Renewable Energy Systems for Isolated Residential Houses: A Case Study Favoring Wind Power

Abstract

This study models different hybrid systems based on renewable energies that can be supported by diesel generators to meet the energy needs of isolated homes in the Canary Islands. The research will cover the energy requirements of a residential house, including the production of fresh water using a reverse osmosis desalination plant. The system is designed to operate independently of the electrical grid. The HOMER software package was used to model and optimize the hybrid systems. The model was fed with data on the electrical demands of residential homes (including the consumption by the small reverse osmosis desalination plant) as well as the technical specifications of the various devices and renewable energy sources, such as solar radiation and wind speed potentials. The software considers various configurations to optimize hybrid systems, selecting the most suitable one based on the available renewable energy sources at the selected location. The data used in the research were collected on the eastern islands of the Canary Islands (Gran Canaria, Lanzarote and Fuerteventura). Based on the system input parameters, the simulation and optimization performed in HOMER, taking into account the lowest “Levelized Cost of Energy”, it can be concluded that the preferred hybrid renewable energy system for this region is a small wind turbine with a nominal power of 1.9 kW, eight batteries, and a small diesel generator with a nominal power of 1.0 kW. The knowledge from this research could be applied to other geographical areas of the world that have similar conditions, namely a shortage of water and plentiful renewable energy sources.

1. Introduction

The Canary Islands are located in the North Atlantic Ocean, to the northwest of Morocco in Africa. The archipelago is made up of eight main islands: the four western ones, El Hierro, La Gomera, Tenerife and La Palma, and the four eastern ones, La Graciosa, Fuerteventura, Lanzarote and Gran Canaria (see Figure 1). The stable population of the eight islands exceeds 2 million people. These volcanic islands are very popular, with an average of over 12 million tourists visiting each year (over 15 million in 2024, which was a record) [1]. This demographic fluctuation would cause problems anywhere in the world, but particularly on small and isolated islands where food, energy, water and other resources are limited.

On the Canary Islands, access to energy and water poses a particular challenge, especially on the eastern islands. Gran Canaria, Fuerteventura and Lanzarote receive over 50%, 86% and 99%, respectively, from desalination plants. Avila, Marichal, Hernández & San Luis [2] and Sadhwani, Álvarez, Melián & Sadhwani [3] explain in their research that the eight islands that make up the Canary Islands archipelago can only cover less than 70% of their demands for fresh water through natural sources (galleries, wells, etc.). Currently, water desalination represents more than 24% of all water resources needed in the Canary Islands.

The only way to meet water needs is through the use of efficient reverse osmosis (RO) desalination systems [2,3,4,5], which consume between 5 and 10% of all the electricity generated on the islands (6.6% on Fuerteventura and 10% on Lanzarote of the total energy generated on these islands) [4,5,6]. This places a high strain not only on the electrical grid but also on the environment due to the greenhouse gases produced by burning fossil fuels, which are associated with the global environmental crisis. The use of renewable energy can be the most effective tool for reducing these harmful emissions and aspiring to an energy system as close to zero emissions as possible [7,8].

The archipelago’s energy system is isolated, with no connections to the continental system. Connections between the islands are also limited due to the depth of the surrounding waters. More than 98% of the energy that the Canary Islands require is sourced from fuels purchased on the international market [3,4,5,9]. Having analyzed all this information, it is clear to conclude that the eastern Canary Islands are the most affected by shortages of water and electricity necessary for their survival.

Therefore, renewable energies that can be used in isolated systems or coupled to the energy grid can be used on these islands to reduce the dependency on fossil fuels and avoid their polluting emissions [10,11]. In light of these issues, this research’s primary contribution is to propose the most suitable hybrid renewable energy system (HRES) to cover the energy demands of isolated residential properties, including the production of fresh water with a small reverse osmosis (RO) desalination plant, in the Canary Islands, from a technical–economic standpoint. The renewable energy sources in the region are taken into consideration, where the average wind and solar radiation levels are considered high. This study focuses on the three largest eastern islands: Gran Canaria, Lanzarote and Fuerteventura. This paper will make an important contribution to research on HRESs for isolated systems on the various islands of the Macaronesia region (Cape Verde, Madeira and the Canary Islands), as well as other coastal regions of Africa, where renewable energy sources and water scarcity are prevalent.

This study was developed using HOMER computer software, version 2.75 [12]. The main input data for the study were the electrical requirements of an isolated family home near the coast (including a small reverse osmosis plant), the technical specifications of all the systems and the renewable energy sources (RESs), such as solar radiation and wind speed, on the eastern islands. HOMER models the HRES and proposes the most suitable option using “load-following” dispatch strategies, whereby the electric generator (if necessary) only produces the necessary energy to cover the electricity load and does not charge the batteries [13].

Sinha & Chandel [14] and Avila, San Luis, Hernández & Marichal [11] highlight the importance of conducting techno-economic studies of hybrid renewable energy systems (HRESs) to ensure that they are designed and sized properly. Poor design can result in an oversized system and high construction costs. The authors of this article have analyzed the characteristics, advantages and limitations of 19 pieces of software for designing, simulating and optimizing HRESs. This classification includes software programs such as IPSYS, SOLSTOR, Hybrid2 and HOMER. The authors agree that HOMER is the most widely used and successful tool, mainly due to its effective integrations of renewable resources, as well as its ability to perform the cost optimization and sensitivity analysis for evaluating multiple system configurations.

HOMER software is an interesting tool that has been used by researchers around the world to simulate the optimization of renewable energy systems for their integration into regional, island or national energy grids [15,16,17,18,19,20]. It has also been used in many cases to identify the most suitable HRES for supplying energy to villages, microgrids [21,22,23,24], health clinics [25], hotels [26], university campuses [27], desalination systems [2,5,11], vehicle charging stations [28,29] and residential homes [30,31,32,33].

The research carried out in the above-described case studies has resulted in numerous combinations of HRESs, including photovoltaic (PV) systems, wind turbines, hydrogen systems, biomass systems and hydroelectric systems. Some of these studies were conducted as either grid-connected or off-grid systems.

This paper is structured into four sections. The introduction section provides an overview of the topic. Section 2 presents the proposed research materials and methods and lists the main techno-economic input variables for the simulation. The results and a discussion are given in Section 3. Finally, the most relevant conclusions of the research are discussed in Section 4.

2. Materials and Methods

After evaluating various factors such as water scarcity, renewable energy sources, renewable energy policies, electrical system, etc., throughout the Canary Islands, this study focuses on the main eastern islands (Gran Canaria, Lanzarote and Fuerteventura). This selection is based on two studies conducted by different research groups, Avila, Marichal, Hernández & San Luis [2] and Subiela & Banat [34]. These studies develop and apply a methodology for the selection of regions and islands with high water stress and abundant renewable energy resources for the implementation of autonomous desalination systems.

Once these islands were selected as the most suitable for this study, the research sites were selected, all of them on the east coast of the islands, near the sea. These locations were chosen due to the accessibility of information from different meteorological stations at the Gran Canaria, Lanzarote and Fuerteventura airports (see Figure 1).

2.1. Technical Variables Inputted on the HOMER Software

The working algorithm of the Homer Software is based on three main tasks: simulation, optimization and sensitivity analysis. Figure 2 displays the relationship between these three parameters. Optimization includes simulation, meaning that multiple simulations are needed to achieve optimization. Similarly, sensitivity analysis includes optimization, suggesting that a multitude of optimizations are needed to achieve a sensitivity analysis [13].

The main goal of this software is to support the design and implementation of any HRES. HOMER is based on modeling and optimization, taking into account the general cost of any installation and the operational cost of any HRES throughout its operational life. All the analysis is based on techno-economic benefits [13].

The architecture of the proposed HRES is shown in Figure 3. The proposed simulated systems and devices include photovoltaic (PV) panels, various wind generators (WT), battery banks and diesel generators (DGs). The system will cover the electrical requirements of an isolated house with a small RO desalination system. It is assumed that the total system is disconnected from the electrical system. The inclusion of diesel generators in the model is due to initial uncertainty regarding the ability of renewable systems to meet total energy demand.

2.1.1. Electrical Loads

This study considered a total electrical load of around 32 kWh/day. Figure 4 shows the yearly distribution of the possible electrical energy spent, the typical consumption and the monthly highs and lows in an isolated home. The average electrical demand can be 1.34 kW, increasing to 2.04 kW during peak hours.

Table 1. Daily electricity consumption of a residential house with a standard number of appliances.

Device	Number of Devices	Power (W)	Daily Operation Time (h/day)	Daily Electrical Consumption (kWh/day)
Indoor lighting	10	12	10	1.200
Outdoor lighting	6	24	8	1.152
Ceiling fan	2	55	10	1.100
Table fan	3	40	10	1.200
Refrigerator	1	300	24	7.200
TV	2	200	6	2.400
Microwaves	1	700	1	0.700
Electric cooktop	1	4000	2	8.000
Washing machine	1	1500	1	1.500
Other loads	one or more	1000	1	1.000
Desalination plant	1	210	24	5.040
Total	27	7831		30.492

shows the suggested electrical loads based on the possible normal electrical consumption of an average family house. The proposal takes into consideration various household appliances such as washing machines, lights, laptops, televisions, refrigerators, fans, microwaves, as well as other loads [30]. The possibility of using a small RO desalination system with a daily freshwater production capacity of up to 1.0 m³ was also considered. The assumed energy requirements for producing water at this reverse osmosis desalination plant were 5.0 kWh/m³. This value was set high because small desalination plants are more inefficient than industrial desalination plants, where the approximate electrical energy required to produce fresh water is less than 3.5 kWh/m³ [2,5,35,36]. The energy inefficiency of small RO plants compared to industrial RO plants is due to the lack of energy recovery systems. In small plants, either these systems are nonexistent, or they are not very efficient.

2.1.2. Solar Radiation

The HOMER software receives monthly solar radiation data from NASA.

Table 2. Coordinates of the meteorological stations on the three eastern islands.

Meteorological Station	Coordinates (Latitude and Longitude)	Altitude (m) (Above Mean Sea Level)
Gran Canaria	Latitude: 27°55′21″ N Longitude: 15°23′22″ W	24
Fuerteventura	Latitude: 28°26′41″ N Longitude: 13°51′47″ W	25
Lanzarote	Latitude: 28°57′7″ N Longitude: 13°36′1″ W	14

shows the latitude and longitude of the meteorological stations used in the study on the islands of Gran Canaria, Fuerteventura and Lanzarote. Graham’s algorithm is the mathematical tool used by the computational software to process solar radiation [2,5,16].

The monthly average solar radiation readings during one year at the coordinates of the stations on the eastern islands are shown in Figure 5, Figure 6 and Figure 7. Daily average radiation is around 4.96 kWh/m² on Gran Canaria and Fuerteventura and 4.86 kWh/m² on Lanzarote, the most easterly island.

2.1.3. Wind Speeds

The monthly average wind speed data were obtained from the three meteorological stations located at the main airport on each island. Figure 1 and Table 2 show the location of each station on the three easternmost islands of the archipelago. Wind speed data at an elevation of 10 m above ground level have been recorded annually at these three stations for more than 40 years. The yearly average wind speed on Lanzarote is 6.28 m/s, on Fuerteventura it is 5.82 m/s and on Gran Canaria it is 7.0 m/s, which can be considered high. Weibull distributions of wind velocity for the three stations are shown in Figure 8, Figure 9 and Figure 10.

Weibull probability density functions (Equation (1)) are the methodology used by HOMER to perform the simulations [2,5,11].

$$f_v=\left({\displaystyle \frac{k}{c}}\right) · {\left({\displaystyle \frac{v}{c}}\right)}^{k-1} · exp\left[-{\left({\displaystyle \frac{v}{c}}\right)}^k\right]$$

(1)

where, k can be defined as the shape factor and c as the scale factor.

2.1.4. Wind Turbine System

To model the behavior of the wind turbines, the standard method is used, which relies on transforming the kinetic energy from the wind into electrical energy using the particular power curve of each wind turbine [37].

The wind energy density per unit area (P/A) can be calculated with Equation (2).

$${\displaystyle \frac{P}{A}}={\displaystyle \frac{1}{2}} · \rho · v^3$$

(2)

Equation (3) is the expression used by HOMER to calculate wind energy production per year (P_wind).

$$P_{wind}={\displaystyle \frac{1}{2}}\tau · \rho · C_p · A · {{\displaystyle \sum }}_{x=1}^j{f}_{\left(v\right)} · v_x^3$$

(3)

where $\tau$ is the time frame (one year), C_p is the capacity factor of each wind generator, v is the wind speed, f_(v) is the Weibull distribution and j is the class number of the data [2,4,8,33].

Figure 11 displays the power curves of the three wind turbines (WTs) selected for the study, which have nominal powers of 1.0 kW, 1.9 kW and 3.0 kW.

Table 3. Commercial characteristics of wind generators. Source [38,39,40].

Technical Parameter	SW 200	E30pro	SW500
Rotor diameter (m)	2.7	3.8	4.5
Nominal power (kW)	1.0	1.9	3.0
Hub height (m)	10	13	13
Cut-in wind speed (m/s)	3.1	1.8	3.4
Survival wind speed (m/s)	55	60	55
Rated power	1000 watts at (11.6 m/s)	1900 watts at (11.0 m/s)	3000 watts at (10.5 m/s)

shows the characteristics of the proposed wind turbines. The primary economic data for the WT and PV systems are shown in

Table 4. Economic data input for HRESs.

Element	Size	I. Capital Cost (ICC) (USD)	Replacement Cost (RC) (USD)	O&M Cost (USD)	Lifetime
PV panels (PV)	0–50 kW	USD 2500/kW	USD 2500/kW	0.01xICCPV	20 years
Wind Turbines (WT)	WT-1.0 kW WT-2.5 kW WT-3.0 kW	USD 6000/unit USD 14,900/unit USD 14,900/unit	USD 3700/unit USD 11,000/unit USD 11,000/unit	0.025xICCWind	20 years
Batteries (360 Ah/6 V)	(0–32) batt.	USD 350/unit	USD 350/unit	USD 8.00/year	10 years
Generator (DG)	0–5.0 kW	USD 700/kW	USD 700/kW	USD 0.40/hour	15,000 h
Converter	0–5.0 kW	USD 1000/kW	USD 1000/kW	USD 50/year	20 years

. The small wind turbine models suggested could be acquired in the Canarian market and are included in the HOMER database.

2.1.5. PV System

Table 4 displays the cost of the PV panels used in the study. The study assumes that PV panels have a useful life of 20 years. However, the HOMER software does not consider variables such as temperature and voltage during operation when modeling the photovoltaic (PV) system [13].

The electrical energy produced (P_PV) by the PV panels is computed using Equation (4)

$$P_{PV}=f_{PV} · Y_{PV} · \left({\displaystyle \frac{I_T}{I_s}}\right)$$

(4)

where f_PV is the debating factor, Y_PV is the total installed capacity of the PV panel arrangement (kW), I_T is the incident global radiation (kW/m²) and I_s is the amount of radiation used to rate the capacity of the PV panel arrangement, equal to 1.0 kW/m² [13].

2.1.6. Generator

The generator considered in the study is a diesel engine connected to an electric generator. The HOMER software, version 2.75, allows the modeling of numerous types of electrical generators, ranging from the simplest (such as the system used in the study, comprising a generator and an internal combustion engine (diesel or Otto)) to the most complex (such as fuel cells, gas turbines and others). The assumed fuel price assumed in the research is USD 0.90/liter (including the logistics and distribution costs of diesel). The main generator parameter considered is the power capacity, which can fluctuate between 0 and 5.0 kW, see Table 4. The generator only produces energy if the isolated house requires its load; it will never start charging the batteries [5,13,41].

2.1.7. Converter

The main purpose of a converter is to change electricity from direct current (DC) to alternating current (AC) or to adjust the voltage or frequency. Table 4 shows the economic properties of the proposed converters, which operate within a range of 0 to 5.0 kW and have a potential useful life of 20 years, with an efficiency of 90% [5,13,41].

2.1.8. Battery Bank

HOMER models the battery as a device that can accumulate a quantity of direct current (DC). Based on the number of battery charge and discharge cycles, the software can determine when the battery needs to be replaced [5,13,41]. In this study, the batteries assumed are commercial lead–acid models, with a capacity of 360 Ah, a voltage of 6.0 V and an energy capacity of 1075 kWh. The battery’s round-trip efficiency is around 85%, the manufacturer-recommended minimum state of charge is 30%, and the expected battery life will be 10 years. Table 4 shows the economic characteristics of the proposed batteries. The simulated battery bank can contain between 0 and 32 units.

2.2. Economic Analysis

The electrical systems of most countries are based on conventional energy systems, such as thermal power plants, which burn fossil fuels. This is because the initial capital cost of these systems is relatively low compared to any RES. However, it is important to note that operating costs in RES-based plants are lower. As mentioned above, one of HOMER’s main advantages is optimizing energy processes. The software compares RESs or HRESs with conventional energy systems in order to recommend the most economically viable option [5,41].

In their research, Avila et al. [2,11], Padrón et al. [5], Atteya & Ali [19] and Hina & Palanisamy [41] present the basis of the economic analysis carried out by HOMER. This analysis uses an economic tool known as “Levelized Cost of Energy” (COE), which calculates the average cost per kWh of the electrical energy produced by any type of system. The system also implements the “Total Net Present Cost” (NPC) (USD), which is capable of calculating the cost of installing and operating the energy system. The methodology and economic equations involved can all be found in the referenced papers.

It is relevant to clarify that the study will not address criteria such as environmental issues, protected areas (navigation routes, fishing areas, military exercise areas, recreational areas, etc.), authorizations, licenses or permits.

3. Results and Discussion

The key issue in designing and proposing any HRES is defining its elements and the size of each, which is restricted by the renewable energy sources in the area where the system will be installed.

The HOMER software, version 2.75, is an exceptional tool for simulating numerous HRES arrangements and allows users to identify viable proposals and discard unfeasible ones.

The technical and economic simulation results produced by the software and its results are presented below. For the simulation, an electrical energy cost of USD 0.15/kWh was assumed for RES. The total electrical load assumed in the simulation is 32 kWh/day, providing a margin of 5.0% over the estimated actual requirement. This energy could power an isolated house with its basic appliances and an RO desalination plant producing 1.0 m³ of water per day.

3.1. Optimization Results on Lanzarote, Fuerteventura and Gran Canaria

Table 5. Technical results.

Island	PV (kW)	WT (Number), (Type)	Battery (Number)	Converter (kW)	DG (kW)	Diesel (L)	GEN (hrs.)
Lanzarote	0	1—(E30pro)	8	2.0	1.0	8	24
Fuerteventura	0	1—(E30pro)	8	2.0	1.0	18	57
Gran Canaria	0	1—(E30pro)	8	2.0	1.0	23	71

and

Table 6. Economic results.

Island	Initial Capital (USD)	Operating Cost (USD/year)	Total NPC (USD)	COE (USD/kWh)
Lanzarote	20,400	830	29,993	0.223
Fuerteventura	20,400	869	30,372	0.227
Gran Canaria	20,400	843	30,073	0.225

show the results proposed by HOMER after modeling the technological and economic variables with HRESs based on photovoltaic–wind–diesel generator systems operating in isolated conditions to supply energy to a family home, with an RO plant included in the proposal, on the islands of Gran Canaria, Fuerteventura and Lanzarote.

Table 5 shows the technical analyses carried out by HOMER. The system allows the simulation and optimization of all possible HRESs to supply energy to the isolated house + RO desalination plant based on the renewable resources available on each analyzed island.

In all cases analyzed (Gran Canaria, Fuerteventura and Lanzarote) the optimal HRES was a system composed of an E30pro wind turbine (WT), eight batteries, a 2.0 kW converter and a 1.0 kW nominal power small diesel generator (DG). The penetration of renewable energies in each case analyzed is greater than 99%. The DG is only used in exceptional situations, when the HRES and batteries cannot meet demand. In all cases, the sum of the total number of hours the DG operates (GEN) is less than three days per year. The DG is never used to charge the batteries; it only operates to meet load demand.

In the event of an emergency, such as a storm or other adverse weather conditions, the HRES must be placed in an emergency mode. Only the following devices may then be connected to the electrical system: lighting, one refrigerator, one television, one microwave and other low-load devices such as radios, iPhone chargers, etc. The RO desalination plant cannot be used to produce drinking water alongside the other system loads. However, if water production is absolutely necessary, the RO plant may only operate in combination with lighting, a television and other low-load devices.

Table 6 shows an initial capital cost of USD 20,400, which is the same for all probable HRESs that can cover the energy needs of an isolated home with a desalination plant. The COE for the different proposed HRES is between USD 0.223 and 0.227/kWh, with only a small difference between them. The lowest cost/kWh is achieved through the renewable energy conditions on Lanzarote. Avila, San Luis, Hernández & Marichal [8] propose in their research that the cost of electricity from renewable energies in the Canary Islands can be assumed to be around USD 0.15/kWh and the cost of energy purchased from the grid is USD 0.10/kW, which is much cheaper than the COE obtained in the proposed HRES. The advantage of the proposed HRES lies in its autonomy, as it operates in isolated conditions without connection to the electrical grid.

As can be seen, the NPC calculated by HOMER, which takes into account system installation and operation costs, shows only a small difference in rates between the analyzed cases. The highest NPC occurs in the case of the HRES proposed for Fuerteventura, due to a slight variation in the system’s operating costs on this island.

Taking into account the different techno-economic situations and renewable sources in the eastern islands, the result of the optimization shows that, despite the high radiation level in the Canary Islands (between 5.0 and 6.0 kWh/m²/day) and more than 10 h of sunlight per day [42,43], the photovoltaic system is not the best option, due to the region’s excellent wind potential. Considering the HOMER sensitivity results, the COE for an HRES that includes photovoltaic systems is 3.0 kW of photovoltaic panels, one E30pro wind turbine, eight batteries, and 1.0 kW nominal power small DG, with a COE greater than USD 0.28/kWh in all three analysis cases. The only advantage of this HRES is the lower DG usage throughout the year.

3.2. Pollution Avoided

Table 7. Pollutants avoided by the proposed HRES.

Proposed HRES	Pollutant Avoided	Emissions Avoided Lanzarote (kg/year)	Emissions Avoided Fuerteventura (kg/year)	Emissions Avoided Gran Canaria (kg/year)
One WT_E30pro Eight batteries One converter (2.0 kW) One D. generator (1.0 kW)	CO2	7244	7214	7204
	SO2	15	14.5	14.5
	NOx	160	159	159

shows the total emissions of polluting gases in kg/year that can be avoided by installing the HRES (one WT_E30pro, eight batteries, one converter in isolated dwellings + RO desalination plant (1.0 m³/day)) on each island.

The main gas emissions considered in the study were carbon dioxide (CO₂), nitrogen oxides (NOx) and sulfur dioxide (SO₂). These are gases that would be released into the atmosphere if the fuel needed to generate the electricity required by the residential house being studied were burned.

Analysis of the results in Table 7 shows that the proposed HRES for each of the cases can avoid the emission of over 7200 kg/year of CO₂, over 14.5 kg/year of SO₂ and around 159 kg/year of NOx. The HRES proposed for Lanzarote has the lowest emissions. This system only requires the diesel generator to be operated for 24 h/year, resulting in emissions of 19.9 kg/year of CO₂, 0.44 kg/year of NOx and 0.04 kg/year of SO₂. This is due to the good renewable energy resources on the island and the distribution of the wind during the year.

4. Conclusions

Having completed the study, the following conclusions can be drawn from the proposal to install HRESs supported by diesel generators to supply electricity to a family home with a reverse osmosis seawater desalination plant. All of these conclusions are based on technical and economic analyses.

The technical and economic analyses carried out on the islands of Lanzarote, Fuerteventura and Gran Canaria, based on the coordinates of the three meteorological stations located at airports on these islands, can be considered successful. Simulation and optimization of the system concludes that the ideal HRES to install on any of the three eastern islands is the system with an E30pro WT, eight batteries, a 2.0 kW converter and a small 1.0 kW DG. In all the cases analyzed, the penetration of renewable energies is greater than 99%. The COEs for all the HRESs are less than USD 0.227/kWh in all three cases. While this may seem very expensive compared to the price of electricity from the electrical grid, the cost is often offset by the expense of routing electrical wiring to homes isolated from the grid.

Each and every one of the systems proposed for each island can avoid the emission of polluting gases; namely, more than 7200 kg/year of CO₂, more than 14.5 kg/year of SO₂ and around 159 kg/year of NOx.

The results show that these strategies could be useful in many analogous scenarios. It is important that these strategies are adapted more effectively for other cases in future work. In this regard, new strategies based on artificial intelligence techniques with integrated “Sensitivity Analysis” (variation in fuel price, seasonal variation in water demand, equipment costs, etc.) could be used to extend the results to other geographic areas. These strategies would account for greater variability in parameters in order to determine the optimal configuration for each case. They would also consider other potential influencing factors, such as the use of alternative fuels like biomass, electrolyzers, or other energy storage concepts such as hydrogen tanks.