# Energy Management Strategy for a Bioethanol Isolated Hybrid System: Simulations and Experiments

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. System Description

#### 2.1. Bioethanol Reformer Subsystem

#### 2.2. PEM Fuel Cell Subsystem

#### 2.3. Wind Power Subsystem

#### 2.4. Solar Power Subsystem

#### 2.5. Energy Storage Subsystem

## 3. Energy Management Strategy

- The RESs deliver their maximum power (MPPT mode), while the batteries do not exceed the maximum charge level.
- When the battery reaches its maximum $SoC$ level, the RESs change to LPT mode, where the power reference follows the load demand.
- Minimize the battery cycles of charging and discharging to preserve their lifetime.
- The fuel cell provides energy to the DC bus during low RES generation periods and maintains the $SoC$ of the batteries at a desirable level.

- State 1:$$\begin{array}{ccc}\hfill {P}_{load}^{del}& =& {P}_{load}^{req},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{res}^{ref}& =& {P}_{res}^{av},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{fc}^{ref}& =& 0,\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{bat}^{ref}& =& {P}_{load}^{del}-{P}_{res}^{ref},\hfill \end{array}$$
- State 2:$$\begin{array}{ccc}\hfill {P}_{load}^{del}& =& {P}_{load}^{req},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{res}^{ref}& =& {P}_{res}^{av},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{fc}^{ref}& =& {P}_{load}^{del}+{P}_{bat}^{ch}+{P}_{h}-{P}_{res}^{ref},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{bat}^{ref}& =& {P}_{load}^{del}+{P}_{h}-{P}_{res}^{ref}-{P}_{fc}^{ref},\hfill \end{array}$$
- State 3:$$\begin{array}{ccc}\hfill {P}_{load}^{del}& =& {P}_{load}^{req},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{res}^{ref}& =& {P}_{res}^{av},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{fc}^{ref}& =& {P}_{load}^{del}+{P}_{h}-{P}_{res}^{ref},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{bat}^{ref}& =& {P}_{load}^{del}+{P}_{h}-{P}_{res}^{ref}-{P}_{fc}^{ref},\hfill \end{array}$$
- State 4:$$\begin{array}{ccc}\hfill {P}_{load}^{del}& =& {P}_{load}^{req},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{res}^{ref}& =& {P}_{load}^{del},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{fc}^{ref}& =& 0,\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{bat}^{ref}& =& {P}_{load}^{del}-{P}_{res}^{ref},\hfill \end{array}$$
- State 5:$$\begin{array}{ccc}\hfill {P}_{load}^{del}& =& {P}_{load}^{vital},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{res}^{ref}& =& {P}_{res}^{av},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{fc}^{ref}& =& {P}_{load}^{del}+{P}_{bat}^{ch}+{P}_{h}-{P}_{res}^{ref},\hfill \end{array}$$$$\begin{array}{ccc}\hfill {P}_{bat}^{ref}& =& {P}_{load}^{del}+{P}_{h}-{P}_{res}^{ref}-{P}_{fc}^{ref},\hfill \end{array}$$

#### SoC Levels Calculation

## 4. Experimental Validation

#### 4.1. Scenario Description

#### 4.2. Simulation Results

#### 4.3. Laboratory Station Description

^{TM}model MAN5100078, manufactured by Ballard

^{TM}. The output voltage can be operated safely in the linear range of voltages from $26\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$ to $36\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$ and currents from $10\phantom{\rule{0.166667em}{0ex}}\mathrm{A}$ to $46\phantom{\rule{0.166667em}{0ex}}\mathrm{A}$. The ESS consists on a $165\phantom{\rule{0.166667em}{0ex}}\mathrm{F}$ Maxwell

^{TM}Supercapacitor model BMOD0165. The nominal voltage is $48\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$ with values up to a maximum of $52\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$. The SC supports a nominal current of 98 in continuous mode and shows a serial resistance of approximately $6\phantom{\rule{0.166667em}{0ex}}\mathrm{m}$. The DC-DC boost converters are implemented with two branches of Semikron

^{TM}IGBTs. The converters are controlled with a $20\phantom{\rule{0.166667em}{0ex}}\mathrm{kHz}$ PWM signal, whose switching duty cycle is set by the EMS. The maximum voltage allowed in each switch is $400\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$, and the nominal current is $75\phantom{\rule{0.166667em}{0ex}}\mathrm{A}$. The inductances in the converters have a value of $35\phantom{\rule{0.166667em}{0ex}}\mathsf{\mu}\mathrm{Hy}$. The DC bus voltage adopted is $75\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$, and the bus capacitance is $2720\phantom{\rule{0.166667em}{0ex}}\mathsf{\mu}\mathrm{F}$. The programmable source is an NL Source-Sink (SS) from Hocherl & Hackl GmbH

^{TM}. The output voltage of this source can reach a maximum of $80\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$ with a nominal power of $3.2\phantom{\rule{0.166667em}{0ex}}\mathrm{kW}$. The installed programmable load (PL) is a ZL Electronic DC Load from Hocherl & Hackl GmbH

^{TM}, which can work with a maximum voltage of $80\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$ and $3.4\phantom{\rule{0.166667em}{0ex}}\mathrm{kW}$. Finally, the control is implemented using a National Instruments

^{TM}controller model Compaq Rio 9035, which has a CPU Dual-Core of $1.33\phantom{\rule{0.166667em}{0ex}}\mathrm{GHz}$ and an FPGA Xilinx Kintex-7 7K70T. The rated values of the fuel cell test station are summarized in Table 7.

#### 4.4. Scaling Methodology

^{TM}platform. The PEMFC stack and the SCs are connected to the DC bus through DC-DC converters. The H&H Source/Sink is used to emulate the RES generation delivered to the bus (${P}_{ps}^{s}$). The scaled version of RES’s available power (${P}_{res}^{s}$) is obtained using the off-line scaling methodology applied to the real RES generation profile (${P}_{res}^{r}$), which is formed by the wind turbines’ (${P}_{wind}^{r}$) and PV array’s (${P}_{pv}^{r}$) available powers. The scaled version of the load demand (${P}_{load}^{s}$) and the power consumed by the BRS heater (${P}_{h}^{s}$) are emulated using the programmable load (${P}_{pl}^{s}$). Note that the energy required by the heater is obtained using the power delivered by the Nexa

^{TM}.

^{TM}fuel cell.

#### 4.5. Experimental Results

^{TM}fuel cell. The power delivered by the fuel cell is set by the EMS, and the power delivered/absorbed by the SC arises as a result of the two PI controllers in cascade that attempt to maintain the DC bus voltage at the desired level, while in the simulation, it is computed from a power balance. A constant power delivered by the SC can be seen that corresponds to the necessary power to maintain the DC bus at the desired value. Then, Figure 12b shows the evolution of the state of charge of the batteries and the state in which the EMS works. The commutations between states according to the occurrence of events can be seen.

^{TM}fuel cell, where the main difference of the experiment evolution with respect to the simulation is a small noise. The major differences observed between experimental and simulation results are found in the SC power of Figure 13d and are due to the low efficiency in the bidirectional DC-DC converter, especially at low powers. This could be solved by incorporating the models of the converters with their efficiency curves. This improvement will be addressed in future works.

## 5. Conclusions

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## Nomenclature

Acronym | Description |

BRS | Bioethanol reform system |

EMS | Energy management strategy |

ESR | Ethanol steam reformer |

ESS | Energy storage subsystem |

FC | Fuel cell |

HRES | Hybrid renewable energy system |

IRI | Institut de Robòtica i Informàtica Industrial |

PEMFC | Polymer electrolyte membrane fuel cells |

PV | Photovoltaic |

RES | Renewable energy source |

SC | Supercapacitor |

WGS | Water-gas shift reactors |

Symbol | Description |

${C}_{bat}$ | Rated capacity of each battery (Ah) |

${E}_{max}^{i}$ | Energy storing capacity in a real (r) or scaled (s) system ($\mathrm{Wh}$) |

${I}_{fc}$ | Fuel cell current ($\mathrm{A}$) |

${I}_{pv}$ | PV cell current ($\mathrm{A}$) |

${I}_{PV}$ | PV module current ($\mathrm{A}$) |

$\Delta {P}_{i}^{max}$ | Maximum power rise rate in a real (r) or scaled (s) system (W/s) |

$\Delta {P}_{fc}$ | Fuel cell power rate (W/s) |

$\Delta {P}_{fc}^{max}$ | Maximum power rise rate (W/s) |

$\Delta {P}_{fc}^{min}$ | Maximum power fall rate (W/s) |

${P}_{res}^{av}$ | RESs available power (W) |

${P}_{bat}$ | Battery power (W) |

${P}_{bat}^{ch}$ | Battery recharge power (W) |

${P}_{bat}^{ref}$ | Battery reference power (W) |

${P}_{fc}$ | Fuel cell power (W) |

${P}_{fc}^{max}$ | Maximum fuel cell power (W) |

${P}_{fc}^{min}$ | Minimum fuel cell power (W) |

${P}_{fc}^{ref}$ | Fuel cell reference power (W) |

${P}_{h}$ | Power supplied to the heater (W) |

${P}_{load}$ | Load demand (W) |

${P}_{load}^{del}$ | Power delivered to the load (W) |

${P}_{load}^{req}$ | Power required by the load (W) |

${P}_{load}^{vital}$ | Vital loads’ power (W) |

${P}_{pv}$ | PV module power (W) |

${P}_{fc,max}^{r}$ | Rated power of the real system FC module (W) |

${P}_{j}^{r}$ | Power profile in the real system (W) |

${P}_{j,max}^{r}$ | Maximum power peaks (W) |

${P}_{h,max}^{r}$ | Power consumed by the electric heater at ${P}_{fc,max}^{r}$ (W) |

${P}_{res}^{ref}$ | RES reference power (W) |

${P}_{fc,max}^{s}$ | Rated power of the laboratory station FC module (W) |

${P}_{j}^{s}$ | Scaled power profile (W) |

${P}_{res}^{ref}$ | RES reference power (W) |

${P}_{w}$ | Wind turbine power (W) |

${P}_{w,r}$ | Wind turbine rated power (W) |

$SoC$ | State of charge of the battery |

$So{C}_{L1}$ | $SoC$ Level 1 |

$So{C}_{max}$ | Maximum $SoC$ |

$So{C}_{min}$ | Minimum $SoC$ |

$So{C}_{th}$ | $SoC$ hysteresis |

${T}_{auto}$ | Autonomy time (min) |

${T}_{ch}$ | Recharge time (min) |

${V}_{bat}^{min}$ | Minimum voltage (V) |

${V}_{bat}^{max}$ | Maximum voltage (V) |

${V}_{bat}^{r}$ | Rated voltage (V) |

${V}_{PV}$ | PV module voltage (V) |

${W}_{{H}_{2}}$ | Rate of hydrogen consumed by the PEM fuel cell (mol/s) |

## References

- Bajpai, P.; Dash, V. Hybrid renewable energy systems for power generation in stand-alone applications: A review. Renew. Sustain. Energy Rev.
**2012**, 16, 2926–2939. [Google Scholar] [CrossRef] - Ming, M.; Wang, R.; Zha, Y.; Zhang, T. Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm. Energies
**2017**, 10, 674. [Google Scholar] [CrossRef] - Dufo-López, R.; Cristóbal-Monreal, I.R.; Yusta, J.M. Stochastic-heuristic methodology for the optimisation of components and control variables of PV-wind-diesel-battery stand-alone systems. Renew. Energy
**2016**, 99, 919–935. [Google Scholar] [CrossRef] - Cabezas, M.D.; Wolfram, E.A.; Franco, J.I.; Fasoli, H.J. Hydrogen vector for using PV energy obtained at Esperanza Base, Antarctica. Int. J. Hydrogen Energy
**2017**, 42, 23455–23463. [Google Scholar] [CrossRef] - Serpi, A.; Porru, M.; Damiano, A. An Optimal Power and Energy Management by Hybrid Energy Storage Systems in Microgrids. Energies
**2017**, 10, 1909. [Google Scholar] [CrossRef] - Kalinci, Y.; Hepbasli, A.; Dincer, I. Techno-economic analysis of a stand-alone hybrid renewable energy system with hydrogen production and storage options. Int. J. Hydrogen Energy
**2015**, 40, 7652–7664. [Google Scholar] [CrossRef] - Khalid, F.; Aydin, M.; Dincer, I.; Rosen, M. Comparative assessment of two integrated hydrogen energy systems using electrolyzers and fuel cells. Int. J. Hydrogen Energy
**2016**, 41, 19836–19846. [Google Scholar] [CrossRef] - Özgirgin, E.; Devrim, Y.; Albostan, A. Modeling and simulation of a hybrid photovoltaic (PV) module-electrolyzer-PEM fuel cell system for micro-cogeneration applications. Int. J. Hydrogen Energy
**2015**, 40, 15336–15342. [Google Scholar] [CrossRef] - Francesconi, J.; Mussati, M.; Mato, R.; Aguirre, P. Analysis of the energy efficiency of an integrated ethanol processor for PEM fuel cell systems. J. Power Sources
**2007**, 167, 151–161. [Google Scholar] [CrossRef] - Torchio, M.; Ocampo-Martinez, C.; Magni, L.; Serra, M.; Braatz, R.D.; Raimondo, D.M. Fast Model Predictive Control for hydrogen outflow regulation in Ethanol Steam Reformers. In Proceedings of the 2016 American Control Conference (ACC), Boston, MA, USA, 6–8 July 2016; pp. 5044–5049. [Google Scholar]
- Serra, M.; Ocampo-Martinez, C.; Li, M.; Llorca, J. Model predictive control for ethanol steam reformers with membrane separation. Int. J. Hydrogen Energy
**2017**, 42, 1949–1961. [Google Scholar] [CrossRef] - Gupta, A.; Verma, J.P. Sustainable bio-ethanol production from agro-residues: A review. Renew. Sustain. Energy Rev.
**2015**, 41, 550–567. [Google Scholar] [CrossRef] - Cutzu, R.; Bardi, L. Production of Bioethanol from Agricultural Wastes Using Residual Thermal Energy of a Cogeneration Plant in the Distillation Phase. Fermentation
**2017**, 3. [Google Scholar] [CrossRef] - Pieragostini, C.; Aguirre, P.; Mussati, M.C. Life cycle assessment of corn-based ethanol production in Argentina. Sci. Total Environ.
**2014**, 472, 212–225. [Google Scholar] [CrossRef] [PubMed] - Vivas, F.; las Heras, A.D.; Segura, F.; Andújar, J. A review of energy management strategies for renewable hybrid energy systems with hydrogen backup. Renew. Sustain. Energy Rev.
**2018**, 82, 126–155. [Google Scholar] [CrossRef] - Chong, L.W.; Wong, Y.W.; Rajkumar, R.K.; Rajkumar, R.K.; Isa, D. Hybrid energy storage systems and control strategies for stand-alone renewable energy power systems. Renew. Sustain. Energy Rev.
**2016**, 66, 174–189. [Google Scholar] [CrossRef] - Valverde, L.; Rosa, F.; Bordons, C.; Guerra, J. Energy Management Strategies in hydrogen Smart-Grids: A laboratory experience. Int. J. Hydrogen Energy
**2016**, 41, 13715–13725. [Google Scholar] [CrossRef] - Torreglosa, J.P.; García, P.; Fernández, L.M.; Jurado, F. Energy dispatching based on predictive controller of an off-grid wind turbine/photovoltaic/hydrogen/battery hybrid system. Renew. Energy
**2015**, 74, 326–336. [Google Scholar] [CrossRef] - Ipsakis, D.; Voutetakis, S.; Seferlis, P.; Stergiopoulos, F.; Elmasides, C. Power management strategies for a stand-alone power system using renewable energy sources and hydrogen storage. Int. J. Hydrogen Energy
**2009**, 34, 7081–7095. [Google Scholar] [CrossRef] - Feroldi, D.; Degliuomini, L.N.; Basualdo, M. Energy management of a hybrid system based on wind-solar power sources and bioethanol. Chem. Eng. Res. Des.
**2013**, 91, 1440–1455. [Google Scholar] [CrossRef] - Feroldi, D.; Rullo, P.; Zumoffen, D. Energy management strategy based on receding horizon for a power hybrid system. Renew. Energy
**2015**, 75, 550–559. [Google Scholar] [CrossRef] - Han, Y.; Chen, W.; Li, Q. Energy Management Strategy Based on Multiple Operating States for a Photovoltaic/Fuel Cell/Energy Storage DC Microgrid. Energies
**2017**, 10, 1–15. [Google Scholar] [CrossRef] - Feroldi, D.; Zumoffen, D. Sizing methodology for hybrid systems based on multiple renewable power sources integrated to the energy management strategy. Int. J. Hydrogen Energy
**2014**, 39, 8609–8620. [Google Scholar] [CrossRef] - Qi, A.; Peppley, B.; Karan, K. Integrated fuel processors for fuel cell application: A review. Fuel Proc. Technol.
**2007**, 88, 3–22. [Google Scholar] [CrossRef] - Rullo, P.; Degliuomini, L.N.; García, M.; Basualdo, M. Model predictive control to ensure high quality hydrogen production for fuel cells. Int. J. Hydrogen Energy
**2014**, 39, 8635–8649. [Google Scholar] [CrossRef] - Basualdo, M.; Feroldi, D.; Outbib, R. PEM Fuel Cells with Bio-Ethanol Processor Systems; Springer: Berlin, Germany, 2011. [Google Scholar]
- Degliuomini, L.N.; Feroldi, D.; Luppi, P.; Basualdo, M. Improvements on hydrogen production efficiency based on switching multiple renewable power sources. In Proceedings of the 22nd European Symposium on Computer Aided Process Engineering, London, UK, 17–20 June 2012; Bogle, D.L., Fairweather, M., Eds.; Elsevier: New York, NY, USA, 2012; pp. 342–346. [Google Scholar]
- Inc, B.P.S. Nexa Power Module User’S Manual. 2003. Available online: http://www.ballard.com/ (accessed on 8 December 2017).
- Feroldi, D.; Serra, M.; Riera, J. Energy management strategies based on efficiency map for fuel cell hybrid vehicles. J. Power Sources
**2009**, 190, 387–401. [Google Scholar] [CrossRef] [Green Version] - Carignano, M.G.; Costa-Castelló, R.; Roda, V.; Nigro, N.M.; Junco, S.; Feroldi, D. Energy management strategy for fuel cell-supercapacitor hybrid vehicles based on prediction of energy demand. J. Power Sources
**2017**, 360, 419–433. [Google Scholar] [CrossRef] - Lei, Y.; Mullane, A.; Lightbody, G.; Yacamini, R. Modeling of the wind turbine with a doubly fed induction generator for grid integration studies. IEEE Trans. Energy Convers.
**2006**, 21, 257–263. [Google Scholar] [CrossRef] - Borowy, B.; Salameh, Z. Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system. IEEE Trans. Energy Convers.
**1996**, 11, 367–375. [Google Scholar] [CrossRef] - Migoni, G.; Rullo, P.; Bergero, F.; Kofman, E. Efficient simulation of Hybrid Renewable Energy Systems. Int. J. Hydrogen Energy
**2016**, 41, 13934–13949. [Google Scholar] [CrossRef] - Tremblay, O.; Dessaint, L.A. Experimental Validation of a Battery Dynamic Model for EV Applications. World Electr. Veh. J.
**2009**, 3, 1–10. [Google Scholar] [CrossRef] - Johnson, V. Battery performance models in ADVISOR. J. Power Sources
**2002**, 110, 321–329. [Google Scholar] [CrossRef] - Li, Q.; Yang, H.; Han, Y.; Li, M.; Chen, W. A state machine strategy based on droop control for an energy management system of PEMFC-battery-supercapacitor hybrid tramway. Int. J. Hydrogen Energy
**2016**, 41, 16148–16159. [Google Scholar] [CrossRef] - Pereira, M.; Limon, D.; de la Peña, D.M.; Valverde, L.; Alamo, T. Periodic economic control of a nonisolated microgrid. IEEE Trans. Ind. Electron.
**2015**, 62, 5247–5255. [Google Scholar] [CrossRef]

**Figure 1.**Schematic diagram of the isolated hybrid renewable energy system with hydrogen production from bioethanol.

**Figure 2.**Statechart diagram describing the energy management strategy based on a finite state machine.

**Figure 3.**Weather variables’ profile of the first week of January 2011: (

**a**) (dashed line) temperature and (solid line) radiation; (

**b**) wind velocity.

**Figure 5.**Simulation results: (

**a**) HRES power distribution; (

**b**) (dashed line) state of charge of the batteries ($SoC$); (solid line) state transitions in the EMS.

**Figure 6.**Critical scenario simulation: (

**a**) HRES power distribution; (

**b**) (dashed line) state of charge of the batteries ($SoC$); (solid line) state transitions in the EMS.

**Figure 8.**Laboratory system control scheme and experiment configuration. The dotted boxes show the elements of the real system to be emulated, and the arrow indicates the laboratory equipment used to emulate this element.

**Figure 9.**(

**a**) Real system, net power ${P}^{r}$; (

**b**) real system, energy variation $\Delta {E}^{r}$; (

**c**) scaled system, net power ${P}^{s}$; (

**d**) scaled system, energy variation $\Delta {E}^{s}$.

**Figure 10.**Solid line: feasible values of the scaling coefficients (${k}_{p},{k}_{t}$) resulting from Equation (48); dashed lines: lower bound (${k}_{{p}_{lb}}$) and most restrictive upper bound (${k}_{{p}_{ub,min}}$). $\mathbf{o}$ points to the adopted pair $(\overline{{k}_{p}},\overline{{k}_{t}})$.

**Figure 12.**Laboratory station power profiles: (

**a**) source/sink, programmable load, Nexa

^{TM}fuel cell, Maxwell

^{TM}SC; (

**b**) (dashed line) state of charge of SCs ($So{C}_{sc}$) and (solid line) states of the EMS.

**Figure 13.**Dashed lines: simulated profiles; solid lines: experiment results: (

**a**) RES generation; (

**b**) load power demand; (

**c**) Nexa

^{TM}fuel cell; (

**d**) Maxwell

^{TM}SC.

HRES Component | Rated Power/Capacity |
---|---|

Wind subsystem | $4.5$ kW |

Solar subsystem | $6\phantom{\rule{0.166667em}{0ex}}{\mathrm{kW}}_{\mathrm{p}}$ |

Energy storage subsystem | $8.11\phantom{\rule{0.166667em}{0ex}}\mathrm{kWh}$ |

PEM fuel cell subsystem | $4.8$ kW |

Bioethanol reformer subsystem (heater) | $1.55$ kW |

Load power demand | $4.1\phantom{\rule{0.166667em}{0ex}}{\mathrm{kW}}_{\mathrm{p}}$ |

**Table 2.**Parameters of each wind turbine ($4.5\phantom{\rule{0.166667em}{0ex}}{\mathrm{kW}}_{\mathrm{p}}$).

Parameter | Value |
---|---|

${\rho}_{a}$ | 1.2 ${\mathrm{kg}/\mathrm{m}}^{3}$ |

r | 1.6 m |

${C}_{p,max}$ | 0.59 |

${v}_{w,r}$ | 12 $\mathrm{m}/\mathrm{s}$ |

${v}_{w,cin}$ | 3 $\mathrm{m}/\mathrm{s}$ |

${v}_{w,cout}$ | 20 $\mathrm{m}/\mathrm{s}$ |

**Table 3.**Parameters of each PV panel ($250\phantom{\rule{0.166667em}{0ex}}{\mathrm{W}}_{\mathrm{p}}$).

Parameter | Value |
---|---|

q | $1.6\times {10}^{-19}\phantom{\rule{0.166667em}{0ex}}C$ |

${A}_{c}$ | $1.6$ |

K | $1.3805\times {10}^{-23}\phantom{\rule{0.166667em}{0ex}}\mathrm{Nm}\phantom{\rule{0.166667em}{0ex}}{\mathrm{K}}^{-1}$ |

${K}_{l}$ | $0.0017\phantom{\rule{0.166667em}{0ex}}A{\phantom{\rule{0.166667em}{0ex}}}^{\circ}$C${}^{-1}$ |

${I}_{or}$ | $2.0793\times {10}^{-6}\mathrm{A}$ |

${T}_{ref}$ | $301.18\phantom{\rule{0.166667em}{0ex}}\mathrm{K}$ |

${E}_{go}$ | $1.10\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$ |

${I}_{sc}$ | $3.27\phantom{\rule{0.166667em}{0ex}}\mathrm{A}$ |

${n}_{p}$ | 1 |

${n}_{s}$ | 60 |

number of PV panels per module | 4 |

**Table 4.**Parameters of each unit of the energy storage subsystem ($1352\phantom{\rule{3.33333pt}{0ex}}\mathrm{Wh}$).

Parameter | Value |
---|---|

Capacity of each battery ${C}_{bat}$ | $104\phantom{\rule{3.33333pt}{0ex}}\mathrm{Ah}\phantom{\rule{0.166667em}{0ex}}/\phantom{\rule{4pt}{0ex}}1352\phantom{\rule{3.33333pt}{0ex}}\mathrm{Wh}$ |

Rated voltage ${V}_{bat}^{r}$ | $13\phantom{\rule{3.33333pt}{0ex}}\mathrm{V}$ |

Event | Description |
---|---|

1 | $SoC\ge So{C}_{L1}$ |

2 | $SoC\le (So{C}_{L1}-So{C}_{th})$ |

3 | $SoC\le So{C}_{min}$ |

4 | $SoC\ge So{C}_{max}$ |

5 | ${p}_{load}^{req}\le {p}_{ren}^{av}$ |

Parameter | Variable | Value |
---|---|---|

System Specifications | ||

Autonomy time | ${T}_{auto}$ | 3 h |

Recharge time | ${T}_{ch}$ | 6 h |

SoC Levels and P_{ch} calculation | ||

Minimum $SoC$ value | $So{C}_{min}$ | $0.4$ |

Maximum $SoC$ value | $So{C}_{max}$ | $0.9$ |

$SoC$ Level 1 | $So{C}_{L1}$ | $0.71$ |

Maximum load power | ${P}_{load}^{max}$ | $4100\phantom{\rule{0.166667em}{0ex}}\mathrm{W}$ |

Maximum fuel cell power | ${P}_{fc}^{max}$ | $4800\phantom{\rule{0.166667em}{0ex}}\mathrm{W}$ |

Maximum power required by the heater | ${P}_{h}^{max}$ | $1553\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}\mathrm{W}$ |

Batteries charging power | ${P}_{ch}$ | $426\phantom{\rule{0.166667em}{0ex}}\mathrm{W}$ |

Parameter | Variable | Value |
---|---|---|

H&H^{TM} SSmaximum power | ${P}_{max}^{ps}$ | $3200\phantom{\rule{0.166667em}{0ex}}\mathrm{W}$ |

H&H^{TM} PLmaximum power | ${P}_{max}^{pl}$ | $1600\phantom{\rule{0.166667em}{0ex}}\mathrm{W}$ |

Nexa^{TM} maximum power | ${P}_{fc,max}^{s}$ | $1200\phantom{\rule{0.166667em}{0ex}}\mathrm{W}$ |

Nexa^{TM} maximum raising rate power | $\Delta {P}_{fc,max}^{s}$ | $120\phantom{\rule{0.166667em}{0ex}}\mathrm{W}/\mathrm{s}$ |

Maxwell^{TM} SC maximum current | ${I}_{sc}^{max}$ | $40\phantom{\rule{0.166667em}{0ex}}\mathrm{A}$ |

Parameter | Variable | Value |
---|---|---|

Real System | ||

Maximum Battery energy storing capacity | ${E}_{max}^{r}$ | $8112\phantom{\rule{0.166667em}{0ex}}\mathrm{Wh}$ |

Fuel cell nominal power | ${P}_{fc,max}^{r}$ | $4800\phantom{\rule{0.166667em}{0ex}}\mathrm{W}$ |

Fuel Cell maximum raising rate power | $\Delta {P}_{fc,max}^{r}$ | $480\phantom{\rule{0.166667em}{0ex}}\mathrm{W}/\mathrm{s}$ |

Heater power at fuel cell nominal power | ${P}_{h,max}^{r}$ | $1553\phantom{\rule{0.166667em}{0ex}}\mathrm{W}$ |

Maximum load power | ${P}_{load,max}^{r}$ | $4100\phantom{\rule{0.166667em}{0ex}}\mathrm{W}$ |

Maximum renewable energy source power | ${P}_{res,max}^{r}$ | $5000\phantom{\rule{0.166667em}{0ex}}\mathrm{W}$ |

Laboratory station: SC voltage values | ||

Maxwell^{TM} SC maximum voltage | ${V}_{max}^{sc}$ | $40\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$ |

Maxwell^{TM} SC minimum voltage | ${V}_{min}^{sc}$ | $30\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$ |

Maxwell^{TM} SC voltage at $So{C}_{min}$ | ${V}_{SoC,min}^{sc}$ | $34.3\phantom{\rule{0.166667em}{0ex}}\mathrm{V}$ |

Scaling Coefficients | ||

Power coefficient | $\overline{{k}_{p}}$ | $0.25$ |

Time coefficient | $\overline{{k}_{t}}$ | $0.0079$ |

Energy coefficient | $\overline{{k}_{e}}$ | $0.0020$ |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Gabriel Rullo, P.; Costa-Castelló, R.; Roda, V.; Feroldi, D.
Energy Management Strategy for a Bioethanol Isolated Hybrid System: Simulations and Experiments. *Energies* **2018**, *11*, 1362.
https://doi.org/10.3390/en11061362

**AMA Style**

Gabriel Rullo P, Costa-Castelló R, Roda V, Feroldi D.
Energy Management Strategy for a Bioethanol Isolated Hybrid System: Simulations and Experiments. *Energies*. 2018; 11(6):1362.
https://doi.org/10.3390/en11061362

**Chicago/Turabian Style**

Gabriel Rullo, Pablo, Ramon Costa-Castelló, Vicente Roda, and Diego Feroldi.
2018. "Energy Management Strategy for a Bioethanol Isolated Hybrid System: Simulations and Experiments" *Energies* 11, no. 6: 1362.
https://doi.org/10.3390/en11061362