Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches

Abstract

The ambitious goals of decarbonization of the European economy by mid-century pose significant challenges, especially when relying heavily on resources whose nature is inherently intermittent, specifically wind and solar energy. The situation is even more serious in isolated regions with limited connections to larger power grids. Using EnergyPLAN software, three scenarios for 2023 were modeled: a diesel-only system, the current hybrid renewable system, and an optimized scenario. This paper evaluates the performance of the usual generation system existing in isolated systems, based on fossil fuels, and proposes an optimized system considering both the cost of the system and the penalties for emissions. All this is applied to the case study of the island of El Hierro, but the findings are applicable to any location with similar characteristics. This system is projected to reduce emissions by over 75% and cut costs by one-third compared to the current configuration. A system has been proposed that preserves the economic viability and reliability of diesel-based systems while achieving low emission levels. This is accomplished primarily through the use of renewable energy generation, supported by pumped hydro storage. The approach is specifically designed for remote regions with small isolated grids, where reliability is critical. Importantly, the system relies on appropriately sized renewable installations, avoiding oversizing, which—although it could further reduce emissions—would lead to significant energy surpluses and require even more efficient storage solutions. This emphasizes the importance of implementing high emission penalties as a key policy measure to phase out fossil fuel generation.

1. Introduction

1.1. Global Energy Transitional Scenario

The “World Energy Outlook 2023” by the International Energy Agency (IEA) [1] highlights the persistent rise in global energy demand, briefly interrupted in 2020 by the CoronaVIrus Disease 2019 (COVID-19) pandemic but which resumed in 2021 [2]. Fossil fuels dominate energy production, supplying nearly two-thirds of electricity [3], though their share has gradually declined in recent years [4]. This reliance is unsustainable due to the medium-term depletion of fossil fuel reserves [5,6] and their significant greenhouse gas (GHG) emissions [7,8].

Renewable energy sources are essential to replace or supplement fossil fuels [9], particularly for electricity generation, to meet ambitious CO₂ reduction targets amid rising electricity demand [10]. Electricity is expected to exceed 30% of total energy consumption in many countries soon [11]. Challenges are exacerbated in remote areas, such as islands [12], which rely heavily on fossil fuels due to limited grid connectivity, leading to emissions, supply chain complexities, price fluctuations, and geopolitical risks [12].

Renewable energy adoption offers strategic energy independence and environmental benefits but presents challenges due to the variability in sources like solar and wind power [13,14,15,16]. This variability necessitates oversized systems with large storage capacities to ensure energy availability during low-generation periods [17,18]. Optimal sizing of generation and storage is usually driven by economic considerations to minimize costs while managing excess energy [19,20].

To meet energy demands using renewable sources, energy systems need to be designed with surplus capacity and equipped with substantial storage solutions to manage excess energy and ensure reliability during periods of low generation [19]. Despite these measures, some surplus energy is unavoidable, though typically at a reduced scale. As a result, the optimal sizing of generation and storage systems is usually guided by economic considerations, striving to fulfill energy needs at the lowest possible cost [18].

Islands, however, encounter unique challenges in deploying large-scale renewable energy systems. These include lacking suitable sites for essential infrastructure such as pumping stations, Compressed Air Energy Storage (CAES), large-scale Battery Energy Storage Systems (BESSs), and expansive solar PV and wind installations [21].

All European Union (EU) countries are mandated to achieve economic decarbonization by mid-century. The Canary Archipelago is actively working towards this goal, aiming to decrease its dependence on fossil-fuel-based generation within a specific timeframe. The archipelago benefits from abundant natural resources, particularly wind and solar energy. The Canary Islands and Spanish national governments are optimistic and have set an accelerated target to complete the decarbonization process 10 years ahead of schedule (PTECan project) [22,23].

The Canary Islands consist of seven islands located between 100 km and 300 km off the Moroccan coast and approximately 1500 km from mainland Europe. More than 2 million people live on these islands, according to official data from 2020 [24]. The largest islands, Gran Canaria and Tenerife, have populations of around 1 million inhabitants, each of them. Lanzarote, Fuerteventura and La Palma all have more than or close to 100,000 inhabitants. The smaller islands, La Gomera and El Hierro, have between 10 and 20 thousand inhabitants.

In 2019, the total final energy demand for the Canary Islands was about 9.4 TWh, which remained relatively constant over the previous decade but decreased in 2020 and 2021 due to the COVID-19 pandemic [24]. Of this demand of almost 10 TWh, only 50 GWh is consumed in El Hierro, i.e., about 0.5%.

This study simulates and evaluates the performance of the autonomous renewable generation system of electric power based on Renewable Energy Sources (RESs) such as wind generation and storage technologies (reversal pumping system) with no CO₂ emissions placed in El Hierro island since 2014. In January 2000, the El Hierro Island was designated as a Biosphere Reserve, honoring its exceptional preservation of environmental and cultural richness, alongside its commitment to progress and the welfare of its inhabitants. Under this basis, the project “Hydro-wind Exploitation of the Island of El Hierro” [25] was launched.

The hydro–wind power plant emerged from this project; it was designed to supply electricity to the island using clean and renewable sources such as water and wind. The wind farm can meet the island’s electricity demand at any time. Surplus energy that is not utilized is employed to pump water between a lower and upper reservoir, accumulating it in the latter to generate electricity through hydraulic jumps during periods of low wind. Consequently, this combination of wind and hydro generation transforms an intermittent and unpredictable source, such as wind, into a constant and controlled supply, thus contributing innovative advancements to the renewable energy sector. However, if Gorona del Viento cannot supply enough energy, the diesel-powered power plant at Llanos Blancos comes into operation.

Although initial estimations of the system performance were expected to reach almost 75% of the energy coverage through renewable facilities, the system has not been able to reach these figures. In fact, diesel-fired generation continues to account for an average of about 50% of electricity generation during the nearly ten years of operation of the hydro–wind facility.

1.2. Modeling and Optimization—Tools and Techniques

Islands and remote regions rely heavily on fossil fuel-based electricity due to their isolation, which complicates achieving green and cost-economical energy solutions [1,14]. Dependence on diesel generates environmental problems and raises fuel costs due to small transport quantities and a remote location [8,26]. To address these challenges and reduce GHG emissions, many islands are aligning with the EU’s goal of sustainable energy by 2050 [27].

Recent research has explored various renewable energy models to provide reliable and emission-free electricity, such as those of Lampedusa Island [28], Terceira Island [29] and the Galapagos Archipelago [30]. Different simulation tools can help in modeling these energy scenarios. For example, HOMER software was used for Grand Canary Island’s 2040 decarbonization plan [14], EnergyPLAN for South Tyrol [31], and H2RES for Cape Verde’s S. Vicente Island [32]. Comprehensive reviews of these tools highlight their effectiveness at scales ranging from tiny islands and microgrids to expansive continental and macro-grid levels, demonstrating their adaptability in diverse energy system contexts [33,34].

EnergyPLAN, developed by Aalborg University in 1999, is an appropriate instrument for analyzing integrated energy systems and was an early adopter of smart energy systems concepts [35]. Consequently, the current EnergyPLAN version 16.3 is the result of more than 25 years of development. Continuously updated, it connects demand sectors like transportation, industrial and buildings with different supply technologies, including electricity, gas, and district heating. It performs hourly simulations over a year, making it suitable for scenarios with high renewable energy shares, and allows comparisons with fossil fuel and nuclear-based systems. The software also calculates costs and integrates with platforms like Excel, MATLAB R2023a, and Python 3.11.13. The review work of Østergaard et al. [36] highlighted EnergyPLAN’s effectiveness in modeling sustainable energy solutions, its application in over 315 peer-reviewed studies, and its credibility when combined with other models.

Most of the previous research documents reach the optimal system only through cost reduction. In contrast, EnergyPLAN is often used in conjunction with supplemental models and advanced optimization algorithms to enhance its effectiveness. For example, the EPLANopt model combines EnergyPLAN with a specialized optimization algorithm designed specifically for capacity expansion [31]. EPLANopt is a Python-based framework tool that facilitates defining optimization problems and constraints [37,38]. The tool uses NSGA-II, one of the most used Multi-Objective Optimization with Evolutionary Algorithm (MOEA), as an optimization algorithm. The EPLANopt source code is freely offered in an open access repository [39]. Samarasinghe et al. [40] reviewed various tools and identified EPLANopt as widely adopted among optimization algorithms, further demonstrating its value in smart energy systems research.

Multiple algorithms are available for addressing multi-objective optimization problems, with Evolutionary Algorithms (EAs) and Particle Swarm Optimization (PSO) being two of the most commonly applied methods. EAs are inspired by the principles of natural selection and genetics, employing mechanisms such as crossover and mutation to evolve a population of candidate solutions. In contrast, PSO draws inspiration from the collective behavior of bird flocks or fish schools, where each solution (particle) navigates the search space by considering both its own experience and that of its neighbors. Among EAs, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is particularly prevalent in energy system planning optimization [37]. For example, Cheraghi and Hossein Jahangir [38] compared NSGA-II and PSO for the multi-objective optimization of a hybrid renewable energy system, finding that NSGA-II produced a more diverse set of solutions and a broader Pareto front, while PSO achieved faster convergence but with less diversity. In addition to these established techniques, newer nature-inspired algorithms such as Grey Wolf Optimization (GWO) [41] and the Moth-Flame Optimization (MFO) algorithm have been increasingly explored for energy system planning [42]. GWO, which mimics the social hierarchy and hunting strategies of grey wolves, has demonstrated strong performance in complex energy optimization tasks, offering a good balance between exploration and exploitation and showing robustness in avoiding local optima. Similarly, MFO, inspired by the navigation patterns of moths in nature, has been applied to power transmission and distribution problems, often in hybrid approaches with PSO to enhance search capabilities.

A highly relevant study by Iqbal et al. [43] addresses the optimization of the Short-Term HydroThermal Scheduling (STHTS) problem. The primary objective of the STHTS problem is to minimize the overall cost of power generation from a water reservoir while simultaneously meeting load demand within a specified time frame. The study presents a comprehensive comparative analysis of multiple algorithms, including genetic algorithms, the chaotic-chimp sine cosine algorithm (C-CHOA-SC), PSO, gravitational search algorithm (GSA), among others. The comparison includes more than 20 different algorithms. The results indicate that, overall, the performance differences among the various algorithms are not substantial, with maximum average performance deviations between algorithms of just over 1%. Nevertheless, the algorithm proposed by the authors (C-CHOA-SC) effectively solved the STHTS problem, achieving notable reductions in both fuel costs and emissions.

1.3. Research Organization and Major Contributions

Despite preliminary projections of EL Hierro’s hydro–wind plant being expected to cover nearly 75% of its energy needs, the system has fallen short of this target. Over nearly a decade of operation, diesel generation has consistently accounted for approximately 50% of electricity production in the hydro–wind facility [25]. A key innovation of this document is the analysis of system sizing to ensure that energy generation is predominantly based on renewables while maintaining high reliability under the various operating conditions that may arise throughout the year. Statistical methods, particularly multi-criteria optimization techniques, have been employed for resource characterization and system optimization. The main optimization criteria include economic viability and the penalization of GHG emissions, as well as constraints related to land use due to the presence of protected areas. Additional limitations concerning the maximum installable capacity of certain technologies have also been considered. For instance, the maximum allowable capacity of solar photovoltaic (PV) systems for self-consumption has been evaluated to prevent excessive visual impact from solar plants or pumped hydro storage facilities. This assessment also considers the ranges of output power variation and capacity availability, ensuring a balanced and sustainable approach to system design.

It should also be highlighted that although the methodology has been applied to a specific case, it could be adapted to any other case. Moreover, the findings presented can be considered almost directly applicable to other cases of small and isolated network systems, as they generally tend to exhibit similar generation typologies as well as the same typical constraints/advantages (limited space, need for reliability, predictability, abundance of resources, etc.).

To carry out these calculations, this document presents a novel approach to redesigning energy systems for full renewable energy integration while ensuring reliability under diverse operating conditions. Managing the EPLANopt model linked with EnergyPLAN tool, this study employs multi-objective optimization to identify the ideal resources and their optimal configurations, specifically for isolated regions. Integrating EPLANopt, which uses a Multi-Objective Evolutionary Algorithm (MOEA), with EnergyPLAN’s deterministic hourly simulation model allows for a comprehensive exploration of energy system optimization.

The EPLANopt model enables the simultaneous optimization of conflicting objectives, such as minimizing costs and CO₂ emissions, yielding a set of trade-offs known as the Pareto front. Constraints like maximum installation capacities for technologies (e.g., rooftop solar to limit visual pollution) and storage parameters (e.g., power and capacity variations) are also factored into the analysis. This combined approach offers an innovative pathway for designing efficient, sustainable energy systems.

Through this multi-criteria analysis, the optimal system configuration was determined by considering two main constraints: economic viability and GHG emissions associated with the system throughout its lifecycle. While similar analyses have been conducted by other authors—sometimes using the same variables or including other factors such as energy surpluses—this study stands out by translating the lifecycle emissions of each technology into direct economic terms. This approach provides updated cost figures for the electrical system that fully incorporate emission costs, representing a methodological advance not commonly found in the literature, especially when applied to highly isolated and small-scale systems where reliability is critical. Furthermore, this study includes projections of how varying emission costs could affect overall system expenses. This transformation is particularly significant because, in most cases, major shifts in energy generation are implemented primarily when they become economically viable. By converting emission penalties into monetary values, it becomes possible to estimate the financial threshold at which a renewable energy scenario, in addition to being environmentally preferable, also becomes more profitable from an economic perspective.

In addition to the optimized renewable scenario, two other reference scenarios have been analyzed to provide a comprehensive comparison. The first scenario reflects the diesel-based power generation system that operated on the island until around 2015, before the hybrid hydro–wind power plant reached near full capacity. This “fully fossil-based” scenario is particularly significant, as it closely mirrors the energy systems still prevalent in most isolated regions worldwide. As such, it serves as a critical benchmark for evaluating the performance and benefits of transitioning to renewable energy, offering a clear point of reference for both economic and environmental metrics.

The second scenario examined is the current hybrid system, which combines renewable sources with diesel generation. This configuration underlines the importance of proper system sizing; without it, even hybrid systems can suffer from inefficiencies and reliability issues, ultimately failing to meet performance expectations. By carefully comparing these three scenarios—the original diesel-only system, the present hybrid renewable-diesel system, and the proposed fully renewable system—it becomes possible to quantify the key differences in terms of both economic costs and greenhouse gas emissions.

The island’s gradual transition, from a system entirely dependent on diesel to a hybrid and ultimately to a fully renewable configuration, exemplifies a significant move toward sustainability. Hybrid systems, in particular, offer a practical intermediate step by enhancing energy reliability and reducing dependence on fossil fuels, which can lead to both cost savings and substantial reductions in emissions. This progression is not only relevant for the island in question but also provides valuable insights for other islands and isolated territories, many of which continue to rely heavily on fossil fuel imports and face unique challenges such as high energy costs and logistical difficulties.

By analyzing and contrasting these three distinct scenarios, this study delivers actionable knowledge that can inform energy planning in similar regions. The findings highlight the tangible environmental and economic advantages of transitioning to renewable energy sources, offering a practical roadmap for isolated communities seeking to improve their energy sustainability and significantly reduce their carbon footprint.

Specifically, the first scenario analyses a system in which electricity generation relies almost entirely on fossil fuels, specifically diesel generator sets. This situation reflects the current reality for many islands and isolated regions. Consequently, this scenario represents a typical generation system for remote areas and regions with low energy demand over recent decades. It provides insight into the characteristics and operational behavior of such systems, highlighting that their GHG emission levels are unacceptable in the context of the ongoing fight against climate change.

The second scenario, in turn, describes the current situation on the island, demonstrating that a strong commitment to decarbonization—merely through the deployment of renewable energy systems with high penetration rates—is not sufficient. Instead, this transition must be preceded by a comprehensive and rigorous analysis. As will be detailed throughout this document, the outcomes achieved do not align with the initial planning, primarily due to shortcomings in the design of the energy storage system, which exhibits insufficient storage capacity and limited pumping power. As a result, the expected levels of renewable energy coverage have not been attained, curtailment rates are higher than anticipated, and the reduction in emissions has fallen short of projections.

Finally, the third scenario presents an optimized system, developed through detailed planning. This scenario achieves moderate emission levels and significantly higher renewable energy penetration, reserving fossil fuel generation exclusively for exceptional circumstances, thereby serving as a backup system.

To accomplish the aforementioned purposes, Section 2 outlines the current electrical supply situation on El Hierro Island. Section 3 elaborates on the methodology developed for conducting the present analysis, alongside contextualizing the issue at hand. This section furnishes a description of the characteristics and requisite data for all systems necessary to conduct simulations within the desired timeframe, encompassing both generation and storage technologies. The primary outcomes of the conducted simulations are detailed in Section 4, accompanied by corresponding analysis and discussion. Section 5 is dedicated to summarizing the conclusions drawn from the present study regarding the generation system. Additionally, potential avenues for future research are outlined within this section.

2. The Electric System of El Hierro

The Canary Islands face structural limitations in their energy generation due to their isolation and the small size of their electrical systems, with only Lanzarote and Fuerteventura having an interconnection. This has led to heavy reliance on fossil fuels to ensure reliability. El Hierro, which is the smallest grid of the Canary Island systems, has historically relied on small diesel generators, which were in a redundant arrangement to provide high security of supply. The Gorona del Viento plant was commissioned in 2014 to achieve energy self-sufficiency while maintaining reliability, but after a decade, fully renewable energy has only been achieved for less than 100 days a year, with an annual renewable energy share of around 50%.

Island systems require significantly higher power reserve margins compared to continental grids, with peak demand-to-installed power ratios reaching 25% and actual available power margins fluctuating between 40% and 70% [44]. These systems prioritize supply security, leading to different operational and economic considerations, including higher costs. The strong seasonal demand, driven by tourism, further complicates energy planning. Electricity costs are mainly driven by fuel prices and transportation expenses, with variable costs accounting for approximately 75% of total expenses. To gain deeper insights into the complexities of decarbonizing small and isolated energy systems, this study conducts a comprehensive analysis of El Hierro’s energy infrastructure and electricity demand.

2.1. The Electric Demand

The hourly electrical demand data of El Hierro was obtained from the website of the Spanish electricity market operator (Red Eléctrica de España—REE) [45]. The data for the island is only available from 1 January 2014, onwards on this site. However, if older annual demand data is considered [24] (Figure 1), a preliminary analysis reveals that the island’s electrical demand has remained quite stable at about 40–50 GWh a year for nearly two decades, even though a slight upward trend could be considered, from less than 40 GWh in 2005 to nearly 50 GWh. The sharpest increase took place between 1985 and 2005, when electricity demand went from less than 10 GWh in 1985 to stabilization at around 40 GWh in 2005. This tendency is also evident in the electrical energy output curve shown in Figure 1, in which the evolution from 2010 to 2022 shows no significant variations.

El Hierro has the mildest climate among the Canary Islands, with temperatures remaining relatively stable throughout the year. Winter temperatures generally range from 13–19 °C, while summer temperatures range from 24 to 25 °C. The climate does not significantly affect electricity demand, but demand patterns are strongly influenced by tourism (Figure 2a). Winter months tend to have the lowest demand, while summer months (especially July, August, and September) experience the highest demand, particularly in the afternoons and evenings. The demand curve typically shows two peaks: one about midday and another towards sunset, with lower consumption at night and more stable levels during the day. These behaviors show the seasonal fluctuations caused by the great weight of the island’s vacation sector, with higher summer demand and lower winter demand (Figure 2b).

As displayed in

Table 1. Yearly contribution of renewable sources and diesel to the demand coverage.

Year	Demand (GWh)	Diesel (%)	Renewable 1 (%)
2013	44.02	99.53%	0.47%
2014	42.08	97.45%	2.55%
2015	42.99	80.10%	19.90%
2016	44.6	59.41%	40.59%
2017	43.83	53.46%	46.54%
2018	41.93	43.58%	56.42%
2019	42.85	45.70%	54.30%
2020	46.81	58.26%	41.74%
2021	47.92	51.82%	48.18%
2022	47.00	51.06%	48.94%
2023	50.24	65.21%	34.79%

¹ Electricity feed to the grid.

, the renewable energy contribution grew sharply from practically zero to approximately 50%, driven by the full operation of the Gorona del Viento plant. The lower renewable generation in 2014 and 2015 was due to the plant not being completely operational. From 2016–2017 onwards, its contribution has become stable at around 50%, though these quantities are significantly below the renewable contribution percentages defined in the project (73.4%).

If the hourly demands shown in Figure 2a are grouped in a histogram (Figure 3), it appears that the demands are between approximately 3 and 8 MW, with a maximum demand of 7.71 MW. However, the greater predominance of values is between about 5 and 7 MW. The range with the highest absolute frequency is between 6.25 and 6.5 MW with around 1200 h, although the range 6.0–6.25 is very close, while the average demand is around 5.75 MW.

2.2. The Generation System

El Hierro, the most isolated landmass in the Canary Islands, has a population of more than 10,000 inhabitants and was designated a World Biosphere Reserve by UNESCO in 2000. The island’s Sustainability Plan, approved in 1997, initiated its transition towards sustainable energy, supported by the environmental awareness of both the local community and government. A key project in this transition is the wind–hydro power initiative, managed by the Gorona del Viento consortium, which aims to meet 100% of energy needs from renewable sources. Traditionally, electricity was supplied by diesel engines with a capacity of around 13 MW. The commissioning of the Gorona del Viento wind–hydro plant in 2014 raised the island’s installed power capacity to close to 40 MW.

2.2.1. The Diesel Generation Plant

Electric power on the island of El Hierro has traditionally been supplied by fossil fuel-based generators, specifically small-scale diesel units. These units are located at the “Llanos Blancos Thermal Power Plant” in the municipality of Valverde [46]. There are 10 units with net power outputs ranging from 670 to 1900 kW and gross power outputs between 780 and 2000 kW, totaling a gross power of 14.91 MW and an effective electrical power of 13.04 MW. Over the last years, its net capacity factor (CF) has stayed at about 40%. Yearly electrical energy generation from fossil fuel units has been reduced from approximately 40 GWh prior to the commissioning of the Gorona plant to between 20 and 25 GWh thereafter (Figure 4) [24].

2.2.2. The Hydro–Wind Power Plant

The various facilities associated with the Gorona del Viento hydro–wind energy station are sited in the municipality of Valverde [47]. The selection of the wind generation with storage combination is not accidental, but it responds to the good characteristics of the island. On the one hand, north-northeast trade winds are predominant (Figure 5a), which are fairly constant throughout the year (Figure 5b). This is coupled with the favorable topography of the island, which allows the construction of reservoirs close to the island, as opposed to higher elevations.

Regarding a detailed description of the Gorona del Viento plant, the system comprises an 11.5 MW wind farm and an 11.3 MW turbine power plant that generates power by harnessing the elevation difference between the facility’s two water storage reservoirs. A pressurized hydraulic circuit connects both tanks through a 6 MW pumping station and 225 MWh of storage capacity [47,48].

The operation of the hydro–wind power system in El Hierro depends on the available generation capacity. During windy periods, the wind turbines supply electricity to the whole island, and excess energy is used to pump seawater between the lower and upper tanks. While during low wind periods, stored water from the upper reservoir is released to drive turbines and generate electricity. In recent years, wind generation has ranged from 30 to 40 GWh annually, with around 20 GWh absorbed by the pumping process. Performance figures for the system over recent years are available on the company’s website [25].

Since the hydro–wind integration into the island’s grid in 2016, the system has faced noteworthy energy losses, particularly due to the storage system’s inability to absorb peak wind generation. With wind turbines with a capacity of 11.5 MW and the pumping system at 6 MW, excess energy often occurs, especially at night or during the winter months when demand is lower. This leads to storage reaching full capacity and causing energy wastage of about 30–40% of renewable generation. Figure 4 shows the system’s performance from 2014, emphasizing the important generation rise during the initial two years because of the aforementioned aspects.

The renewable energy contribution to the island’s electricity generation varies throughout the year, with the highest contribution in the summer months, reaching 80% in July and around 20% in April. Over the years, renewable energy contributions have fluctuated between 60–80% from May to August and 20–40% in other months. Figure 6 illustrates El Hierro’s monthly electricity generation in 2018 and 2023; the values are broken down by renewable and diesel generation.

This shift to renewable energy has significantly reduced GHG emissions. In 2018, around 50% of emissions were avoided compared to pre-renewable energy operations (Figure 6a), though reductions were more modest in less windy years, such as 2023, where the reduction was around 25% (Figure 6b). The hydro–wind plant avoided approximately 18 kt CO₂ in 2018 and 10 kt CO₂ in 2023 [25].

3. Materials and Methods

The third section describes the theoretical background and principles, in addition to providing the necessary information about the tools employed, the method followed, and the procedures that form the basis of the proposed methodologies. The fundamentals for implementing the old fossil fuel-based scenario, the current diesel-renewable energy scenario, will be developed, and from there, how to evolve towards an innovative scenario that combines synergies to achieve an electric energy system totally based on renewable generation. The new system will be based on the participation of renewable generation and storage, specifically through wind and solar photovoltaic energies and storage via reverse pumped hydro, all aimed at achieving full autonomy on an isolated island.

3.1. EnergyPLAN Software

In the last few years, several estimation methods have been defined and used in various studies to evaluate electricity demand and generation in different types of scenarios with varying levels of GHG emissions. For example, as particular applications of different code simulation tools, Vargas-Salgado et al. [14] presented a forecast for Gran Canaria for 2040 in a scenario of total decarbonization of the economy using HOMER software. Prina et al. [31] performed several forecast simulations for the South Tyrol region using EnergyPLAN software. Segurado et al. [32] simulated different scenarios varying the renewable penetration on the island of S. Vicente in Cape Verde using the H2RES tool, and Mirjat et al. [49] conducted a long-term analysis of Pakistan using the LEAP tool. Other research works compile reviews of different energy simulation tools. Hall and Buckley [33] reviewed many energy simulation tools for the United Kingdom. Similarly, Ringkjob et al. [50] reviewed the most used tools for energy and electricity systems employing highly renewable resources, and Prina et al. presented a review of existing simulation tools for energy system scenarios applied at the island level [34].

Based on the previous comments, it has been considered that several tools are useful for the objective pursued in the current work. In this context, the widely known tool EnergyPLAN has been used [51]. EnergyPLAN is a broadly used tool for designing energy systems, mainly for future sustainable energy solutions [36]. The software is a deterministic input/output tool that simulates the operation of energy systems from the local to the global level, making balances even of the hourly or lower order. The scientific community widely uses this software for various applications. The primary purpose of EnergyPLAN is to analyze the energy, environmental and economic effect of different energy strategies by modeling a variety of options that can be compared, rather than optimizing a single solution [52]. It focuses on modeling the future energy system and how it will operate. For instance, additional criteria can be considered, such as the need for less installed power, minimizing energy wastage, the weighted combination of several factors, penalties for land occupation or visual pollution, etc.

EnergyPLAN is a simulation tool for analyzing energy system scenarios without being constrained by cost minimization, distinguishing it from other calculation tools. It operates through an energy balance at defined time steps, typically hourly, comparing energy demand with available generation and optimizing generator and storage operation [36,53]. The economic analysis seeks a balance between generation and storage sizing to minimize costs while ensuring reliability. Using 2023 economic data, the methodology is tested in two scenarios: one replicating the current system and another applying multi-criteria optimization to transition toward a fully renewable system while maintaining 100% reliability in isolated grids. A precise methodology was followed to perform the computer code estimates, which included a detailed introduction of the necessary input information and an outline of the steps used, as shown in Figure 7. The required input data includes annual information on energy demand or, for future estimates, their forecasts; technical and cost information on the generation system to be considered (in the current study, wind and photovoltaic power plants); technical and cost information on the storage system (mega-batteries and reversible pumping); the energy resources available for each generation system (wind and solar resources available at the selected sites); and additional economic data (such as the annual interest rate and the project’s useful life).

3.2. Optimization Procedures of Multi-Objective Problems

EnergyPLAN is a powerful simulation tool that enables the analysis of energy balances beyond just cost concerns. Unlike traditional approaches focused solely on minimizing expenses, EnergyPLAN allows for the examination of different future energy generation mixes, ensuring a balance between economic feasibility and environmental sustainability, particularly by achieving zero CO₂ emissions. It operates by conducting hourly energy balances, usually over an entire year, in which the energy demand at each time step is compared with the supply capacity of the generation systems plus the existing storage capacity [51]. Once all possible system arrangements are simulated, the software provides key performance characteristics, helping researchers assess different energy strategies.

While single-objective optimization, such as cost minimization, is useful, energy system planning often concerns numerous competing goals. These can include reducing overall system costs, minimizing GHG emissions, and maximizing renewable energy integration. Multi-objective optimization techniques are employed to address these complex trade-offs. These approaches allow for the simultaneous consideration of several goals by exploring a wide range of possible solutions and ranking them based on their performance.

Multi-objective optimization algorithms follow an iterative process inspired by natural selection and evolutionary strategies. The key steps include generating an initial population of solutions, evaluating them based on predefined objectives such as cost and emissions, and improving solutions through selection, crossover, and mutation. Over multiple generations, these solutions evolved to enhance their performance. The final result is often represented as a Pareto front, which illustrates the trade-offs between competing objectives. This front consists of optimal solutions where improving one objective, such as lowering costs, may compromise another, such as reducing emissions. By analyzing the Pareto front, decision-makers can select the best energy system configuration based on their priorities.

EnergyPLAN’s integration with optimization algorithms offers a structured approach to energy system planning, enabling decision-makers to explore diverse solutions rather than relying on a single predefined objective. As energy transitions become more complex, adopting a multi-objective optimization framework ensures that economic, environmental, and technical criteria are adequately balanced.

To start, a multi-objective minimization problem generally follows a structure like the following [54]:

$$\begin{gathered} Optimizationfunction \\ \underset{x}{\mathit{min}}\left[f_k\left(x\right)\right]\hspace{1em}k=\mathrm{1,2},3,\dots ,K \end{gathered}$$

(1)

$$\begin{gathered} Subjecttoconstraints \\ {x}_i^{LB}\le x_i\le x_i^{UB}\hspace{1em}\hspace{1em}i=\mathrm{1,2},3,\dots ,I \\ {h}_n\left(x\right)\ge 0\hspace{1em}n=\mathrm{1,2},3,\dots ,N \end{gathered}$$

(2)

where $f_k\left(x\right)$ are the k objective functions to be minimized and x is the vector of the i decision variables or parameters. The decision variables’ space is constrained by Lower and Upper Bounds (LB/UB, respectively) and might have n inequality constraints of type $h\left(x\right)$. If an x_i solution space meets the constraints, then it is named a possible solution.

The specific context of the current research, specifically the energy situation of El Hierro, must be integrated into the optimization framework. This study aims to minimize both the system cost and GHG emissions. The decision variables include the installed capacities of various technologies such as wind, solar photovoltaic, hydropower, energy storage, and diesel generators. The optimization is subject to constraints like maximum installable power for each technology and, in the case of storage systems, limits on both power capacity and total energy storage. Detailed information about these parameters and constraints specific to this research is provided in Section 4.2.

Multi-Objective Optimization (MOO) problems can be solved using various algorithms, with Evolutionary Algorithms (MOEAs) and Multi-Objective Particle Swarm Optimization (MOPSO) being among the most widely used. An MOEA is based on natural selection and genetic principles, employing crossover and mutation to evolve a population of solutions. MOPSO, an extension of Particle Swarm Optimization (PSO), mimics the social behavior of birds or fish, guiding solutions through the search space based on individual and collective experiences. Among MOEAs, NSGA-II is the most commonly used in energy system planning [37]. Studies comparing NSGA-II and MOPSO in optimizing hybrid renewable energy systems have found that NSGA-II provides more diverse solutions along the Pareto front, whereas MOPSO converges faster but with less diversity. Other algorithms, such as the cuckoo search algorithm, have also been applied to long-term energy system planning.

NSGA-II can be coupled with EnergyPLAN through EPLANopt, a Python package that integrates optimization frameworks with energy system modeling. EPLANopt, built on the DEAP evolutionary algorithm library, permits users to identify optimization problems and constraints to achieve cost-optimal or environmentally optimal arrangements of renewable technologies and storage systems [31,55]. This approach has been applied at both national and island scales.

The optimization process presents solutions on a Pareto front, where no single solution is best for every target [31]. Decision-makers select from this set built on predefined trade-offs and/or criteria. Some research works employ the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to order results according to policymakers’ choice order, adjusting subjective weights for different scenarios 79. Another approach, the Weighted Sum Method (WSM) [54], transforms a multi-objective problem into a single-objective one by assigning relative importance to each factor. These methods help navigate complex decision-making in energy system planning by balancing cost, environmental impact, and reliability. The general form of WSM is:

$$\mathit{min}F\left(x_i\right)=w_1·f_1\left(x_i\right)+w_2·f_2\left(x_i\right)+\cdots +w_n·f_n\left(x_i\right)$$

(3)

where $f_i\left(x\right)$ denotes the single objective functions, $w_i$ corresponds to the weights given to each respective objective function, and $F\left(x\right)$ is the combined objective function to be minimized.

In our issue, optimization implies two competing objectives: the total annual cost and GHG emissions. The conversion of CO₂ emissions into economic terminology effectively leads to a weighted sum of these objectives, permitting us to pose a simplified optimization problem from two objectives to a single one. The EU Emissions Trading Scheme (EU ETS) sets a price for each GHG equivalent emitted ton [56].

3.3. Definition of Scenarios

Coverage of electrical demand in El Hierro up to 2014, as previously detailed, was achieved employing diesel-powered generators, a technology that entailed a substantial impact on the environment. This situation significantly improved after that date with the establishment of the fully renewable wind–hydro power plant of Gorona del Viento. Based on this, there are two different options or models for the supply of electrical energy for El Hierro. The first possibility involves continuing to use the current energy generation system, which means using the wind–hydro plant and supplementing it with the thermal power plant during periods when it is necessary (approximately 50% generation from each over these years). The second option aims to evolve the current model towards one with a greater renewable share, with the intention of achieving a 100% renewable system. But in order to have a reference point of the performance of El Hierro’s electricity system prior to the installation of the wind–hydro power plant, the analysis of the system completely based on fossil fuels is also presented. Thus, the three models can be summarized as follows:

• Generation completely based on the diesel thermal power plant (generation systems up to 2014).
• Integrated wind generation with energy storage, alongside a diesel thermal power plant (current generation system).
• Optimizing the configuration of these renewable systems to achieve generation levels as close as possible to a full renewable energy system while considering existing constraints.

Additionally, the accuracy of the software was assessed by evaluating its 2023 simulation outcomes against existing data sources. As the monthly data of the two systems for the current generation system is available, the accuracy of the code forecasts has been tested against that of these facilities. Subsequently, through this approach, the correct performance of the code was assessed, and a code analysis was carried out, which will subsequently be used for detailed analyses of the established scenarios.

After validating the software’s performance, the first step is to conduct an analysis of the optimum generation system from an integrated energy planning approach. This line of investigation considers the analysis of the electric system’s performance with the aim of reaching the point of optimal efficiency over the thorough energy landscape. Once the various possible contributions are taken into account, an hourly analysis of the balances between renewable energy production, storage technologies, and energy demand is carried out, considering the effect produced by the inherently oscillatory nature of this type of energy source with an adequate level of detail. These analyses evaluate the performance of the current energy system. Additionally, an in-depth analysis of the optimized multi-criteria scenario is conducted, so that the main findings related to the effects of the analyzed constraints on the considered factors can be presented and discussed.

Within the developed multi-criteria optimization procedure, the prospective use of other sources of extra generation and storage should be explored, i.e., the data gathered from wind generation and storage performance for El Hierro’s wind–hydro plant. In the current study, the consideration of solar generation was explored, adding to unavoidable additional storage capacity. This additional storage capacity can be reached by increasing the current facility or by finding other appropriate locations for new plants.

Particularly for solar photovoltaic generation, Trina Solar Vertex 550 W+ panels have been used as a reference model, with an initial investment of 1300 EUR/kW and Operating and Maintenance costs (O&M) of 3.5 EUR/kW per year [15,18]. The relatively high costs are due to the exploitation of the resource being considered only on rooftops (minimizing visual pollution, given the island’s prominent tourist nature), with a maximal usable surface on the island of 1.2 km², of which a maximum exploitable surface of 0.8 km² was considered, representing 83.5 MW of peak solar PV generation capacity [57]. These figures are well above the required installed power values, the current peak demand of which is below 10 MW. Therefore, the island’s capacity is more than sufficient for solar photovoltaic installations for self-consumption.

Importantly, as part of the transition to a fully sustainable energy system, several projects are already underway to advance the decarbonization of the island. The ultimate goal is to achieve a completely carbon-free economy within a few years.

In the first phase, the plans include installing 5 MW of solar photovoltaic capacity along with 5 MW of battery storage, aiming to boost renewable electricity generation from the current level of about 50% up to nearly 80%. In a subsequent phase, an additional 7 MW of solar generation and a second 5 MW battery storage group are scheduled to be added, with the objective of reaching 100% decarbonization.

However, the characteristics of the storage systems using mega-batteries were analyzed, reaching the conclusion that the landscape pollution, the associated emissions in the life cycle and the costs of this system are higher than those that would involve the expansion of the current reversible pumping system existing on the island. Therefore, only the use of the latter was considered. However, in terms of the performance of both systems and characteristics in terms of technical issues, both systems would be equally valid and yield similar results.

Consequently, for additional storage needs, the aforementioned Canary Islands Government study shows that any future expansion of the island’s storage capacity by pumping would require, as the best option, an increase in the size of the reservoirs used in the current system and an improvement in pumping and/or turbination powers [48]. Another possibility would be the installation of an additional reverse pumping station; anyway, to reach a completely renewable system, at least the storage capacity and probably the generation capacity must be augmented.

A summary of the technology costs used is shown in

Table 2. Sub-system costs for the El Hierro forecasts (based on [15,18,58,59,60]).

Production Type	Investment Costs (M EUR/MW)	O&M (%)	Lifetime (Years)
Diesel power generator 1	0.4	5	20
Wind power	1.32	2.97	20
Hydro turbination	3	1.5	50
Hydro storage 2	7.5 × 10−3	1.5	50
Hydro pump	0.6	2.5	30
Solar Photovoltaic	1.3	0.27	20

¹ O&M costs do not include fuel consumption, as it strongly depends on the use. ² Investment cost of hydro storage is in M EUR/MWh of installed storage capacity.

, in which the investment costs are normalized versus installed capacity, while fixed O&M costs are given as an investment cost percentage. For diesel generation, a fuel cost of 150 EUR/MWhe was considered. Although the table provides the initial investment costs for diesel technology, these costs were not considered in the final calculations. This is because, given the years this system has been in operation, it is assumed that its investment has already been amortized. Conversely, for the other two cases, the amortization costs of the Gorona del Viento system were included, as its operation has not yet reached 10 full years, meaning this period has not yet elapsed. This approach is considered appropriate to ensure the general applicability of this document. When extrapolating to other cases, it is common to encounter fossil fuel power plants that are intended to be either fully or partially replaced by renewable technologies.

Alternatively, as is widely recognized, taking advantage of renewable technologies, such as the ones used here, i.e., wind, solar PV and reverse pumping, significantly reduces GHG emissions with respect to fossil-fuel-based technologies since renewable energy technologies produce far fewer emissions than fossil fuels because they do not rely on burning fuel, which is the main source of CO₂ and other pollutants. While manufacturing and installing renewables do generate some emissions, these are much lower than those from fossil fuel extraction and use. Renewables also use abundant natural resources like sunlight and wind, reducing reliance on limited and polluting fuels, and they prevent the release of other harmful pollutants such as SO₂, NOx, and particulate matter. However, it should be noted that renewable energy systems are not entirely free of emissions.

Noting divergences in reported emissions for various generation technologies over their life cycles highlights that emission estimates can vary significantly between technologies. These variations are the result of several factors, such as geographic location, the scale of the facility, methods of construction and operation, and the particular technology employed.

The location of a generation facility can influence emissions due to factors such as climate, availability of natural resources, local regulations, and existing infrastructure. Similarly, a plant’s size or capacity plays a role, i.e., larger facilities tend to be more efficient; consequently, their emissions per unit of energy are generally lower than those of smaller plants, which tend to be less efficient. Emissions are also affected by the construction, operation, and maintenance practices of the plant; for instance, equipment efficiency and waste management approaches can vary widely. Even among systems using the same technology, such as solar PV or wind, different facility designs and configurations might lead to changing life cycle emissions due to differences in efficiency and material usage [61].

However, to provide a more general and manageable estimate of emissions from different generation technologies, average values are used. These average values are obtained from studies and analyses conducted by specialized organizations such as the United Nations Economic Commission for Europe (UNECE) [62] and the National Renewable Energy Laboratory (NREL) in the United States [63]. These organizations collect and analyze data from numerous bases and conditions, generating average values that serve as standard references for comparison of GHG emissions from different generation technologies. Thus, in

Table 3. Average GHG emission factors over the technology’s life cycle for different technologies.

Generation Technology	Life Cycle Emissions (gCO2eq/kWhe)
Diesel power plant	795
Solar photovoltaic	40
Wind power	13
Pumped storage	7.4

, the quantities employed for estimating the life cycle emissions of the technologies used in the current work are shown. In the particular case of diesel generation, the emissions shown in the table are corroborated by the emission data provided in reports relating to energy audits carried out on the diesel installation itself on the island of El Hierro [46]. The remaining values are based on research papers and reports, with values widely spread in the scientific world [62,63].

In the battle against global warming, there are two main options for penalizing GHG emissions: a carbon tax and a GHG emission allowance trading market [64,65]. A carbon tax directly establishes a cost per ton of emissions. The resulting reduction in emissions depends on how much emitters change their behavior in response to the tax. Emission-trading systems (or cap-and-trade systems) set the total amount of GHG emissions that can be released. The government then issues a limited number of emission permits, either by giving them to emitters for free or through an auction. For each ton of emissions released, the emitter must hold one permit. Permits can be traded; so, emitters who cannot reduce their emissions cost-effectively must buy additional permits from emitters who can. The resulting carbon price then depends on the supply and demand for permits.

There are several countries around the world that impose taxes (like Mexico, Japan, or most of Europe countries) and/or others in which emission-trading systems operate, such as the EU Emissions Trading System (EU ETS), the California Emissions Trading System and the China Emissions Trading Scheme. These markets set a price per ton of CO₂ equivalent emitted [56,66]. In the EU, the price of carbon has increased significantly in recent years, reaching levels close to EUR 100 per ton on several occasions over the last few years (Figure 8). The rise in emission prices is mainly due to stricter regulations and fewer available permits. Since 2016, prices have increased from under 10 EUR/tCO₂ to nearly 100 EUR/tCO₂, stabilizing around 80 EUR/tCO₂ since early 2022. Consequently, the 2022 average price is used as a reference to evaluate the costs of GHG emissions in the scenarios analyzed in this study.

4. Results and Discussion

As outlined in previous sections, the analysis conducted includes an initial simulation of the scenario prior to the installation of the Gorona del Viento renewable power station. In the second step, the current scenario is simulated, where for average resource years, approximately 50% of the generation comes from diesel groups and 50% from the renewable plant. In the final step, analyses are carried out that include a multivariate optimization process aimed at achieving the optimal combination of renewable generation. The main criteria considered in this optimization process are economic factors and the reduction in GHG emissions during the life cycle of the employed technologies, although other criteria such as land occupation, landscape pollution, restricted aerial or maritime use, and other existing restrictions have also been considered.

Regarding economic criteria, a cost evaluation and financial feasibility analysis of the proposed generation mix will be conducted. In terms of GHG emission reduction, the decrease in CO₂ equivalent emissions achieved with the optimal generation mix will be measured. Finally, both criteria will be reduced to economic terms by estimating the costs per ton of GHG emitted during the life cycle of the installations, resulting in the “optimal scenario”. In this way, the main stages between fully fossil fuel-based and fully renewable generation can be compared. Thus, the findings are applicable to various locations around the world, as most isolated systems are still largely or entirely fossil fuel-based, and the inevitable transition to renewable generation must be addressed or, in many cases, is already being addressed. For these purposes, the Results section is organized into several subsections that will be developed in the following pages.

4.1. Scenarios Pre- and Post-Full Operation of the Hydro–Wind Power Plant

As outlined in the initial sections regarding the energy scenario of El Hierro island, at the beginning of the 21st century, a project was initiated to install a renewable generation plant that was pioneering for its time and remains advanced today. This plant started its operation in 2014 but became fully operational close to the end of 2015. Until then, the island’s energy system relied entirely on electricity generation using fossil fuels, specifically through the Llanos Blancos diesel plant. Consequently, to establish a maximum reference value for GHG emissions and a cost reference for a fossil fuel-based system, analyzing this scenario was considered important. Indeed, similar scenarios exist in many parts of the world due to various factors. These influences, which can favor the use of fossil fuel-based systems or their combinations with renewables, include those related to locations with significant isolation, cheap and continuous access to fossil fuels, low technological advancement in electrical systems, and a desire for high reliability of the generation system at reduced costs, among other possible reasons.

First, a detailed analysis of the diesel generators’ performance for 2023 will be made, which is summarized in

Table 4. Main characteristics of El Hierro’s all-diesel generation system used to meet the 2023 demand.

Variable	Results for 2023
Installed power	13.04 MW
Generation	50.50 GWh
Average CF	43.97%
Peak CF	59.13%
Total annual costs	6.835 M EUR
Fixed operation costs	0.377 M EUR	5.52%
Variable costs	6.458 M EUR	94.48%
Annual investment costs	0 M EUR	-----
GHG emissions	40.16 kt

. It is worth noting that there are 10 units of between 670 and 2000 kW, totaling a maximum power of 13.04 MW, which facilitates straightforward coverage of the entire demand and its regulation. In the year 2023, there was a total demand of 50.23 GWh, which means an average hourly demand of 5.75 MWh. These hourly demand values range between 3 MW and 8 MW approximately, with a maximum hourly demand value of 7.71 MW (Figure 3). In other words, the average capacity factor (CF) of diesel engines is 43.97%, while the peak CF is 59.13%. As for the economic data, the total system costs are almost 7 M EUR, of which variable costs (fuel costs) account for almost 95%, and the remaining slightly above 5% is for fixed O&M costs, whereas in relation to GHG emissions, these are around 40 kt.

After presenting the results provided by the code for the scenario on El Hierro island prior to the hydro–wind power plant becoming operational, i.e., demand covered exclusively by diesel generation, the results of the current generation system have been evaluated. This system features the hybridization between renewable generation from the Gorona del Viento power plant and the Llanos Blancos diesel station. Specifically, it combines wind generation and reversible pumped storage with diesel generation. To further analyze the accuracy performance of the code, the simulation results shown by the code for the current island system are compared with the real values available on the REE website for the year 2023 [45].

The choice of the year 2023 is justified after the analysis of historical data, specifically since the beginning of the current century, given that the year had adverse environmental conditions from the point of view of generation, thus being conservative in the analysis carried out. To estimate solar and wind resources, NASA’s POWER Data Access Viewer was used [67]. Hourly solar data comes from satellite observations combined with surface solar irradiance data from NASA’s Global Energy and Water Exchange Project (GEWEX)/Surface Radiation Budget (SRB) Release 3 and NASA’s CERES Fast Longwave And Shortwave Radiative project (FLASHFlux). Similarly, for the wind resource, its magnitude was obtained using the global wind data access viewer POWER developed by NASA [67]. This database is based on wind measurements from the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) assimilation model products from the Goddard Global Modeling and Assimilation Office (GMAO) and the near-real-time GEOS 5.12.4 products from the Advanced Processing Instrument Teams (FP-IT) of the GMAO. Finally, when analyzing the data, it was observed that the year 2023 is abnormally low in its wind resource, while for the solar resource, it is a typical year. Hence, in general, 2023 is a very adverse year in terms of climatic conditions for power generation. Therefore, by using this data, a conservative approach is taken, i.e., the system’s performance will be better for any other year with more favorable conditions. Additionally, in the subsequent process of optimizing the sizing of the energy system, a conservative approach is also taken, so that in those years with more favorable climatic conditions, there will be higher electricity generation, thus comfortably covering the demands under any situation. However, it is true that the system’s excesses will be greater than those presented. For this reason, it is considered a good option with this optimized model to see its performance when introducing favorable conditions of solar and wind resources, thereby quantifying the minimum and maximum values of both the associated emissions and the excesses for the optimized system.

To confirm this proper code performance, the attached figure shows the hourly values of the four generation technologies’ contributions along with the demand for a representative week in every main group of the year, both for the results provided by the code and for the actual system data. As shown in Figure 9b, during a typical summer week, there is a clear predominance of renewable technologies. This situation is completely opposite to what is observed during the winter months (Figure 9c), specifically the weeks near the winter solstice, where demand is almost entirely met by diesel generation. An intermediate situation is observed for the rest of the year, as illustrated in Figure 9a. During these periods, diesel generation made a notable contribution, although renewable generation also played a significant role.

When comparing the actual values from the plants (Figure 9-1) with the calculations made by the software (Figure 9-2), it is obvious that, in general, both show similar behavior. While analyzing specific moments may reveal certain differences, the overall behavior is quite similar. For example, a more detailed analysis of Figure 9a shows that the general performance is quite similar. However, there are five major periods of reversible pumping recharge in both cases, although the simulation shows sharpest recharges but with lower durations. As also observed, the areas dominated by diesel and wind generation are very similar, and the simulation presents a slightly lower diesel dependency, since the valley values of the diesel generation are lower in the EnergyPlan simulation. Even the turbine use of the reverse pumping facility is quite similar (red color in the figures). Regarding the summer and winter performance of both systems, comments, which are likely to be those made for Figure 9a, can be made. Then, the overall performance of both systems, real data and simulation results, is similar in general terms, but if the specific performance in a particular time period is analyzed, it may present appreciable differences.

The incorporation of the wind–hydro plant completely changed the technical performance of the island. This hybrid system, which combines wind turbines with a pumped hydro storage facility, was designed to maximize the use of renewable energy and minimize reliance on diesel generation. However, this shift introduced new operational dynamics, especially concerning the conventional diesel power plant.

Diesel-plant-operating patterns have become more variable and less predictable. As depicted in Figure 9, there are frequent rises and falls in the plant’s operation, characterized by numerous start-ups, shutdowns and fluctuations in output. These changes correspond to the inherently intermittent nature of wind energy and the need to balance supply with demand. When wind conditions are favorable, wind turbines generate electricity, reducing the need for diesel power. Conversely, when wind speeds drop, the diesel generators must quickly ramp up to compensate for the shortfall. A summary of the system performance is shown in

Table 5. Overview of the main features of the current generating systems.

Technology	Results for 2023 Simulation
Diesel power plant
Installed diesel power	13.04 MW
Generation	31.41 GWh
Average CF	27.50%
Peak CF	59.13%
Annual costs	4.278 M EUR 1
Fixed operation costs	0.261 M EUR	6.10%
Variable costs	4.017 M EUR	93.90%
Annual investment costs	0 M EUR	----
Hydro–wind plant
Wind
Installed wind power	11.50 MW
Generation	23.93 GWh
Average CF	23.75%
Peak CF	67.05%
Annual costs	1.471 M EUR 1
Fixed operation costs	0.45 M EUR	30.66%
Annual investment costs 1	1.02 M EUR	69.34%
Hydro storage
Installed pump power	6 MW
Installed turbine power	11.30 MW
Storage capacity	225 MWh
Energy pumped	7.52 GWh
Hydro pump average CF	14.31%
Hydro pump peak CF	100%
Energy turbinated	2.78 GWh
Turbine average CF	2.76%
Turbine peak CF	67.05%
Annual costs	2.410 M EUR 1
Fixed operation costs	0.685 M EUR	28.42%
Annual investment costs1	1.725 M EUR	71.58%
Total annual costs	8.159 M EUR
GHG emissions	24.063 kt

¹ An annual interest rate of 3% is assumed.

.

The integration of the wind–hydro plant into the system has led to a reduction in the efficiency of the traditional plant, along with an increase in fuel consumption. This increase amounts to 39.4% of specific consumption, resulting in a drop in plant efficiency 9.4 points from 33.55% to 24.08% [44]. The inefficiency and increased fuel consumption translate directly into higher variable costs. Higher fuel consumption directly leads to an increase in CO₂ emissions compared to the expected. Even though the wind–hydro system is designed to reduce dependence on fossil fuels, the inefficiencies introduced by the need to balance intermittent wind energy with diesel generation result in a net increase in emissions. Consequently, despite the expectation that emissions and costs would significantly decrease with the installation of the renewable plant, the data showed that these reductions were not as substantial as anticipated. This is attributed to the considerable drop in diesel performance. The performance issues are due to the continuous start–stop maneuvers required to adjust its generation to the renewable plant’s intermittent production. Furthermore, the overall efficiency of the reverse pumping cycle is not within the generally discussed interval of 70–80% [68], but rather significantly lower, around 30–40% [44]. All these issues are shown in Table 5, which offers a comprehensive summary of the main features of the current generation system as simulated for 2023, considering the efficiency decrease of the conventional power plant. It comprises detailed data on installed power, electric generation, capacity factors, and costs for the diesel power plant, wind, and reverse pumping components.

4.2. Multi-Objective Optimization

The shift towards sustainable and efficient energy systems is a complex challenge as the most suitable configuration that balances multiple, often conflicting, objectives must be selected. In smart energy systems, the optimization problem typically involves minimizing objectives, such as total annual cost, lifecycle GHG emissions, energy losses, or other relevant factors. These objectives can be in conflict, meaning that improving one objective may come at the expense of another. To address this challenge, multi-objective optimization techniques are employed to identify optimal solutions representing the best trade-offs between the competing objectives.

As mentioned, the EnergyPLAN software is used to conduct the current analysis. Although this software is a simulation tool rather than an optimization model, it allows for analyzing any conceivable energy system scenario. Scripts in other tools like MATLAB or Python can simulate different scenarios where various constraints and objectives can be implemented. For example, EPLANopt is publicly accessible under the GNU Lesser General Public License and published on a GitLab repository [31]. EPLANopt is used as an optimization framework that interfaces with EnergyPLAN to optimize the energy system while considering multiple conflicting objectives. This tool uses a multi-objective evolutionary algorithm (MOEA) built on the Python library DEAP. The MOEA in EPLANopt is designed to find optimal future energy mixes that satisfy minimum criteria for various energy strategies and energy system costs [68].

EPLANopt allows for the definition of optimization problems with EnergyPLAN as the underlying simulation model. The problem must be specified by decision variables, constraints and objective functions to find the optimal arrangement of the energy generation system.

Based on Equation (1), the general multi-objective optimization problem to minimize total annual cost and lifecycle CO₂ emissions can be defined:

$$\underset{x}{\mathit{min}}\left[\begin{array}{c}TotalAnnualCost\left[M€\right]\\ C{O}_{2}LifeCycleEmissions\left(tons\right)\end{array}\right]$$

(4)

In the context of El Hierro’s energy grid, the decision variables may include the installed capacities of various technologies, such as wind power, solar photovoltaics, hydropower, energy storage and diesel generators. The optimization problem may be subject to various constraints, such as usually the installable capacity limits of each technology. These limits are typically in the form of maximum power to be installed, and in the case of storage systems, are also usually given by the total energy that can be stored by the system. Then, in the current case, the optimization decision function to minimize the total annual costs is:

$$TotalAnnualCost\left[M€\right]=\left[\sum _i{(C}_i·x_i)·\left({\displaystyle \frac{1}{L_i}}+O_i\right)\right]+G_{Diesel}·F_{Diesel}$$

(5)

where $x_{\mathrm{i}}$ and $C_i,$ respectively, represent the installed power (MW) and the cost per unit of installed power (EUR/MW) of each subsystem (solar PV, wind, hydro turbination, hydro pumping and diesel), except $x_{\mathrm{S}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e}}$ and $C_{\mathrm{S}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e}}$ which represent the total installed capacity (MWh) and its unitary cost of storage (EUR/MWh) for the hydro storage subsystem (dam-related equipment). While $L_i$ are the lifetimes of each technology, in the diesel contribution, this term has not been considered since this subsystem is considered to be amortized. In the same manner, $O_i$ represents the annual O&M costs of each technology (usually a percentage of the $C_i$/100) per technology, but in this case, diesel generation has to be considered as the rest of the contributions. Diesel generation also has fuel costs, the contribution which is accounted for in the total diesel generation, $G_{Diesel}$ (MWh) and the unitary cost of this diesel fuel, $F_{Diesel}$ (EUR/MWh). All these costs are summarized in Table 2.

On the other hand, there is the expression associated with the second function to be optimized, the one related to GHG emissions. This is given by the following expression:

$$C{O}_{2}LifeCycleEmissions\left(tons\right)=\sum _i{G}_i·E_i$$

(6)

where $E_i$ is the life cycle emissions of each generation technology (tCO₂eq/MWhe) and, as in the previous equation, $G_i$ is the total generation of each technology. In this case, the contributions are diesel, solar PV, wind and storage (where the emissions of the life cycle of the complete installation, i.e., pumping, turbination, dams and other components of the reverse pumping system, have been considered). The data on the life cycle emissions for each technology is shown in Table 3.

These variables are subject to the following group of constraints:

$$\begin{gathered} CurrentInstalledPower\le x_i\le PotentialInstalledPower\left(MW\right) \\ Constraints:\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} \hspace{1em} \\ 11.5\le x_{Wind}\le 25.3 \\ 0\le x_{PV}\le 25 \\ 11.25\le x_{Turb.}\le 15.0 \\ 6.0\le x_{Pump}\le 12.0 \end{gathered}$$

(7)

$$\begin{gathered} CurrentInstalledCapacity\le x_i\le PotentialInstalledCapacity\left(MWh\right) \\ Constraint:\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} \hspace{1em} \\ 225\le x_{Storage}\le 500 \end{gathered}$$

(8)

The inferior limits for wind power and reverse pumping storage (which are 11.5 MW and 6 MW, respectively) are established based on existing installed powers, ensuring that optimization does not propose lowering the current arrangement. The superior constraints for wind (25.3 MW) and solar PV (25 MW) are determined considering land availability and resources in El Hierro. For example, reports suggest that rooftop solar capacity could reach up to 85 MW [57]. However, such an elevated upper limit would create an excessively large solution space, making analysis and visualization difficult. It would also be unrealistic due to visual impact and potential technical–electrical concerns. The upper bound for reverse pumping storage (12 MW) is established based on expected maximum demand and system flexibility needs. Turbine power remains constant at its actual value, as the peak demand of the island is around 8 MW, indicating no need for an increase. However, it is maintained to ensure it can meet future demand growth.

For the capacity of the hydro storage system, the inferior limit (225 MWh) denotes the present existing size, while the upper bound (500 MWh) reflects the maximum possible expansion of existing facilities, as no other suitable locations are available for new installations on the island. Otherwise, in all likelihood, the optimal system would have used this technology to a greater extent.

The objective is to meet the island’s energy demand using a mix of renewable energy sources, but minimizing dependence on fossil fuels, i.e., diesel generation remains an option for backup purposes. Then, consequently, the limits for diesel generators (0–13 MW) range from the current values to total elimination:

$$\begin{gathered} PotentialInstalledPower\le x_{Diesel}\le CurrentInstalledPower\left(MW\right) \\ Constraint:\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} \\ 0\le x_{Diesel}\le 13.04 \end{gathered}$$

(9)

Given that El Hierro is an island, the energy supply must always meet or exceed the demand to prevent shortages since there is no possibility of connection to an external grid. This constraint can be formulated as follows:

$$Generation\left(x\right)-Demand\ge 0$$

(10)

When optimizing El Hierro’s energy grid using EPLANopt and EnergyPLAN, the goal is to find the best balance between competing objectives like minimizing total annual cost and lifecycle CO₂ emissions. This leads to identifying a Pareto front, which is an important concept in multi-objective optimization. In a two-objective optimization problem, the Pareto front can be visualized as a curve in a two-dimensional plot where each axis represents one of the objectives, i.e., the total annual cost vs. lifecycle CO₂ emissions. Points on this curve represent the Pareto-optimal solutions. The EPLANopt framework uses multi-objective optimization techniques to weigh the trade-offs between different goals and identify Pareto-optimal solutions. These solutions can offer valuable insights into the best combination of renewable energy sources, storage technologies, and backup generators (if needed) to meet energy demands while also minimizing costs and emissions.

The EPLANopt script was launched using baseline data, allowing for a comprehensive energy system analysis. The results of this optimization process are illustrated in Figure 10, which shows the Pareto front and all the simulations, including the dominated solutions. Each point on the Pareto front signifies a different mix of energy sources and technologies that collectively offer a balance between minimizing costs and reducing CO₂ emissions.

The blue points represent dominated solutions, while the red points depict the Pareto front. The green points labeled (D) and (WH) highlight the above-analyzed scenarios. Point (D) represents the alternative option of just operating with diesel, whereas point (WH) represents the actual situation with the wind–hydro plant. As can be seen in Figure 10, the change between diesel generation and the current diesel/wind–hydro plant mix has led to a substantial reduction in emissions, but with an appreciable increase in costs. It is therefore necessary to make progress on both fronts to continue to reduce emissions and at the same time, if possible, costs. The reference case, i.e., the actual current energy setup and the situation with only the conventional power plant, are compared against the numerous points on the Pareto front. The existence of numerous Pareto optimal solutions demonstrates the value of multi-objective optimization in energy system design and planning. By simultaneously considering economic and environmental objectives, the optimization process can identify solutions that offer significant improvements over the current or alternative setups.

Remarkably, many solutions significantly reduce CO₂ emissions without an increase in the total annual costs, compared to the reference case (WH). This finding highlights the potential for achieving environmental benefits through system optimization without compromising economic efficiency.

4.3. Collapsing on a Single Optimization Metric

In a two-objective minimization problem, all feasible solutions are located in a two-dimensional Pareto front. It is often beneficial to simplify the problem by converting both objectives into a common metric that aligns with the other objectives. By converting CO₂ emissions into a monetary cost, we can merge these objectives into a single cost-based metric, thus facilitating a more straightforward optimization process. The carbon price reference is used to convert the emissions into a cost (Figure 8). Based on recent trends in the EU ETS, an average carbon price of 80 EUR/tCO₂ has been used as a reference value. Then, as shown in Equation (11), the lifecycle CO₂ emissions ($E_{CO2}$) are multiplied by this carbon price ($P_{CO2}$) and added to the total costs for each scenario ($T_{EUR}$), leading to a final system cost considering GHG emissions ($Tota{l}_{costs}$).

$$C_{CO2}=E_{CO2}·P_{CO2}$$

(11)

$$Tota{l}_{costs}=C_{CO2}+T_{€}$$

(12)

The calculated emission costs are added to the total annual cost of each scenario to obtain a combined metric (Equation (12)). The minimum added cost is the optimal energy solution setup that minimizes both costs and emissions. The total costs considered for each scenario range from 6.591 M EUR for the minimum and, therefore, the optimum (highlighted with a red dot in Figure 11), to about 13 M EUR for the most costly, with the current and previous scenarios for El Hierro being in the range of 10 M EUR. The actual energy setup (WH) is far from the minimum, even from any of the solutions obtained by EPLANopt. The scenario with only diesel generators, named (D), although it is classified as not environmentally friendly, has similar characteristics as other possible solutions obtained by the algorithm. The scenario with the minimum of both objectives is detailed in

Table 6. Overview of the main features of the optimal generating system for 2023.

Technology	Results for Optimal Setup
Diesel power plant
Installed diesel power	9 MW
Generation	7.35 GWh
Average CF	9.33%
Peak CF	85.67%
Annual costs	1.187 M EUR 1
Fixed operation costs	0.180 M EUR	20.58%
Variable costs	1.007 M EUR	79.42%
Annual investment costs	0 M EUR	-%
Wind–hydro plant
Wind
Installed wind power	11.50 MW
Generation	25.48 GWh
Average CF	24.65%
Peak CF	65.34%
Annual costs	1.51 M EUR 1
Fixed operation costs	0.463 M EUR	30.66%
Annual investment costs 1	1.047 M EUR	69.34%
Hydro storage
Installed pump power	7.2 MW
Installed turbine power	11.3 MW
Storage capacity	500 MWh
Energy pumped	16.64 GWh
Hydro pump average CF	26.38%
Hydro pump peak CF	100%
Energy turbinated	8.37 GWh
Turbine average CF	8.10%
Turbine peak CF	65.34%
Annual costs	2.628 M EUR 1
Fixed operation costs	0.749 M EUR	28.50%
Annual investment costs 1	1.879 M EUR	71.50%
Solar PV
Installed Power	13.30 MW
Generation	27.36 GWh
Average CF	23.48%
Peak CF	57.97%
Annual costs	1.266 M EUR 1
Fixed operation costs	0.104 M EUR	8.21%
Annual investment costs 1	1.162 M EUR	91.79%
Total annual costs	6.591 M EUR
GHG emissions	6.26 kt

¹ An annual interest rate of 3% is assumed.

. Similarly to Table 5, the current table includes detailed data on the installed power, generation, capacity factors, and costs for the diesel power plant, wind, and reverse pumping storage components.

There are several aspects to highlight from the information provided in Table 6. For example, the installed diesel power is reduced appreciably, from more than 13 MW in the current case to 9 MW, more than 30%; this reduction is not more pronounced since the system acts as a “backup” in those specific situations in which there is no other generation alternative available. The diesel generation is reduced from over 30 GWh in the current mix by 2023 to just over 7 GWh in the proposed mix. It should also be noted that in the optimized system, wind generation has been maintained at the current level (in all probability, it has not been reduced since it has been considered appropriate to maintain the existing renewable equipment as a restriction), incorporating solar PV generation with an appreciable contribution, an installed power of more than 13 MW, given its greater cost competitiveness, despite its lack of dispatchability and generation concentration in the central day hours. It should also be noted that the use of storage has increased appreciably with respect to the current case, from just under 8 GWh in the current scenario to almost 17 GWh in the proposed mix. In summary, it can be said that the proposed mix achieves a very significant reduction in emissions while also reducing costs. The reduction in emissions is 75% compared to the figures of the 2023 scenario, 24 kt compared to slightly more than 6 kt in the proposed scenario. On the other hand, the reduction in costs is over 30%, from around 10 M EUR to just under 7 M EUR.

In Figure 12, the monthly performance of different generation technologies and the demand are summarized (it is important to note that the actual balances were calculated in hourly intervals, with the monthly representation provided for better visual synthesis). As shown, during the summer months, the generation is significantly greater despite higher demands, leading to more significant surpluses. July and August, followed by May, exhibit the best system performance. Conversely, the winter months, particularly December and February, show considerably lower generation, and then, despite lower demands, the system experiences critical performance moments. January, along with October and November, occupies an intermediate position in these energy balances. Specifically, January has the lowest energy demand and low generation, allowing for balanced generation and demand. In October and November, higher demand is accompanied by higher generation, also leading to a balance of generation and demand. A similar situation is observed in March, April, June, and September, where higher generation meets higher demand without major issues. This balance is even more favorable in July, August, and May, where the highest generation levels are recorded in the system.

4.4. Performance of the Optimized Generation System

The optimization algorithm has produced an adjusted scenario based on the hourly wind speed and solar irradiance data for the year 2023. This year preserves a slightly increasing tendency in electric energy consumption, while solar irradiance has been fairly constant over the years, even though wind is not as constant, representing the smallest contribution of recent years. In fact, as shown in Table 1, there is some variability between renewable production in different years. Consequently, as outlined previously, 2023 was the worst-performing year for the wind–hydro plant, with only 35% of renewable generation, while in 2018, the wind–hydro plant provided over half of the required energy to the grid, specifically more than 56% of renewable generation, which has been the most favorable year. The year 2017 is a typical example of a year in which renewable power plants contributed almost half of the energy to the grid, particularly almost 47% of renewable generation. The optimized energy setup is exposed to the wind speed and solar irradiance of the years 2018 and 2017 to analyze the performance of the 2023 electric demand.

Consequently, the performance that the proposed system would have under the different climatic conditions that have been observed over the last few years should be analyzed in order to provide predictions of how the system would perform under the different climatic conditions that have historically occurred on the island. As mentioned in Section 4.1, data from the last 20 years have been analyzed to determine the hourly distribution of renewable resources, i.e., sun and wind, for a typical year (2017), a favorable year (2018) and an unfavorable year (2023). Obviously, as it is an isolated system, the generation mix must be able to cover the demand in the adverse years; so, the system optimization analysis was justified for this adverse year. But precisely because of this variability, it is interesting to analyze the performance of the system for those typical years or even for those more favorable years. To this end, Figure 13 shows how the system would perform the demand coverage for these situations.

We began by highlighting how a system that is near its limits covers the demand during the winter months, especially in December, when it performs under adverse conditions for renewable resources (Figure 12), while the same generation mix easily covers demand when annual environmental conditions of an average year are considered, and how this margin of coverage becomes even more pronounced for favorable years (Figure 13).

Additionally, as shown in the above-mentioned figures, it should be noted that diesel is used much less in a favorable year, has intermediate use in an average year, and is used much more in an unfavorable year, with usage spiking during the fall–winter months, from October to December.

Another aspect to highlight is that in average and favorable years, there is less use of pumped storage, as the rest of the system is capable of covering the demand on its own most of the time (Figure 13). It is also important to note that solar power remains much more consistent across all three scenarios, with the lowest generation in winter and the highest from April to approximately October.

In the same vein, when comparing surpluses, there are practically zero excesses for 2023 (since diesel generation, being a dispatchable source, adjusts to demand) compared to appreciable values for a year with average renewable resources, in almost all months, with lower values in the winter months due to the poorer performance of renewable generation. In contrast, a favorable meteorological year shows even higher surplus values. However, due to the intrinsically variable nature of these renewable resources, the most adverse month in the average year presented is not December but October (a situation that is not uncommon in the Canary Islands, as this month is prone to having lower winds).

We end by pointing out that the excesses are extremely high in the last two scenarios (Figure 13). These surpluses reach values close to 50% of the energy produced by the system in the favorable case, and around 30% in the average year.

This indicates how these systems face the issue of large surpluses. Given that the generation relies heavily on highly variable and unreliable resources, there is a significant difference in system performance depending on when and how these resources are available. For this reason, there is always a substantial oversizing of the generation and/or storage system to provide the energy demanded under any circumstances. This situation can be mitigated or even eliminated if there is a strong capacity for dispatchable generation, which can cover this variability when necessary. In El Hierro’s system, the dispatchable energy source is diesel engines, which could not be completely eliminated from the proposed mix due to system constraints.

While a fossil fuel source is still maintained, another alternative for a fully renewable system would be to use a dispatchable renewable generation source, such as biomass or geothermal energy. However, geothermal energy is not available on the island, leaving biomass as the only option. Unfortunately, no significant biomass fuel is available on the island, nor on any other islands in the archipelago. Therefore, most of the biomass would have to be imported, but it is important to remember that the system would act as a backup, not as a continuous generation source. It should also be noted that when the other renewable generation sources cannot provide the required energy, the backup would need to operate significantly during certain periods. In fact, as shown for a year with abnormally low renewable resources, there is substantial diesel generation, which is greatly reduced in a year with high renewable resources.

4.5. Discussion and Major Findings

This discussion reviews the application of multi-objective optimization techniques for decarbonizing isolated electrical grids, with a focus on the case of El Hierro. While this case study provides a detailed methodology, the approach is broadly applicable to other systems.

The global decarbonization process faces substantial challenges, primarily due to the continued reliance on fossil fuels for energy production. Transitioning to renewable energy sources, primarily through wind and solar power, introduces challenges related to reliability and stability. This is because these sources are intermittent and non-dispatchable, challenges that are further compounded in islands and other isolated regions, which must be self-independent due to their lack of connection to larger power grids. For these regions to transition to renewables, effective energy storage solutions are essential to ensure a continued, reliable and stable energy supply, particularly during periods of low renewable generation.

EnergyPLAN software, a recognized tool for simulating energy system performance, was employed to model various energy scenarios. The tool enables hourly simulations of energy system operations, providing detailed insights into energy balance, reliability, and the economic impact of different renewable energy configurations. This capability allows for the evaluation of multiple energy source combinations, assessing their ability to meet demand, ensure stability, and minimize costs in a sustainable manner.

To optimize the system, a multi-objective optimization method was implemented, aiming to minimize system costs, GHG emissions, and excess generation. This approach incorporates key constraints, such as generation technology capacities, storage limits, demand coverage levels, and land use restrictions, to ensure the system’s feasibility and efficiency. The optimization was carried out using the EPLANopt tool, an extension of EnergyPLAN that provides enhanced optimization functionalities. EPLANopt allows for the determination of the optimal system configuration by balancing performance, costs, emissions, and excess generation while respecting technical and operational constraints. This multi-objective approach offers a robust framework for designing economically viable and sustainable energy systems.

4.5.1. Synthesis of Essential Aspects in This Study

Fully renewable energy systems encounter intrinsic challenges in matching financial, ecological, and operating factors. By optimizing storage solutions and integrating a combination of renewable and dispatchable generation sources, it is feasible to design systems that meet multi-criteria optimization objectives. This approach provides a sustainable route to energy self-sufficiency for autonomous systems like El Hierro while also reducing their environmental footprint. However, due to the island’s isolation, these must also ensure its self-sufficiency and the ability to meet all its energy demands. Based on the studies conducted, several key aspects can be highlighted:

1.

Current Model Validation

• The validation process has confirmed that the model successfully replicates the operational performance of El Hierro’s current energy systems. While minor discrepancies are observed in the hourly values, the overall trends and contributions from the different subsystems are well aligned. This validation demonstrates that the model is suitable for further analysis.

2.

Fossil-Fuel-Based Systems

• Fossil-fuel-dependent systems are characterized by the absence of energy surpluses, as all generations are fully dispatchable. However, this reliability comes at the expense of significantly higher GHG emissions. El Hierro’s energy systems consisted of diesel generators (13 MW) up to 2014.

3.

Evaluation of Current Non-Optimized Renewable System of El Hierro

• The integration of the hydro–wind system on El Hierro has enabled a shift toward renewable energy, but challenges persist due to system over-sizing and continued reliance on fossil-fuel backup. The current energy setup combines diesel generators (13 MW) with a hydro–wind system (11.5 MW wind, 6 MW pumping power, 11.25 MW turbination power, and 225 MWh storage). The hydro–wind plant enhances flexibility by managing wind power surplus, reducing surplus generation. However, diesel backup is still necessary when renewable energy is lacking, contributing to GHG emissions.

4.

Multi-Criteria Optimization Analysis

• A multi-criteria optimization framework was used to design energy systems that minimize costs and emissions while balancing these factors. The analysis highlights the need for a primarily renewable system with moderate dispatchable generation, which could include biomass or geothermal sources if available. Expanding storage capacities, such as BESS or CAES, could address energy shortfalls. A balanced installed power of solar and wind generation (close to current wind capacity), in addition to an increased reversible pumping storage capacity (somewhat more than double the current level), while preserving the powers of pumping and turbination, with a significant diesel capacity (roughly equal to peak demand) would continue as a backup system, contributing about 10% of the overall generation.

5.

Drawbacks of Optimized Systems based on Non-Dispatchable Resources

• The performance of renewable energy systems varies significantly over the years due to resource variability and unpredictability, especially when storage is limited. Two main options for system dimensioning:
  • System sizing is based on the worst-case renewable resource year. In favorable years, renewable energy generation largely exceeds demand, leading to significant energy surpluses and reduced need for backup systems.
  • System sizing is based on the average-case renewable resource year. In unfavorable years, renewable energy generation is largely lacking, leading to substantial use for backup systems.

6.

Fully Renewable Systems

• Transitioning to an entirely renewable energy system requires over-dimensioning of generation and storage to account for the variability in renewable resources. This ensures a reliable energy supply during periods of low generation. While necessary for energy security, over-dimensioning increases costs due to the need for additional capacity and infrastructure.
  • Challenges for system over-dimensioning: excess renewable capacity can result in energy excesses, economic ineffectiveness, and environmental impacts, especially when storage options are restricted.
  • Importance of large-scale storage: sufficient storage solutions (e.g., pumped hydro, CAES, large BESS) can help manage energy surpluses, but their implementation is often constrained by geographical, technical, and economic factors.
  • Need for backup systems: fundamental in coupling generation and demand in renewable energy systems due to their intermittent nature. The following are viable options:

    -

    Fossil fuel backup systems: while reliable and cost-effective, fossil fuels are unsustainable due to their high GHG emissions.

    -

    Renewable backup systems: options such as geothermal and biomass have reduced GHG emissions but face challenges related to resource availability, elevated costs and operational complexities.

4.5.2. Fundamental Insights for Improving Renewable Generation Integration

After modeling the scenarios examined and analyzing the results, different key issues that were discussed in previous points have been reviewed, addressed and considered in depth. Accordingly, these analyses lead to a number of main points at a more global level that can help to enhance the integration of renewable energies:

-

Decarbonization challenges: Transitioning to renewable energy faces challenges due to the current heavy reliance on fossil fuels. Wind and solar are intermittent, which makes it difficult to ensure a stable energy supply, particularly in isolated regions without connections to larger grids.

-

Isolated regions’ issues: Islands and remote regions face additional difficulties because they must be self-sufficient. They rely on fossil fuels for their stability, as renewables alone cannot always meet their energy needs.

-

Renewable energy and storage needs: To support decarbonization, large-scale renewable energy generation is necessary, but storage is essential to bridge gaps during times of low renewable generation, ensuring a reliable power supply.

-

EnergyPLAN Tool: The EnergyPLAN software was used to simulate various renewable energy system scenarios. It helps analyze different configurations by modeling energy systems’ performance in terms of energy balance, reliability, and economic impact.

-

Optimization technique: A multi-objective optimization technique was employed to balance system costs and GHG emissions, taking into account system constraints such as technology capacities and storage. This approach optimizes the energy system for both cost-effectiveness and environmental impact. The NSGA-II algorithm was selected for the optimization process, as it has demonstrated strong performance and yielded results comparable to those of other commonly used algorithms [43].

-

El Hierro case study: The optimization technique was applied to El Hierro, which has an existing renewable energy system. This case study highlights the system’s performance and the potential for improvement, showing a hybrid hydro–wind system with significant diesel reliance.

-

Adverse conditions and system design: in years with poor renewable resource conditions (e.g., 2023), the renewable system could only cover 35% of the demand, indicating that system designs must account for low generation periods, still relying on diesel generation.

-

Excess generation in favorable years: while renewable energy systems show surplus generation during favorable years (30–50%), this excess energy represents inefficiencies. A 100% renewable system would require significant over-sizing of both generation and storage.

-

Renewable backup alternatives: To fully eliminate diesel, alternatives like biomass or geothermal would be necessary. However, their availability is limited, especially on islands like El Hierro, where imports would be required, adding complexity and cost.

-

Optimized system design: the proposed optimized system reduces diesel use, adds more solar capacity, and increases storage, cutting emissions by 75% and reducing costs by one-third compared to the current system.

-

Pathway to decarbonization: Achieving full decarbonization requires careful system planning and optimization. Without it, oversizing systems could lead to wasted resources, making it crucial to select the right path to reach sustainable energy goals, especially in isolated regions.

The findings emphasize the complexity of achieving a fully renewable energy system, particularly in isolated regions like El Hierro. Balancing renewable generation with storage and backup systems, while minimizing reliance on fossil fuels, is key to both reducing costs and achieving environmental goals. The use of advanced optimization tools and real-world case studies highlights the need for careful planning to ensure energy independence, grid stability, and decarbonization in challenging conditions.

5. Conclusions

This research investigates key aspects of global economy decarbonization, focusing on isolated regions and islands. The transition to renewable energy sources like wind and solar poses challenging issues due to their intermittency, unpredictability and variability. Isolated systems face additional difficulties as they must ensure self-sufficiency, traditionally relying on fossil fuels for stability despite advancements in renewable technologies.

This study highlights the necessity of substantial energy storage to support renewables and applies a multi-criteria optimization method to minimize the entire system costs and GHG emissions. EnergyPLAN, a widely used energy system analysis tool, is employed to simulate different energy scenarios, while EPLANopt enhances the process by optimizing system configurations.

El Hierro, an isolated island with an existing renewable energy system, serves as a case study to evaluate performance under various conditions. The island currently operates a hybrid hydro–wind system that meets approximately 50% of its electricity demand. However, even with optimized configurations, a small share of diesel generation remains necessary to prevent excessive over-dimensioning of renewables and storage. This study underscores the role of dispatchable energy sources in maintaining grid stability.

Using real-world data from El Hierro, this analysis validates the modeling tools while incorporating economic and environmental considerations. This study tests system resilience by modeling conditions from 2023, including the worst wind and solar resource year in the past decade. The proposed optimized system, developed with EPLANopt, achieves significant cost and emission reductions, lowering GHG emissions to approximately 6 kt and costs to 6.5 M EUR, a 75% and 30% reduction, respectively, compared to the current system.

The main findings obtained from the studies conducted include the following general conclusions:

-

Diesel-based systems are cost-effective but environmentally harmful, and they remain essential in small-scale networks until renewable infrastructure is fully developed.

-

Fossil fuels may act as transitional backups in renewable systems due to the unpredictability of wind and solar energy.

-

Islands and remote regions face greater reliance on fossil fuels or large storage due to lack of grid connections.

-

The most used sustainable renewable backup options, biomass and geothermal, are often unavailable, leading to more fossil fuel dependence.

-

There is a need to promote other renewable backup energies, like solar thermal energy, micro-hydropower, tidal and wave energy, to avoid using fossil-fuel-based systems.

-

Relying on a single renewable backup source is difficult due to variability, geographic limits, and infrastructure constraints. Using multiple backup systems can fill gaps left by others.

-

Large-scale energy storage is fundamental but economically challenging due to high costs.

-

Oversizing renewable systems can reduce emissions but create inefficiencies, making storage vital for balancing supply and demand.

-

GHG penalties are necessary to phase out fossil-fuel-based systems, but significant taxes are required to make renewable solutions economically viable.

-

Policies should balance emissions reduction and storage investments to ensure an efficient and sustainable energy transition.

Future research could build upon this study by conducting predictive investigations of demand progress under various scenarios, including Business-As-Usual (BAU) trends, energy efficiency improvements, and disruptive changes such as the adoption of Electric Vehicles (EV), full electrification of major energy consumers, and the integration of hydrogen as an energy vector. Additionally, a more detailed economic analysis of future technology costs and energy prices would enhance the accuracy of projections.

Further studies should focus on modeling predominantly renewable energy systems to meet changing demands, incorporating multi-objective optimization to identify optimal energy mixes based on costs, emissions, and energy surpluses. Developing synthetic curves for solar and wind resources using historical data could also improve scenario accuracy, ensuring a more precise representation of renewable variability. Demand-Side Management (DSM) measures should be explored to better align generation with consumption, reducing the need for oversized systems while maintaining reliability.

The primary limitations of this study stem from the constraints of simulation models, which operate at the system level and cannot replicate the precise control mechanisms of individual generation plants or grid operators. Instead, energy generation and storage follow pre-defined operational sequences to meet demand. Additional uncertainties arise in forecasting generation, storage, and consumption patterns, as well as in assessing the impact of DSM strategies on shifting energy demand. Addressing these limitations through future research could significantly enhance the robustness of renewable energy system modeling and optimization.