Cost-Optimized Battery Capacity and Short-Term Power ... - IEEE Xplore

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 51, NO. 1, JANUARY/FEBRUARY 2015

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Cost-Optimized Battery Capacity and Short-Term Power Dispatch Control for Wind Farm Cong-Long Nguyen, Student Member, IEEE, Hong-Hee Lee, Senior Member, IEEE, and Tae-Won Chun, Member, IEEE

Abstract—Utilizing the optimal capacity of a battery in wind–battery hybrid power systems is crucial to minimize costs. In this paper, we modify the min–max dispatch method to effectively integrate wind power into the grid. In line with the dispatch principle, we define a lifetime cost function, which indicates the battery energy storage system cost of dispatching 1 kWh of wind energy, to determine the optimal battery capacity. By using the optimal battery capacity, the operation costs are minimized, and the hybrid system is able to dispatch the scheduled power at any dispatching time. Moreover, the short-term power dispatch control is also considered; we smooth the transient power between two consecutive dispatching intervals and control the state of charge of the battery by an online control algorithm. To evaluate the performance of the proposed optimization method and the short-term power dispatch control, we perform several numerical studies with a 3-MW wind turbine generator and real wind speed data. Index Terms—Battery energy storage system (BESS), optimization methods, short-term power dispatch, state of charge (SOC) control, wind–battery hybrid power system (WBS).

I. I NTRODUCTION

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ENEWABLE energy sources such as wind energy, solar energy, and tidal energy can be used to address the current energy crisis and environmental pollution issues [1]. Among these energy resources, electricity generation from wind turbines (WTs) is favored because of its lower investment cost compared with photovoltaic or tidal systems [2]. In addition, improvements in technology to manufacture high-power WTs have resulted in rapid increases in wind-power capacity year after year, i.e., from 159 GW in 2009 to 283 GW in 2012 [3]. However, similar to other renewable resources, wind generation is unsteady and uncontrollable because wind speed depends on natural and meteorological conditions. Moreover, the high penetration of intermittent power into the grid is associated with serious technical challenges related to grid connections, power quality, and electric system reliability [4], [5]. Therefore, the Manuscript received December 3, 2013; revised March 24, 2014; accepted May 15, 2014. Date of publication June 10, 2014; date of current version January 16, 2015. Paper 2013-SECSC-940.R1, presented at the 2013 IEEE Energy Conversion Congress and Exposition, Denver, CO, USA, September 15– 19, and approved for publication in the IEEE T RANSACTIONS ON I NDUSTRY A PPLICATIONS by the Sustainable Energy Conversion Systems Committee of the IEEE Industry Applications Society. This work was supported by a National Research Foundation of Korea grant funded by the Korean Government under Grant 2013R1A2A2A01016398. The authors are with the School of Electrical Engineering, University of Ulsan, Ulsan 680-749, Korea (e-mail: [email protected]; [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIA.2014.2330073

intermittent characteristic of wind power needs to be overcome before dispatching high wind power to the grid [5]–[7]. In order to mitigate wind-power fluctuations, pitch angle control for a variable-speed WT generator was introduced [8], [9]. Usually, the pitch angle control aims to capture the maximum wind power. However, when a high wind-power level penetrates into the grid, the wind-power fluctuation needs to be strictly considered, and the pitch angle control is actively adopted for smoothing the WT output power. However, this method makes the system become more complex, and the wind farm (WF) might not capture the maximum power from the available wind [9]. Recently, utilizing a battery as an energy buffer to compensate for the wind-power fluctuations has been introduced and has been getting attention in several studies [10]–[16], [22]–[26]. Using a battery can help the wind system to harness the maximum power available in the wind, which is an essential demand in all renewable energy conversion systems. However, large-scale batteries are expensive; the use of an optimal battery capacity is therefore critical to reduce system costs [10]. A battery energy storage system (BESS) and a WT cooperate to stably dispatch power to the grid. This system is referred to as a wind–battery hybrid power system (WBS), in which the dispatched power influences the BESS output power. As a result, to optimize the battery energy storage capacity, the dispatch power needs to be identified primarily. In [10], the power dispatch in a day was set to be a constant level, and the battery capacity was optimized by varying the dispatched power within the WT power rating. At the optimal capacity, the WF achieves maximum income, but the designed capacity is not unique because the method depends on the day that the wind was studied. Another widely used dispatch method involves the use of a low-pass filter to smoothen the WT output power [11]. However, this paper just concentrates on the control of the power dispatch in order to guarantee the state of charge (SOC) of the battery in a safe range, but it does not mention how to determine the optimal battery capacity. In [14], a min–max windpower dispatching method was presented; during charging the battery, the dispatched power was set to the minimum available wind power, whereas the dispatched power was allocated to the maximum wind-power range when the battery switches to the discharging stage. Based on this dispatching principle and an index function composed of the battery lifetime and costs, the economical battery capacity was then defined. The larger BESS power rating compared with other dispatching methods and the complicated determination program are the disadvantages of this dispatching method [16].

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In this paper, we develop a modified min–max dispatching method to effectively dispatch wind power into the grid, and then, a lifetime cost function is newly defined to determine the optimal BESS capacity. Two steps are required to decide which BESS capacity makes the system have the minimum cost. In the first step, the basic battery capacity that is capable of compensating for the wind-power fluctuations in any dispatching interval of the modified min–max dispatching method is assigned. In the second step, we define the lifetime cost function that shows how much money needs to be spent on the BESS to integrate 1 kWh of wind energy into the grid. Subsequently, by increasing the battery capacity step by step from the basic capacity, the minimum lifetime cost function value is allocated; hence, the optimal battery capacity is defined correspondingly. In order to effectively manage the WBS using the optimal BESS capacity, we control the short-term power dispatch by considering technical problems such as the inevitable error in the wind-power forecast, the power dispatch during the initial time of each dispatching interval, and the control of the battery SOC. Finally, we apply the proposed optimization method and a short-term power dispatch control algorithm to a 3-MW WT generator model and real wind speed data to evaluate the system performance. II. C ONFIGURATIONS AND P OWER D ISPATCHING M ETHODS FOR WBS The firm development on battery technology and the significant decrease in battery costs in recent years are two main factors that decide the use of the BESS to address the intermittent problem of wind power [16]. The BESS functions as an energy buffer for the WF: It absorbs and releases power to compensate for the intermittent wind power, allowing the integration of stable power into the grid.

Fig. 1. Hybrid WT and BESS system configurations. (a) BESS connected to the dc link. (b) BESS directly connected to the grid at the PCC.

in this paper can be applied to determine the power dispatch and the BESS output power for both systems without any distinction. Among the many available types of WT generators, the permanent-magnet synchronous generator (PMSG) and the doubly fed induction generator (DFIG) are the most popular types for industrial WTs [20]. To integrate the BESS and the PMSG-based WTs, the configuration shown in Fig. 1(a) is usually employed [24]. Meanwhile, the other configuration is recommended for the hybrid DFIG-based WT and battery system [25]. B. Definition of Required BESS Capacity

A. Configurations of WBSs Fig. 1 illustrates two common configurations of a WBS. In Fig. 1(a), the output of the WT generator is rectified and stored in the dc link to which the BESS is also connected via a bidirectional dc-to-dc converter. A grid-connected converter delivers the total power of the BESS and the WT into the grid. This configuration has two main advantageous features, i.e., the fault ride through capability and the lower battery nominal voltage. However, this configuration seems to increase the overall system losses and requires the inverter with the higher power rating. In Fig. 1(b), the BESS and the WT generator are independently connected to the grid at a point of common coupling (PCC) through power converter systems PCS1 and PCS2, respectively. Due to the reduced number of converters, the system conversion efficiency may be improved compared with the configuration in Fig. 1(a). Moreover, the BESS side converter can play a role of the static synchronous compensator (STATCOM) to improve the power system quality because it is directly connected to the grid [19]. However, it requires the battery nominal voltage to be higher. Although the two configurations have different electrical connection schemes, the proposed power dispatching strategy

The required BESS capacity, which is normally specified in terms of energy rating Ebrat and power rating Pbrat , is defined based on the BESS power flow [10], [21]. If the assumption is made that the power losses in the system are negligible, BESS power Pb (t) can be expressed as Pb (t) = Pw (t) − Pd (t).

(1)

For a system operating over time period T , the BESS power rating required is Pbrat = MAX |Pb (t)| = MAX |Pw (t) − Pd (t)| . 0≤t≤T

0≤t≤T

(2)

Fig. 2(a) shows the Pb (t) response when Pw (t) and Pd (t) are known. Because the BESS power is maximal at instant t1 , this value is defined as the BESS power rating. The net energy injected into or drawn from the battery up to time t can be calculated as follows: t Eb (t) =

t [Pw (τ ) − Pd (τ )] dτ.

Pb (τ )dτ = 0

0

(3)

NGUYEN et al.: COST-OPTIMIZED BATTERY CAPACITY AND SHORT-TERM POWER DISPATCH CONTROL FOR WF

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Fig. 3. Modified min–max dispatching power strategy.

Fig. 2. Definition of the required BESS capacity. (a) BESS power flow. (b) BESS energy response.

Similar to the power rating, the energy rating is the maximum volume defined as Ebrat = MAX |Eb (t)| . 0≤t≤T

(4)

The BESS energy response, in which the energy rating is designated at instant t0 when the BESS stores the largest amount of energy, is illustrated in Fig. 2(b). C. Overview of Power Dispatching Methods In [11]–[13], the wind power can be smoothened by a lowpass filter to assign the power dispatch. The implementation of this dispatching method is actually simple because the wind power does not need to be predicted. In addition, the SOC of the BESS can be mathematically expressed in terms of the filter smoothing time constant, allowing the system controller to manage the BESS effectively. However, this power dispatching strategy does not consider the cooperation between the WF and the transmission system operator (TSO) so that the dispatching method is not able to handle the integration of the WF into the modern electric system [16]. According to modern electric power market rules, all generation units have to submit their output power schedule to the TSO several hours ahead of time, and then, they have to commit to the scheduled power in the next dispatching interval Td (usually Td = 1 h) [23]. In [10], [15], and [26], the power dispatch is determined by means of averaging the available wind power in Td . Using the wind-power forecast data, the dispatching method is able to cooperate with the TSO. However, similar to the previous method, the BESS power flow quickly alternates from positive to negative or vice versa, which

increases the battery charge–discharge cycles with a shallow depth of discharge (DOD) level. Jiang et al. [13] verified that a shallow DOD leading to the increased charge–discharge cycle may significantly reduce the battery lifetime. To dispatch power more effectively, the min–max dispatching method was introduced in [14]. Using this approach, the power dispatch is set to the maximum or minimum wind-power levels depending on whether the BESS is in a discharging or charging phase. This method has many advantageous features, such as the ability to cooperate with the TSO, and it addresses the BESS power flow direction changing problem leading to a full charge and discharge. However, it requires higher BESS power and energy ratings than other dispatching methods [16]. D. Modified Min–Max Dispatching Method We introduce a modified min–max dispatching method to decrease the required BESS capacity, which is illustrated in Fig. 3. The power dispatch is set to the maximum or minimum wind-power level in each dispatching time Td not in all charging or discharging phases. This modified method not only has all the advantages of the original min–max dispatching strategy but also reduces the required BESS capacity. Indeed, during the discharging phase, the power dispatch of the original method is always higher than the power dispatch of the modified method, and during the charging phase, the power dispatch is always smaller. These lead to fewer BESS power fluctuations when the modified method is used compared with the original method, hence decreasing the required BESS capacity. In addition, the control of the modified dispatching system, which will be presented in Section IV, is straightforward. III. O PTIMIZATION OF BESS C APACITY As a vital part of system planning and design, the BESS capacity, i.e., how large the battery needs to be taking into account the compensation for unsteady wind power and the minimization of system operating costs, needs to be determined. We recommend using the modified min–max dispatching strategy to integrate the WBS into the grid because of its advantages of communication with the TSO, ease of control of the system, and enhancement of the operational lifetime of the BESS. To determine the optimal BESS capacity for the WF in agreement with this dispatching method, foremost,

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the basic battery capacity that is the basically required BESS volume is identified. Then, the most beneficial BESS capacity is determined to minimize the system cost. Moreover, a long-term wind-power profile is generally needed to obtain the optimal BESS capacity precisely. For example, Li et al. [14] utilized two years of wind-power data, and another project reported in [26] used one year of wind-power data to design their own system.

power and charge energy of the battery up to time t, respectively, are i i (t) = Pwi (t) − Pd; Pch ch i Pd; ch =

A. Basic Capacity of BESS

i i (t) = Pwi (t) − Pd; Pdis dis , where i i Pw (t) Pd; dis = MAX (i−1)Td ≤t