Load Sharing and Arrangement through an Effective

Load Sharing and Arrangement through an Effective Utilization of SOFC/Supercapacitor/Battery in a Hybrid Power System Syed Zulqadar Hassan, Hui Li, Tariq Kamal, María Jesús Espinosa Trujillo & Barış Cevher Iranian Journal of Science and Technology, Transactions of Electrical Engineering ISSN 2228-6179 Iran J Sci Technol Trans Electr Eng DOI 10.1007/s40998-018-0107-z

1 23

Your article is protected by copyright and all rights are held exclusively by Shiraz University. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”.

1 23

Author's personal copy Iran J Sci Technol Trans Electr Eng https://doi.org/10.1007/s40998-018-0107-z

(0123456789().,-volV)(0123456789(). ,- volV)

RESEARCH PAPER

Load Sharing and Arrangement through an Effective Utilization of SOFC/Super-capacitor/Battery in a Hybrid Power System Syed Zulqadar Hassan1

•

Hui Li1 • Tariq Kamal2 • Marıá Jesu´s Espinosa Trujillo3 • Barıs¸ Cevher2

Received: 10 February 2016 / Accepted: 10 July 2018 Shiraz University 2018

Abstract Solid oxide fuel cell (SOFC) provides several benefits such as high efficiency, modularity, quiet operation and cogeneration alternatives. Nevertheless, the main weakness in SOFC-based power plant has the slow dynamic response during transient situations in peak demand since this problem can be addressed by using complementary system such as a super-capacitor (SC). This paper provides an optimal load sharing and arrangement strategy (LSAS) for a hybrid power system which combines a SOFC stack, a SC module and a battery bank that support local grid. According to LSAS, the SOFC is the primary power source. The SC module is utilized as a backup and complement device to take care of the load following problems of SOFC during transient. The battery bank is added as a high energy density and/or backup device to stabilize the DC bus voltage, while an electrolyzer (ELYZ) is used as a dump load during surplus power. The LSAS operates in two layers. The external layer accomplished the overall power management system. Depending upon load demand, this layer generates references to the internal layer. The internal layer controls the individual subsystems, i.e., SOFC, ELYZ, SC and battery according to the references coming from the external layer. A complete MATLAB/Simulink model has been established to check the performance of the proposed system for the real load conditions. Simulation results show the effectiveness of the proposed system in terms of power transfer, load tracking and grid stability. Keywords SOFC Hybrid storage system Load sharing control strategy Stability Power quality analysis

1 Introduction & Syed Zulqadar Hassan [email protected] Hui Li [email protected] Tariq Kamal [email protected] Marıá Jesu´s Espinosa Trujillo [email protected] Barıs¸ Cevher [email protected] 1

State Key Laboratory of Power Transmission Equipment and System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China

2

Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Serdivan, Sakarya, Turkey

3

Universidad Tecnolo´gica Metropolitana, 115 404, Santa Rosa, 97279 Me´rida, YUC, Mexico

Growing pollution issues, continuous diminution of fossil fuels, strict demand about the quality and reliability of electrical power has provided an opportunity to motivate researchers towards the development of renewable energy sources (RES) based power generation. Many RES, such as solar photovoltaic systems, hydrogen fuel cell (FC) power systems and wind power systems, are polluted free and abundantly available [1–5]. Recently, FC technology for grid enhancement has exposed its importance and can be considered an indispensable energy source for the future power system [6]. FC is a stationary electrochemical device that generates electricity from hydrogen through electrolysis. The superior reliability, with practically zero noise level or no moving parts, is an extra advantage of FC system as compared to the diesel generator. Among the various types of FCs, solid oxide fuel cell (SOFC) is selected, because it works at high temperatures (800–1000 C) [7]. This makes SOFC the highest

123

Author's personal copy Iran J Sci Technol Trans Electr Eng

efficiency FC among others [6, 8, 9]. Some special characteristics, like internal reforming or larger fuel flexibility, very fast kinetics reaction without platinum catalysts and tolerance to impurities, make the performance of SOFC better among all FCs [10–12]. Other advantages of SOFC are the distributed configurations, cogeneration options and reusability of heat in bottoming cycle. Despite high efficiency and flexibility, there are some weaknesses in SOFC. One common drawback in all FCs is their slow dynamic load tracking capabilities [13]. When a SOFC is subjected to a large power fluctuations, it experiences an instant drop off of the voltage in the VI curve and take several seconds to deliver the required power. In the meantime, hydrogen starvation can occurs which results the detrimental of cell life and efficiency [14]. The problem of slow dynamic response of SOFC can be addressed through a power density device such as SC. Therefore, it is essential to operate SOFC under steady-state condition. Without SC or battery, the SOFC power system must meet all the power demand, which consequently increases the size and cost of the SOFC power plant. Many hybrid FC-based power systems are suggested in the literature. For example, in [15–27], the authors have described the control of various hybrid systems including FC, SC and battery. In [15, 16], the authors have designed a FC power generation with SC storage for electric vehicle applications. In both studies, the authors have explained the combined benefit of FC and SC. The modeling and power control for hybrid power topology including FC/SC are simulated in [17]. In [18], the authors have designed a FC/ battery hybrid system through an adaptive control scheme. The advantages of the FC generation as a green technology is appeared in [19]. In [20], the researchers have simulated a hybrid system including FC and energy storage system for the purpose to remove voltage sag in distributed generation system. A simple FC battery hybrid system with improved efficiency and experimental prototypes are described in [21, 22]. In both sides, the FC was used as an auxiliary power supply to recharge the battery. An energy management and control schemes based on proportional integral (PI) sliding mode controllers of a hybrid FC/SC source for load requirements with high robust performance are the aims of [23]. In [24], the authors have developed an energy management scheme for FC/battery/SC hybrid system using model predictive control. Similarly, particle swarm optimizationbased optimal power flow control of FC/SC and FC/battery hybrid electric vehicles is simulated in [25]. In [26], the researchers have concluded that the integration of FC/SC is effective to use in electric vehicles. The SOFC has the advantage to use the syngas as a fuel produced during gasification from solid waste products [27].

123

This paper provides an optimal load sharing and arrangement power control approach combining SOFC, ELYZ, SC, battery and a set of residential loads for grid-on and grid-off applications. The proposed test bed works on LSAS. The LSAS managed the overall power management system for the proposed system and controlled all the individual subsystem on the basis of dynamic references. The LSAS ensures premium quality and reliability of power for the 24-h simulation. This paper is structured as follows. First, an overall system configuration is provided in Sect. 2. Sections 3 and 4 present the modeling and control of individual components. Section 5 hosts the LSAS. Simulation results and conclusion are described in Sects. 6 and 7.

2 System Configuration It is possible to accomplish much higher generating capacity factors by an integrating FC stack and with a hybrid storage configuration to overcome the fluctuations in plant’s output. An efficient energy storage scheme is required to get continuous and constant power, and the electrical power delivered by the FC stack has to be converted into SC or battery energy. Figure 1 explains the configuration of proposed hybrid system which consists of a 50-kW SOFC stack as a prior energy source. A 58 F SC and 50 Ah battery hybrid storage systems in the proposed architecture are used as a high power/energy density and/or backup energy source. A pressurized 50-kW alkaline electrolyzer is used as a dump load during surplus power to generate hydrogen which is stored in hydrogen tank for later use in the SOFC stack. The four subsystems, i.e., FC, ELYZ, SC and battery are controlled through four separate control units. All the subsystems are combined in parallel to a common DC bus through their individual non-isolated DC–DC converters. The output of DC bus is then integrated to the grid or gridconnected load through an inverter. All the energy sources and their controllers are designed in MATLAB/Simulink. The complete hybrid system is simulated for 24 h including different operating and load conditions under LSAS supervision. The simulation results conclude the operating principle and effectiveness of proposed system. It is essential to point out that the architecture of proposed system is modular, and thus, easily expandable as long as a new FC, SC or battery are added to the existing ones with no need to increase the circuit and control complexity. Furthermore, it is also possible to upgrade the existing system by adding another parallel inverter. Thus, the proposed system is beneficial for distributed energy generation.


Fig. 1 Configuration of proposed hybrid power system

3 System Modeling Formulation

Vcell ¼ Ecell

This section explains the dynamic modeling of the individual components involved in the proposed system.

with

X

ð6Þ

Vlosses;cell

Ecell ¼Eo;cell kB ðTFC 298Þ þ

3.1 SOFC Modeling

pffiffiffiffiffiffiffi RTFC pH2 pO2 ln pH 2 O 2F

VFC ¼Ncell Vcell ; PFC ¼ VFC IFC The model used in this study is based on the dynamic SOFC model as explained in [28]. During modeling, diffusion phenomena of hydrogen and oxygen gases, constant stable operating temperature and the double-layer charging effect are considered. The chemical reactions that occur at respective electrodes are written as: Anode : H2 þ O2 ! H2 O þ 2e

ð1Þ

1 Cathode : O2 þ 2e ! O2 2

ð2Þ

The dynamics of the reactants partial pressure associated in the system can be expressed as: V1 d IFC pH ¼ rHin2 rHout2 RTFC dt 2 2F

ð3Þ

V2 d IFC pH2 O ¼ rHin2 O rHout2 O RTFC dt 2F

ð4Þ

V3 d IFC pO ¼ rOin2 rOout2 RTFC dt 2 4F

ð5Þ

where Vj refers to the number of volumes of electrode side; pj represents the partial pressure of jth species (j ¼ H2 ; H2 O; O2 ); and R and F stand for gas constant and Faraday’s constant, respectively. rj is the mass flow rate of jth species; and IFC is FC current. Depending on the chemical reactions occur at cathode and anode, the voltage developed in a cell (Vcell ) can be expressed in (6) according to Nernst equation.

ð7Þ ð8Þ

where Ecell is the Nernst’s voltage; Eo;cell stands for Gibbs potential; Ncell is the number of cells in the FC stack; TFC is the operating temperature; and PFC is power delivered from FC; and kB is Boltzmann constant. Remember that Ecell in (7) expresses reactants partial pressure dynamics, and value of pressures in (3), (4) and (5) is derived under open-circuit voltage condition. The individual potential drop term in (6) can be written as: X Vlosses;cell ¼Va;cell þ Vc;cell þ Vo;cell ð9Þ Va;cell ¼k0 þ ðTFC 298Þx þ TFC y ln IFC RTFC IFC ln 1 Vc;cell ¼ cF Il Vo;cell ¼Ro IFC

ð10Þ ð11Þ ð12Þ

where Vc;cell is concentration potential; Va;cell is cell-activation potential; and Vo;cell is ohmic potential drop. k0 , x, y and Ro are the parameters of FC; IFC is the current of FC; and Il is the limiting current. To provide the require voltage and power level, a FC stack is modeled according to the parameters given in Appendix. Figure 2 shows the electrical model of FC explaining the three regions, i.e., activation loss occurs due to the starting of chemical reaction inside in FC, voltage drop due to flow resistance of ions in the electrolyte which is the middle of the curve is an ohmic loss (approximately linear), and voltage drop due to the mass transfer inside the porous media is concentration loss.

123

Author's personal copy Iran J Sci Technol Trans Electr Eng Fig. 2 Equivalent electrical model of fuel cell

3.2 Electrolyzer Modeling ELYZ is a static electrochemical device, which consumes water and heat to generate hydrogen and oxygen, and technically classified as the opposite of FC system. The modeling of ELYZ involves different dynamics: the hydraulic dynamic, electrochemical dynamic, thermal dynamic and electrical dynamic. The thermal modeling is omitted in this paper, and constant temperature approach is adopted considering the large time constant of the thermal model. Here, the potassium hydroxide (KOH), i.e., alkaline ELYZ model, is taken as proposed in [29–31]. The chemical reactions occur in the ELYZ is written as: Anode : 2OH aq ! H2 O þ 2e

ð13Þ

Cathode : 2H2 OðlÞ þ 2e ! H2 ðgÞ þ 2OH

ð14Þ

Faraday’s explains molar hydrogen production rate as a function of applied current and given as: rH2 ;p ¼ bðTE ; JE Þ

NE IE 2F

ð15Þ

where NE and TE represent the number of cells in a FC stack and temperature of the ELYZ, respectively. The Faraday efficiency b in terms of current density (JE ) and temperature is written as: b¼

JE2 z2 z1 þ JE2

ð16Þ 4

z1 ¼ 50 þ 2:5TE ; z2 ¼ 1 7:5 10 TE

ð17Þ

The electrical dynamic of the ELYZ is established upon the empirical parameters whose values are taken from the experiments. The VI characteristics of the ELYZ can be written in (18) and (19) by nonlinear and empirical relationship as: ! k1 þ Tk2E þ Tk32 IE ðr1 þ r2 TE Þ E vcell ¼ v0 þ þ v1 log þ1 AE AE ð18Þ VE ¼NE vcell

ð19Þ

where vo is the thermodynamic potential of FC; and vcell is the voltage drop across ELYZ. Note that vo is the function

123

of pressure and temperature. ri s are the parameters of ohmic resistance; while v1 and ki s represent the parameters for the ELYZ over voltage. The values of all parameters are given in Appendix. During the designing of rated FC stack, it is assumed that both no-load voltages of ELYZ and SOFC are equal. Broadly speaking by doing Ncell Ecell ¼ NE vo , there would be a continuous static phase for FC/ELYZ unit. Thus, smooth transition happens between two modes, i.e., FC mode and ELYZ mode. The hydraulic part of an ELYZ contains the dynamics of the resultant gases. Considering the ideal gas law, the dynamics of resultant pressure can be defined as: 0

VE d pH e ¼ rH2 p rH2 O RTE dt 2

ð20Þ

where rH2 p and rH2 O are the molar hydrogen production rate 0 and molar hydrogen outflow rate, respectively. VE is the cathode volume; and pH2 e is the partial pressure of hydrogen associated with cathode. Under steady state, rH2 p ¼ rH2 O must be satisfied to keep constant pressure.

3.3 Compressor and Hydrogen Tank Modeling Based on polytrophic model, the compressor power is expressed in terms of hydrogen molar flow rate as: c rH2 O ¼ c Pcomp ð21Þ a " d1 # dRTE pt d ð22Þ 1 a¼ d 1 pE where cc stands for compression efficiency; pt is the pressure of hydrogen tank; and a is the polytropic work. Furthermore, the pressure of storing hydrogen depends on the difference between input and output flow rates and expresses in the following differential equation Vt d pt ¼ rH2 O rHin2 RTt dt

ð23Þ

where Tt and Vt are the temperature and the volume of hydrogen inside the tank, respectively.

3.4 Super-capacitor Modeling This study works on the classical SC model as provided in Fig. 3, which consists of a double-layer capacitance Fig. 3 Equivalent model of SC

electrical


(C) and two resistances, viz., an equivalent parallel leakage resistance (Rp ) and an equivalent charging/discharging series resistance (Rs ) [32]. The amount of energy consumed/drawn from the SC bank (ESC ) depends on the capacitance of SC and the potential develop across its terminals, and is given as: 1 2 2 ð24Þ ESC ¼ C Vinitial Vfinal 2 When the SC is subjected to release energy, the magnitude of terminal voltage across SC is decreased and vice versa. To provide the required voltage level, multiple numbers of SC modules are joined in series and/or parallel configuration. All the parameters of the SC are given in Appendix.

3.5 Battery Modeling The battery model consists of two RC circuits as proposed in [20, 33] and as shown in Fig. 4. The two voltages (ea ; eb ) across two capacitances (Ca ; Cb ) and the state of charge (TB ) are three state variables of the battery. The open-circuit voltage (Voc ) and the value of resistance can be defined as: Voc ¼ 338:8 ½0:94246 þ 0:05754 TB ! dea Ra þ R b Rb Ra Ca þ ea ¼ Voc þ ea dt Ra Ra Rb Cb

deb þ e b ¼ e b Rb I t dt

This section describes the control and power electronics interfacing of individual components system.

4.1 Control System of SOFC The output voltage or power of SOFC depends upon load demand, and the increase in load demand decrease its voltage or power. Therefore, a DC–DC boost converter is applied at the SOFC system to maintain the DC bus output voltage constant, i.e., 700 V. The boost controller is controlled though a conventional proportional integral differentiator (PID) controllers. The PIDs work on the error, which is the difference between the measured voltage and actual voltage of SOFC. Based on the error, the PID adjusts the duty cycle according to the requirement. The control system for SOFC is depicted in Fig. 5.

4.2 Control System of Electrolyzer

ð26Þ

The output power of ELYZ is controlled by controlling its input current [34, 35]. A buck converter is used to regulate the input current of ELYZ which consequently controls the output power. The buck converter is itself controlled by PI controllers. The control system for ELYZ is shown in Fig. 6.

ð27Þ

ð29Þ

where r and r1 represent the internal and polarization resistance of battery, respectively. Qmax and P represent the

Fig. 4 Equivalent electrical model of battery

4 Control of System Components

ð25Þ

The battery state of charge (SOC) (TB ) is an indication of the energy reserve and is written as: R IB dt ð28Þ TB ¼ 100 1 % Qmax IB is the battery current and determined by: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 4ðr þ r ÞP Voc Voc 1 IB ¼ 2ðr þ r1 Þ

maximum battery charge (Ah) and output power (W) of the battery, respectively.

4.3 Control System of Super-capacitor/Battery The control strategy is developed for both SC and battery on the basis of their charging and discharging capabilities. The output voltage or power of SC and/or battery is always smaller than the DC bus voltage. A boost converter is used to increase the output voltage SC and/or battery to the desire value, i.e., 700 V. Similarly, a buck converter is used to charge the SC and/or battery from DC bus under LSAS. The buck-boost converter is itself controlled by PID

Fig. 5 Control system of fuel cell

123


Fig. 6 Control system of ELYZ Fig. 8 Control system of grid-connected inverter

controller. Due to an inner control loops, SC and/or battery must follow (30). The control system for SC/battery is shown in Fig. 7. I ¼ P =V

ð30Þ

I is the reference current, and P ; V are the reference power and voltage, respectively.

of the grid voltage is calculated through phase-locked loop. The gird current is controlled using outer current control loop through PI voltage controllers, while the unity power factor is adjusted using inner current control loop through PI current controllers as defined in (31) and (32). Z VL ref ¼ kpv ðVdc ref Vdc Þ þ kiv ðVdc ref Vdc Þdt

4.4 Control System of Grid-Connected Inverter Figure 8 explains the control strategy of the proposed three-phase inverter connecting the DC bus of the hybrid system to the grid and/or grid-connected load. The inverter operates on power conversion from the DC bus power to the grid and/or grid-connected load and also regulates the grid current at unity power factor. The current controlled inverter is controlled to provide sinusoidal current after filtering, which is fed to the utility line to distribute the required power. It also stabilizes the DC bus voltage. The inverter increases the output power when the DC bus voltage is increased and vice versa. The proposed control technique uses PI controllers and hysteresis current control pulse width modulation technique to generate suitable gate signals for driving the controllable switches of the inverter. The inputs to PI controllers are the errors calculated between the measure and actual values of the active and the reactive powers. The PI controllers try to diminish the error in order to achieve the desired active and the reactive powers. Furthermore, it is desirable for the grid current to be controlled in phase with the grid voltage and have unity power factor. To perform such functions, the phase angle

IL

ref

¼ kpi ðIref IL Þ þ kii

Z

ð31Þ ðIref IL Þdt

ð32Þ

where kpv ; kiv ; kpi and kii are the gains of the PI voltage and PI current controllers, respectively. ILref is the reference current of grid; and Vdc ; IL ; Vdc ref and Iref are the DC bus actual voltage, DC bus actual current, DC bus reference voltage and DC bus reference current, respectively.

5 Coordinated Power Control Strategy (LSAS) The LSAS ensures optimal use and energy management of SOFC, SC, battery and even grid. The LSAS generates the dynamic references for each subsystem which confirms: (1) continuity of power, (2) energy management of storage systems, (3) tracking of loads for 24 h and (4) reducing burden on the grid.

5.1 Load Arrangement Based on available renewable energy sources, the power balance equation for proposed HPS can be written as follows: PFC ¼ PL PB PSC PG þ PE

ð33Þ

where PL ; PSC ; PB ; PG and PE are the load demand, SC output power, battery output power, utility grid output power and ELYZ power consumption, respectively. The excess power generated by FC (PEX ) is utilized by battery, SC, utility grid and ELYZ as follows: Fig. 7 Control system of SC/battery

123


PEX ¼ PL PFC ¼ ðPB þ PSC þ PE þ PG Þ ;

ð34Þ

whereas, due to maximum power limitation of FC, the shortage of power (PS ) is supplied by SC, battery and utility grid, respectively, as follows: PS ¼ PL PFC ¼ PSC þ PB þ PG

ð35Þ

Based on above power balance equations, a load management algorithm is presented in Fig. 9 in which the following conditions are incorporated. •

•

The use of power produced by the FC system has priority in satisfying load demand over that delivered by the SC/battery system or grid. The use of power produced by the FC system has priority in satisfying load demand over that delivered by the SC/battery system or grid. If the power produced by FC system is higher than the demand, the excess power is used to charge the SC or battery bank. If still excess power is available, then it will be sent to the grid. If still excess power is available, then it will be used for electrolysis to generate hydrogen for later use in FC.

•

Similarly, if the total power generated by the FC system is less than the demand, power will be delivered from the battery bank. If the load demand exceeds the power generated by the FC/battery combination, the difference is supplied by the SC. If still load demand is not satisfying, then grid’s power will be used to fill the gap. If still demand is not satisfied, then load shedding is executed.

The LSAS is based on an above load management algorithm. For better understanding, the operation of LSAS is organized in the modes described in Table 1 and discussed as follows.

5.2 Modes 1 to 4 The primary condition for these modes is that the load is less than the maximum power rating of FC. Hence, the overall demand is satisfied by FC itself. Mode 1 and mode 2 contain the off-grid condition. In mode 1, due to slow response of FC, the FC does not fulfill the sudden changed demand and that deficient power is supplied by battery and SC, whereas in mode 2, the sudden change in load demand

Fig. 9 Flow chart of load management algorithm

123

Author's personal copy Iran J Sci Technol Trans Electr Eng Table 1 Operating modes of coordinated power control strategy

Mode

Primary condition

Secondary condition

Tertiary condition

1

Off grid (OG)

PD ¼ PB þ PSC

2

OG

PD ¼ PB PSC

3

Non-rush hour of grid (NRHG)

PD ¼ PB PSC þ PG

4

Rush hour of grid (RHG)

PD ¼ PB PSC PG

5

OG

PD ¼ PB

NRHG

PD ¼ PB PG

7

OG

PD ¼ PB PSC

8

NRHG

PD ¼ PB PSC þ PG

9

RHG

PD ¼ PB PSC PG

10

OG

PD ¼ PB

11

NRHG

PD ¼ PB þ PG

12

RHG

PD ¼ PB PG

13

OG

PD ¼ PB þ PSC

14

NRHG

PD ¼ PB þ PSC þ PG

15 16

RHG OG

PD ¼ PB þ PSC PG PD ¼ PB

6

PL \PFCMAX

OG

PD ¼ PB PSC

18

NRHG

PD ¼ PB PSC þ PG

19

RHG

PD ¼ PB PSC PG

20

OG

PD ¼ PB PSC

21

OG

PD ¼ PB

22

NRHG

PD ¼ PB þ PG

17

PL [ PFCMAX

is fulfilled by battery and excess power in system is absorbed by SC and ELYZ. Mode 3 and mode 4 contain NRHG and RHG, respectively. In NRHG, the grid supports the FC and fulfills the power deficiency, whereas in RHG the grid acts as a load and draw power from FC.

excess power generated by FC is used to charge battery and SC. Similarly, in mode 10, the grid is disconnected and excess power is solely consumed by the battery. In mode 11 and mode 12, the grid is at NRHG and RHG conditions, respectively, whereas in both the cases the excess power is utilized by battery only.

5.3 Modes 5 to 8 5.5 Modes 13 to 17 The primary condition remains the same for these modes, i.e., that the load demand is less than the maximum power rating of FC. In mode 5, grid and SC are disconnected and only the battery assists the FC. In mode 6, due to NRHG, the grid along with battery supports the FC during rapid change in load. In mode 7, the power generated by FC is greater than the load and off-grid condition. Hereafter, the excess power is consumed by battery and SC. In mode 8, FC and grid (NRHG) produce power more than load demand. The excess power is distributed among battery and SC as per algorithm.

5.4 Modes 9 to 12 Alike previous modes, the load demand is less than the maximum power rating of FC. In mode 9, due to RHG, the grid also acts as a load and draw power from FC, while the

123

After mode 12, the primary condition is changed that the load demand is greater than the maximum power rating of FC. Therefore, other energy sources such as battery, SC and grid are used to fulfill the deficient power. In mode 13, the grid is detached and battery/SC achieves the remaining power deficiency. Similarly, in mode 14 and mode 15, the grid is at NRHG and RHG conditions. During NRHG condition, the grid supports the battery/SC to accomplish the power deficiency, whereas in RHG condition, the grid also acts as a load on battery/SC. In mode 16 and mode 17, the grid is disconnected. Moreover, the difference between the two modes is only the consumption strategy. In mode 16, the shortage of power is supplied by battery only, while in mode 17, the battery not only fulfills the deficiency, but also produces some excess power, which is consumed by SC.


5.6 Modes 18 to 22 In mode 18 and mode 19, the grid is at NRHG and RHG condition, respectively. In both modes, the battery generates more power than deficiency and this excess power is consumed by SC. The only difference is that these modes are of grid condition that the grid supports battery in mode 18 and acts a load in mode 19. Last three modes, i.e., modes 20, 21 and 22, are exceptional modes. These modes exist for a very short time. These modes exist only when a load is changed suddenly nearly equal to the maximum power rating of FC; then, the FC output power overshoots and PFC becomes greater than PL (although PL \PFCMAX ). At this stage, the battery and SC consume the excess power. In modes 20 and 21, the grid is isolated. In mode 20, only the battery consumes the excess power wherein mode 21 the excess power is shared between battery and SC. Similarly, in mode 22, due to NRHG condition, the excess power generated by FC plus grid power is totally utilized by battery for charging.

6 Simulation Results and Discussion For validation of proposed algorithm and performance of LSAS, the proposed architecture is tested in MATLAB/ Simulink. Using the SimPower System, the simulations are performed for different load conditions. The parameters used in modeling of FC, battery, SC, ELYZ, grid and inverter are presented in Appendix. The proposed system provides power to residential load of a small community at Chongqing, China. The peak load and average load are calculated as 2.8 and 2.02 kW per home, respectively. The peak load starts from 18 h and ends at 21 h. The simulation results of all the modes are discussed below.

Fig. 10 Time versus operating mode

Figure 10 shows the time versus operating mode generated by LSAS. Between 1–8 and 16–18 h, the PL \PFCMAX , so, that the operating modes lie between 0 and 12. Similarly, for remaining duration, the operating mode lies above 13. Figure 11 shows the simulation results of output and reference powers of residential load, grid, FC, battery, SC and electrolyzer for t ¼ 0–3 h. From Fig. 11a, the solid blue line shows the actual residential load before load shedding (P-WLS), whereas the dotted black and solid red lines show the reference and actual residential loads after execution of load shedding. The operation of load shedding is incorporated due to shortage of power from energy sources which is clearly revealed from Fig. 11. However, at t = 2–3 h, the load is fully satisfied by FC/SC/battery and there is no need of load shedding. Figure 11b shows the reference and actual power supplied by the grid. Similarly, Fig. 11c–e represents the reference and actual powers of FC, battery and SC, respectively. The ELYZ consumes any excess power generated by energy sources to keep system stable as shown in Fig. 11f. Throughout Fig. 11, the positive value of power represents that the energy source is supplying power and vice versa. Figure 12 represents the simulation results of different energy sources for a duration of 3–6 h. It is clearly revealed from Fig. 12a that for a short period of time, load shedding is needed. All the load demand is satisfied with the collaboration of FC/SC/battery as shown in Fig. 12c–e. After 5 h, grid also supports FC to overcome the load demand and any excess present inside the system is consumed by ELYZ as illustrated in Fig. 12b, f. In this interval, the LSAS varies from 1 to 11 operating modes.

Fig. 11 Simulation results for t = 0–3 h a PL b PG c PFC d PB e PSC f PE

123


Fig. 12 Simulation results for t ¼ 3–6 h a PL b PG c PFC d PB e PSC f PE

From 6–9-h interval, both of the primary conditions exist. The rated power of FC is 50 kW, while the maximum power provided by FC is 55 kW. From Fig. 13a, the load demand varies from 43 to 53 kW. Above 50 kW, i.e., after 8 h, the second primary condition is started. FC provides its maximum power to satisfy the load demand, and the remaining load demand is satisfied by battery as shown in Fig. 13c, d. From Fig. 13e, due to slow response of FC at rapidly changing load at 8 h, the SC attempts to fulfill the power gap by using its power density property. On the other side, the utility grid assists FC and ELYZ which play


123

its role to keep the system stable as depicted in Fig. 13b, f. Due to existence of both primary conditions, the LSAS also shifts between 8 and 21 operating modes. For a duration of 9–12 h, the residential load demand is greater than the rated power of FC and FC which delivers its maximum power of 50 kW as shown in Fig. 14a, c. Hence, the battery, grid and SC endeavor to satisfy the remaining load demand, but from Fig. 14b–e, they are unable to meet their reference power. Therefore, a load shedding is executed as shown in Fig. 14a. In this interval, due to shortage of power there is no excess power in the system and ELYZ power consumption is zero as presented in Fig. 14f. For a period of 12–15 h, the residential load demand is again greater than the rated power of FC as shown in Fig. 15c. Therefore, LSAS generates the remaining power references to grid, battery and SC to overcome the power shortage. From Fig. 15, the battery, SC and grid almost accomplish the power gap except 13–13.2 h in which it is necessary to execute load shedding. Since, battery also produces some excess power at 14 h which is successfully utilized by ELYZ as shown in Fig. 15f. As PL [ PFCMAX , the LSAS operates in between 13 and 19 operating mode. From Fig. 16, the load that demands varies from 53 to 43 kW. In this interval, both the primary conditions exist. Accordingly, the FC delivers its maximum power and deficient power is satisfied by battery, SC and grid as shown in Fig. 16. In this case, all the energy sources efficiently fulfill the load demand. So, there is no need of load curtailment. Some excess power is generated in system during load changes, which is properly disposed by ELYZ as shown in Fig. 16f.



Fig. 15 Simulation results for t = 12–15 h a PL , b PG , c PFC , d PB , e PSC , f PE




Between 18 and 24 h, PL [ PFCMAX as shown in Figs. 17a and 18a. Due to rush hour of grid, it also acts as a load. Hence, FC supplies its maximum power of 50 kW and the shortage of about 0–20 kW of power is supplied by battery and SC as shown in Figs. 17 and 18. Due to nonfulfillment of load demand at certain points (especially when the load is changing), the load curtailment is executed. Figure 19 shows the overall load shedding executed by LSAS for entire 24 h. Usually, it is executed when residential load demand changes (at every hour). Due to efficient load management algorithm, LSAS kept the load

Fig. 19 Load shedding executed versus time

curtailment below 2 kW. On the other side, the SOCs of battery and SC are shown in Fig. 20. Battery is discharging

123


Fig. 20 State of charge a Battery, b SC

most of the time, while SC is both charging and discharging depending upon the power available. Since, all the electrical equipments are highly sensitive, so, the power quality is a serious concern. Power quality is particularly addressed in proposed LSAS, which is clearly shown in Fig. 21. Load voltage frequency, RMS load voltage and THD for load voltage and current are all in their acceptable limits [36], which ensures that the system provides quality power. The system is said to be a stable one when the sum of all powers inside it is zero. If a system contains real power, then it alters the line-to-line voltage of the load, while if a system contains reactive power, it alters the frequency of load voltage. From Fig. 22, it is clearly revealed that the net power on both DC and AC buses is zero and the system is said to be stable.

Fig. 22 System stability parameters a Net power on DC bus b Net real power on AC bus c Net reactive power on AC bus

7 Conclusion An optimal control strategy for a hybrid power system with different energy storage systems for a grid-on and grid-off application was presented. The drawback of a single power source, i.e., FC, was addressed by proposing a hybrid energy storage system in the proposed system. A complete simulation model of FC/ELYZ/SC/battery was prepared which facilities the grid and/or grid-connected load under the supervision of LSAS. The proposed system was shown excellent dynamic performance in terms of power sharing, grid stability, power quality and reliability which was verified by simulations performed in MATLAB/Simulink. Acknowledgements This research work is supported by the National Natural Science Foundation of China (Nos. 51675354 and51377184), the International Science and Technology Cooperation Program of China (No. 2013DFG61520) and the Fundamental Research Funds for the Central Universities (No. 106112016CDJZR158802).

Appendix Electrical Network VDC ¼ 700 V; VLL;rms ¼ 440 V; f ¼ 50 Hz; PG;rated ¼ 10 MVA

SOFC Prated ¼ 50 kW, TFC ¼ 1173 K, Ncell ¼ 325, Stack ¼ 4 kW; Array size ¼ 13; Td ¼ 5 s, F ¼ 96484:6 C/mol Fig. 21 Power quality parameters

123


Battery (CINCOFM/BB12100T) Capacity ¼ 200 Ah; Voltage/string ¼ 12 V, Np ¼ 3; NS ¼ 34; Vrated ¼ 12 34 ¼ 400 V

SC (Maxwell Technologies BMOD0058) C ¼ 58 F, Vmax ¼ 16:2 V, ESR ¼ 22 mX, Imax ¼ 20 A, NP ¼ 20, Ileakage ¼ 1 m A, NS ¼ 6

Electrolyzer (QualeanQL-85000) PE;rated ¼ 50 kW, vo ¼ 1:038 V, AE ¼ 0:25 m2 , c ¼ 2; NE ¼ 350; k1 ¼ 1:002 A1 m2 , k2 ¼ 8:424 A1 m2 C, v1 ¼ 0:185 V, k3 ¼ 247:3 A1 m2 C2 , r1 ¼ 8:05105 X m2 ,r2 ¼ 2:5107 Xm2

References 1. Zahran MB (2003) Photovoltaic hybrid systems reliability and availability. J Power Electron 3(3):145–150 2. Rubbia C (2006) Today the world of tomorrow—the energy challenge. Energy Convers Manag 47(17):2695–2697 3. Zhou T, Francois B, el Hadi Lebbal M, Lecoeuche S (2009) Realtime emulation of a hydrogen-production process for assessment of an active wind-energy conversion system. IEEE Trans Ind Electron 56(3):737–746 4. Kim YH, Kim SS (1999) An electrical modeling and fuzzy logic control of a fuel cell generation system. IEEE Trans Energy Convers 14(2):239–244 5. Papadopoulos DP, Dermentzoglou JC (2002) Economic viability analysis of planned wec system installations for electrical power production. Renew Energy 25(2):199–217 6. Hoseinnia S, Sadeghzadeh SM (2009) A comparative study of fuel cell technologies and their role in distributed generation. In: EUROCON 2009, EUROCON’09. IEEE, pp 464–469 7. Das T, Snyder S (2011) Adaptive control of a solid oxide fuel cell ultra-capacitor hybrid system. In: American control conference (ACC), 2011, pp 3892–3898. https://doi.org/10.1109/acc.2011. 5991519 8. Giddey S, Badwal S, Kulkarni A, Munnings C (2012) A comprehensive review of direct carbon fuel cell technology. Prog Energy Combust Sci 38(3):360–399 9. Mekhilef S, Saidur R, Safari A (2012) Comparative study of different fuel cell technologies. Renew Sustain Energy Rev 16(1):981–989 10. Nehrir MH, Wang C (2009) Modeling and control of fuel cells: distributed generation applications, vol 41. Wiley, Hoboken 11. Li X (2005) Principles of fuel cells. Taylor & Francis, London 12. Larminie J, Dicks A, McDonald MS (2003) Fuel cell systems explained, vol 2. Wiley, New York 13. Meacham JR, Jabbari F, Brouwer J, Mauzey JL, Samuelsen GS (2006) Analysis of stationary fuel cell dynamic ramping capabilities and ultra capacitor energy storage using high resolution demand data. J Power Sources 156(2):472–479 14. Thounthong P, Sethakul P (2007) Analysis of a fuel starvation phenomenon of a pem fuel cell. In: Proceedings of the power conversion conference-Nagoya PCC, pp 731–738

15. Paladini V, Donateo T, De Risi A, Laforgia D (2007) Supercapacitors fuel-cell hybrid electric vehicle optimization and control strategy development. Energy Convers Manag 48(11):3001–3008 16. Rodatz P, Paganelli G, Sciarretta A, Guzzella L (2005) Optimal power management of an experimental fuel cell/supercapacitorpowered hybrid vehicle. Control Eng Pract 13(1):41–53 17. Uzunoglu M, Alam M (2007) Dynamic modeling, design and simulation of a pem fuel cell/ultra-capacitor hybrid system for vehicular applications. Energy Convers Manag 48(5):1544–1553 18. Jiang Z, Gao L, Dougal R et al (2007) Adaptive control strategy for active power sharing in hybrid fuel cell/battery power sources. IEEE Trans Energy Convers 22(2):507–515 19. Rajashekara K (2005) Hybrid fuel-cell strategies for clean power generation. IEEE Trans Ind Appl 41(3):682–689 20. Hajizadeh A, Golkar MA (2010) Control of hybrid fuel cell/ energy storage distributed generation system against voltage sag. Int J Electr Power Energy Syst 32(5):488–497 21. Chao C, Shieh J (2012) A new control strategy for hybrid fuel cell-battery power systems with improved efficiency. Int J Hydrog Energy 37(17):13141–13146 22. Yan WT, Yang LS, Hong CM, Liang TJ, Chen JF, Yang HT (2008) Fuel cell and battery hybrid supplied power system. In: 2008 international conference on electrical machines and systems, pp 2676–2680 23. Kraa O, Ghodbane H, Saadi R, Ayad MY, Becherif M, Aboubou A, Bahri M (2015) Energy management of fuel cell/ supercapacitor hybrid source based on linear and sliding mode control. Energy Procedia 74:1258–1264 24. Amin Bambang RT, Rohman AS, Dronkers CJ, Ortega R, Sasongko A (2014) Energy management of fuel cell/battery/supercapacitor hybrid power sources using model predictive control. IEEE Trans Ind Inform 10(4):1992–2002 25. Omar H, Mierlo JV, Barrero R, Omar N, Lataire P (2012) PSO algorithm-based optimal power flow control of fuel cell/supercapacitor and fuel cell/battery hybrid electric vehicles. COMPEL Int J Comput Math Electr Electron Eng 32(1):86–107 26. Uzunoglu M, Alam MS (2008) Modeling and analysis of an FC/ UC hybrid vehicular power system using a novel wavelet-based load sharing algorithm. IEEE Trans Energy Convers 23(1):263–272 27. Bellomare F, Rokni M (2013) Integration of a municipal solid waste gasication plant with solid oxide fuel cell and gas turbine. Renew Energy 55:490–500 28. Wang C, Nehrir HM (2007) A physically based dynamic model for solid oxide fuel cells. IEEE Trans Energy Convers 22(4):887–897 29. Ulleberg Ø (2003) Modeling of advanced alkaline electrolyzers: a system simulation approach. Int J Hydrog Energy 28(1):21–33 30. Artuso P, Gammon R, Orecchini F, Watson SJ (2011) Alkaline electrolysers: model and real data analysis. Int J Hydrog Energy 36(13):7956–7962 31. Zhou T, Francois B (2009) Modeling and control design of hydrogen production process for an active hydrogen/wind hybrid power system. Int J Hydrog Energy 34(1):21–30 32. Onar O, Uzunoglu M, Alam M (2008) Modeling, control and simulation of an autonomous wind turbine/photovoltaic/fuel cell/ ultra-capacitor hybrid power system. J Power Sources 185(2):1273–1283 33. Gao L, Jiang Z, Dougal RA (2004) An actively controlled fuel cell/battery hybrid to meet pulsed power demands. J Power Sources 130(1):202–207 34. Wang L, Singh C (2009) Multicriteria design of hybrid power generation systems based on a modified particle swarm optimization algorithm. IEEE Trans Energy Convers 24(1):163–172

123

Author's personal copy Iran J Sci Technol Trans Electr Eng 35. Kim SK, Jeon JH, Cho CH, Ahn JB, Kwon SH (2008) Dynamic modeling and control of a grid-connected hybrid generation system with versatile power transfer. IEEE Trans Ind Electron 55(4):1677–1688

123

36. IEEE standard for interconnecting distributed resources with electric power systems. IEEE Std 1547-2003, pp 1–28 (2003). https://doi.org/10.1109/ieeestd.2003.94285