*2.2. Home-Load Profile*

The home-load profile with1hresolution represents an average energy consumption (EC) of 7.5 kWh/day for a home on the Mexican coast [31,32]. Energy consumption was obtained from the statistical yearbook Quintana Roo state in Mexico in 2017, developed by the National Institute of Statistics and Geography [33]; Figure 4 shows the daily standardload profile.

**Figure 4.** Daily standard-load profile of coastal house in Cozumel.

#### *2.3. Sizing and Design of the Renewable Energy Hybrid System*

SMHES sizing is based on the analysis of the profiles of solar irradiance (Figure 5) and marine-current speed (Figure 6). The sizing of both renewable energies was carried out based on the assumption that the energy supplies are provided individually and independently of each other. Figures 5a and 6a show the daily solar irradiance and marinecurrent-speed variations in a one-year timelapse. Figures 5b and 6b show the minima, maxima, and year averages to identify the maximum variations during the year; this shows the occurrence of days with higher or lower power generation than that estimated for sizing. The SMHES was dimensioned with respect to the seasonal potential minima [34]. In the case of the PVS, it was dimensioned with the minimum daily irradiation in December (4.72 kWh/day) (Figure 5c), while the SCM dimensioning was carried out with the minimum speeds in November (0.65 m/s) (Figure 6c).

**Figure 5.** Solar potential in Cozumel: (**a**) solar-irradiance daily variations; (**b**) maximum, minimum, and average solar potential; (**c**) daily solar irradiation in different months of the year.

**Figure 6.** Marine-current potential in Cozumel; (**a**) marine-current-speed daily variations; (**b**) maximum, minimum, and average marine-current potential; (**c**) daily speed variation of the different days of the year.

2.3.1. PV Solar System Sizing

The sizing of the PVS was carried out according to annual irradiation minimums (Equation (1)) [34,35].

$$P\_{PVS} = \frac{E\_{PVOut} G\_{CEM}}{G\_{dm} \eta} \tag{1}$$

where *PPVS* is the minimum power-installed capacity (kW) of the photovoltaic system, which was obtained directly through energy consumption per day (*EPVOut*, kWh/day); *GCEM* is the solar standard-test irradiance (1 kW/m2); *Gdm* is the solar irradiation, which in

this case is proposed as the media in the month of lower irradiation, December (4.72 kWh/m2 day); and *η* is the overall efficiency of the auxiliary equipment (DC–DC controller regulator). Note that solar panel efficiency is not considered in Equation (1); the installed capacity is evaluated through commercial parameters where 300 W is proposed for solar panels (tested at 1 kW/m2).

#### 2.3.2. Marine-Current-Energy System Sizing

The marine-current system (MCS) is mainly composed of the marine-current turbine (MCT) and the permanent magnet synchronous generator (PMSG). The MCT instantaneous power *PMCT* (kW) can be calculated using Equation (2) [36].

$$P\_{\rm MCT} = \frac{1}{2} \rho \mathbb{C}\_{\mathcal{P}} A \ V^3 \tag{2}$$

where *ρ* is the seawater density, *V* is the current speed in m/s, and *A* is the cross-area section of the turbine. *Cp* is the power coefficient (dimensionless), which is a function of the tip speed ratio and pitch angle; for the typical marine-current turbine (MCT), *Cp* values are considered in the 0.35–0.5 range. A turbine with a diameter of 2 m is proposed, based on that reported by Shirasawa et al., 2016 [37], which allows the turbine to operate at the current speeds of Cozumel (~0.7 m/s).

To obtain the daily energy production, the integration of the power-generation profile was carried out. The daily marine-current energy (*EMCS*) supplied by the MCS is given by Equation (3), where *t* is the time and *η* is the overall efficiency of the equipment (PMSG and AC–DC controller regulator).

$$E\_{MCS} = \left(\int\_0^t P\_{MCT} \, dt\right) \eta \tag{3}$$

#### *2.4. Solar PV–Marine Current and Energy-Storage System Hybridization*

The SMHES hybridization analysis consisted of evaluating the energy supply with different PVS and MCS degrees of utilization for the 365 days of the year. A 0 HD considers that energy is provided only from the PVS and 1 HD when the energy is provided completely from the MCS. Therefore, hybridization supposes a decrease in the installed capacity of the PVS or MCS. In this study, the hybridization analysis was carried out using an algorithm developed in Matlab® software (Figure 7). The analysis procedure consisted of four steps; first, the power of each system was integrated as a function of time intervals of 1 h in a whole year; second, the HD was proposed from zero to one; third, the energy generated and consumed was obtained; and fourth, the energy storage as well as the number of charge and discharge cycles of the storage system were calculated considering the points of intersection between the generation and consumption profiles. In Section 3.1 the results are analyzed.

Daily electricity-power supply (*ESMHES*) was determined by the *HD*, which defines the *PVS* (*EHpvs*) and *MCS* (*EHMCS*) installed capacity (Equations (4)–(6)).

$$E\_{HPVS} = E\_{PVS} \ (1 - HD) \tag{4}$$

$$E\_{HMCS} = E\_{MCS} \ HD D \tag{5}$$

$$E\_{SMHES} = E\_{Hp\text{vs}} + E\_{HMCS} \tag{6}$$

**Figure 7.** Energy-balance analysis algorithm to evaluate the daily and seasonal energy-storage system.

#### 2.4.1. Hybrid Energy Storage

A hybrid battery–hydrogen storage system was proposed, and the sizing was carried out based on hybridization analysis, while its relevance was validated through the TOPSIS method. Figure 8 shows the SMHES diagram. The energy balance from Equation (7) determined the daily energy surplus–deficit that was proposed to be covered by the hydrogen energy-storage system (HESS). For hourly fluctuations, a battery energy-storage system (BESS) was proposed, where time fluctuations determined the energy balance; thus, a dynamic model was required. In this analysis, energy balance was carried out at different time intervals (Δt) with 1 h resolution (Equation (8)). The variation between the SMHES generation and the energy consumption (*EC*) determines the moments in which surplus energy is available to store and when energy deficits are present, and thus, the stored energy must be used.

$$E\_{HESS} = \left(E\_{SMHES} - E\_C\right) \eta\_{HESS} \tag{7}$$

$$E\_{BESS} = \left(E\_{SMHES} - E\_{\mathbb{C}}\right) \eta\_{BESS} \tag{8}$$

**Figure 8.** Solar–MarineCurrent Energy Hybrid System diagram.

#### 2.4.2. Energy-Storage Selection

Due to the variety of ESS options to evaluate the relevance of the hybrid battery– hydrogen storage system, a MCDA analysis was proposed. In this work, ESSs were evaluated based on three objectives: (a) the Regulation Energy-Storage System (RESS), (b) the Post-Consumption Energy-Storage System (PCESS) for daily, monthly, or seasonal periods; and (c) the Regulation & Post-Consumption Energy-Storage System (RPCESS) for uses where the ESS is considered for the regulation and control of energy supply autonomy on certain days, e.g., batteries [9,38,39]. The proposed MCDA is a combination of the Analytic Hierarchy Process (AHP) and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS); this method provides solutions to problems involving conflicting and multiple objectives [10]. TOPSIS was developed by Hwang and Yoon (1981) [40] and is based on the concept that the best alternative should have the shortest geometric distance from the ideal positive solution but the largest geometric distance from the ideal negative solution [41–43].

The study evaluated eight ESSs with four classifications, (1) mechanical, (2) electrical, (3) electrochemical, and (4) chemical, and ten criteria, lifetime (C1), cycle life (C2), energy efficiency (C3), power rating (C4), response time (C5), storage duration (C6), power density (C7), energy density (C8), installed system cost (C9), and maturity (C10). The TOPSIS method was conducted according to Garduño-Ruiz et al. (2021) [44] and consisted of feeding a decision matrix with a set of alternatives and criteria (Figure 9) and then assigning levels of importance or weightings to each criterion through the AHP technique using the method of Saaty (1990) [45]. To determine the best alternative, the TOPSIS tool was used by means of a Python algorithm.

**Figure 9.** Classification of energy-storage technologies and alternatives for decision matrix.
