# Modelling Methodologies to Design and Control Renewables and Hydrogen-Based Telecom Towers Power Supply Systems

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## Abstract

**:**

## 1. Introduction

## 2. Plant Description

## 3. Model Description

#### 3.1. PV and Wind Turbine Calculation Procedure

_{PV}is the panel efficiency, n

_{PV}is the number of the installed panels, while λ is a reduction coefficient that is calculated for each month. It is introduced to quantify the difference, in terms of power, when using panels in ideal and in real weather conditions, and it is defined as the ratio between G

_{real weather conditions}and G

_{without clouding effects}; consequently, λ values lay between 0 and 1. Specifically, the monthly values of G

_{real weather conditions}are provided by [21], while those of G

_{without clouding effects}are calculated according to [9].

_{w,n}depends on the characteristic speeds of the turbines, and those values are the cut-in V

_{ci}, nominal V

_{n}and cut-out V

_{co}speeds. The working power P

_{w}can be expressed as a function of nominal power P

_{w,n}in Equation (2):

#### 3.2. PEMFC and PEMEL Systems

_{min}. Therefore, the working time is defined as described in Equation (6):

#### 3.3. PEMFCS Mode

#### 3.4. PEMELS Mode

#### 3.5. Hydrogen Storage Hourly State Calculation

**HST**can be expressed as in Equation (10):

## 4. Optimisation Procedure

_{ge}[11] is the CAPEX for grid extension, D is the grid distance and ${Q}_{{H}_{{2}_{yr}=0}}$ is the hydrogen mass initially needed (at year 0) to ensure the optimal initial level of the HST (it is a result of the optimisation procedure). In the denominator, ABC is the annual bill cost, which refers to the electricity required by the telecommunication towers. Since the latter is not a cost in case of an islanded configuration, this term is associated with annual savings. MC

_{ge}[10] is the maintenance cost associated with the grid extension, p

_{H2}is the hydrogen price [29], ${\u2206}_{{H}_{2}}$ is the hydrogen mass that must be purchased year by year to restore the optimal HST initial level, while R is defined as the replacement cost for each main component of the plant (i.e., wind turbines, tanks and so on) and in this analysis it is set equal to 0 since it is assumed that it will be performed once the payback time is reached.

_{y}are defined over the most durable investment for the plant element (here considered as n = 25 years for PV panels) and reduced by a factor (1 + d)

^{i}that increases with time and where d is defined as the discount rate and it is set equal to 5%. The goal is to minimise the SPB or LCOE; consequently, the variables to be optimised are the number of PV modules n

_{PV}, the number of wind turbines n

_{w}, the capacity of HST HST

_{size}and the quantity of stored hydrogen at the beginning of the year SOC

_{0,HST}. The first two variables are used to satisfy the average electric power request, while the storage system interacts with both PEMFCS and PEMELS to improve their operations, making possible the accumulation of electric power in the form of hydrogen when the production exceeds the demand. Finally, managing SOC

_{0,HST}makes it possible to take full advantage of the fuel cell and the electrolyser systems.

- (1)
- the state of charge of the hydrogen tank has to lay between 15% and 95%;
- (2)
- the nominal power of the PEMELS must be equal to the summation of installed PV and wind turbine nominal power, so as to avoid having renewable energy wasted;
- (3)
- the nominal power of the PEMFCS must be equal to the maximum value of the load oversized by 10% to cover intermittency of renewable sources;
- (4)
- the final level of the tank must be equal to the initial level in case of charge sustaining strategy. This implies that, considering SPB and LCOE formulas, the term representing the hydrogen bought (${\u2206}_{{{H}_{2}}_{i}})$ is always negligible. Meanwhile, if the charge depleting strategy is used, the final level of the tank must be equal to half of its initial level.

_{size}and SOC

_{0,HST}initial state. Blue and light blue arrows, respectively, stand for the load demand and renewable production flows, while the green arrows indicate the energy flows in the HST. The balance between the load request and production gives the electric excess/lack, which determines the PEMFCS and PEMELS operating mode, as well as the increase or reduction in terms of

**HST**state of charge. Once the SPB or LCOE, which depend on every component cost, are defined, the optimisation procedure is terminated if one of the parameters reaches the lowest possible value and the constraints are verified, thus the optimal sizing of the plant is achieved. It is worth noting that such a minimisation problem was solved relying on a code developed by the authors in the Matlab

^{®}environment, adopting the ‘interior-point’ optimisation method [31].

## 5. DP Routine

_{j}helps to define hour by hour if wind and solar production from the optimisation can meet the telecommunication tower needs, with the j subscript that is used to refer the balance to the jth hour of the year:

_{j}can be written in Equation (16):

_{PEM,j}assumes negative values, the system is working in PEMELS mode. In the same way, when P

_{grid,j}assumes negative values, the system is selling electricity to the grid. To this value, P

_{grid}, is associated the cost–function variable J, estimated in Equation (17):

_{bought}and P

_{sold}are, respectively, the hourly electric power purchased and sold to the grid. In addition, C

_{el,bought}and C

_{el,sold}are the cost for buying electric energy and its selling price, which, here, are considered constantly equal to 0.35 €/kWh and 0.15 €/kWh, respectively [34]. This term will affect the objective function SPB, in Equation (18), or LCOE, in Equation (19), as follows:

_{HST}as a state variable. The state variable is directly controlled by the control variable U, which, in this case, is P

_{grid,j}. Assumptions on the state and control variable are needed:

- (1)
- ${P}_{bought}\le {P}_{PEMFCS,nominalvalue}$;
- (2)
- ${P}_{sold}\le \mathrm{max}\left({P}_{PV}+{P}_{W}-TL\right)-{P}_{PEMELS,nominalvalue}$;
- (3)
- $SO{C}_{end,HST}=SO{C}_{0,HST}$, considering charge sustaining strategy;
- (4)
- $SO{C}_{end,HST}=0.5\xb7SO{C}_{0,HST}$, considering charge depleting strategy.

_{HST}, which strictly depends on the hydrogen mass produced and consumed, we considered the same procedure defined in the previous sections. It is worth remarking that in this case, the system will also respect the constraints defined earlier on the fuel cell system and the electrolyser system and the term P

_{PEM,j}, defined in Equation (16). In this case, it will never work under the minimum threshold defined earlier, so the working time in this procedure will always be 1 h. Given the value of the optimal control variable that minimises the cost function over the entire year, it is possible to calculate the SOC

_{HST}trajectory.

## 6. Results

_{size}, which will be slightly reduced, in order to maximise the utilisation of the tank. Its initial state is indeed a bit higher in case of the charge depleting strategy, as it is shown in Figure 5.

## 7. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Nomenclature

CAPEX | Capital expenditure |

DP | Dynamic programming |

HST | Hydrogen storage tank |

LHV | Lower heating value |

MH | Metal hydride |

OPEX | Operational expenditure |

PEMELS | Proton exchange membrane electrolyser system |

PEMFCS | Proton exchange membrane fuel cell system |

RESs | Renewable energy sources |

rSOC | Reversible oxide cell |

Symbols | |

ABC | Annual bill cost [€] |

C_{ge} | Capex for grid extension [€/km] |

D | Grid extension distance |

d | Discount rate |

E_{y,i} | Energy required from the microgrid at the ith year [kWh] |

IC | Installation cost of the microgrid [€] |

J | Performance index of DP routine |

LCOE | Levelised cost of energy [€/kWh] |

MC | Annual maintenance cost for the microgrid [€] |

MC_{ge} | Annual maintenance cost for grid extension [€] |

m_{H2, PEMELS, j} | Mass of hydrogen generated in PEMELS mode at the jth hour [kg] |

m_{H2, PEMFCS, j} | Mass of hydrogen consumed in PEMFCS mode at the jth hour [kg] |

m_{H2, tank, j} | Mass of hydrogen entering/leaving the HST at the j^{th} hour [kg] |

n | Longest investment period |

P_{PEMELS,nominal} | PEMELS nominal electric power [kW] |

P_{PEMFCS,nominal} | PEMFCS nominal electric power [kW] |

P_{grid,j} | Electric power from the electric grid at the j^{th} hour [kW] |

p_{H2} | Hydrogen price [€/kg] |

P_{min} | PEMFCS or PEMELS minimum working power [kW] |

P_{PEM,j} | PEMFCS and PEMELS power split at the jth hour [kW] |

P_{PV,j} | Photovoltaics electric power production at the jth hour [kW] |

P_{W,j} | Wind power production at the j^{th} hour [kW] |

R | Replacement cost [€] |

S | Renewable (i.e., generated by PV and/or wind turbine) power surplus or shortage [kW] |

SOC | State of charge |

SPB | Simple payback period [years] |

t | Fuel cell system or electrolyser working time [h] |

TC | Total microgrid cost (excluding installation cost) [€] |

TD | Tax deduction |

TL | Total electric load [kW] |

Case Scenarios Nomenclature | |

- Renewable sources:
- $\mathrm{P}1\mathrm{W}1=\mathrm{PV}\mathrm{panels}\mathrm{and}\mathrm{wind}\mathrm{turbines}$
- $\mathrm{P}1\mathrm{W}0=\mathrm{PV}\mathrm{panels}\mathrm{only}$
| |

- 2
- Grid Connection:
- $\mathrm{G}\mathrm{o}\mathrm{f}\mathrm{f}=\mathrm{P}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{o}\mathrm{f}\mathrm{f}\mathrm{g}\mathrm{r}\mathrm{i}\mathrm{d}$
- $\mathrm{G}\mathrm{o}\mathrm{n}=\mathrm{P}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{o}\mathrm{n}\mathrm{g}\mathrm{r}\mathrm{i}\mathrm{d}$
| |

- 3
- HST Charge Strategy
- $\mathrm{C}\mathrm{S}=\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{S}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}$
- $\mathrm{C}\mathrm{D}=\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{D}\mathrm{e}\mathrm{p}\mathrm{l}\mathrm{e}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g}$
| |

- 4
- This level depends on the economic optimisation parameter used:
- 4.1
- $\mathrm{d}\mathrm{x}=\mathrm{G}\mathrm{r}\mathrm{i}\mathrm{d}\mathrm{E}\mathrm{x}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n},\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e}\mathrm{x}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{i}\mathrm{n}\mathrm{k}\mathrm{m}$
- 4.2
- $\mathrm{L}=\mathrm{o}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{D}\mathrm{P}\mathrm{h}\mathrm{e}\mathrm{l}\mathrm{d}\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}LCOE$
| |

Example | |

Plant with no wind turbines, grid connected not grid extended in a charge depleting strategy: | |

$\mathrm{P}1\mathrm{W}0\mathrm{G}\mathrm{o}\mathrm{n}\mathrm{C}\mathrm{D}\mathrm{d}0$ | |

Plant with wind turbines, off-grid in a charge sustaining strategy optimised with LCOE: | |

$\mathrm{P}1\mathrm{W}1\mathrm{G}\mathrm{o}\mathrm{f}\mathrm{f}\mathrm{C}\mathrm{S}\mathrm{L}$ |

## References

- Tazay, A.F.; Hazza GA, W.; Zerkaoui, S.; Alghamdi, S.A. Optimal Design and Techno-economic Analysis of a Hybrid Solar-wind Power Resource: A Case Study at Al baha University, KSA. Int. J. Energy Prod. Manag.
**2022**, 7, 13–34. [Google Scholar] [CrossRef] - Wang, Y.; Wang, J.; Yang, L.; Ma, B.; Sun, G.; Youssefi, N. Optimal designing of a hybrid renewable energy system connected to an unreliable grid based on enhanced African vulture optimizer. ISA Trans.
**2022**, 129, 424–435. [Google Scholar] [CrossRef] [PubMed] - Usman, M. HOMER Analysis for Integrating Solar Energy in Off-Grid and On-Grid SCO Telecommunication Sites. In Proceedings of the 2019 1st Global Power, Energy and Communication Conference (GPECOM), Cappadocia, Turkey, 12–15 June 2019; pp. 270–275. [Google Scholar]
- Haddad, A.; Ramadan, M.; Khaled, M. Triple hybrid system coupling fuel cell with wind turbine thermal solar system. Int. J. Hydrogen Energy
**2019**, 45, 11484–11491. [Google Scholar] [CrossRef] - Amutha, W.M.; Rajini, V. Techno-economic evaluation of various hybrid power systems for rural telecom. Renew. Sustain. Energy Rev.
**2015**, 43, 553–561. [Google Scholar] [CrossRef] - Li, Z.; Zheng, Z.; Xu, L.; Lu, X. A review of the applications of fuel cells in microgrids: Opportunities and challenges. BMC Energy
**2019**, 1, 8. [Google Scholar] [CrossRef] - Maoulida, F.; Rabah, D.; El Ganaoui, M.; Aboudou, K.M. PV-Wind-Diesel System for Energy Supply on Remote Area Applied for Telecommunication Towers in Comoros. Open J. Energy Effic.
**2021**, 10, 50–72. [Google Scholar] [CrossRef] - Schmidt, O.; Gambhir, A.; Staffell, I.; Hawkes, A.; Nelson, J.; Few, S. Future cost and performance of water electrolysis: An expert elicitation study. Int. J. Hydrogen Energy
**2017**, 42, 30470–30492. [Google Scholar] [CrossRef] - Rispoli, N.; Vitale, F.; Califano, F.; Califano, M.; Polverino, P.; Rosen, M.A.; Sorrentino, M. Constrained optimal design of a reversible solid oxide cell-based multiple load renewable microgrid. J. Energy Storage
**2020**, 31, 101570. [Google Scholar] [CrossRef] - Jansen, G.; Dehouche, Z.; Corrigan, H. Cost-effective sizing of a hybrid Regenerative Hydrogen Fuel Cell energy storage system for remote & off-grid telecom towers. Int. J. Hydrogen Energy
**2021**, 46, 18153–18166. [Google Scholar] [CrossRef] - Ayodele, T.R.; Mosetlhe, T.C.; Yusuff, A.A.; Ogunjuyigbe, A.S.O. Off-grid hybrid renewable energy system with hydrogen storage for South African rural community health clinic. Int. J. Hydrogen Energy
**2021**, 46, 19871–19885. [Google Scholar] [CrossRef] - Luta, D.N.; Raji, A.K. Decision-making between a grid extension and a rural renewable off-grid system with hydrogen generation. Int. J. Hydrogen Energy
**2018**, 43, 9535–9548. [Google Scholar] [CrossRef] - Pellegrini, M.; Guzzini, A.; Saccani, C. Experimental measurements of the performance of a micro-wind turbine located in an urban area. Energy Rep.
**2021**, 7, 3922–3934. [Google Scholar] [CrossRef] - Lai, C.S.; McCulloch, M.D. simple cost of electricity for solar photovoltaic and electrical energy storage. Appl. Energy
**2017**, 190, 191–203. [Google Scholar] [CrossRef] - Califano, M.; Sorrentino, M.; Rosen, M.A.; Pianese, C. Optimal heat and power management of a reversible solid oxide cell based microgrid for effective technoeconomic hydrogen consumption and storage. Appl. Energy
**2022**, 319, 11926. [Google Scholar] [CrossRef] - Taghavifar, H.; Zomorodian, Z.S. Techno-economic viability of on grid micro-hybrid PV/Wind/Gen System for an educational building in Iran. Renew. Sustain. Energy Rev.
**2021**, 143, 110877. [Google Scholar] [CrossRef] - Vitale, F.; Rispoli, N.; Sorrentino, M.; Rosen, M.A.; Pianese, C. On the use of dynamic programming for optimal energy management of grid-connected reversible solid oxide cell-based renewable microgrids. Energy
**2021**, 225, 120304. [Google Scholar] [CrossRef] - Parida, B.; Iniyan, S.; Goic, R. A review of solar photovoltaic technologies. Renew. Sustain. Energy Rev.
**2011**, 15, 1625–1636. [Google Scholar] [CrossRef] - Alma Solar. Panneau Solaire I’M SOLAR 280p. Available online: https://www.alma-solarshop.fr/panneau-i-m-solar/1335-panneau-solaire-i-m-solar-370w-mono-noir.html (accessed on 4 February 2023).
- Aeolos. Wind Turbine. Available online: http://www.windturbinestar.com/3kwv-v-aeolos-wind-turbine.html (accessed on 4 February 2023).
- ENEA. Solar Database. Available online: http://www.solaritaly.enea.it/%0ACalcRggmmOrizz/Calcola.php.%0A (accessed on 4 February 2023).
- Marocco, P.; Ferrero, D.; Lanzini, A.; Santarelli, M. The role of hydrogen in the optimal design of off-grid hybrid renewable energy systems. J. Energy Storage
**2022**, 46, 103893. [Google Scholar] [CrossRef] - Sorrentino, M.; Pianese, C.; Maiorino, M. An integrated mathematical tool aimed at developing highly performing and cost-effective fuel cell hybrid vehicles. J. Power Sources
**2013**, 221, 308–317. [Google Scholar] [CrossRef] - Kotowicz, J.; Jurczyk, M.; Węcel, D. Equipment Exploitation in Power to Gas Installation. New Trends Prod. Eng.
**2018**, 1, 409–418. [Google Scholar] [CrossRef] - Hancke, R.; Holm, T.; Ulleberg, Ø. The case for high-pressure PEM water electrolysis. Energy Convers. Manag.
**2022**, 261, 115642. [Google Scholar] [CrossRef] - Roy, A.; Watson, S.; Infield, D. Comparison of electrical energy efficiency of atmospheric and high-pressure electrolysers. Int. J. Hydrogen Energy
**2006**, 31, 1964–1979. [Google Scholar] [CrossRef] - Scheepers, F.; Stähler, M.; Stähler, A.; Rauls, E.; Müller, M.; Carmo, M.; Lehnert, W. Improving the efficiency of PEM electrolyzers through membrane-specific pressure optimization. Energies
**2020**, 13, 612. [Google Scholar] [CrossRef] - Orioli, A.; Franzitta, V.; Di Gangi, A.; Foresta, F. The recent change in the italian policies for photovoltaics: Effects on the energy demand coverage of grid-connected pv systems installed in urban contexts. Energies
**2016**, 9, 944. [Google Scholar] [CrossRef] - Hydrogen Council. Path to Hydrogen Competitiveness: A Cost Perspective. 88. 2020. Available online: www.hydrogencouncil.com (accessed on 4 February 2023).
- Sparber, W.; Weiss, W.; Sanner, B.; Angelino, L.; De Gregorio, M.; Février, N.; Haslinger, W.; Kujbus, A.; Landolina, S.; Stryi-Hipp, G.; et al. Strategic Research and Innovation Agenda for RHC; Publications Office of the European Union: Luxembourg, 2022. [Google Scholar] [CrossRef]
- MathWorks. Find Minimum of Constrained Nonlinear Multivariable Function—MATLAB Fmincon. Available online: https://it.mathworks.com/help/optim/ug/fmincon.html#busog7r-options (accessed on 4 February 2023).
- Sundström, O.; Guzzella, L. A generic dynamic programming Matlab function. In Proceedings of the 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC), St. Petersburg, Russia, 8–10 July 2009; pp. 1625–1630. [Google Scholar] [CrossRef]
- Anderson, E.; Ayers, K.; Capuano, C. R&D Focus Areas Based on 60,000 hr Life PEM Water Electrolysis Stack Experience. In Proceedings of the 1st International Workshop on Durability and Degradation Issues in PEM Electrolysis Cells and Its Components, Freiburg, Germany, 12 March 2013. [Google Scholar]
- ARERA. 2023. Available online: https://www.arera.it/it/dati/eep35.htm# (accessed on 4 February 2023).

**Figure 1.**Plant scheme made up of renewables (PV panels and wind turbines), electrical grid, PEMELS, PEMFCS, metal hydride HST and telecommunication towers.

**Figure 4.**Detailed information flows within the developed constrained optimisation tool. The figure shows how the different model features interact with each other in order to obtain the optimal results. Note: PV plant and wind turbine unitary costs are given in [19,20], respectively. For PEMFCS, PEMELS and HST, a cost projection to 2030 is considered [30].

**Figure 5.**Year-through HST trajectory for charge sustaining (

**a**) and charge depleting strategy (

**b**). The figures show every state of the tank during the year.

**Figure 6.**SPB variation as a function of the grid extension distance. The SPB decreases with increasing distance from the grid and reaches 0 at 6.75 km.

**Figure 7.**Monthly energy shares of PV, wind turbine, electric load and hydrogen (H

_{2}) in the charge sustaining (

**a**,

**b**) and charge depleting (

**c**,

**d**) strategy. In (

**a**,

**c**) the islanded microgrid energy shares are shown, while in 7b and 7d, there are the grid connected ones.

**Figure 8.**Working hours and efficiencies in PEMFCS and PEMELS mode comparison between charge sustaining and charge depleting strategies in an islanded microgrid (

**a**,

**b**) and grid-connected microgrid (

**c**,

**d**). The ordinates on the left show the operating hours, while on the right, the efficiency of the PEMFCS and PEMELS are shown. The number of bars in off-grid and on-grid scenarios was chosen aiming at improving the visualisation and understanding of simulated PEMFCS and PEMELS scheduling outcomes.

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

Maximum power | [W] | 270 |

Efficiency | - | 0.16 |

Dimensions (length × width × height) | [mm] | 992 × 40 × 1640 |

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

Maximum power | [kW] | 3.6 |

Cut-in wind speed | [m/s] | 1.5 |

Nominal wind speed | [m/s] | 10 |

Cut-out wind speed | [m/s] | 25 |

Components | Unit | Unit Cost |
---|---|---|

PV plant: panel, inverter | [€/kW] | 817 (600, 217) |

Wind turbine: tower, rotor, inverter, grid-on controller | [€/kW] | 2513 (504, 1108, 635, 266) |

HST [30] | [€/kg] | 1200 |

PEMFCS [30] | [€/kW] | 1200 |

PEMELS [30] | [€/kW] | 500 |

**Table 4.**Plant characteristics in the charge sustaining strategy, in terms of renewable, PEMFCS and PEMELS power installed, HST size and initial state, SPB and LCOE results and Capital investment.

Parameter | Unit | P1W1GoffCSd0 | P1W1GonCSd0 | P1W1GoffCSd5 | P1W1GoffCSL | P1W1GonCSL |
---|---|---|---|---|---|---|

PV power | [kW] | 16.71 | 16.71 | 16.71 | 16.71 | 16.71 |

Wind power | [kW] | 31.45 | 31.45 | 31.45 | 31.45 | 31.45 |

HST_{size} | [kg] | 104.57 | 10.457 | 104.57 | 104.57 | 10.457 |

SOC_{0,HST} | [%] | 34.81 | 34.81 | 34.81 | 34.81 | 34.81 |

PEMFCS power | [kW] | 4.12 | 4.12 | 4.12 | 4.12 | 4.12 |

PEMELS power | [kW] | 48.16 | 12.36 | 48.16 | 48.16 | 12.36 |

SPB | [years] | 33.348 | 6.997 | 7.397 | / | / |

LCOE | [€/kWh] | / | / | / | 0.520 | 0.209 |

Capital invest. | [€] | 247,200 | 116,520 | 247,200 | 247,200 | 116,520 |

**Table 5.**Plant characteristics in the charge depleting strategy in terms of renewable, PEMFCS and PEMELS power installed, HST size and initial state, SPB and LCOE results and Capital investment.

Parameter | Unit | P1W1GoffCDd0 | P1W1GonCDd0 | P1W1GoffCDd5 | P1W1GoffCDL | P1W1GonCDL |
---|---|---|---|---|---|---|

PV power | [kW] | 19.80 | 19.80 | 19.79 | 19.79 | 19.79 |

Wind power | [kW] | 27.11 | 27.11 | 27.12 | 27.13 | 27.13 |

HST_{size} | [kg] | 99.03 | 9.903 | 98.96 | 98.92 | 9.892 |

SOC_{0,HST} | [%] | 41.51 | 41.51 | 41.49 | 41.47 | 41.47 |

PEMFCS power | [kW] | 4.12 | 4.12 | 4.12 | 4.12 | 4.12 |

PEMELS power | [kW] | 46.91 | 12.36 | 46.91 | 46.91 | 12.36 |

SPB | [years] | 28.533 | 6.361 | 5.213 | / | / |

LCOE | [€/kWh] | / | / | / | 0.489 | 0.195 |

Capital invest. | [€] | 231,540 | 107,470 | 231,480 | 231,450 | 107,490 |

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## Share and Cite

**MDPI and ACS Style**

Aliberti, P.; Sorrentino, M.; Califano, M.; Pianese, C.; Capozucca, L.; Cristiani, L.; Lops, G.; Mancini, R.
Modelling Methodologies to Design and Control Renewables and Hydrogen-Based Telecom Towers Power Supply Systems. *Energies* **2023**, *16*, 6316.
https://doi.org/10.3390/en16176316

**AMA Style**

Aliberti P, Sorrentino M, Califano M, Pianese C, Capozucca L, Cristiani L, Lops G, Mancini R.
Modelling Methodologies to Design and Control Renewables and Hydrogen-Based Telecom Towers Power Supply Systems. *Energies*. 2023; 16(17):6316.
https://doi.org/10.3390/en16176316

**Chicago/Turabian Style**

Aliberti, Paolo, Marco Sorrentino, Marco Califano, Cesare Pianese, Luca Capozucca, Laura Cristiani, Gianpiero Lops, and Roberto Mancini.
2023. "Modelling Methodologies to Design and Control Renewables and Hydrogen-Based Telecom Towers Power Supply Systems" *Energies* 16, no. 17: 6316.
https://doi.org/10.3390/en16176316