Hybrid Energy Storage With Multimode Fuzzy Power ... - IEEE Xplore

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 5, NO. 2, APRIL 2014

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Hybrid Energy Storage With Multimode Fuzzy Power Allocator for PV Systems Xue Feng, Graduate Student Member, IEEE, H. B. Gooi, Senior Member, IEEE, and S. X. Chen, Member, IEEE

Abstract—The ineluctable variations of the solar radiation, ambient temperature, and partial shading affect the performance of the photovoltaic (PV) arrays, and hence incur supply–demand mismatches in the PV-based system. This paper proposes a hybrid energy storage (HES) composed of lithium–ion batteries and ultracapacitors that can be incorporated in the PV-based system to complement the supply–demand mismatches by using a multimode fuzzy-logic power allocator. The battery used in the HES was modeled based on the experimental data and can accurately reflect the battery dynamics under varying conditions. The power allocator optimally adjusts the power contributions of the batteries and ultracapacitors and exchanges energy between them, thus compensating the supply–demand mismatches without accidentally depleting or saturating the two components. The proposed HES was evaluated in both short- and long-term scenarios. A good ac-side performance in the short-term scenario is observed due to the instant response of the ultracapacitors to the high-frequency requests. In the long-term scenario, the power allocator restrains the frequency fluctuations caused by the supply–demand mismatches within 0.2 Hz, while maintaining the batteries and ultracapacitors in a safe working region. Additionally, the participation of ultracapacitors in supplying high-frequency power is beneficial for relaxing the stress on batteries, and the simulation results show that the lifetime of batteries in the HES can be extended. Index Terms—Fuzzy logic, hybrid energy storage (HES), lithium–ion battery modeling, photovoltaic (PV) generation, ultracapacitor.

I. INTRODUCTION HOTOVOLTAIC (PV)-based power generation systems have been proliferating rapidly in recent years due to the pressing requests of renewable energy sources. Although PV is superior in terms of cleanliness and sustainability, its stability may be easily affected by temporary variations of the operating conditions such as temperature fluctuations, intermittent solar radiation, and partial shading effects [1]. One of the primary countermeasures to overcome the stability issues is to deploy the energy storage devices to preserve the surplus energy and use it to compensate the deficiency of power generation due to the degraded performance of the PV arrays. Lithium–ion batteries with high-energy density are regarded as promising candidates by virtue of their capability of smoothing

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Manuscript received August 20, 2013; accepted November 06, 2013. Date of publication January 02, 2014; date of current version March 18, 2014. This work was supported by the Singapore National Research Foundation under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. The authors are with the School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore, Singapore (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/TSTE.2013.2290543

out the temporary slow moving power fluctuations [1]–[3]. However, the lithium–ion batteries are unable to deal with the high-frequency components in the power generated from PV-based system and load profiles and may suffer from aging problems when stressed by the highly fluctuating power requests [4], [5]. To solve this problem, a hybrid energy storage (HES) combining the lithium–ion batteries and another energy preservation module targeting specifically at handling the highfrequency components should be used. Ultracapacitors can be employed as such collaborators owing to their faster response speed to high-frequency demands than batteries [6]–[9]. By taking full advantages of each of the two energy storage units, HES can effectively satisfy the varying power and energy requirements, and hence can stabilize the PV-based power generation system. In order to design and evaluate an HES system, device models that are suitable for the system-level simulations are required. Due to the complexity of internal characteristics and sensitivity to the ambient conditions, lithium–ion batteries are of special interest in the device modeling phase. It can be found in the literature that several battery models that are compatible with system-level simulations have been proposed for different purposes such as mitigating pulsed load in the hybrid microgrids [10] and virtual prototyping of portable battery-powered systems [11]. However, to delineate the behaviors of HES precisely, a sophisticated battery model that can capture the battery dynamics under varying conditions yet incur marginal computational overhead may be required. Additionally, to maximally exploit the features of the batteries and ultracapacitors in HES while protecting them from being over-charged/discharged, power sharing and energy exchange policies should be dedicatedly designed. Existing strategies such as low-pass-filter (LPF)-based approaches and the Haar wavelet method [9], [12]–[15] resolve the problem to some extent, but either neglect the impacts of the state of charge (SOC) or lack a fine-grained SOC monitoring scheme. Another motivation of our research is that most of the existing HES research activities are focused on either short- or long-term scenarios. Combined with the considerations on short-term power delivery efficiency and device safety, battery aging problems that may arise in the long-term applications should also be taken into account carefully when designing the power allocating schemes to ensure that the HES works reliably in all possible cases. In our paper, an HES that is composed of batteries and ultracapacitors is proposed to deal with generation-demand power mismatches for the PV-based power system. The main contributions of the paper are as follows: a lithium–ion battery model suitable for the system-level applications is constructed based on the intensive experimental tests. By considering the

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Fig. 1. (a) Proposed system structure and (b) overall control scheme for the proposed system.

internal resistance changes due to varying conditions and entropy heat in the thermal dynamic description, the proposed model reflects the battery dynamics accurately without relying on complicated calculations. A fuzzy-logic-based power allocator that includes three power sharing modes is proposed for HES power distribution. One fuzzy-logic controller adaptively allocates power to the batteries and the ultracapacitors under varying operating conditions according to their SOC conditions, and sufficiently exploits the energy density of batteries and the fast reactivity of the ultracapacitors to high-frequency components, thus improving the efficiency of power compensation. Another fuzzy-logic controller is used to exchange energy dynamically between the batteries and ultracapacitors to avoid overly charging or discharging the energy storage units. As a result, SOC of the ultracapacitors can be maintained within the safe working range so that the transient mismatches between the supply and demand can be fully compensated. More importantly, adopting the ultracapacitors in the HES to balance the supply and demand relaxes the burdens on the batteries and hence prolongs the life span of the batteries effectively. The rest of the paper is organized as follows. Section II describes the proposed HES system. The lithium–ion battery model validated by experimental tests and ultracapacitor model as well as the proposed multimode fuzzy-logic-based power allocator are introduced. Section III presents the evaluation of the system performance in both short- and long-term scenarios, in comparison to the traditional HES power sharing strategies. The paper is concluded in Section IV. II. SYSTEM DESCRIPTION

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METHODOLOGY

The HES system proposed in this paper is shown in Fig. 1(a). The PV arrays work as the primary source. The HES compensates the mismatches between the PV generation and load demand. The PV array output is fed to the dc link through a boost converter. Storage units are connected to the dc bus through bi-directional converters using the parallel active hybrid topology [16]. A dc-side energy is then transferred to the ac grid through a voltage-source inverter (VSI). The overall control scheme of the system is shown in Fig. 1(b), in which is the output current of PV arrays, is the power output of PV arrays, and are the power outputs of the ultracapacitors and batteries, respectively, is the HES power output, and are the three-phase voltages and currents of the inverter, respectively, and are the active and reactive power of the load demand, respectively, is the VSI power on the dc side which is calculated as the sum of and

Fig. 2. Equivalent circuits for: (a) Ultralife UBBL10 battery, (b) BMOD0058 ultracapacitor, and (c) Sanyo PV cell.

is the power reference for the power losses across the VSI, the HES which is considered as the power mismatches between and , , and are the power references for the ultracapacitors and batteries, respectively, and are the SOC of the ultracapacitors and batteries, respectively. The maximum power point tracking (MPPT) method is used to control the boost converter. The proportional resonance (PR) controller is used to control the VSI. Power sharing of the HES is managed by the multimode fuzzy-logic-based allocator which will be discussed in detail in Section II-B3. A. Device Modeling 1) Lithium–Ion Battery Modeling: We propose an electrical circuit-based model to characterize the lithium–ion batteries. Calculations of the battery dynamics using the proposed model are suitable for the system-level simulations, where computational simplicity is one of the utmost concerns. Additionally, due to the consideration of the internal resistance variations, the proposed model is able to capture the battery dynamics under different currents, temperature, and state of discharge (SOD). The model was validated by intensive experimental tests. The battery under test is the Ultralife UBBL10 battery pack. The controllable electronic load CHROMA 63200 is applied to discharge the battery using the LabVIEW program. The equivalent circuit for the battery model is shown in Fig. 2(a), in which stands for the open-circuit voltage, represents the battery terminal voltage, refers to the battery current, and and are the internal resistances. results in the instant voltage drop at the beginning of discharge and can be determined as follows [17]:

FENG et al.: HYBRID ENERGY STORAGE WITH MULTIMODE FUZZY POWER ALLOCATOR FOR PV SYSTEMS

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TABLE I CALCULATION

Fig. 3. Experimental data with 2 A discharge current at different temperatures.

, which varies as a function of the SOD changes and temperature variations, can be expressed as Fig. 4. Working mechanism of the proposed battery model.

can be calculated as follows. 1) at the reference temperature is expressed as a polynomial function of SOD. 2) Based on the experiments, battery terminal voltage changes with 2 A discharge current at different temperature values can be obtained, as shown in Fig. 3. The disparities between the voltage at the reference temperature and that at other temperature can be expressed as . can be fitted in the polynomial functions of SOD as

, , , and are where the polynomial functions that are used to represent the relationship between and SOD. 3) under a certain temperature range can be calculated by using the linear interpolation method as shown in Table I, where lin represents the linear interpolation. Working principles of the proposed model can be described by the following equations:

is the ambient of the battery, is the battery temperature, temperature, is the cooling coefficient, and is the surface area of the battery. Equation (7) was defined in [18]. The working mechanism of the proposed model is depicted in Fig. 4. Here, is kept the same as the initial under ideal cooling conditions. Otherwise for normal cooling conditions, use (7) to update . The simulation results using the proposed model are compared with the experimental data, as shown in Fig. 5(a) and (b). It can be seen that simulation results align well with the measurement data for different discharge currents and under various temperatures. 2) Ultracapacitor Modeling: The Maxwell Boostcap ultracapacitor BMOD0058 E015 B1 is used in our paper. It is shown in [19] that the relationship between the discharge/charge voltage of the ultracapacitors and time is approximately linear. The equivalent circuit shown in Fig. 2(b) is used to model the ultracapacitor behaviors, in which represents the voltage of the main capacitor, refers to the terminal voltage, is the discharge or charge current, and is the equivalent series resistance given in the manual. Equation (8) can be used to model the dynamics of ultracapacitors. is defined as the ratio of and the voltage of the fully charged ultracapacitor denoted as and can be expressed as in (9)

3) PV Cell Modeling: The PV array used in our paper is Sanyo HIT-215SJ01. The PV model is built based on a typical PV equivalent circuit as shown in Fig. 2(c), in which depends on the irradiation rate and is approximated as the short-circuit current, is the current in the diode, is the leakage current through , is assigned with a small value depending on the PV cell quality, and is the terminal voltage [20]. where is the battery capacity, is the coefficient of the polynomial function describing the dependence of on SOD changes, is the battery mass, is the specific heat capacity

B. Control Methods 1) MPPT Control for PV Arrays: The perturb-and-observe MPPT method is adopted in a discrete controller. The discrete

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Fig. 6. Mode transitions controlled by the fuzzy-logic controllers.

Fig. 5. Comparison of simulation results and measurement data: (a) with different discharge currents at the nominal temperature (25 C) and (b) at different temperatures with the nominal discharge current (2 A).

controller adjusts by a small amount in a certain time interval and then calculates the output power. If the power increases in the present time interval, then the controller will adjust the current further in the same direction for the following time intervals. The adjusting process will be iteratively performed until the power reaches the peak value [21]. 2) PR Control for VSI: VSI is controlled by a PR controller. The basic theory of the PR control is to eliminate the steady-state error at a specific resonant frequency by introducing an infinite gain at that frequency [22]. 3) Multimode Fuzzy-Logic-Based Energy Management for HES: Fuzzy logic is known as an effective technique to intelligently control the complicated systems [23]–[25]. A multimode fuzzy-logic-based allocator which contains three working modes is designed, as shown in Fig. 6. Mode-I is considered as the main working mode which runs at the start of each application. Fuzzy-logic controller, FLC I, adjusts power references for storage units in Mode-I so that the ultracapacitors capture most transient power changes to take full advantage of the fast load-following abilities of the ultracapacitors. Meanwhile, the ultracapacitors are protected from instant energy saturation or depletion. Batteries are provided with the steady power request and protected from frequent power changes. Additionally, the ultracapacitors whose energy density is relatively low should be protected from being overly discharged/charged ( < or > ); therefore, Mode-II and Mode-III are included. In Mode-II and Mode-III, the ultracapacitors are not distributed with power request from the system, instead they are controlled to exchange energy with the batteries so that is restrained within a safe working region ( < < ). Fuzzylogic controller, FLC II, adjusts the charge/discharge power

Fig. 7. (a) FLC I and (b) membership functions for FLC I.

released/received by the batteries to/from ultracapacitors. FLC II is designed to prevent batteries from being over-discharged/ charged in Mode-II and Mode-III. and , As shown in Fig. 7(a), FLC I has two inputs, and two outputs, and which are ratios of distributed to the ultracapacitors and batteries, respectively. The membership functions for FLC I are shown in Fig. 7(b). FLC I links inputs to outputs through a series of “if ,then ” fuzzy rules. The if-part describes the conditions and the then-part determines the conclusion when the conditions hold. The rules of FLC I are shown in Table II. The th rule can be expressed as

Fuzzy reasoning for FLC I is performed in three steps: 1) fuzzy matching, 2) inference and conclusion combination, and 3) defuzzification [25]. Inputs are mapped into linguistic descriptions, and matching degrees are determined according to how inputs satisfy the “if” conditions. Matching degrees of the inputs to membership functions are denoted as and . The matching degree of a rule invoked by inputs and is obtained as

Based on the matching degree, conclusions are inferred using the clipping method which suppresses the output membership


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TABLE II FUZZY RULES OF FLC I

NH: negative high, NLM: negative low to medium, ZO: zero, PH: positive high, PLM: positive low to medium, negative: charge, positive: discharge, L: low, M: medium, H: high, MH: medium to high, and LM: low to medium.

functions. A larger matching degree results in a lower suppression extent. Multiple inferred conclusions are combined to get a comprehensive one. In defuzzification, the conclusions which are in the form of fuzzy sets are transformed to a crisp number that can be used by the controller. The center of area method is deployed to complete the defuzzification which can be expressed as follows:

Fig. 8. (a) FLC II and (b) membership functions for FLC II.

TABLE III FUZZY RULES OF FLC II

Using , in Fig. 7(a), power references of the storage devices in Mode-I can be obtained as

passes through an LPF to create a smooth power demand for batteries, whereas the remaining high-frequency component is allocated to as in (10). The remaining part of will be matched by batteries as in (11). As shown in Fig. 8(a), FLC II is used for HES power distribution in Mode-II and Mode-III. It has two inputs, and , and two outputs, and , which are the charge/discharge power released/received by the batteries to/from ultracapacitors, respectively. Membership functions for FLC II are shown in Fig. 8(b). Fuzzy rules of FLC II are shown in Table III. The fuzzy reasoning of FLC II is similar to that of FLC I, hence it is not illustrated here in detail. Based on the outputs of FLC II, power references of the storage devices in Mode-II are calculated as

and those in Mode-III are calculated as

Note that the load shedding or other strategies can be applied when batteries are over-charged/discharged. III. EVALUATION AND DISCUSSION The proposed HES was evaluated in both short- and long-term scenarios. The short-term scenario is to evaluate the instant

load-matching capabilities of the HES and the ac-side performance. The PV irradiation data are collected every second in one minute on April 10, 2011, in Singapore. The long-term scenario is to evaluate the flexibility of the multimode fuzzy-logic-based energy management for the HES. The PV irradiation data are recorded every minute from 10:00 to 16:30 on April 11, 2011, in Singapore. Load data are scaled down from a typical Singapore load demand to suit our applications [26]. In both scenarios, and are set to 0.5 initially. The parameters of the devices are shown in Table IV. A. Short-Term Scenario The PV power output and ac load variations are depicted in Fig. 9(a). The active power of the load fluctuates at 21 and 41 s. The reactive power of the load is kept constant at 100 Var. The power drawn from the ultracapacitors and the batteries are shown in Fig. 9(b), where positive power means the batteries and ultracapacitors are being discharged and the negative power means they are being charged. It can be observed that the power output of the ultracapacitors fluctuates while the output of the batteries is stable. The three-phase voltages and currents on the ac side at the time of load changes are shown in Fig. 9(c) and (d). At 21 s, the active power of the load decreases from 1200 to 800 W and three-phase voltages are almost constant while the currents decrease to match the smaller load demand. At 41 s, the real power of the load increases from 800 to 1300 W and the voltages are still kept constant while currents increase to deliver more power to the load. The HES controlled by FLC I is able to effectively compensate the fluctuating power mismatches between the

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TABLE IV PARAMETERS OF DEVICES

Fig. 10. (a) Load variations and (b) PV outputs for the long-term scenario.

Fig. 11. Outputs of FLC I in Mode-I for the long-term scenario in different periods: (a) from 10:00 to 10:05, (b) from 10:18 to 13:40, and (c) from 14:45 to 16:22.

Fig. 9. (a) PV output and load variations, (b) power outputs of batteries and ultracapacitors, three-phase voltages and currents when the load, (c) decreases, and (d) increases for the short-term scenario.

PV generation and load demand. Therefore, a satisfactory loadfollowing performance is achieved on the ac side. B. Long-Term Scenario The load variations and PV power output for the long-term scenario are displayed in Fig. 10. Fig. 11 shows the power sharing of the ultracapacitors and batteries controlled by FLC I in Mode-I for different periodsin the long-term scenario. The power supplied by the batteries and ultracapacitors is depicted in Fig. 12(a). Changes of and are shown in Fig. 12(b). From 10:00 to 11:00, power generation by the PV arrays is less than that demanded by the load. HES works at Mode-I where ultracapacitors instantly release power to compensate the power deficiency and the batteries slowly increase the discharge power. From 10:00 to 10:05, decreases immediately [Fig. 12(b)],

so batteries are allocated with more power to avoid fast depletion of the ultracapacitors. This is controlled by FLC I following the is and is PH, then is and rule of “if is ” [Fig. 11(a)]. While the ultracapacitors are being discharged, Mode-II is triggered when falls below 0.1 (at about 10:05). Then, the ultracapacitors are charged from the batteries [Fig. 12(a)]. The power provided by the batteries to charge the ultracapacitors is determined by FLC II based on and . FLC II protects the batteries from being overdischarged in Mode-II. Until reaches a higher level (larger than 0.15), Mode-I is resumed at about 10:18 so that the ultracapacitors are able to compensate the high-frequency components. From 10:18 to 11:00, HES works in Mode-I where FLC I allocates the power between the storage units. Fig. 11(b) shows that from 10:18 to 11:00, is larger than because is low and is the discharge demand, so more discharge power is allocated to the batteries. From 11:00 to 15:00, as shown in Fig. 10(b), the PV arrays generate surplus power due to high irradiation, so extra power is stored in the HES. From 12:00 onward, gradually increases to a high level [Fig. 12(b)], so batteries are commanded to store more charge power to prevent the ultracapacitors from reaching saturation. This is achieved by following the rule of “if is and is , then is and is ” in FLC I [Fig. 11(b)]. While the ultracapacitors are being charged, Mode-III is enabled when becomes larger than 0.9, at about 13:40 in Fig. 12(b), then the ultracapacitors release some power which is absorbed by the batteries to avoid saturation. The power transferred from ultracapacitors to batteries is controlled by FLC II based on and . FLC II protects the batteries from being over-charged in Mode-III. When decreases to a lower value (smaller than 0.85) at about 14:45,


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Fig. 14. Comparisons of the standard deviations of power fluctuations for the batteries and ultracapacitors with two traditional methods in (a) the short-term scenario and (b) the long-term scenario.

Fig. 12. (a) Power outputs and (b) SOC changes of the batteries and ultracapacitors for the long-term scenario.

Fig. 13. Frequency fluctuations for the long-term scenario.

Mode-I is restored so that the ultracapacitors can be controlled to compensate the high-frequency components. During the period from 15:00 to 16:30, power generated from the PV arrays is less than that requested by the load due to the dwindling solar irradiation. HES works in Mode-I where the power sharing is controlled by FLC I. From 16:00 onward, gradually decreases to a low value so that the batteries are distributed with a larger ratio of discharge power [Fig. 11(c)] and that the ultracapacitors are protected from instantly being depleted. While the ultracapacitors are being discharged, ModeII is triggered when ultracapacitors almost drain their energy at about 16:22. The ultracapacitors are then charged from the batteries [Fig. 12(a)]. The supply–demand mismatches result in the frequency deviations [27]. Frequency fluctuations for the long-term scenario are shown in Fig. 13. It is shown that the frequency variations are limited to for the entire period [28]. Relatively salient frequency variations appear when Mode-I transfers to the other two modes, because the ultracapacitors are forced to rest while there are still frequent power mismatches that can only be compensated by the batteries whose response speed is slower. C. Performance Comparison Other methods have also been applied for the power allocation. One representative of them is the LPF-based approach proposed in [9] and [12]–[14]. Another is the Haar wavelet transformation method used in [15]. The LPF-based approach is to pass the power demand through an LPF. Then, the lowfrequency components are dispatched to slow-response units such as fuel cells or batteries, whereas the high-frequency signals

are compensated by the ultracapacitors. The Haar wavelet transformation method decomposes the power demand into low- and high-frequency components, the former of which is supplied by the slow-response units, whereas the latter is provided by the ultracapacitors. The LPF-based approach, Haar wavelet transformation method, and our proposed multimode fuzzy-logic-based strategy are compared in terms of standard deviations for power fluctuations of the batteries and the ultracapacitors, denoted as and , for both the short- and long-term scenarios, respectively. The LPF-based approach uses an LPF with the same time constant of 40 s as in [13]. The wavelet transformation method uses a three-level Haar wavelet decomposition suggested in [15]. Fig. 14 shows the comparison results. For the short-term scenario, of all the three methods is notably large, whereas is relatively small. Compared to the two conventional methods, the proposed method achieves the largest and the smallest . The advantages of the proposed method may be attributed to the more effective participation of ultracapacitors in the transient power compensation, so that a better load-following capability and more stable ac-side performance can be achieved. In the long-term scenario, the allocator shows obvious superiority over the other two methods. of the proposed method is observed to be 18.64% and 18.79% smaller than the LPF-based approach and Haar wavelet method, respectively. Meanwhile, of the proposed method is 121.60% and 162.81% larger than that of the LPF-based and the Haar wavelet method, respectively. This is because the proposed allocator dynamically adjusts the power distribution to the batteries and ultracapacitors according to their SOC, so that more high-frequency components of the are supplied by the ultracapacitors, whereas the low-frequency ones are met by the batteries. The battery lifetime is another concern for their long-term applications. The equivalent battery charge/discharge cycles can be used as a metric for battery lifetime estimation, because a battery which experiences fewer cycles will have a longer lifetime, and vice versa. is calculated as

where is the battery power output and is the energy rating of batteries. The numerator calculates the totally

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COMPARISON RESULTS


OF

TABLE V EQUIVALENT DISCHARGE/CHARGE CYCLE NUMBER

consumed battery energy. The denominator represents the energy needed to fulfill a charge/discharge cycle. Table V shows the calculated for the three approaches given the same operating time. It can be seen that using the proposed method is the smallest due to the effective exploitation of the ultracapacitors, which indicates the longest battery lifetime. IV. CONCLUSION In this paper, an HES composed of lithium–ion batteries and ultracapacitors has been proposed to compensate the power supply and load demand mismatches in the PV-based systems. The battery model was dedicatedly established and validated by the experimental data for appropriate system-level simulations. A multimode fuzzy-logic-based allocator distributes power to the batteries and the ultracapacitors. The fuzzy rules were elaborately designed so that both the batteries and the ultracapacitors can be efficiently utilized as well as prevented from working under extreme conditions. The performance of the proposed HES was evaluated under both the short- and longterm scenarios using solar irradiation data collected from the field and a typical load demand in Singapore. For the short-term scenario, the HES ensures a good load-following performance on the ac side due to the fast response of the ultracapacitors. On the other hand, for the long-term scenario, power contributions of the batteries and ultracapacitors were observed to be optimally allocated to maximally offset the supply–demand mismatches. Both of these two components were protected well against any accidental entry into the undesired operating conditions through specially designed timely mode transitions. The proposed HES was compared with the conventional power allocation methods in terms of both power fluctuations and battery lifetime extension, and the results show that the proposed method is superior. REFERENCES [1] C. A. Hill, M. C. Such, D. Chen, J. Gonzalez, and W. M. Grady, “Battery energy storage for enabling integration of distributed solar power generation,” IEEE Trans. Smart Grid, vol. 3, no. 2, pp. 850–857, Jun. 2012. [2] H. Fakham, D. Lu, and B. Francois, “Power control design of a battery charger in a hybrid active PV generator for load-following applications,” IEEE Trans. Ind. Electron., vol. 58, no. 1, pp. 85–94, Jan. 2011. [3] S. Teleke, M. E. Baran, S. Bhattacharya, and A. Q. Huang, “Rule-based control of battery energy storage for dispatching intermittent renewable sources,” IEEE Trans. Sustain. Energy, vol. 1, no. 3, pp. 117–124, Oct. 2010. [4] A. E. Curtright and J. Apt, “The character of power output from utility-scale photovoltaic systems,” Prog. Photovolt.: Res. Appl., vol. 16, no. 3, pp. 241–247, 2008. [5] D. Shin, Y. Kim, Y. Wang, N. Chang, and M. Pedram, “Constant-current regulator-based battery-supercapacitor hybrid architecture for high-rate pulsed load applications,” J. Power Sourc., vol. 205, pp. 516–524, 2012. [6] A. Burke, “Ultracapacitors: Why, how, and where is the technology,” J. Power Sourc., vol. 91, no. 1, pp. 37–50, 2000. [7] L. Gao, R. A. Dougal, and S. Liu, “Power enhancement of an actively controlled battery/ultracapacitor hybrid,” IEEE Trans. Power Electron., vol. 20, no. 1, pp. 236–243, Jan. 2005.

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Xue Feng (GS’11) received the B.S. degree in power engineering from Wuhan University, Wuhan, China, in 2011. She is currently working toward the Ph.D. degree at Nanyang Technological University, Singapore. She is also working as a Research Associate at TUM CREATE Centre for Electromobility, Singapore. Her research interests are energy storage management, energy efficiency, and renewable energy sources.


H. B. Gooi (S’80–M’83–SM’95) received the B.S. degree from National Taiwan University, the M.S. degree from the University of New Brunswick, and the Ph.D. degree from Ohio State University, in 1978, 1980, and 1983, respectively. From 1983 to 1985, he was an Assistant Professor in the EE Department at Lafayette College, Easton, PA, USA. From 1985 to 1991, he was a Senior Engineer with Empros (now Siemens), Minneapolis, MN, USA, where he was responsible for the design and testing coordination of domestic and international energy management system (EMS) projects. In 1991, he joined the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore as a Senior Lecturer. Since 1999, he has been an Associate Professor.

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His current research focuses on microgrid energy management systems, electricity markets, spinning reserve, energy efficiency, and renewable energy sources. S. X. Chen (S’08–M’12) received the B.S. dual degree in power engineering and business administration from Wuhan University, Wuhan, China, in 2007, and the M.S. degree in power engineering from Nanyang Technological University, Singapore, in 2008. He is now a Ph.D. student at Nanyang Technological University, Singapore. His research interests are smart energy management systems, energy efficiency, renewable energy sources, and energy storage systems.