Next Article in Journal
Sensorless Control of Dual Three-Phase Permanent Magnet Synchronous Machines—A Review
Previous Article in Journal
Transfer Functions of Ammonia and Partly Cracked Ammonia Swirl Flames
Previous Article in Special Issue
Calibration of Grid Models for Analyzing Energy Policies
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Energies
Volume 16
Issue 3
10.3390/en16031328
energies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Perspective

Hybrid Renewable Energy Systems for Sustainable Rural Development: Perspectives and Challenges in Energy Systems Modeling

by
Lauren E. Natividad
1 and
Pablo Benalcazar
2,*
1
Chemical Engineering Department, California State Polytechnic University Pomona, 3801 W Temple Ave, Pomona, CA 91768, USA
2
Division of Energy Economics, Department of Policy and Strategic Research, Mineral and Energy Economy Research Institute of the Polish Academy of Sciences, 31-261 Krakow, Poland
*
Author to whom correspondence should be addressed.
Energies 2023, 16(3), 1328; https://0-doi-org.brum.beds.ac.uk/10.3390/en16031328
Submission received: 22 December 2022 / Revised: 23 January 2023 / Accepted: 25 January 2023 / Published: 27 January 2023
(This article belongs to the Special Issue Advances in Scientific, Economic and Policy Analysis for Sustainable Development)
Browse Figures
Versions Notes

Abstract

:
Hybrid Renewable Energy Systems (HRESs) have proven to be viable solutions for rural electrification. They not only electrify rural locations but also provide environmentally sustainable, secure, and affordable energy if optimized. These systems can best be described as generators of electricity from multiple energy sources that complement each other. Optimized HRESs often generate affordable electricity by minimizing the levelized cost of electricity (LCOE) and carbon emissions. The investigation of social benefit factors within energy poverty is a relatively new discussion in the study of modeling off-grid energy systems. In this perspective article, we examine the importance of computational tools for the energy transition of rural and remote communities. We show that classical and heuristic models possess the capability of optimizing hybrid renewable energy systems considering social parameters including health, education, and income. This is followed by a discussion about the potential changes these computational tools would need to go through to integrate interdisciplinary factors and address societal transformations. The essence of this paper showcases the influx of literature about this topic; additionally, we look beyond the traditional optimization approaches to disclose that new contributions are evolving based on both current and potential needs within society.

1. Introduction

The global energy sector has been experiencing a transition from the usage of fossil fuels to renewable sources over the past few decades. This transition has been primarily motivated by the need to mitigate climate change, a term which can be described as the abnormal variation in weather patterns caused directly or indirectly by human activity and the release of greenhouse gasses from fossil fuel combustion into the atmosphere [1]. It is widely known that the effects of climate change may intensify the impacts of flooding and extreme temperatures, consequently affecting poor, rural households and millions of lives globally. Moreover, the damage from fossil fuels has not only impacted the environment but also caused harmful health effects to those constantly exposed to combusted toxins [2]. By 2057, it is projected that the global population will reach ten billion from its current number of eight billion [3]. The growing population will increase global demand for fossil fuels in the coming years as the need for secondary energy sources will subsequentially rise. As a result, driven by the abovementioned factors, there is a need for systemic solutions that can contribute to sustainable rural development and the ongoing transformation of the global energy sector.
Although significant progress has been made in developed countries to support climate change policy and accelerate the deployment of renewable energy technologies, numerous research works have shown that there is much to be performed to prepare those who live in rural areas for climate change, as it is frequently seen that those who live in these locations are more reliant on fossil fuels and most vulnerable to climate change impacts [4]. It has been found that dwellers in developing countries experience higher exposure to air pollution and suffer from pulmonary diseases due to a lack of regulation on greenhouse gases. Moreover, in line with the seventh sustainable development goal, ensuring clean, sustainable electricity to all is of the utmost importance from a global perspective. In this paper, the discussion of HRES will be with respect to rural areas since these regions generally lack clean, sustainable energy, and HRES are viable solutions for this problem. However, it should be noted that HRES are effective solutions for cities, towns, and urban regions; they are not limited to rural areas [5]. The figure seen below (Figure 1) shows the share of a region’s electricity supply between rural and urban populations.
The recent awareness of the potential effects of climate change on rural areas has urged scientists, engineers, and energy system modelers to take a more active role in the design of renewable and sustainable energy systems for isolated communities. When investigating attempts of developing new ways to generate clean energy in rural areas, a spectrum of distributed energy supply systems known as Hybrid Renewable Energy Systems (HRESs) present themselves in the literature. These energy production systems can be best described as generators of electricity and heat that utilize multiple complementary energy sources to supply a local load [8]. In previous decades, a plethora of research works have shown that through optimization, such systems have been successful in electrifying rural areas while taking into account carbon emission outputs, energy security, and energy equity [9]. However, the success of rural electrification by HRES extends beyond the scope of formulating objective functions associated with economic and environmental issues. One must consider critical technical aspects within the system which greatly contribute to the outcome of the models’ results. Ensuring that models have appropriate voltage and power operation control is significant for ensuring electricity stability [10]. Additionally, selecting appropriate parameters is important as it can impact the reliability of a system and the accuracy of forecasted results [11]. The reliability of a system can be determined by the use of equations that measure the consistency of energy supply, such as the loss of power supply probability (LPSP) and battery’s state of charge (SOC); these measures provide insight into how reliable a system is performing [5]. Energy storage systems must be mentioned when discussing reliability measures as they ensure continuous operation of power supply in a microgrid; selecting the energy capacity of such storage systems is critical when developing a model as it is related to the load of the system [12]. By discussing the significance of the aforementioned components, it is evident that the uniqueness of a HRES is what makes it capable of electrifying rural regions with complex problems.
Rural electrification by the use of HRES has also shown success when considering carbon emission outputs, energy security, and energy equity as previously mentioned. The reduction in carbon emissions within energy system modeling is achieved through the minimization of representative equations and/or metrics. For instance, in HRES models that utilize pyrolysis and gasification technologies, the reduction in carbon emissions is remarkably effective when greenhouse gas (GHG) emission targets have been implemented [13]. In models that employ the traditional approach of including diesel generators within their development, the reduction in GHG emissions is successful when using carbon emission equations ( CO 2 emission ) [14]. Similarly, ensuring energy security within a system has been achieved by minimizing the loss of power supply probability (LPSP), an equation that represents the total energy deficit of a system divided by total demand [15]. In some cases, the LPSP has been constrained to zero, demonstrating that these systems can ensure complete energy security [16]. Other studies have shown that when the loss of power supply probability is unconstrained, the LPSP may result in higher values. For example, in the case of a remote PV–Wind–Diesel–Battery system, the results showed an LPSP value of 10.63% [17]. When considering rural electrification, optimized HRES provide affordable energy by utilizing different cost-representing equations. For a system that includes PV, wind turbines, and hydrogen technology, the cost of supplying electricity has been successfully minimized and represented as the levelized cost of electricity (LCOE) [18]. Similarly, for HRES that include PV, diesel, and battery technologies, the cost of energy has been used as the model’s objective function, resulting in values of USD 0.207/kWh [19]. Another example of cost-representing equations is the minimization of the total net present cost (TNPC) of energy systems that include multiple technologies (e.g., biogas system, biomass system model, fuel cell system, battery bank system, etc.) [20].
The literature on HRES has expanded and evolved rapidly, opening up new research areas related to sustainable rural development and climate change. A novel research agenda in HRES modeling is the consideration of social benefit factors that help address the issue of energy poverty.

Contributions

This perspective article focuses on the role that classical and heuristic optimization models have played in the transition from diesel-based to renewable energy systems in rural and remote communities. Additionally, it highlights the importance of energy system models that utilize social benefit factors within their design to overcome societal issues. Furthermore, an outlook on the direction of future models is proposed based on the influx of literature and current trends while providing potential endeavors for challenges that have not been met by existing models. The contribution of this paper is a critical discussion on the evolution of HRES along with an outlook of what future models may include. We evaluate the relationship between energy system modeling and the exogenous factors that influence their purpose and formulation while providing an outlook on potential hybrid renewable energy system modeling. These exogenous factors are the challenges involving energy poverty and climate change, two closely related topics that affect rural communities.

2. Evolving Approaches for Hybrid Renewable Energy Systems: Challenges and Solutions

The development of hybrid renewable energy system models has historically been seen as a response to relevant energy-related issues at the time of deployment. The pressing issues most classical and heuristic optimization models for HRES aim to alleviate have been consistent in nature since their creation with most regarding financial, computational, and environmental problems. During the mid-1970s, the expansion of interest in wind–diesel systems was primarily driven by the uncertainty of oil supply [21]. The introduction of hybrid systems dates to the 1980s when the practical application of alternative energy sources began. The Papago Indian (Native American) Village of Schuchuli, Arizona, was the first community, globally, to become reliant on photovoltaic-generated energy [22]. It was also brought to attention that at the time (the 1980s), one of the significant challenges in generating renewable energy and using PV systems was the high capital cost and the intermittency of electricity generation [22]. Despite the many economic and technological challenges, it was anticipated that disruptive innovations would enhance the deployment of wind–diesel–PV systems and extend the application of such systems to remote areas of developing countries; a prediction that has come to fulfillment [21]. Figure 2 shows the share of the global population with access to electricity by country in 2020.
The development and advancement of computational power between the late 1980s and early 1990s enabled the enhancement of HRES with global and meta-heuristic optimization techniques. The inclusion of linear programming models in energy systems provided a way to minimize system costs and carbon emissions, though challenges in ensuring energy security were not addressed. To the best of the authors’ knowledge, The Brookhaven Energy System Optimization Model (BESOM) might be one of the first energy systems to have utilized linear programming for the minimization of the total system cost [24]. It is interesting to note that the concept of including social factors such as energy security and environmental concerns within energy system development was mentioned; however, it was stated that due to model complexity, these challenges might be difficult to address when developing optimized hybrid systems [24].
Since the late 2000s, research in hybrid systems has increased exponentially, peaking in 2021 with 402 publications made in the domain of hybrid renewable energy systems [25]. Until recently, the focus on hybrid energy system optimization was centered around minimizing system costs and, occasionally, carbon emission outputs; minimizing system components along with energy costs has proven to be a practical approach to solving design and operational problems related to HRES [26,27,28]. There are various computational approaches to obtain this objective; however, linear programming (i.e., linear (LP), mixed-integer linear (MILP), and mixed-integer non-linear (MINLP)) and metaheuristic algorithms (i.e., genetic algorithms (GA), particle swarm optimization (PSO), simulated annealing (SA), and others) are among the most commonly used methods for the minimization of costs and emissions in rural electrification projects.
The use of linear programming models for hybrid energy system optimization has proven to be a reliable method for planning rural electrification, given the broad range of parameters each HRES is designed for LP and MILP models allow for the representation of non-linearities (through linear transformations) and acceptable computational times (for solution convergence) by using float and binary variables [29,30]. Concerning rural electrification, linear programming models for hybrid wind–PV systems have been shown to significantly reduce project costs [12]. Moreover, the computational solutions obtained by LP and MILP models have proven to be robust in terms of location, application needs, and energy demand.
Like other computable models, the use of energy demand requirements specific to a community is used to determine the best possible solution. The aforementioned optimization approaches have successfully electrified homes and schools in rural regions [31,32]. When considering different locations/regions, linear programming models have been widely used for optimizing HRES in rural locations with both high and low renewable energy potential [29,31]. Additionally, the energy technologies considered in such models can change depending on the local resources available in the region. The significance of linear programming models is demonstrated in their continued success in planning and scheduling hybrid renewable energy systems with a wide range of parameters. It is worth highlighting that although optimized HRES are solutions for sustainable rural electrification, not all modeling approaches take into consideration carbon emission outputs.
Though linear programming models (LP, MILP, and NMILP) have successfully minimized costs, limitations such as convergence time for larger, complex models with multiple objective (MO) functions and stochastic elements have hindered the adaptability of this optimization approach. Other optimization methods, particularly meta-heuristics, have been proven to reduce computational times while allowing for multiple objective functions. Note that both classical and heuristic methods can provide optimal or near-optimal solutions for rural electrification, yet the scope and complexity of the problem influence their selection.
Genetic algorithms, for instance, use cross-mutation to select the most efficient solution from a set of feasible solutions. In the hybrid renewable energy system domain, GAs have been shown to minimize energy costs in rural communities [33]. For example, hybrid PV–wind systems, diesel–PV–wind systems, and systems that utilize geothermal energy for remote communities have all been optimized using genetic algorithms [34]. This optimization approach has numerous applications, ranging from electrifying small fishing communities in warmer regions to indigenous villages that experience freezing temperatures [9,35]. The nature of the GAs design allows for multiple objective functions to be considered such as carbon emission output [35], the levelized cost of electricity (LCOE), and the total lifecycle cost (TLCC) [36]. Overall, this metaheuristic approach has demonstrated its success in supporting sustainable electrification and rural development in many regions.
As discussed in this section, the optimization approaches that have been explored since the 1970s have shown to be capable of minimizing system costs and carbon emissions. The electrification of rural communities using optimized hybrid systems has proven to be a sustainable solution that assists the global initiative of reducing carbon emissions. In this regard, it can be argued that the contributions to literature in the last decades and the advancements in computational models have set the stage for new ventures in decentralized rural electrification. Table 1 is provided to offer the reader more information regarding relevant studies surrounding the domain of HRES optimization. A list of optimization approaches and notable studies are indicated along with the technologies applied, objective function(s), and the specific location for which the system was tested.

3. Incorporating Social Factors in Hybrid Renewable Energy Systems

The installation of optimized hybrid energy systems in rural locations has successfully electrified these regions while demonstrating the capability of minimizing system cost and carbon emission output. In early literature, the optimization of hybrid renewable energy system models in rural locations noticeably focused on the minimization of system costs to create affordable energy. The system cost can be represented as both the levelized cost of electricity (LCOE) and/or the total lifecycle cost (TLCC). The importance of providing clean, affordable electricity for rural communities is largely due to the direct relationship between negative social effects and lack of electricity. This deprivation of clean electricity has subsequentially inclined rural inhabitants to divert to the use of natural resources (e.g., carbon, wood, and biomass) in order to provide heating and cooking abilities. Thus, the term energy poverty has been coined to describe those who live without electricity and do not have access to clean, sustainable energy. In striving to mitigate energy poverty and rural electrification, a shift in the research domain of optimized HRES models has occurred which has since led to novel contributions that aid in the alleviation of these issues.
The adverse effects caused by the inaccessibility to clean energy have presented themselves within rural communities, these issues have caused an evident educational decline, health diminishment, and economic burden to these inhabitants [47]. Research shows that the continual use of natural resources as a fuel source can have significant damage to human health; in some regions this detriment disproportionally affects women [48]. The use of natural resources in rural locations as a direct form of energy supply has subtly contributed to global climate change since the byproduct is largely composed of carbon emissions. Along with negative environmental impacts, a link between educational decline and the lack of energy was found in households who spend over 10–15% of income on energy needs (energy-poor households) displaying a higher illiteracy rate [49]. When developing models to electrify remote and rural regions, it is significant to take into consideration the alleviation of these social problems and not just providing affordable electricity. Additionally, one must consider the issue of energy poverty since clean, sustainable, and affordable energy is of the utmost importance [2]. Fortunately, providing clean, sustainable energy in rural communities has high importance on the global scale. In 2013, the United Nations collectively agreed to tackle climate change and ecological preservation while ending poverty through a set of 17 Sustainable Development Goals (SDGs) [50]. The seventh SDG aims to, “...ensure access to affordable, reliable, sustainable, and modern energy for all...”, a target that simultaneously works to prevent climate change, alleviate energy poverty, and guarantee energy for all [50]. Correspondingly, other SDGs seek to issue quality education and improve human health [51]. The SDGs are significant as they broadcast rural issues on a global platform and further expose the need for new developments in rural electrification. The broadcasting of the SDGs introduced by the United Nations further transmits the need for HRES models that include social factors, urging modelers to design energy systems through a social lens.
Optimized HRES have shown to simultaneously prevent climate change, resolve energy poverty, and alleviate the social impacts resulting from an inaccessibility to clean electricity by including the Energy Trilemma Index (ETI) within model development [52]. The ETI has provided a means to quantitatively describe the level of clean, sustainable energy for a region along with the availability and affordability of energy being supplied [52,53]. In terms of model formulation, the index can be broken up into three weighted components; energy sustainability, energy security, and energy equity –these indicators are recognized as the three core factors (TCF) by The World Energy Council. The aforementioned three factors can each be represented on a point-basis scale—with greater scores in a sector portraying a closer achievement to the specific goal.
In order to include the ETI within HRES models, each factor must be represented by an equation; by doing this, the indicators (energy security, energy equity, and energy sustainability) can be optimized. The three components can be represented by minimizing carbon emissions or fossil fuel consumption (energy sustainability), constraining the loss of power supply probability to zero percent (energy security), and minimizing the TLCC of a system (energy affordability). The inclusion of the ETI within energy system modeling for rural locations has proven successful for a number of different parameters involving location, size, and modeling technology used. For example, the robustness of this technique is revealed as it has been found effective in reducing carbon emissions by 79.89% for areas that may be solely reliant on solar irradiance as a renewable source [54]. Additionally, rural locations that contain high renewable energy potential have generated scenarios with optimal ETI scores for energy systems models and have even shown a 79% decrease in electricity price and a 68.6% reduction in carbon emission when compared with scenarios that do not contain renewable energy [55]. The ETI has been successful given a variety of scenarios and has also effectively been included with various optimization approaches and software, including HOMER Pro, PLEXOS Integrated Energy Model, and heuristic approaches in MATLAB environments. Upon adjusting the weight of each three core factors within the ETI, it has been found that the lowest combined score appeared when the optimized system prioritized energy equity (affordability), and highest when emphasized on energy security (reliability)—demonstrating that in some cases, affordable energy comes at a cost to other social factors [54]. Including the ETI in optimized HRES models proves that this technique is significant as it has the capability to aid in alleviating energy poverty and climate change. However, optimizing HRES models while including the ETI does not assist in resolving the resulting social issues that have occurred from energy poverty and climate change. To better resolve the aftereffects caused by the aforementioned issues, the addition of social benefit factors to existing models in the form of equations is a promising solution that can directly help to solve these matters.
In addition to including the ETI within model development—the introduction of social indicators (social benefit factors) assists in alleviating the aftereffects caused by energy poverty. Social benefit factors are generally qualitative properties that can prevent or significantly reduce negative aspects of our society. In the context of hybrid energy systems, these factors have been expressed as equations included within energy system models; they work by directly targeting the adverse effects of energy poverty and climate change. To better clarify the relationship between the ETI, sustainable development goals (SDGs), social benefit factors, and rural electrification, Figure 3 is included to demonstrate the interconnections between these topics.
There are several types of social benefit factors in literature, an example of one can be seen by the measurement of particulate matter (PM), an indicator that seeks to prevent the damaging health effects of particles in the air [56]. The particulate matter (PM) environmental factor works by breaking up particulate matter size into two categories based on its diameter which allows for the differentiation between matter that is extremely harmful to human health and matter that is not as harmful [56].
Another social benefit factor that assesses the damage to human health caused by energy poverty is the disability adjusted life years (DALYs), an equation that describes the years of life lost caused by premature death and years of life lost due to disability caused from the amount of carbon emissions outputted [57]. Both the PM and DALYS social benefit factors are relevant as they provide a means to help promote healthier lifestyles within energy system modeling, this demonstrates the current trend in literature of developing models that include social components. Other indicators that consider the adverse effects of energy poverty and climate change are centered around job loss and job creation; for example, the job creation factor (JCF) indicator works by estimating how many jobs will be created from a system’s excess energy (in terms of jobs/(GWh/yr)) [58]. The inclusion of social benefit factors in model development takes into consideration an overlooked concept, that is social well-being and human health.
Incorporating social benefit factors within model development has proven to alleviate the aftereffects of energy poverty, including job shortages, job loss, and health hazards, while still considering system cost, carbon emission control, and energy security. The literature shows that the cost of energy has been successfully minimized along with the minimization of DALYs through the use of HRES models that employ heuristic methods such as non-dominated sorting genetic algorithm-II (NSGA-II) [57]. In addition, the maximization of JC and the HDI are successful in conjunction with minimizing environmental impact [57]. In some unique cases, solutions found with the incorporation of social benefit factors have been shown to provide better economic and environmental outcomes than models without these indicators [57]. The use of NSGA-II optimization in HRES is successful in maximizing JC and sometimes even including it within the objective function alongside the cost of energy (COE) and lifecycle emission (LCE), demonstrating that energy system models can incorporate social factors alongside conventional equations [59]. As a result of this, the multi-objective approach is frequently employed when including social benefit factors alongside standard objective function equations (LCOE, LPSP, and TLCC) as a way to ensure results are generated considering the most optimal system, some systems have even contain up to six objective functions [60]. Using conventional equations along with economic and social benefit factor equations such as the levelized cost of electricity (LCOE), LPSP, HDI, JC, and emission control (EMC) in conjunction with a hybrid two-stage particle swarm differential evolution (HPSO-DE) model for optimization, jointly provides the best possible energy system solution [60].
Social benefit factors have been effectively included in HRES models for a variety of case study regions and applications [57,61]. Additionally, HRES optimization models that include social benefit factors are successful for different load sizes and can be unique for the specific needs of each location [59,60]. The integration of social benefit factors alongside traditional functions has provided a way to enhance the livelihood of those experiencing energy poverty. Though these factors are successful in alleviating societal issues, it is important to acknowledge that their creation is driven by current problems. As the world continues to change, the issues seen in rural regions today have the potential to change, especially when considering the unavoidable increase in energy demand and the influx of data regarding futuristic climate change scenarios. With that being said, prospective energy system modelers must investigate how these inevitable issues can potentially affect the social well-being and environment of rural dwellers and furthermore, determine a sustainable way to address these problems. Taking this into consideration, the questions posed for prospective modelers to consider in upcoming energy systems are as follows:
  • What energy problems will the developing rural world face in the coming future?
  • What are potential ways to prevent and/or alleviate these problems using computational tools and models for HRES?

4. Moving Forwards

The past few decades have shown a dramatic shift in the optimization of HRES, starting from the minimization of system cost to now taking into consideration social issues in model development. The surfacing of new social issues as a result of energy poverty and climate change in rural regions has been alleviated by energy system modelers who have shifted their model formulations to meet these issues; for example, the need for jobs has been met with the JC social factor and the steady decline of health has been addressed by the PM environmental factor. As the world continues to change, it is only a matter of time before new problems arise and another paradigm in energy systems is seen. With that being said, anticipating upcoming issues is significant in order to accelerate model development and thus, sooner meet the needs of rural communities and prevent negative aftereffects.
Including the ETI within the design process of HRES has shown that pre-existing metric systems can be successfully included in energy system models in order to minimize social issues and prevent future problems. With the growing number of problems surrounding rural electrification, creating models that utilize different metric systems can potentially alleviate these drawbacks on a distinctive, case-by-case basis. Although the ETI has the capability to measure energy security, energy sustainability, and energy equity, other metric systems can measure the same social factors, if not more. Thus, it can be expected that in the future, the domain of rural HRES modeling will include various indices that can fully represent different social factors and modeling challenges for unique scenarios.
The projection that futuristic models will include other types of indices within energy system model development is postulated based on the increasing influx of HRES models that contain social parameters and pre-existing models that employ similar indices (e.g., ETI). With that being said, potential indices such as the multi-dimensional energy poverty index (MEPI) may be useful to include within energy system model development as it has the capability of measuring energy poverty through multiple indicators. This well-renowned index considers the multi-dimensionality character of energy poverty as it takes into consideration five dimensions that represent energy services such as cooking, lighting, entertainment/education, communication, and services provided by means of household appliances [61]. Additionally, there are six indicators within these dimensions that involve modern cooking fuel, indoor pollution, electricity access, household appliance ownership, entertainment/education appliance ownership, and telecommunication. The index also defines a household as energy poor if there is deprivation within a dimension that breaches a certain level, it also mandates that 40% of the total weight in scale is connected to cooking for health prioritization [61]. The usefulness of the MEPI is evident as it accurately describes energy poverty from a multi-dimensional standpoint and does not limit its definition to having or not having electricity. Additionally, it measures the incidence and intensity of energy poverty in a region by its multi-dimensional characteristic. Subsequentially, based on the existing literature and the modeling trends observed in the past few years, future works are likely to consider this index or similar indices when developing rural HRES models as they offer more social indicators that can thoroughly represent energy poverty.
The problems that current HRES models aim to address are quantitative social deprivations that exist in rural settings caused by energy poverty. Although optimized HRES successfully mitigate energy poverty and the various aftereffects of climate change for these communities, there have been very few attempts to develop models that consider the negative impacts climate change has had on local ecosystems. One of the relevant problems that is frequently overlooked in HRES modeling is the eradication of species caused by climate change and its shifting global temperatures. The eradication of species can be classified as an environmental and social issue, this problem will impact global communities if ignored. Given that energy system modelers have steered into developing HRES that take into consideration social issues, it is plausible that the issue of species extinction may appear in future works.
To further support the hypothesis that prospective energy system modelers will consider species eradication in the future, statistics surrounding the topic are provided to broadcast the relevancy of this social issue. It is widely acknowledged that disruptions to sensitive ecosystems can have unwavering effects on humans. According to the International Union for Conservation of Nature (IUCN), there are at least 10,967 species on the IUCN threatened species list, better known as the ICUN Red List of Threatened Species. The number of species on this list shows the high risk of extinction and further exposes the need for models to consider ecological concerns. Aside from their eradication, there are many physical changes occurring in species because of global warming [62]. In addition to the ecological aftereffects of climate change, it has been found that of 4500 islands, 6–19% of them may be entirely submerged as a result of rising sea levels, revealing the necessity of modelers to consider social effects caused by climate change [63]. The need for modelers to consider ecological impacts from climate change within energy system development is evident, some authors have already considered these impacts within HRES by determining the extent of ecosystem damage and the loss of local species for a given year [57]. It is important to emphasize that previous trends of HRES models in literature have been designed to consider the Sustainable Development Goals (SDGs) since they highlight current global problems. It is possible that future hybrid renewable energy system models will consider other goals in conjunction with the seventh goal (which aims to provide clean and sustainable energy). Examples of such that align with the inclusion of ecological damage can be found in the 13th through 15th goals.
These specific goals highlight the need to combat the effects of climate change, conserve the oceans, protect and restore forests and habitats, and halt biodiversity loss [51]. It is possible that in the future, rural HRES models will work towards using excess energy to restore and protect species while dealing with the aftereffects of climate change, considering that this is an issue that will plague future generations. Additionally, it is possible that indices used to measure species extinction such as the Red List of Threatened Species will be included in model development.
Moving forward, the domain of HRES might expect to see another paradigm in model development. Although models have successfully taken into consideration current problems and provided viable solutions to them; with the number of new problems arising because of climate change it is likely that based on modeling evidence and the ecological effects of climate change, future developments in energy system modeling will consider the eradication of species and other social indices.

5. Conclusions

In this perspective paper, the authors highlight the developmental causes for the transition from traditional rural hybrid energy systems to current systems while providing an evidence-based outlook on prospective systems. To provide clarity on the concepts addressed in this article, a visual representation of themes, perspectives, and challenges are expressed below in Table 2, providing the reader with a visual diagram of the topics discussed.
From this perspective article, the following conclusions can be drawn:
  • The mention of rural electrification and its multi-dimensional nature was covered in detail, along with an extensive discussion on how it can be solved by optimization models, specifically HRES. From the studies mentioned regarding optimization approaches (MILP, GA, PSO, and LP), it is evident that these approaches combined with HRES are effective solutions to the progressing issue of energy poverty and furthermore, rural electrification. This article emphasized rural electrification using HRES in contrast to urban electrification due to the dramatic increase in the lack of clean and sustainable energy in rural areas;
  • The authors discuss in detail the global energy transition occurring, concluding that HRES model development is influenced by social needs. Over the past few years, the global transition from fossil fuel energy to renewable energy has been enhanced by classical and heuristic models. When investigating these models, a common trend was seen in that they take into consideration various issues depending on global importance. An example of this global importance is seen in the broadcasting of the Sustainable Development Goals (SDGs) by the United Nations. The problems associated with rural electrification have traditionally involved cost limitations; however, due to climate change, new problems have risen;
  • A discussion on recent energy system models was mentioned to show readers the direction that HRES modeling is taking. It was discovered that new models are taking into consideration Social Benefit Factors (SBFs) to adequately meet the needs of rural locations. These social factors can alleviate energy poverty by improving human health and education while also minimizing the cost of electricity. Furthermore, the use of SBFs in conjunction with traditional objective functions was successfully achieved, it is also important to mention the discovery that various optimization approaches can be used to include SBFs;
  • Given that optimization models have successfully minimized costs and included social benefit factors, it is likely that in the future other indices to measure energy poverty will be incorporated in model development. The mention of other indices is seen in the outlook section, where the potential of using the well-renowned MEPI is mentioned as a possibility. Additionally, considering that models have been developed based on social needs, it has been projected by research and the inclusion of new global initiatives that preserving and preventing ecological problems from climate change will be the next challenge that HRES solve. A brief discussion of climate change is included in this article and the impact it has on species extinction along with local ecosystem damage. Furthermore, it was noted that new models have already begun to consider species eradication, supporting the conclusion that energy system modelers may begin to consider ecological issues.
In the future, it is possible that more government policies will apply enough pressure to make ecological issues a priority. With an increase in energy system modelers attempting to design systems with respect to the Sustainable Development Goals along with countries prioritizing this initiative, it is plausible that major advances to alleviate climate change will be developed. With this perspective, it can be said that there is a substantial amount of literature that discusses social benefit factors in hybrid renewable energy system models. The global energy transition has influenced the design of energy system models from only considering system costs, to now including a resolution for social issues in rural communities. The scope of hybrid renewable energy system modeling has extended far beyond what it was initially designed for, the current trends of energy system applications in the social realm have provoked new ideas which are now venturing into the ecological domain.

Author Contributions

Conceptualization, L.E.N. and P.B.; methodology, L.E.N. and P.B.; formal analysis, L.E.N. and P.B.; investigation, L.E.N. and P.B.; data curation, L.E.N. and P.B.; writing—original draft preparation, L.E.N. and P.B.; writing—review and editing, L.E.N. and P.B.; visualization, L.E.N. and P.B.; supervision, P.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank and acknowledge the Student Success and Transfer Articulation through Research and Support Services (STARS program) and the Office of Undergraduate Research at Cal Poly Pomona for their continued support. The authors are grateful for the constructive comments of the anonymous reviewers, which greatly improved the quality of this paper.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

COECost of Energy
CSCuckoo Search
DEDifferential Evolution
DPSPDeficiency of Power Supply Probability
EGEnergy Generation
EMCEmission Control
ETIEnergy Trilemma Index
GAGenetic Algorithm
HDIHuman Developmental Index
HPSOHybrid Particle Swarm Optimization
HRESHybrid Renewable
ICCInitial Capital Cost
ICUNInternational Union for Conservation of Nature
JCJob Creation
LCELifecycle Emission
LCOELevelized Cost of Electricity
LPLinear Programming
MEPIMulti-Dimensional Energy Poverty Index
MILPMixed-Integer Linear Programming
MOMultiple Objective
NGSA IINon-dominated Genetic Sorting Algorithm-II
NMILPNon-linear Mixed-Integer Linear Programming
NPCNet Present Cost
OCOperation Cost
PSO-GWOParticle Swarm Optimization- Grey Wolf Optimizer
RFRenewable Fraction
SBFsSocial Benefit Factors
SDGsSustainable Development Goals
TCFThree Core Factors
TLCCTotal Lifecycle Cost
TNPCTotal Net Present Cost

References

  1. Lilienfeld, E.; Nicholas, P.K.; Breakey, S.; Corless, I.B. Addressing Climate Change through a Nursing Lens within the Framework of the United Nations Sustainable Development Goals. Nurs. Outlook 2018, 66, 482–494. [Google Scholar] [CrossRef]
  2. Mohsin, M.; Taghizadeh-Hesary, F.; Shahbaz, M. Nexus between Financial Development and Energy Poverty in Latin America. Energy Policy 2022, 165, 112925. [Google Scholar] [CrossRef]
  3. Holechek, J.L.; Geli, H.M.E.; Sawalhah, M.N.; Valdez, R. A Global Assessment: Can Renewable Energy Replace Fossil Fuels by 2050? Sustainability 2022, 14, 4792. [Google Scholar] [CrossRef]
  4. Buenfil Román, V.; Espadas Baños, G.A.; Quej Solís, C.A.; Flota-Bañuelos, M.I.; Rivero, M.; Escalante Soberanis, M.A. Comparative Study on the Cost of Hybrid Energy and Energy Storage Systems in Remote Rural Communities near Yucatan, Mexico. Appl. Energy 2022, 308, 118334. [Google Scholar] [CrossRef]
  5. Fathima, A.H.; Palanisamy, K. Optimization in Microgrids with Hybrid Energy Systems—A Review. Renew. Sustain. Energy Rev. 2015, 45, 431–446. [Google Scholar] [CrossRef]
  6. IEA; IRENA; UNSD; WHO. Access to Electricity, Rural (% of Rural Population). Available online: https://data.worldbank.org/indicator/EG.ELC.ACCS.RU.ZS (accessed on 21 December 2022).
  7. IEA; IRENA; UNSD; WHO. Access to Electricity, Urban (% of Urban Population). Available online: https://data.worldbank.org/indicator/EG.ELC.ACCS.UR.ZS?end=2020&start=1990&view=chart (accessed on 21 December 2022).
  8. Ammari, C.; Belatrache, D.; Touhami, B.; Makhloufi, S. Sizing, Optimization, Control and Energy Management of Hybrid Renewable Energy System—A Review. Energy Built. Environ. 2022, 3, 399–411. [Google Scholar] [CrossRef]
  9. Quitoras, M.R.; Campana, P.E.; Crawford, C. Exploring Electricity Generation Alternatives for Canadian Arctic Communities Using a Multi-Objective Genetic Algorithm Approach. Energy Convers. Manag. 2020, 210, 112471. [Google Scholar] [CrossRef]
  10. Arul, P.G.; Ramachandaramurthy, V.K.; Rajkumar, R.K. Control Strategies for a Hybrid Renewable Energy System: A Review. Renew. Sustain. Energy Rev. 2015, 42, 597–608. [Google Scholar] [CrossRef]
  11. Seyedmahmoudian, M.; Jamei, E.; Thirunavukkarasu, G.S.; Soon, T.K.; Mortimer, M.; Horan, B.; Stojcevski, A.; Mekhilef, S. Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach. Energies 2018, 11, 1260. [Google Scholar] [CrossRef] [Green Version]
  12. Sadullaev, N.; Nematov, S. Micro-Grid Based Power Supply of Remote Consumers Located Away from the Centralized Power Grid. In Proceedings of the IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation, ICATMRI 2020, Buldhana, India, 30 December 2020; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2020. [Google Scholar] [CrossRef]
  13. Li, L.; You, S.; Wang, X. Optimal Design of Standalone Hybrid Renewable Energy Systems with Biochar Production in Remote Rural Areas: A Case Study. Energy Procedia 2019, 158, 688–693. [Google Scholar] [CrossRef]
  14. Fodhil, F.; Hamidat, A.; Nadjemi, O. Potential, Optimization and Sensitivity Analysis of Photovoltaic-Diesel-Battery Hybrid Energy System for Rural Electrification in Algeria. Energy 2019, 169, 613–624. [Google Scholar] [CrossRef]
  15. Mokhtara, C.; Negrou, B.; Settou, N.; Settou, B.; Samy, M.M. Design Optimization of Off-Grid Hybrid Renewable Energy Systems Considering the Effects of Building Energy Performance and Climate Change: Case Study of Algeria. Energy 2021, 219, 119605. [Google Scholar] [CrossRef]
  16. Pal, A.; Bhattacharjee, S. Effectuation of Biogas Based Hybrid Energy System for Cost-Effective Decentralized Application in Small Rural Community. Energy 2020, 203, 117819. [Google Scholar] [CrossRef]
  17. Cadmiel, R.; Castro, C. Hybrid Particle Swarm Optimization and Gravitational Search Algorithm for Optimal Sizing of Hybrid Renewable Energy Systems. In Proceedings of the 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Macao, China, 1–4 December 2019. [Google Scholar] [CrossRef]
  18. Marocco, P.; Ferrero, D.; Lanzini, A.; Santarelli, M. The Role of Hydrogen in the Optimal Design of Off-Grid Hybrid Renewable Energy Systems. J. Energy Storage 2022, 46, 103893. [Google Scholar] [CrossRef]
  19. Odou, O.D.T.; Bhandari, R.; Adamou, R. Hybrid Off-Grid Renewable Power System for Sustainable Rural Electrification in Benin. Renew. Energy 2020, 145, 1266–1279. [Google Scholar] [CrossRef]
  20. Suresh, V.; Muralidhar, M.; Kiranmayi, R. Modelling and Optimization of an Off-Grid Hybrid Renewable Energy System for Electrification in a Rural Areas. Energy Rep. 2020, 6, 594–604. [Google Scholar] [CrossRef]
  21. Mcgowan, J.G.; Manwell, J.F.; Connors, S.R. Wind/Diesel Energy Systems: Review of Design Options and Recent Developments. Sol. Energy 1988, 41, 561–575. [Google Scholar] [CrossRef]
  22. Groumpos, P.P. Overview of Control Aspects of a Village Stand-Alone Photovoltaic Power System. IEEE Trans. Power Appar. Syst. 1984, PAS-103, 2845–2853. [Google Scholar] [CrossRef]
  23. IEA; IRENA; UNSD; WHO. Access to Electricity (% of Population). Available online: https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS (accessed on 21 December 2022).
  24. Kydes, A.S. An Overview of The Principal Brookhaven Energy System Optimization Models-F. Resour. Energy 1980, 3, 65–92. [Google Scholar] [CrossRef]
  25. Sohail, M.; Afrouzi, H.N.; Mehranzamir, K.; Ahmed, J.; Mobin Siddique, M.B.; Tabassum, M. A Comprehensive Scientometric Analysis on Hybrid Renewable Energy Systems in Developing Regions of the World. Results Eng. 2022, 16, 100481. [Google Scholar] [CrossRef]
  26. Huneke, F.; Henkel, J.; González, J.A.B.; Erdmann, G. Optimisation of Hybrid Off-Grid Energy Systems by Linear Programming. Energy Sustain. Soc. 2012, 2, 7. [Google Scholar] [CrossRef] [Green Version]
  27. Deshmukh, M.K.; Deshmukh, S.S. Modeling of Hybrid Renewable Energy Systems. Renew. Sustain. Energy Rev. 2008, 12, 235–249. [Google Scholar] [CrossRef]
  28. Benalcazar, P.; Suski, A.; Kamiński, J. The Effects of Capital and Energy Subsidies on the Optimal Design of Microgrid Systems. Energies 2020, 13, 955. [Google Scholar] [CrossRef] [Green Version]
  29. Ranaboldo, M.; Lega, B.D.; Ferrenbach, D.V.; Ferrer-Martí, L.; Moreno, R.P.; García-Villoria, A. Renewable Energy Projects to Electrify Rural Communities in Cape Verde. Appl. Energy 2014, 118, 280–291. [Google Scholar] [CrossRef]
  30. Malheiro, A.; Castro, P.M.; Lima, R.M.; Estanqueiro, A. Integrated Sizing and Scheduling of Wind/PV/Diesel/Battery Isolated Systems. Renew. Energy 2015, 83, 646–657. [Google Scholar] [CrossRef] [Green Version]
  31. Ferrer-Martí, L.; Domenech, B.; García-Villoria, A.; Pastor, R. A MILP Model to Design Hybrid Wind-Photovoltaic Isolated Rural Electrification Projects in Developing Countries. Eur. J. Oper. Res. 2013, 226, 293–300. [Google Scholar] [CrossRef]
  32. Benalcazar, P.; Suski, A.; Kamiński, J. Optimal Sizing and Scheduling of Hybrid Energy Systems: The Cases of Morona Santiago and the Galapagos Islands. Energies 2020, 13, 3933. [Google Scholar] [CrossRef]
  33. Khatib, T.; Mohamed, A.; Sopian, K. Optimization of a PV/Wind Micro-Grid for Rural Housing Electrification Using a Hybrid Iterative/Genetic Algorithm: Case Study of Kuala Terengganu, Malaysia. Energy Build. 2012, 47, 321–331. [Google Scholar] [CrossRef]
  34. Naseh, M.R.; Behdani, E. Feasibility Study for Size Optimisation of a Geothermal/PV/Wind/Diesel Hybrid Power Plant Using the Harmony Search Algorithm. Int. J. Sustain. Energy 2021, 40, 584–601. [Google Scholar] [CrossRef]
  35. Bala, B.K.; Siddique, S.A. Optimal Design of a PV-Diesel Hybrid System for Electrification of an Isolated Island-Sandwip in Bangladesh Using Genetic Algorithm. Energy Sustain. Dev. 2009, 13, 137–142. [Google Scholar] [CrossRef]
  36. Quitoras, M.R.; Cabrera, P.; Campana, P.E.; Rowley, P.; Crawford, C. Towards Robust Investment Decisions and Policies in Integrated Energy Systems Planning: Evaluating Trade-Offs and Risk Hedging Strategies for Remote Communities. Energy Convers. Manag. 2021, 229, 113748. [Google Scholar] [CrossRef]
  37. Salameh, T.; Abdelkareem, M.A.; Olabi, A.G.; Sayed, E.T.; Al-Chaderchi, M.; Rezk, H. Integrated Standalone Hybrid Solar PV, Fuel Cell and Diesel Generator Power System for Battery or Supercapacitor Storage Systems in Khorfakkan, United Arab Emirates. Int. J. Hydrog. Energy 2021, 46, 6014–6027. [Google Scholar] [CrossRef]
  38. Sanajaoba, S.; Fernandez, E. Maiden Application of Cuckoo Search Algorithm for Optimal Sizing of a Remote Hybrid Renewable Energy System. Renew. Energy 2016, 96, 1–10. [Google Scholar] [CrossRef]
  39. Haghighat Mamaghani, A.; Avella Escandon, S.A.; Najafi, B.; Shirazi, A.; Rinaldi, F. Techno-Economic Feasibility of Photovoltaic, Wind, Diesel and Hybrid Electrification Systems for off-Grid Rural Electrification in Colombia. Renew. Energy 2016, 97, 293–305. [Google Scholar] [CrossRef]
  40. Sanchez, V.M.; Chavez-Ramirez, A.U.; Duron-Torres, S.M.; Hernandez, J.; Arriaga, L.G.; Ramirez, J.M. Techno-Economical Optimization Based on Swarm Intelligence Algorithm for a Stand-Alone Wind-Photovoltaic-Hydrogen Power System at South-East Region of Mexico. Int. J. Hydrog. Energy 2014, 39, 16646–16655. [Google Scholar] [CrossRef]
  41. Maatallah, T.; Ghodhbane, N.; ben Nasrallah, S. Assessment Viability for Hybrid Energy System (PV/Wind/Diesel) with Storage in the Northernmost City in Africa, Bizerte, Tunisia. Renew. Sustain. Energy Rev. 2016, 59, 1639–1652. [Google Scholar] [CrossRef]
  42. Suman, G.K.; Guerrero, J.M.; Roy, O.P. Optimisation of Solar/Wind/Bio-Generator/Diesel/Battery Based Microgrids for Rural Areas: A PSO-GWO Approach. Sustain. Cities Soc. 2021, 67, 102723. [Google Scholar] [CrossRef]
  43. Ismail, M.S.; Moghavvemi, M.; Mahlia, T.M.I. Genetic Algorithm Based Optimization on Modeling and Design of Hybrid Renewable Energy Systems. Energy Convers. Manag. 2014, 85, 120–130. [Google Scholar] [CrossRef]
  44. Alberizzi, J.C.; Rossi, M.; Renzi, M. A MILP Algorithm for the Optimal Sizing of an Off-Grid Hybrid Renewable Energy System in South Tyrol. Energy Rep. 2020, 6, 21–26. [Google Scholar] [CrossRef]
  45. Kumar, R.; Channi, H.K. A PV-Biomass off-Grid Hybrid Renewable Energy System (HRES) for Rural Electrification: Design, Optimization and Techno-Economic-Environmental Analysis. J. Clean. Prod. 2022, 349, 131347. [Google Scholar] [CrossRef]
  46. Balderrama, S.; Lombardi, F.; Riva, F.; Canedo, W.; Colombo, E.; Quoilin, S. A Two-Stage Linear Programming Optimization Framework for Isolated Hybrid Microgrids in a Rural Context: The Case Study of the “El Espino” Community. Energy 2019, 188, 116073. [Google Scholar] [CrossRef]
  47. Li, W.; Chien, F.; Hsu, C.C.; Zhang, Y.Q.; Nawaz, M.A.; Iqbal, S.; Mohsin, M. Nexus between Energy Poverty and Energy Efficiency: Estimating the Long-Run Dynamics. Resour. Policy 2021, 72, 102063. [Google Scholar] [CrossRef]
  48. Abbas, K.; Xu, D.; Li, S.; Baz, K. Health Implications of Household Multi-dimensional Energy Poverty for Women: A Structural Equation Modeling Technique. Energy Build. 2021, 234, 110661. [Google Scholar] [CrossRef]
  49. Apergis, N.; Polemis, M.; Soursou, S.E. Energy Poverty and Education: Fresh Evidence from a Panel of Developing Countries. Energy Econ. 2022, 106, 105430. [Google Scholar] [CrossRef]
  50. Acheampong, M.; Ertem, F.C.; Kappler, B.; Neubauer, P. In Pursuit of Sustainable Development Goal (SDG) Number 7: Will Biofuels Be Reliable? Renew. Sustain. Energy Rev. 2017, 75, 927–937. [Google Scholar] [CrossRef]
  51. Reid, A.J.; Brooks, J.L.; Dolgova, L.; Laurich, B.; Sullivan, B.G.; Szekeres, P.; Wood, S.L.R.; Bennett, J.R.; Cooke, S.J. Post-2015 Sustainable Development Goals Still Neglecting Their Environmental Roots in the Anthropocene. Env. Sci. Policy 2017, 77, 179–184. [Google Scholar] [CrossRef]
  52. Quitoras, M.R.; Campana, P.E.; Rowley, P.; Crawford, C. Remote Community Integrated Energy System Optimization Including Building Enclosure Improvements and Quantitative Energy Trilemma Metrics. Appl. Energy 2020, 267, 115017. [Google Scholar] [CrossRef]
  53. Gibellato, S.; Ballestra, L.V.; Fiano, F.; Graziano, D.; Luca Gregori, G. The Impact of Education on the Energy Trilemma Index: A Sustainable Innovativeness Perspective for Resilient Energy Systems. Appl. Energy 2023, 330, 120352. [Google Scholar] [CrossRef]
  54. Lacea, J.; Querikiol, E.; Taboada, E. Balancing Energy Trilemma Using Hybrid Distributed Rooftop Solar Pv (Drsp)/Battery/Diesel Microgrid: A Case Study in Gilutongan Island, Cordova, Cebu, Philippines. Energies 2021, 14, 7358. [Google Scholar] [CrossRef]
  55. Zafeiratou, E.; Spataru, C. Sustainable Island Power System—Scenario Analysis for Crete under the Energy Trilemma Index. Sustain. Cities Soc. 2018, 41, 378–391. [Google Scholar] [CrossRef]
  56. Sawle, Y.; Gupta, S.C.; Bohre, A.K. Socio-Techno-Economic Design of Hybrid Renewable Energy System Using Optimization Techniques. Renew. Energy 2018, 119, 459–472. [Google Scholar] [CrossRef]
  57. Roy, D.; Hassan, R.; Das, B.K. A Hybrid Renewable-Based Solution to Electricity and Freshwater Problems in the off-Grid Sundarbans Region of India: Optimum Sizing and Socio-Enviro-Economic Evaluation. J. Clean. Prod. 2022, 372, 133761. [Google Scholar] [CrossRef]
  58. Dufo-López, R.; Cristóbal-Monreal, I.R.; Yusta, J.M. Optimisation of PV-Wind-Diesel-Battery Stand-Alone Systems to Minimise Cost and Maximise Human Development Index and Job Creation. Renew. Energy 2016, 94, 280–293. [Google Scholar] [CrossRef]
  59. Hassan, R.; Das, B.K.; Hasan, M. Integrated Off-Grid Hybrid Renewable Energy System Optimization Based on Economic, Environmental, and Social Indicators for Sustainable Development. Energy 2022, 250, 123823. [Google Scholar] [CrossRef]
  60. Singh, P.; Pandit, M.; Srivastava, L. Techno-Socio-Economic-Environmental Estimation of Hybrid Renewable Energy System Using Two-Phase Swarm-Evolutionary Algorithm. Sustain. Energy Technol. Assess. 2022, 53, 102483. [Google Scholar] [CrossRef]
  61. Nussbaumer, P.; Bazilian, M.; Modi, V. Measuring Energy Poverty: Focusing on What Matters. Renew. Sustain. Energy Rev. 2012, 16, 231–243. [Google Scholar] [CrossRef] [Green Version]
  62. IUCN. Species and Climate Change. Available online: https://www.iucn.org/sites/default/files/2022-04/species_and_climate_change_issues_brief-2019-12.pdf (accessed on 17 December 2022).
  63. Courchamp, F.; Hoffmann, B.D.; Russell, J.C.; Leclerc, C.; Bellard, C. Climate Change, Sea-Level Rise, and Conservation: Keeping Island Biodiversity Afloat. Trends Ecol. Evol. 2014, 29, 127–130. [Google Scholar] [CrossRef] [PubMed]
Energies 16 01328 g001 550
Figure 1. Share of rural and urban population with access to electricity (2020). Own work based on data from: [6,7].
Figure 1. Share of rural and urban population with access to electricity (2020). Own work based on data from: [6,7].
Energies 16 01328 g001
Energies 16 01328 g002 550
Figure 2. Share of population with access to electricity by country (2020). Own work based on data from: [23].
Figure 2. Share of population with access to electricity by country (2020). Own work based on data from: [23].
Energies 16 01328 g002
Energies 16 01328 g003 550
Figure 3. Model considerations based on SDG7: Relation between ETI driving forces (left) and model equations (right).
Figure 3. Model considerations based on SDG7: Relation between ETI driving forces (left) and model equations (right).
Energies 16 01328 g003
Table
Table 1. Relevant studies surrounding HRES.
Table 1. Relevant studies surrounding HRES.
ReferenceOptimization
Approach		Analyzed TechnologyObjective Function(s)Location
PVWind TurbineHydroDieselFuel CellStorage and Other
[37]HOMER● ●●●Minimize LCOE, NPC, and CO2 emissionSarjah, UAE
[38]CS●● ●Minimize total system costUttarakhand, India
[39]HOMER●● ● ●Minimize TNPC, COE, Capital CostsGuajira, Choco, Boyaca, Colombia
[40]PSO●● ●●Minimize COE, LPSPChetumal city, Mexico
[41]HOMER●● ● ●Minimize NPC, LCOE, CO2 emissionBizerte, Tunisia
[42]PSO-GWO●● ● ●Minimize COE, DPSPBihar, India
[43]GA●● ● ●Minimize COE and LLPPalestine
[44]MILP●● ●Minimize total system costSouth-Tyrol, Italy
[45]HOMER ● ●Minimize NPC, COE, OC, ICC, Maximize EG and RFPunjab, India
[46]LP●●●● ●Maximize NPC“El Espino”, Bolivia
Table
Table 2. Description of modeling challenges and perspectives discussed with respect to the associated themes.
Table 2. Description of modeling challenges and perspectives discussed with respect to the associated themes.
ThemeChallengePerspective
Technological AdvancementsAccurate representation of new technologies in HRESNew advancements in computational techniques/formulations
Energy PovertyProvide sustainable, clean rural electrificationNew advancements in model formulations that include social benefit factors
Optimization ApproachesComputational time, model complexityHybridization of computational approaches (Machine Learning, LP, heuristics, etc.)
Climate ChangeMinimizing carbon emissions, and reduction in environmental degradation, and biodiversity lossNew advancements in model formulation that alleviate ecological issues caused by climate change
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Natividad, L.E.; Benalcazar, P. Hybrid Renewable Energy Systems for Sustainable Rural Development: Perspectives and Challenges in Energy Systems Modeling. Energies 2023, 16, 1328. https://0-doi-org.brum.beds.ac.uk/10.3390/en16031328

AMA Style

Natividad LE, Benalcazar P. Hybrid Renewable Energy Systems for Sustainable Rural Development: Perspectives and Challenges in Energy Systems Modeling. Energies. 2023; 16(3):1328. https://0-doi-org.brum.beds.ac.uk/10.3390/en16031328

Chicago/Turabian Style

Natividad, Lauren E., and Pablo Benalcazar. 2023. "Hybrid Renewable Energy Systems for Sustainable Rural Development: Perspectives and Challenges in Energy Systems Modeling" Energies 16, no. 3: 1328. https://0-doi-org.brum.beds.ac.uk/10.3390/en16031328

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop