based on the PSO method with 50 particles is proposed, whose performance is demonstrated in Fig. 7. At the beginning of each dispatching interval, the PSO is executed to minimize the system cost TSC by searching the optimal smoothing time constant Tc . In detail, Fig. 7(a) shows the search result of the PSO in each dispatching interval, and Fig. 7(b) shows the search objective outcome (i.e., TSC) respecting to the search variable (i.e., Tc ) at the 22th dispatching interval. When the particle owns a value of 641, the TSC becomes minimal at 445,000$; this particle defines the optimal value of the smoothing time constant. To determine the rating of the HESS capacity, Fig. 8 and 9 show the responses of HESS power and energy in a day. In 3.0

Fig. 5. Flowchart of the process used to define the optimal HESS capacity.

Power (MW)

• Compute E smax [ n], Emax [ n] from (7)&(8) b

rat max b , Eb

{

These three steps are summarized in the flowchart shown in Fig. 5. By increasing index n by one in each interval, the minimum HESS capacity requirement in each interval Td is calculated from (5)–(8). When index n reaches N, all the wind power data sets are considered, and the HESS capacity rating is determined from (17)-(20).

• Execute PSO to search the optimal Tc

Ebrat

(19)

Ebrat = MAX Ebmax [ n]

• Compute Psmax [ n], Pbmax [ n] from (5)&(6)

{ { = MAX { E

}

1≤ n ≤ N

Corresponding to a value of the variable TC , the SCSS power and BESS power in the evaluated dispatching interval are determined from (16)-(19). Subsequently, the required capacity of SCSS and BESS in the dispatching interval is defined from (5)-(8). After computing the TSC as (9), all TSC values are compared each other to select the smallest one. The smoothing time constant makes the TSC smallest is the optimal solution. In this paper, we apply the PSO method with 50 particles, which is one of the most enhanced optimization methods, to search the optimal smoothing time constant. The optimization of HESS capacity is taken into account during the design and planning of the wind-storages system. In order to ensure that the HESS capacity is able to compensate the wind power variation under any wind condition, a longterm wind power profile from several months to years is generally required. Based on the given wind power profile in T, the following steps are suggested to determine the optimal HESS capacity: Step 1: Divide the wind power profile into N sets, in which each set contains the wind power for the time interval Td. In n the nth set, the wind power is denoted Pw (t ) , where 1≤ n ≤ N

1≤ n ≤ N

{

Pbrat = MAX Pbmax [ n]

2.0

1.0

0 0

Pf ( t )

Pw ( t ) Pd ( t )

10 12 14 16 18 20 22 24 Time (h) Fig. 6. The wind power, filtered power, and dispatch power in the sampled day.

003013

2

4

6

8

(a) (a) 1.2

5.0

Power (MW)

TSC ($)

5.4

x 105

4.6 4.2 0

600

1200

1800

Tc (sampling)

Psmax ( n)

0.8

0.4

0

0

2

4

6

8

10

(b)

Pbrat

Ebrat

(MW)

(MWh)

(MW)

(MWh)

TSC ($)

Conventional [22]

1.5

0.65

0.85

0.25

1,142,500

Proposed

1.25

0.2

1.08

0.4

760,250

18

20

22

24

1.0

Energy (MWh)

E srat

16

Fig. 8. Determination of HESS power rating. (a) HESS power flow. (b) Maximum of HESS power in each dispatching interval.

COST COMPARISION OF THE PROPOSED OPTIMIZATION METHOD WITH THE CONVENTIONAL METHOD Psrat

12 14 n (Td )

(b)

Fig. 7. The PSO performance. (a) The optimal value of smoothing time constant in each dispatching interval. (b) The PSO performance at the 22nd dispatching interval. TABLE I.

Pbmax ( n)

0.5 0 −0.5 −1.0 −1.5 0

MW, Esrat = 0.2 MWh, Pbrat = 1.08 MW, and Ebrat = 0.4 MW. In order to highlight the effectiveness of the proposed optimization method, the proposed method is compared with the conventional one. In Table I, the required HESS capacity and the TSC in each method are listed. Notably, the conventional method makes the system cost be 1,142,500 $, whereas the system utilizing the proposed method can significantly reduce the cost to be 760,250 $. In addition, Fig. 10 shows the system cost respecting to four methods with the conventional method, only BESS, only SCSS and the proposed one. It is clear to conclude that the proposed method leads the system cost being the smallest; the proposed method is the most economized solution.

2

4

6

8

10

12 14 n (Td )

16

18

20

22

24

22

24

(a) 0.25

Energy (MWh)

Fig. 8(a), the SCSS power and BESS power are depicted. Based on the power flow, the maximum of SCSS power and BESS power in each dispatching interval are determined as shown in Fig. 8(b). Based on the maximum SCSS power, the required SCSS power rating in a day is defined as 1.085 MW at the 22nd dispatching interval. Similarly, the maximum BESS power at 8th dispatching interval is the most dominant value, so that 1.05 MW is defined as the BESS power rating in a day. In terms of the HESS energy, the results are shown in Fig. 9. The required SCSS energy rating in the day is 0.175 MWh at 12th dispatching interval, and the required BESS energy rating in a day is 0.22 MWh at 6th dispatching interval. By running the proposed process for all year of wind power data, we obtained that the SCSS and BESS capacity were Psrat = 1, 25

E bn ( t )

E sn ( t )

E smax ( n)

0.2

Ebmax ( n)

0.15

0. 1 0.05 0

0

2

4

6

8

10

12 14 n (Td )

16

18

20

(b) Fig. 9. Determination of HESS energy rating. (a) HESS energy flow. (b) Maximum of HESS energy in each dispatching interval.

Fig. 10. Comparision of the proposed HESS with the conventional systems.

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[2]

VI. CONCLUSIONS In this paper, an optimal HESS is proposed to stabilize the wind power variation. The HESS including the BESS and SCSS holds both merits of individual storage system, so that the HESS is able to compensate the wind power variation to dispatch constantly power to grid under any wind condition. To allocate effectively the power for each storage device in HESS, we propose a power allocation control strategy to determine the SCSS power based on a zero-phase low-pass filter as well as the BESS power to commit an average dispatching method for constant power dispatch to grid. At the beginning of each dispatching interval, the smoothing time constant of the filter is optimized by the PSO to minimize the total system cost. In order to evaluate the proposed optimal system, a numerical example is performed by using a 3-MW wind turbine generator with a real wind speed data, and the proposed optimization method is demonstrated as the economized solution for HESS in wind power applications. In future, we will consider the system operation in transient time between two consecutive dispatching intervals to reduce the HESS power fluctuation during this period. In addition, the state of charge of each storage device in HESS will also be taken into account to manage the system for short-time operation. ACKNOWLEDGMENT

[4]

[5]

[6] [7] [8] [9] [10]

[11]

This work was supported by the National Research Foundation of Korea grant funded by the Korean Government (NRF-2013R1A2A2A01016398). REFERENCES [1]

[3]

[12] [13]

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