Lifetime Cost Optimized Wind Power Control Using ... - IEEE Xplore

Lifetime Cost Optimized Wind Power Control Using Hybrid Energy Storage System §

Dongsheng Li† , Fenglong Lu§ , Qin Lv§ , Li Shang§ † Tongji University, Shanghai 201804 P.R.China University of Colorado Boulder, Boulder, CO 80309 USA

Abstract— This paper presents the use of hybrid energy storage, composed of ultracapacitor and Lithiumion battery, to improve wind power stability. A control algorithm based on artificial neural network is proposed to manage the run-time use of the hybrid energy storage system to (1) optimize wind power predictability hence power grid stability, and (2) minimize the overall lifetime cost of the energy storage system. Evaluations using wind farm data demonstrate that, compared with two recently proposed control methods, the proposed control algorithm can extend system lifetime by 62% and 143%, and reduce the overall lifetime energy storage system cost (20 years) by 41% and 59%, respectively.

I. I NTRODUCTION During recent years, wind power generation has become one of the fastest growing electric power forms. Base on the 2011 Wind Technologies Market Report [1], 6,816 MW of new capacity was added in the U.S. in 2011, bringing the total wind power capacity of the U.S. to nearly 47,000 MW, and new wind power capacity additions in 2012 are anticipated to exceed that of 2011. However, wind energy has a nature of intermittency and time variability. A key challenge facing on-grid connected wind farms comes from the unpredictable and fast-ramping output power of wind turbines [2]. Various energy storage systems have been proposed to regulate and smooth wind farm output power. Due to the smaller size and higher energy density, using Lithium-ion battery energy storage systems for wind power stability control, is drawing more attention from the research community [3], [4], [5], [6]. There are two main roadblocks when introducing Lithium-ion battery energy storage systems into wind farms: system cost and lifetime [2], [4], [6], [7]. Existing works mainly focus on system cost optimization, namely one-time system installation cost [2], [4], [7]. But few work considers the cost related to the system’s lifetime [6] and capacity degradation [8]. Wind turbines typically have a lifetime of 20 years [9], while Lithium-ion battery has a shorter lifetime which is closely related to its limited cycle life (typically around 1000 [8]) and the workload throughput situation (e.g., average number of daily charging/discharging cycles). Once the battery capacity decreases to a certain level which cannot support the regulation function, it needs to be replaced with a new set. Thus, an optimal control system should have long energy storage system lifetime and low overall system cost in long-term,

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e.g., 20 years, while smoothing the unpredictable wind power output. We observe that hybrid energy storage system, combining an ultracapacitor and a Lithiumion battery system, has the potential to significantly extend the overall system’s lifetime and reduce the lifetime cost, due to the fact that ultracapacitor has much larger power density, much longer cycle life, and lower cost per unit power delivery compared with Lithium-ion battery [10]. In the hybrid energy storage system, the ultracapacitor can absorb frequent small variations from the wind power output, while the battery system can be used to buffer infrequent large output variations, such that the overall system lifetime could be extended and lifetime energy storage system cost could be reduced. In this paper, we highlight the significance of lifetime cost of energy storage system in wind power control, and tackle the problem by proposing both design-time optimization of the system structure and run-time control algorithm on top of the system. To optimize the overall lifetime energy storage system cost, a control strategy based on artificial neural network (ANN) is proposed to control the input and output of ultracapacitor and battery system. Meanwhile, genetic algorithm is adopted to train the ANN, and the system’s optimal configuration parameters. Furthermore, the proposed control algorithm is evaluated using seven-month wind power generation data from a real 50MW wind farm. Results demonstrate that an ultracapacitor-Lithium battery hybrid system can keep wind farm output transients within the predefined level. Meanwhile, compared with two recently proposed solutions — a hybrid energy storage system and a pure battery system, our method achieves 62% and 143% system lifetime extension, 41% and 59% 20-year energy storage system cost reduction, respectively. The key contributions of this paper are as follows: • A hybrid energy storage system, which consists of ultracapacitor and Lithium-ion battery system, is proposed to keep the variable wind farm output at a predictable level. • An artificial neural network based control algorithm is proposed to control the input and output of each component of the hybrid energy storage system, which could ensure minimal overall lifetime energy storage system cost. • Detailed evaluations on real wind farm data demon-

strate that the proposed system can meet wind power control requirement and achieve longer system lifetime and less overall lifetime energy storage system cost compared with two recently proposed solutions. The rest of this paper is organized as follows. Section II discusses related work. Section III presents the detailed design of the proposed hybrid energy storage system. Section IV presents the ANN-based control algorithm, system design optimization, and run-time control method. Section V evaluates the proposed solution and compares it with two recently proposed solutions. And we conclude this work in Section VI.

Transformer

AC/DC

Wind Farm

Grid Hybrid Energy Storage System (HESS) Fig. 1.

Architecture of the wind power control system.

II. R ELATED W ORK Energy storage system (ESS), such as Ultracapacitor or battery, has been adopted to control the unstable output of wind power in previous works [2], [5]. Abbey et al. proposed an ultracapacitor system to damp short-term power oscillations in wind turbine generators [11]. Qu et al. proposed a constant power control scheme for a wind farm equipped with doubly fed induction generator wind turbines using ultracapacitor [12]. Teleke et al. proposed a model predictive control strategy to control battery storage systems, which can make combined power output as smooth as possible on an hourly basis [13]. Zeng et al. proposed a decoupling and feed forward compensating control strategy, and used battery energy storage system for wind power smoothing [14]. The goals of the above works are either to make the best use of the battery system or control the wind power as smooth as possible. However, the goal of this paper is to keep wind power within predictable output and minimize overall lifetime energy storage system cost. Cost analysis has also been conducted by previous works [4], [7], [6], [15]. Le et al. proposed an economic analysis to estimate the profitability of using energy storage systems to control wind power output [7]. Gao et al. proposed an analytical technique, which can find the optimal size of battery energy storage systems for wind farm output control based on reliability cost and worth analysis [15]. Brekken et al. proposed sizing and control methods for a zinc-bromine flow batterybased energy storage system to smooth the variability of wind power [4], and artificial neural network was also adopted to control the energy storage system. But their ANN control method is of different network structure and optimization goal compared with our ANN-based control algorithm. All the above works only consider system’s initial cost, while our work focuses on minimizing lifetime system cost. The work of Babazadeh et al. shares a similar goal with this work. They proposed a hybrid energy storage system as well as a control strategy, which is to use ultracapacitor as much as possible and use battery system when the ultracapacitor cannot meet the control requirements [6]. However, their work focused on longest lifetime of the hybrid energy storage system,

while our work aims to ensure minimal lifetime system cost. III. S YSTEM D ESCRIPTION In this section, we first present the wind power control requirement. Then, the system architecture of the hybrid energy storage system is presented in detail. A. Wind Power Control Requirement The wind power control requirement is to allow the combined output of wind farm and the associated energy storage system to meet an hour-ahead forecast power output within 4% during 90% of the time [4], [16]. This constraint is based on penalties specified by the Bonneville Power Administration (BPA) [17], in which penalties occur if actual wind farm output is 4% over or under forecasted wind farm output. Wind power forecast could be performed in various ways, we adopt the hourly persistence model [4] in this paper. Let h be the hour of the day, the forecasted wind power output at h could be computed as follows: Pˆ (h) = mean(Pˆ (h − 10min), Pˆ (h + 10min)) (1) where Pˆ (h + 10min) is estimated by the real power output at time h − 20min, and Pˆ (h − 10min) is estimated by the real power output at time (h − 1) − 20min. Based on Equation 1, wind power forecast is performed every 20 minutes before the top of each hour. Let P˙ (h) be the combined output of wind farm and the hybrid energy storage system, then the control requirement is expressed as follows: T (|

Pˆ (h) − P˙ (h) | ≤ 0.04) ≥ 90% Pˆ (h)

(2)

where T (x) stands for the percentage of time when event x occurs. B. System Architecture As illustrated in Figure 1, the hybrid energy storage system consists of an ultracapacitor and a Lithium-ion battery system. Generally, ultracapacitors are of high cost per kWh, but its cost per charge cycle is relatively lower than that of battery system due to its long cycle life (e.g. 100,000 [10]). A pure ultracapacitor would be not practical, because it is too expensive

for initial installation. And a pure Lithium-ion battery system would be very expensive in terms of life-time cost, because the lifetime number of charge cycles are only 500 — 2000 [8]. Thus, a hybrid energy storage system (HESS) which consists of an ultracapacitor and a Lithium-ion battery system could be a good combination. In the HESS, ultracapacitor is used to smooth small frequent variations, and Lithium-ion battery system is used when the large unfrequent varitions occur. This would increase the total cost of the energy storage system compared with pure Lithiumion battery system, but could reduce the overall lifetime system cost greatly, because cost per charge cycle of ultracapacitor is much lower than that of Lithiumion battery. Meanwhile, the total cost of the hybrid energy storage system would not be too high to accept compared with pure ultracapacitor. IV. W IND P OWER C ONTROL S TRATEGY In this section, we present the proposed wind power control algorithm. First, an HESS cost model is proposed to evaluate the overall life-time system cost of utilizing such systems for wind power control. Then, an ANN-based control algorithm is proposed to decide the outputs of the HESS to meet the control requirement and ensure minimal life-time ESS cost. Meanwhile, genetic algorithm is adopted to train the artificial neural network parameters, and help find optimal configuration parameters of the HESS. A. Cost Modeling Lithium-ion battery’s lifetime may be affected by battery’s material, manufacturing, environment of operation, and battery management. Therefore lifetime is usually decided by whichever condition is reached first, number of charge/discharge cycle, calendar life, and capacity degration (retention ratio). In our case, the limit of cycle life comes earlier than calendar life does, and our model ensures control requirement can still be satisfied even the capacity retention at the end of lifetime cycles is included. The lifetime charge/discharge cycles at 100% depth-of-discharge (DoD) ranges from 500 to 2,000 cycles [8]. In our cost modeling, the following assumptions are used. The battery system we proposed lasts 1,000 cycles before replacement, which means during the 20 years’ operation of the wind power plant, extra battery replacement cost will be included. Moreover we convert the integrated energy processed by the battery to the equivalent full DoD cycle numbers, based on the assumption that the cycle life is a weak function of the experienced DoD [18]. Moreover, we assume 60% of capacity retention ratio when the battery’s cycle life is reached [8]. To ensure minimal overall lifetime ESS cost in 20 years, we calculate one-time cost of the HESS as well as system replacement costs after ends of cycle lives of ESS. For ultracapacitor, its cost is measured as 6000$/kWh and its cycle life is measured as 100,000 [10]. For Lithium-ion battery system, its

cost is measured as 500$/kWh [19], and its cycle life could be 500 to 2,000 [8], so we choose 1000 in this paper. Based on the above cost and cycle life measurements, the overall HESS cost in 20 years could be measured as follows: µb Jb Cb Cost = µu Ju + × 20 (3) CPb Nb where µu and µb are costs per kWh of ultracapacitor and Lithium-ion battery, respectively. Ju and Jb are rated energy capacities required during wind power control of ultracapacitor and Lithium-ion battery, respectively. CPb , the Lithium-ion battery capacity retention ratio, is adopted here to calculate the initial cost of Lithium-ion battery so that the active capacity is enough throughout its lifetime. Cb20 is the total number of charge cycles of Lithium-ion battery required C in 20 years. Then, Nb20 reflects how many Lithiumb ion batteries are replaced in 20 years. It should be noted that cycle life of ultracapacitor would be enough for 20 years, so replacement of ultracapacitor is not necessary. Regarding the maximum power rating, we use 6C for both ultracapacitor [20] and Lithium-ion battery [21]. And we use 85% as the averaged energy conversion efficiency of both ultracapacitor system and Lithium-ion battery system [6]. However, it should be noted that for ultracapacitor systems, the energy loss mainly comes from the power electronics interfaced between ultracapacitor and the power bus, because the efficiency of power electronics converters is function of the runtime voltage of ultracapacitor array, dynamically ranging from 75% to 95%. For Lithium-ion battery system, the loss in battery cells dominates. B. ANN-based Control Algorithm Artificial Neural Network (ANN) is a widely adopted machine learning model, which can be used to model complex relationships between inputs and outputs. In wind power control, ANN could be trained to control runtime inputs and outputs of ultracapacitor and Lithium-ion battery system, with the goal of optimizing overall lifetime cost of HESS. Meanwhile, optimal system configuration parameters, i.e., rated energy capacities of ultracapacitor and Lithium-ion battery system, could also be obtained during the system optimization process. The structure of the proposed artificial neural network is as follows: • Input layer: Pwind , Pˆwind , Ju , SOCu , Jb , SOCb ; • Hidden layer: six hidden neurons; • Output layer: ultracapacitor power output, Lithiumion battery system power output. where Pwind is the actual wind power output, Pˆwind is the forecasted wind power output, Ju and SOCu are the rated energy capacity and state of charge (SOC) of ultracapacitor, and Jb and SOCb are those of Lithium-ion battery system. The activation function of the hidden layer is Bipolar Sigmoid function, and the

activation function of the output layer is linear output function. Given the ANN defined above, the output of the ANN will be used to determine the runtime input/output of HESS. For instance, the ultracapacitor will charge if its output in the network is negative and discharge if its output in the network is positive, and so will the Lithium-ion battery system. C. ANN Training and System Optimization The genetic algorithm [22], which is proved to be effective for training ANN [23], is adopted to train the proposed ANN using historical wind farm data. The purposes of the genetic algorithm based training are: 1) to decide optimal sizing configuration of the HESS, and 2) to find out optimal weights among neurons for runtime charge/discharge control of the HESS. During the training process, Equation 3 is used as the train function, which can help optimize system lifetime and reduce overall lifetime energy storage system cost while ensure the wind power control requirement. After the training, optimal system configuration parameters, i.e., the rated capacities of ultracapacitor (Ju ) and Lithium-ion battery system (Jb ) can be found, which can be applied in the design of the HESS. Meanwhile, optimal weights among neurons of three layers could also be obtained, so that these weights could be adopted in runtime wind power control scenario. The chromosome in the genetic algorithm is defined as follows: {Ju , Jb , w1 , ..., wm , w˙ 1 , ..., w˙ n }

(4)

where w1 , ..., wm are weights between neurons in input layer and neurons in hidden layer and w˙ 1 , ..., w˙ n are weights between neurons in hidden layer and neurons in output layer. The basic steps in the genetic algorithm are defined as follows: • selection: choose 1/2 members in population with lowest costs (as computed by Equation 3); • crossover: each element of two chromosomes are swapped with probability ρ; • mutation: each element in a chromosome is added with a random noise within [−η%, η%], where η > 0 is the mutation rate; Given the definitions above, the genetic algorithm based ANN training and hybrid energy storage system configuration optimization method is presented in Algorithm 1. D. ANN-based Runtime Wind Power Control Inputs: Pwind, Ṕwind, Ju, Jb, SOCu, SOCb

Fig. 2.

outputu The Proposed Artificial Neural Network (ANN) outputb

Constrains of HESS: Efficiency, Rated Power, Rated Capacity

outputu'

outputb'

ANN-based Runtime Wind Power Control Process.

Algorithm 1 GAOptimization(k, N ) Require: k is the initial population size, and N is the maximum number of iterations. 1: Let P = {p1 , p2 , ..., pk } be randomly generated initial population, and n = 0; 2: while n < N do 3: Run simulations on wind farm data with each pi ∈ P , and obtain costs Cp1 , ..., Cpk ; 4: for each pi ∈ P do 5: if |Pwind − Pˆwind | ≤ 4% less than 90% time then 6: Cpi = +∞; 7: end if 8: end for 9: Do selection on P ; 10: Randomly divide P into two subsets P1 and P2 , and r = 0; 11: while r < |P |/2 do 12: Randomly choose pi ∈ P1 , pj ∈ P2 ; 13: Do crossover on pi and pj ; 14: r++; 15: end while 16: Combine P1 and P2 as new P ; 17: for each pi ∈ P do 18: Do mutation on pi ; 19: end for 20: n++; 21: end while 22: return p ∈ P with lowest Cp ;

After the HESS is sized and all weights among neurons in the ANN are obtained, the runtime control process is illustrated in Figure 2. The key steps are as follows: 1) all the inputs data are put into the proposed ANN, and outputs of ANN is computed based on all weights among neurons and activation functions among layers; 2) check if the commanded outputs of ultracapacitor and Lithium-ion battery system are within the constrains of each component of the HESS, and regulates the outputs if energy conversion efficiencies, rated powers, or SOCs are out of the constraints; and 3) apply the regulated outputs to control the runtime charge/discharge of the HESS. V. E VALUATIONS A. Experimental Setup Our evaluations are conducted on real wind power generation data from a 50 MW wind farm, collected at seven-second scale lasting for seven months. The proposed hybrid ANN-based wind power control solution (HANN) is compared against two recently proposed solutions. The first, named as Hybrid algorithm [6], can control an HESS consisting of a ultracapacitor and a Lithium-ion battery system to smooth the variable wind power output. Their method is to use ultracapacitor whenever possible, and to introduce battery system if ultracapacitor cannot meet the control requirements.

1.5

1.5

Wind Power (One-hour forecast)

Wind Power (One-hour forecast) System Power

1

Power [pu]

Power [pu]

Actual W ind Power

0.5 0 10

11 12

13

14 15

16 17

18 19

1 0.5 0 10

11 12

13 14

day # Fig. 3.

CDF of error [%]

wind power wind + HESS 50

0

0.05

17 18 19

Illustration of real wind power, forecasted wind power and wind power after control.

100

0

15 16

day #

0.1

0.15

0.2

control error Fig. 4. Cumulative distribution functions (CDFs) of errors between wind power and forecasted wind power before and after control.

In their hybrid system, the designed energy capacity of ultracapacitor system consists 10% of the overall HESS capacity. The second is an ANN-based control strategy (BANN) [4], which can control a batterybased ESS to smooth the variable wind power output with minimal initial system cost. Their ANN regulates only the battery system’s input/output power. Meanwhile, their optimization goal is to achieve lowest initial system cost, while the overall lifetime cost of ESS is not considered. B. Wind Power Control Performance Figure 3 shows the actual power output of wind farm, forecasted wind power and the combined output of wind power and HESS. From the results, we could see that the actual wind power is different with forecasted wind power remarkably. However, the combined output of wind power and HESS is very close to that of the forecasted wind power. This intuitive result demonstrate that the wind power control strategy is indeed effective. Figure 4 shows the cumulative error distributions between output wind power and forecasted wind power before and after control. From the results, we can clearly see that most of errors between combined output wind power and forecasted wind power are within 4%. However, the errors between real wind power and forecasted wind power are greater than 4% in 80% of the time. This result indicates that the wind farm will be penalized in 80% of the time without wind power control, and the penalty time will be reduced to less than 10% after wind power control. C. Cost Analysis and Comparison The analysis and comparisons of average energy storage system cost per day are listed in Table I.

Daily numbers of charge cycles of ultracapacitor and Lithium-ion battery are measured by average over all the seven month data. Cost per charge cycle is measured from dividing initial system costs by cycle life. Then, average energy storage system cycle cost per day is measured by multiplying daily numbers of charge cycles with costs per charge cycle. From the results, we can see that the proposed HANN method achieves much lower average daily system cost compared with the other two solutions (about 41% lower than Hybrid method and 70% lower than BANN method). The reason HANN method outperforms Hybrid method is that HANN adopts an ANN-based control strategy, which can ensure less daily charge cycles of HESS during control process compared with the Hybrid method. And the reason HANN method outperforms BANN method is that BANN method only adopts battery system, which has much higher cost per charge cycle compared with hybrid system. One Time ESS Cost (million $) 20

10

0

ESS Lifetime (year) 4 3.5 3 2.5 2 1.5 1 0.5 0

Hy BA HA bri NN NN d

140 120 100 80 60 40 20 0 Hy BA HA bri NN NN d

20-year ESS Cost (million $)

Hy BA HA bri NN NN d

Fig. 5. Long-term cost comparison of three control systems in a 50-MW wind farm.

Figure 5 shows the initial energy storage system cost, system lifetime, and overall energy storage system cost comparison of the three wind power control solution in a 50-MW wind farm. The overall energy storage system costs are measured as Equation 3. Compared with Hybrid method, HANN method achieves 30% lower initial energy storage system (ESS) cost, 62% longer ESS lifetime and 41% lower 20-year ESS cost. The reason HANN method outperforms Hybrid method in all three aspects is that HANN requires less rated energy capacities and less daily number of charge cycles, which is due to the optimal control process of HANN. Compared with BANN method, HANN method requires higher initial ESS cost, which is around 41%. But HANN achieves about 143% longer ESS lifetime and 59% lower 20-year ESS cost

TABLE I AVERAGE DAILY SYSTEM

Control Method Hybrid BANN HANN

CYCLE COST COMPARISON OF DIFFERENT CONTROL METHODS IN A

Rated Capacity (MWh) Ultracapacitor Battery 1.9 18.8 NA 20.6 0.9 18.5

#Daily Charge Cycle Ultracapacitor 7.06 NA 4.54

compared with BANN method. In the comparisons, BANN method achieves the lowest one-time cost, that is because pure Lithium-ion battery is cheaper than hybrid energy storage systems which contain ultracapacitors. But as battery systems has much lower lifetime charge cycles, the ESS lifetime of BANN is shorter than the other two hybrid methods. Overall, the proposed HANN method achieves longest ESS lifetime and lowest 20-year ESS cost compared with the other two solutions, so that HANN is more beneficial to wind farms in long-term. VI. C ONCLUSION Wind power control has recently become an emerging challenge due to the large-scale penetration of wind power. As wind power control system should be run during the lifetime of wind turbine (20 years, typically), a lower lifetime energy storage system cost would be more beneficial to wind farms. In this paper, a hybrid energy storage system, composed of a ultracapacitor and a Lithium-ion battery system, is adopted to smooth variable wind power. An ANN-based control algorithm is proposed to control the input/output of the hybrid energy storage system. Using genetic algorithm, optimal rated energy capacities of ultracapacitor and Lithium-ion battery as well as the weights among neurons of ANN are trained. Evaluations on real wind farm data demonstrate that the proposed control solution could extend energy storage system lifetime and reduce the overall lifetime energy storage system cost compared with two recently proposed solutions. ACKNOWLEDGEMENT This work is supported in part by National Natural Science Foundation of China under Grant No. 61233016 and the Fundamental Research Funds for the Central Universities. R EFERENCES [1] U.S. Department of Energy, “2011 wind technologies market report,” 2012. [2] M. Swierczynski, R. Teodorescu, C. N. Rasmussen, P. Rodriguez, and H. Vikelgaard, “Overview of the energy storage systems for wind power integration enhancement,” in 2010 IEEE Int. Symp. on Industrial Electronics, 2010, pp. 3749– 3756. [3] A. Arulampalam, M. Barnes, N. Jenkins, and J. Ekanayake, “Power quality and stability improvement of a wind farm using statcom supported with hybrid battery energy storage,” Generation, Transmission and Distribution, IEE Proceedings-, vol. 153, no. 6, pp. 701–710, 2006. [4] T. Brekken, A. Yokochi, A. Von Jouanne, Z. Yen, H. Hapke, and D. Halamay, “Optimal energy storage sizing and control for wind power applications,” IEEE Transactions on Sustainable Energy, vol. 2, no. 1, pp. 69–77, 2011.

Battery 1.1 1.65 0.68

50-MW WIND FARM .

Time of Error≤0.04

Average Daily System Cycle Cost($)

0.9000 0.9008 0.9007

11,150 21,500 6,550

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