Empowering Remote and Off-Grid Renewable Energy Communities: Case Studies in Congo, Australia, and Canada

Abstract

This paper aims to explore the feasibility of establishing self-sufficient electricity generation systems in off-grid remote communities using renewable energy sources. It provides an overview of current trends and developments in Renewable Energy Communities worldwide, with a focus on remote locations. To assess the technical feasibility, simulations were conducted using PVGIS for irradiation data and a load generator for energy consumption data. Different scenarios combining photovoltaic modules with lithium-ion battery systems were simulated using a dedicated optimization model developed in the PYTHON environment. The simulations aimed to size the entire system for three distinct locations: Congo, Australia, and Canada. The optimal number of PV modules determined for each location was 50 for Congo and 55 for Australia, and the battery system sizes were found to be 225 kWh and 150 kWh, respectively, admitting usual practices for the energy not supplied. The results obtained regarding Canada pointed to a system difficult to justify from an economic standpoint due to challenging weather conditions, namely, the existence of several consecutive days without irradiation.

1. Introduction

One of the biggest challenges of the 21st century, or even human history, is global warming and climate change. Global warming has significant consequences for the environment, such as disruption of ecosystems, biodiversity loss, increases in extreme weather events, ocean acidification, melting ice caps, and rising sea levels, among others. The usage of renewable energy sources (RESs) as an alternative seems essential to avoid disaster.

Since its widespread adoption in the late 19th and early 20th centuries, electricity has revolutionized the way energy is used. Electricity enabled the development of lighting, communication systems, appliances, and powered machinery. It became the backbone of modern society and fueled advancements in technology, industry, and infrastructure. Unfortunately, the electric sector has a great responsibility for the global warming crisis, since a large share of it is still produced by fossil fuels [1]. Electricity production from renewables seems therefore to be one of the keys to the success of the ongoing energy transition, more so given human dependence on electricity.

Indeed, nowadays, and especially for the new generation, having access to the electricity grid seems normal. For over 40 years, the final consumption of electricity in the world has not stopped growing, reaching an amount of 22,848 TWh in 2019 [2]. Furthermore, it is safe to say that individuals have developed a strong reliance on electricity, as a quick observation of their daily routines would easily demonstrate. Individuals can effortlessly illuminate their surroundings whenever needed and swiftly charge all their electronic devices. Beyond personal convenience, the vast majority of vital sectors in contemporary society, such as industry, housing, communication, and healthcare, heavily rely on electrical power. Moreover, the residential sector represents more than 25 per cent of the final electrical consumption [2].

The residents of developed countries find it effortless to access basic amenities, leading them to perceive this as the norm, although this is far from reality. In fact, there are still today around 770 million people who live without access to electricity, with two-thirds of them residing in Africa [3]. Unfortunately, the COVID-19 pandemic did not help, and even if the number of people without access to electricity has decreased in recent years, it is foreseen that this number will stagnate in the future, population growth being one of the reasons. Those people often live in remote communities, which makes access to energy very difficult for them.

Therefore, the problem that humanity has two faces: people need to have easier access to electricity, but to avoid a climate disaster, this electricity needs to come from clean energy sources.

Several methodologies exist in the literature for planning off-grid Renewable Energy Communities (RECs), each offering distinct advantages and limitations. Techno-economic models, such as HOMER and RETScreen, are widely used for optimizing system sizing and costs by integrating renewable energy sources like solar and wind. These models are data-intensive and primarily focus on cost-efficiency, potentially neglecting socio-environmental considerations. In contrast, participatory approaches like Participatory Rural Appraisal (PRA) involve local communities in the planning process, leading to more socially equitable and culturally relevant outcomes, though these methods tend to be slower. Sustainability-focused approaches emphasize long-term environmental and social benefits, ensuring that energy systems contribute to local development. However, they may result in higher initial costs. Additionally, geospatial planning models, such as GIS-based Multi-Criteria Analysis (GIS-MCA), use spatial data to optimize the placement of renewable energy systems, though they are constrained by the availability of high-quality geographic information.

This paper aims to find out how a remote and off-grid community could produce its own electricity from RESs to fulfil its needs. It gives an overview of the current trends in energy production and storage that could help to develop Renewable Energy Communities (RECs) in different remote places of the world, with case studies in Congo, Australia, and Canada. Furthermore, the present study investigates the optimal configuration for the hybrid supply energy system that combines photovoltaic (PV) panels and lithium-ion batteries. The relevant data are collected from the Photovoltaic Geographical Information System (PVGIS) platform and a load generator for the solar irradiation and energy consumption data, respectively. A first approximation of the number of panels required to supply the energy consumed is based on a simple model. This result is used as an initial guess in a subsequent iterative method to find the optimal system configuration. The process involves the simulation of diverse scenarios with different configurations using the PYTHON environment.

This study introduces a cross-continental approach to off-grid renewable energy systems, with case studies from Congo, Australia, and Canada. Although renewable energy solutions have been explored in many regions, our research is one of the first to compare these diverse regions, specifically addressing the lack of research on Sub-Saharan Africa’s remote communities. By comparing the solar PV systems’ performance across these different regions, this study provides valuable insights into the benefits and feasibility of using solar PV to empower remote locations. The analysis reveals that solar PV systems hold considerable promise for alleviating energy poverty, especially in regions with high solar irradiance levels like Congo and Australia. However, the effectiveness of such systems also depends on the local climate, infrastructure, and socio-economic factors. This comparative analysis offers a first estimation of the potential for solar PV deployment in off-grid communities and highlights the importance of adapting solutions to the specific conditions of each region.

The primary objective of this paper is to assess the technical feasibility of implementing the hybrid energy system (PV plus batteries) to supply the electricity needs of an off-grid REC. Additionally, it examines and evaluates the reproducibility of the developed model across various locations (Congo, Australia, and Canada) characterized by distinct and specific conditions. Furthermore, it compares the energy system requirements for each community, enabling a comprehensive assessment of their individual needs.

This document has the following structure. In Section 2, besides the definition of an REC, a literature review of the current RESs and the ways of storing this energy are presented. In Section 3, the model and methodology used to size the renewable energy systems are described. Section 4 presents and discusses the results obtained and compares the studied locations. Finally, a conclusion addressing the impact of the discoveries and referring to what has been learned during this project is made in Section 5.

2. Literature Review

2.1. What Is a Renewable Energy Community?

At first, an energy community was described as “Energy communities that organize collective and citizen-driven energy actions that help pave the way for a clean energy transition while moving citizens to the fore” [4]. However, nowadays the concept has started to be used more to describe a community or ecosystem of people with several houses. Those people pool their energy production and storage resources to make financial savings and to have access to different and sometimes more efficient technologies [5]. The scale of energy transactions within the ecosystem surpasses those at the individual household level, enabling the establishment of a larger infrastructure and the exploration of new energy sources. For instance, a small-scale hydroelectric plant may not be suitable for powering a single house, but it becomes a viable option when serving a community. This concept can also be applied to storage technologies. Several studies have shown the technological and economic benefits of an REC (see for instance [6]).

Although in the beginning the main interest was economic, more and more similar projects are emerging to help with the energy transition [7]. Moreover, sometimes these communities just do not have the choice. Indeed, some of them live remotely and do not have access to the public power grid. Therefore, they are autonomous and themselves need to produce the energy they need. The term “renewable” only indicates the type of sources used and the desire of the community to aim towards clean energy. According to the United Nations, “renewable energy is energy derived from natural sources that are replenished at a higher rate than they are consumed. Sunlight and wind, for example, are such sources that are constantly being replenished” [8]. Coal, oil, and gas are not considered as such.

A renewable energy community is therefore a group of people, organizations, or businesses that collaborate to generate and consume renewable energy locally. The community may be organized around a shared goal of reducing carbon emissions and promoting sustainable energy, or it may simply be a group of individuals who have come together to install renewable energy systems for their own benefit [7]. RECs can take various forms, depending on their goals and the resources available to them. They may include residential neighborhoods, rural villages, or urban districts, and they may be organized as cooperatives, non-profits, or for-profit entities.

The primary objective of an REC is to generate clean energy through renewable sources such as solar, wind, and hydroelectric power. Members of the community may work together to install and maintain renewable energy systems, or they may purchase energy generated by a shared system. Some renewable energy communities may also incorporate energy storage technologies to ensure a reliable supply of power. In addition to the environmental benefits of renewable energy, an REC can also offer economic and social benefits. By producing energy locally, the community can reduce dependence on imported fossil fuels, create jobs within the sector, and benefit from the financial outcomes within the local economy. An REC can also foster a sense of community and shared responsibility among its members, as they work together towards a common goal of being sustainable.

2.2. Current Energy Sources for RECs

Most of the remote and off-grid communities still lack energy. Traditionally, the only way to meet their electricity and heating demands is using diesel generation, an analysis of which has been included in several case studies [9,10,11,12,13]. Indeed, diesel has several advantages that make it very attractive: it has a high energetic potential (1 L of diesel corresponds to around 10 kWh), is easily transported, and is low cost to produce (at least before the current war in Ukraine). Unfortunately, diesel has significant environmental impacts. Diesel usage emits between 2.4 and 2.8 kg CO₂/L and other greenhouses gases (GHGs) [14]. Other than the environmental impacts, the option of diesel is very costly due to the high cost of transport and delivery to remote communities. Finally, the diesel generator contributes to the dependence of the community on an external supply.

For those reasons, a lot of work and research has been conducted to find alternative ways to supply energy to the community. Currently, RESs have gained a lot of attention due to their significantly reduced CO₂ emissions. Many case studies have been made on this subject and, although they concern different places of the world (Canada [9], Italy [10,15,16,17], Nigeria [18], Amazon [11], Australia [13]), some general conclusions can be drawn.

Firstly, wind and especially solar seem to be the best options to produce the required energy [19]. In fact, solar energy, using PV, was always described in every case study as the main source, regardless of the location. This trend of using more solar and wind in the communities follows the global evolution of RES development. Indeed, solar PV is the fastest-growing technology, followed by wind energy [20].

Gandiglio et al. address the advantages of PV over diesel in their case study of Ginostra, Italy [10]. The configuration of their renewable energy system consists of 170 kWp of PV and 600 kWh of lithium-ion battery storage coupled with a hydrogen storage system (50 kW alkaline electrolyzer and 50 kW proton-electron membrane fuel cell). However, a diesel generator of 47 kW is still needed as a backup.

Mokthara et al. [19] confirmed the importance of PV in a hybrid energy system. They studied the optimal design of off-grid hybrid renewable energy systems for residential electrification in arid climates. Using an in-house MATLAB code for modeling the system, and after using HOMER software to validate the model, they found that PV combined with a lithium-ion battery is the best configuration.

For other climates, such as in the United Kingdom (UK), wind energy is more important. In their analysis, Miao et al. [21] explore several hybrid power systems for a household in the UK using HOMER. The result is a wind turbine coupled with battery units and a biogas genset. The latter is an ecological alternative to the diesel generator.

The choice of using mostly PV and wind energy can be easily explained. On the one hand, those two resources are the ones with the lowest levelized cost of energy (LCOE). The LCOE measures the average net present cost of electricity generation for a generator over its lifetime; therefore, the lower the better. In recent years, the LCOE of PV and wind have sharply decreased, which has made them both very attractive solutions.

Sánchez et al. [11] show that PV and wind systems often have higher capital costs than diesel generators, but their operating costs and maintenance needs are lower. Additionally, those technologies are also capable of producing a wide range of energy scale. For PV, the use can be from pocket calculators or batteries with a power between 0.1 and 1 kW to utility-scale PV parks with powers of tens of megawatts, passing through tens to hundreds of kilowatts, which is what interests us in our REC case. Wind energy also exhibits a wide energy range, with powers between 500 W and 10 kW when applied in RECs. The combination of wind and solar energy allows the production of energy over a whole year in many places across the globe.

Samy et al. [22] studied the comparison between such a hybrid system and a single PV or wind configuration for the electrification of a community in Egypt. Using several optimization techniques, they demonstrate that a wind–PV combined system shows a lower total LCOE. The same conclusion was drawn by Babaei et al. [23] in Canada, even despite the much lower irradiation.

However, PV and wind are both intermittent sources of energy. Indeed, they do depend on the weather and cannot always produce energy on demand (unlike hydro and nuclear energies). Case studies conducted in Nigeria showed that the maximum reachable percentage of electricity supplied by combined PV–wind sources without storage and that was economically viable was 95% [18]. This introduces the need for energy storage systems, which will be explained further in this paper.

The second conclusion that can be drawn from the literature review is that two other technologies have been operating in RECs: small hydro plants (SHPs) and biomass. Of course, their utilization will depend on the location.

An SHP (a hydro plant with a capacity lower than 10 MW) is very interesting because it only uses water, which can also be a storable resource. However, it requires a water flow (from a river for example) with a large enough head (difference between the higher and lower elevations). Gbalimene Richard Ileberi and Pu Li studied the integration of hydrokinetic energy into a hybrid renewable energy system in an off-grid community in Nigeria [24]. The hydro energy was used to compensate for the intermittence of solar and wind energy and to use less diesel. Using a genetic algorithm, the most efficient configuration found was 320 kWp of PV panels, 120 units of batteries with a capacity of 6.91 kWh each, two hydrokinetic turbines with a power output of 27 kW, 120 kW converters, and no wind turbines. However, a diesel generator with a power capacity of 100 kW was still required. Unfortunately, those resources are not available everywhere on the planet and therefore SHPs for remote communities are only operating in some parts of the world, like in the Amazon [11] and in Canada [9]. The same can be said for PV and wind in terms of weather constraints; however, these are still widely used due to the lower LCOE.

Biomass is another source of energy used in some cases; it is mainly utilized in the Amazon, Canada, and Nordic countries. Furthermore, biomass represents 55% of the renewable energy supply, making it the largest supplier globally [25]. Bioenergy is less attractive in RECs to produce electricity due to price and size constraints but is still one of the main alternatives for heating purposes. Since biomass could help to reduce waste, it remains a very interesting alternative. For instance, Sarker [26] used HOMER to demonstrate the feasibility of a system including bioenergy to compensate for the intermittence of wind and solar energy in the south of Norway. A comparison with mono-energy systems showed the essential roles of bioenergy, batteries, and capacitors in communities in this area.

To summarize what can be found in the literature, one could say that there is currently a trend for the uptake of PV and wind turbines in renewable energy communities. Solar power is the most widely used source, mainly due to its ease of installation and ability to generate energy on various scales. Many communities install solar panels on rooftops or on the ground to generate electricity for their own consumption. Additionally, community solar projects allow residents to purchase or lease solar panels located offsite and receive credits on their electricity bills for the energy generated.

Wind power is also commonly used in renewable energy communities, particularly in areas with high wind speeds. Wind turbines can be installed individually or in groups and can generate significant amounts of electricity. Community wind projects can also allow residents to invest in or own a share of the wind turbines, providing financial benefits and local ownership. Depending on the place in the world, PV and wind technologies can be combined with a small hydro plants or biomass to produce the rest of the electricity required.

2.3. Energy Storage Possibilities

RESs, especially PV and wind, face the issue of intermittence. Indeed, PV energy depends on irradiation and wind energy on the wind. This way, the energy production does not always match the demand. Therefore, energy storage is essential and crucial in the energy transition. Other than overcoming the RES intermittency and balancing the load, storage systems allow grid stability, reduce transmission and distribution constraints, and enhance RES penetration. Remote RECs particularly need it because they are off-grid and rely on decentralized energy systems. A lot of research has been performed in recent years on this subject and several storage technologies have been developed. They are often classified as mechanical, electrochemical, electrical, chemical, and thermal energy storage. However, not all of them fit the criteria required for an REC. The following discussion will not encompass thermal and electrical storage technologies as they are primarily utilized in different applications. The main focus will therefore be on chemical (hydrogen), electrochemical (batteries), and mechanical (flywheel) storage. Those three technologies convert the electrical into chemical or kinetic energy. This is very interesting since the energy produced by PV or wind is electrical as well, which makes the process more efficient overall.

2.3.1. Hydrogen-Based Energy Storage

Recent studies have shown that hydrogen could be very interesting to store energy. Indeed, hydrogen has advantages such as long-term storage capabilities, a high storage density, is adapted to various geographical locations, is well-suited for decentralized applications, and produces minimal GHG emissions [27]. In a power-to-power (P2P) system that relies on hydrogen, the excess renewable energy generated can be utilized for water electrolysis to produce hydrogen when there is no immediate demand for power [28]. Hydrogen can be stored for long periods of time, as has been mentioned above. Later, the stored hydrogen can be converted back into electricity by employing fuel cells. For instance, in their study, Raimondi and Spazzafumo [16] explore the usage of hydrogen as a means of P2P in Italy. The result is an increase of 15% in the energy autonomy of the community. Nevertheless, this solution is not yet economically viable.

Indeed, when it comes to price competitiveness, there are several factors to consider. Currently, hydrogen production through electrolysis is considered relatively expensive compared to other energy storage methods. The cost of hydrogen production is influenced by the electricity prices and the capital costs of electrolyzers [29]. Moreover, the infrastructure required to store hydrogen poses a challenge. Hydrogen exhibits high gravimetric density, meaning it has a relatively high energy content per unit mass. However, it has low volumetric density, indicating that it requires a large volume to store it in gaseous form. Two possible solutions to overcome this limitation are compressing hydrogen into pressurized tanks, sometimes employing cryogenic compression, or liquefying hydrogen. However, both options come with significant cost implications, rendering them expensive alternatives [30].

2.3.2. Electrochemical Energy Storage

Batteries convert chemical energy to electrical energy thanks to redox reactions occurring in the electrode materials. Electrochemical energy storage covers different types of secondary batteries. The secondary batteries are electrochemical devices that can be consecutively charged and discharged since the redox reactions in the electrode materials are quasi-reversible. Therefore, they are also called rechargeable batteries, in contrast to primary batteries, which are disposable. There are plenty of types of secondary batteries, such as lead-acid, nickel-cadmium, nickel-metal hydride, and lithium-ion (which include different types themselves) [31].

Because of their different features, they are used in different applications. Lead-acid batteries have high operating safety and are mostly operated in the automotive industry and tractions applications, in uninterruptible power supplies (UPS), or medium to large grid-scale energy storage. This technology is very mature and nowadays lead is effectively recycled. Moreover, the cost of these batteries is low and they have good power density. The main disadvantage is the modest energy density [32].

Nickel-cadmium batteries are found in motorized equipment, emergency lighting, or toys. Since cadmium is toxic, metal-hydride has started replacing it in portable products. Moreover, nickel is difficult to extract and to recycle; it is therefore classified as critical raw material with an increasing price [33]. As the aim of an REC is to be more sustainable, so nickel-based batteries are not envisaged to be used.

Lithium-ion batteries are present in nearly all electric vehicles due to their high energy density and increased power density compared to lead-acid batteries. However, they are very costly. Different types of batteries containing lithium have started to emerge, such as lithium-organic or lithium-air, which would be both easy to recycle and have an excellent carbon footprint [34,35]. For applications such as in an REC, mid-scale to large-scale applications are needed. Therefore, lead-acid and lithium-ion have been mostly investigated in the literature.

A recent research paper on optimizing and sizing a stand-alone hybrid energy system coupled with various battery technologies on Pelee Island (Canada) gives a comparison between battery storage technologies [23]. Three of the scenarios considered are a 1 kWh lead-acid battery, a 1 kWh lithium-ion battery, and a 100 kWh lithium-ion battery. For each of the scenarios, three schemes with different energy sources are envisaged. Only one of them does not use a diesel generator and is therefore interesting for this paper. From an economic standpoint, the study shows that the second scenario, including the 1 kWh lithium-ion battery, is the best. Indeed, it has the lowest LCOE and NPC (net present cost). The same conclusion was confirmed by other studies focusing on stationary storage systems [36,37]. Moreover, Kebede et al. [36] show that lithium-ion batteries have longer lifetime characteristics and require fewer units (around 40% less) of batteries than lead-acid batteries for the same output.

2.3.3. Promising Storage Technologies

Three other technologies seem to have a bright future in storage systems for microgrids: long-duration flywheels, redox flow batteries, and supercapacitors.

Flywheels are a mechanical technology that has gained significant attention recently. Indeed, in comparison with batteries, they have higher power capacity and lower cost per power capacity. Flywheels use electrical energy to spin a rotor at very high speed and the energy is then retrieved by slowing the rotor. Since it is chemical-free, flywheel energy storage systems (FESSs) have less environmental impact. They also have a fast response time and long cycle lives [38]. The main issue remains the rapid loss of capacity over time, but a long-duration flywheel can cope with that. For example, Hitachi Energy installed two flywheels of 1 MW each on Kodiak Island in Alaska [39]. This island is inhabited by around 15,000 people and intends to run entirely on renewable energy. The flywheels are combined with a battery system, which could be a promising solution for the future. Indeed, the FESS could protect the batteries from regular charging and discharging, extending their lifetime. This approach improves performance and reduces overall costs. In fact, integrating an FESS into batteries could considerably slow the battery ageing process by a factor of 300% [40]. However, a techno-economic comparison of long-duration flywheels, lithium-ion batteries, and lead-acid battery energy storage technologies for isolated microgrid applications showed that long-duration flywheels have low chances of gaining some market share based on their LCOE and the rapid price decrease of lithium batteries [41].

Redox flow batteries are a type of electrochemical energy storage device used for relatively large (1 kWh–10 MWh) stationary energy storage applications. They differ from conventional batteries and offer certain advantages; for example, power and energy can be set independently. Three common types of flow batteries are polysulfide bromine, vanadium, and zinc-bromine redox flow batteries. These flow batteries have specific technical characteristics within certain ranges. The specific energy of flow batteries ranges from 10 to 35 Wh/kg, the specific power ranges from 100 to 160 W/kg, and the energy efficiency is between 70% and 85% [42]. Those first features are not particularly interesting. However, redox flow batteries have two main advantages. First, the lifespan is approximately 15 years, with around 20,000 cycles. This long lifespan is due to the presence of reversible electrochemical reactions in the anolyte and catholyte side over a carbon support [42]. They can therefore be fully charged and discharged without hampering the electrode materials. Second, their self-discharge rate is close to zero [37]. These technical features make flow batteries advantageous for stationary storage applications due to their low self-discharge, long service life, and fast response characteristics. The principle of a redox flow battery is simple. They utilize two tanks of electrolyte, with one tank being positively charged and the other negatively charged. These tanks are separated by electrodes and a membrane. The difference in the chemical oxidation of species between the two tanks creates a flow of ions and electricity across the membrane. These batteries are primarily designed for large-scale grid storage purposes. They are capable of efficiently storing significant amounts of energy, surpassing many other technologies in terms of effectiveness. To increase the battery’s capacity, it is possible to add more electrolyte [43]. The most important disadvantage is that parts, such as pumps and pipelines, are susceptible to corrosion.

A supercapacitor is an energy storage device that can rapidly store and release electrical energy. It operates based on the principles of electrostatic charge storage [37]. It consists of electrodes made of highly porous materials and carbon, separated by an electrolyte. When a voltage is applied, an electrostatic double layer forms at the electrode–electrolyte interface, storing energy in the form of separated charges. Supercapacitors can store a larger amount of charge compared to traditional capacitors due to the higher surface area of the used carbon materials. During charging, ions from the electrolyte are attracted to the electrode surfaces, storing energy. During discharge, the stored energy is released as charges neutralize and ions return to the electrolyte [44]. Supercapacitors show interesting features such as a long lifespan, sustaining millions of cycles of charge, discharge near room temperature, and an efficiency surpassing 95%. Although their energy density is significantly lower than lithium-ion batteries, their specific power output is significantly higher. This characteristic makes them particularly well-suited for applications demanding high power density and rapid energy transfer [45]. Nevertheless, the relatively high cost of supercapacitors poses a challenge, which limits their feasibility in remote communities.

To sum up, lithium-ion batteries seem to be the best option at the moment. However, a hybrid storage system combining flywheels and batteries could be interesting since it would increase the lifespan of the battery. Moreover, redox flow batteries are promising for the future due to their long lifespan and large capacity.

3. Models and Methods

3.1. Definition of the Energy Community Needs

To define the community’s needs, the different electricity consumption profiles of the houses have been set up using a load profile generator [46]. The community chosen is assumed to be outside of the city, meaning that it will be constituted mostly of families with children and of older and retired people. For instance, young workers and students in a shared flat will not be considered. The focus will be on the electricity. The used load profile generator separates the electricity needs from the cold and hot water; however, the latter will not be considered.

Table 1. Electrical consumption profiles of each house and total electricity consumption.

Description	Consumption (kWh)
Couple both at work	2623.2
Family, 1 child both at work	2613.5
2 families with 3 children each, all parents at work	8002.5
Multigenerational home: working couple, 2 children, 2 seniors	8179.5
Couple over 65 years old (retired)	3700.7
Family, 2 children, 1 at work, 1 at home	4224.7
Family, 2 children, both at work	4312.3
Total	33,656.4

shows the different consumption profiles and the houses’ resident information. All the data come from the load profile generator [46].

Figure 1 represents the evolution of the electricity consumption for the whole community in kWh over a year, a random week, and a random day, respectively. The curves were obtained by summing up all the houses’ individual profiles.

3.2. Sizing the RES System to Supply the Required Energy

PV energy was selected as the energy production technology based on the literature review findings.

3.2.1. Number of Modules

The first step is to compute the area occupied by the PV installation. One needs the average efficiency of the PV modules and the average solar irradiation. The first one can be approximated at 14% for a crystalline silicon cell, as suggested by the PVGIS tool [47]. Indeed, current PV technologies have efficiencies ranging between 15 and 20 percent, but the efficiency of the power electronics, namely the inverter, needs to be considered. The irradiation depends directly on the location. To have a good comparison, the choice of three places with different irradiation levels was most appropriate. Moreover, as the objective is to supply remote and off-grid communities, the localization cannot be cities. The decision was made based on three criteria: a location needs to have a very low population density [48], be far from a big city, and have a different irradiation level than the two other chosen locations. The three places chosen with those criteria and that will be used further in this study are in Congo, Australia, and Canada.

The PVGIS software gives information about solar irradiation for different locations on the globe and was used to determine the average irradiation in each selected location. The retrieved results are 1782, 2854, and 1264 kWh/m² per year, for Congo, Australia, and Canada, respectively [47]. It is assumed that the total area occupied by the PV installation doubles the useful area of the PV modules [49]. Indeed, the shadow that the modules cast on each other needs to be minimized and therefore they need to be spaced out. Finally, the choice of a typical panel with a peak power of 500 Wp, a derate factor of 0.85, and an area of 2 m² allows us to determine the number of modules that should be used.

Using the following simple model (Equations (1)–(4)) [49], the results in

Table 2. First approximation of the number of panels required for each location.

Location	Coordinates	${\mathit{H}}_{\mathit{i}}^{\mathit{a}}$	${\mathit{E}}_{\mathit{P}\mathit{V}}$	${\mathit{A}}_{\mathit{u}}$	${\mathit{A}}_{\mathit{t}}$	${\mathit{N}}_{\mathit{P}\mathit{V}}$
		(kWh/m2)	(kWh/m2)	(m2)	(m2)
Congo	(2.482, 17.579)	1782	250	135	270	67
Australia	(−24.151, 114.583)	2854	400	84	168	41
Canada	(67.102, −127.800)	1264	177	190	380	95

can be obtained. It is highlighted that this simple model is used to obtain a first approximation of the number of modules to be used. Further refinements will be made in the sequence.

$$E_{PV}=H_i^a{\eta }_{PV}$$

(1)

$$A_u={\displaystyle \frac{E_d}{E_{PV}}}$$

(2)

$$N_{PV}={\displaystyle \frac{A_u}{A_{PV}}}={\displaystyle \frac{A_u}{2}}$$

(3)

$$A_t=2A_u$$

(4)

where $E_{PV}$ is the specific annual energy produced by the PV modules (kWh/m²), $H_i^a$ is the annual solar irradiation (kWh/m²), ${\eta }_{PV}$ is the average PV modules efficiency, $A_u$ is the useful area occupied by the PV modules (m²), $E_d$ is the annual energy demanded by the consumers (kWh), $N_{PV}$ is the number of PV modules, $A_{PV}$ is the area of each PV module (2 m²), and${ A}_t$ is the total area occupied by the PV installation (m²).

Equation (2) represents an idealized case where solar PV systems meet all energy demand. This simplified approach was employed to provide a preliminary assessment of the spatial requirements for solar PV panels needed to power the community. The intent was to establish a baseline for evaluating the feasibility of solar energy in isolation, which helps in understanding at what point solar PV might be practical. In practice, integrating other renewable energy sources, such as wind, diesel, or hydropower, could reduce the required space for PV panels, leading to a more efficient and comprehensive energy solution. This approach allows for a preliminary evaluation of the requirements before considering hybrid systems.

From those results, it can easily be explained and understood why PV are so attractive. Even for areas with low irradiation (Canada, for instance), only 380 m² of PV modules would be required to supply enough energy. Of course, those first results are based on some simplifying assumptions. However, they provide a good first estimate of the number of modules that should be used in each location and will be used as starting point for the next phase of the model.

3.2.2. Hourly Energy Production

The hourly produced energy, $E_{hj}$, in each hour $j,$can be calculated using Equation (5) [49].

$$E_{hj}={\displaystyle \frac{H_{ij}^h}{G^r}}{P}_p{f}_{derate}{N}_{PV}$$

(5)

where $H_{ij}^h$ is the hourly solar irradiation in hour $j$, $G^r$ equals 1000 W/m² and is the irradiance under standard test conditions (a cell temperature of 25 °C and irradiance equal to 1000 W/m²), $P_p$ is the peak power (500 Wp), and $f_{derate}$ is the derate factor (0.85), accounting for external losses, such as soiling, shading, mismatch, wiring, connections, availability, etc. Equation (5) indicates how much electricity is likely to be produced in real-life conditions, taking into consideration all efficiency losses.

The electricity production can be now compared with the electricity consumption on an hourly basis. Figure 2 shows this comparison over a period of one year for each of the locations. For all the locations, the same energy consumption is assumed. This can be a good approximation since, as mentioned above, this consumption represents only the electricity needs and not the cooling and heating services. It can be observed in Figure 2 that Congo has a steady energy production over the course of the year. Contrastingly, Australia and Canada have a peak of production during their respective summers. However, it is less marked in Australia.

As is apparent from Figure 2, Canada experiences very low energy generation towards the end of the year, which is attributed to the extended winter season and the substantially low solar irradiation during this period. This situation suggests that the feasibility of solar PV systems in Canada may be more challenging compared to regions with higher solar exposure. The low energy output indicates that a much larger energy storage system would be required to compensate for the reduced generation during winter months. This consideration is crucial for evaluating the practical implementation of solar PV systems in such environments, where extended periods of low solar availability can significantly affect the overall system performance.

A general trend across all regions indicates that the available energy consistently falls short of meeting the demand throughout the year. This ongoing imbalance suggests that energy storage solutions are essential for addressing the gap between supply and demand. Currently, lithium-ion batteries represent the most practical and effective option for energy storage, providing a reliable means to manage fluctuations in energy generation. However, redox flow batteries are emerging as a promising alternative for the future due to their potential for greater scalability and longer-duration energy storage, which could offer significant advantages for balancing renewable energy resources and meeting energy needs more effectively.

3.2.3. Battery Sizing and Energy Not Supplied

The choice of 1 kWh batteries modules was made since it was shown to be most efficient in different studies (see, for instance, [22]). The storage system will therefore consist of several stacks of those batteries. To be able to match the demand at any time, the battery size needs to be big enough. However, the price of the battery increases with its size, and it would not be efficient to select a very big battery that would be half empty most of the time. The last thing to consider is the state of charge (SOC), which has an impact on the cycle life of the battery. Therefore, to maximize its lifespan and the number of use cycles, a battery needs to stay within certain limits of its state of charge: it cannot be fully discharged nor fully charged. For a lithium-ion battery, a common good practice is to stay between 20% and 80% [50]. Therefore, those are the minimum and maximum limits of the battery state of charge.

Knowing all this, a function depending only on the size of the battery can be created. The output is the stock of the battery, which varies with time. For any moment (in this case any hour, since the data are on an hourly basis), if the production of energy is bigger than the consumption, the excess energy goes to the battery and is added to the previous value of the battery state of charge. In contrast, if the production is lower than the consumption, the energy missing goes out of the battery and is subtracted from the previous value of the battery state of charge. Since the communities are completely off-grid, the function is made so that the battery is never below the minimum level nor above the maximum level.

$$B{S^{\prime }}_j=B{S}_j+B{S}_{j-1} \mathrm{if} E_{hj}>E_{dj} \mathrm{and} 20\%<SO{C}_j<80\%$$

(6)

$$B{S^{\prime }}_j=B{S}_j-B{S}_{j-1} \mathrm{i}\mathrm{f} E_{hj}<E_{dj} \mathrm{a}\mathrm{n}\mathrm{d} 20\%<SO{C}_j<80\%$$

(7)

$$B{S}_0=0$$

(8)

$$BS=B{S}_{8760}$$

(9)

where $B{S^{\prime }}_j$ is the updated battery size at hour $j$, $B{S}_j$ is the initial battery size at hour $j$, $B{S}_0$ is the initial battery size, $BS$ is the final battery size, and $B{S}_{8760}$ is the battery size at hour 8760.

However, another problem may occur: every time there is a surplus of energy (the production of energy is bigger than the consumption) and the battery is already full ($SOC=80\%)$, there is a waste of energy since it cannot be used or sent to the grid.

The energy surplus in hour $j$, $E_{sj},$ is computed by:

$$E_{sj}=E_{hj}-E_{dj} \mathrm{and} SO{C}_j>80\%$$

(10)

where $E_{dj}$ is the demanded energy in hour $j,$ and $SO{C}_j$ is the battery state of charge in hour $j$. The total energy surplus, $E_s,$ is:

$$E_s=\sum _{j=1}^{8760}{E}_{sj}$$

(11)

In fact, this excess could be used for other energy needs since, as mentioned previously, the focus here is on the electrical needs, but there are still the heating and cooling services that need energy. Anyway, both the battery size and the surplus need to be minimized.

Along the year, a certain amount of energy is not supplied to the load. This represents the Energy Not Supplied (ENS). This happens when, for each hour $j$, the battery SOC is equal to the defined minimum of 20%.

$$EN{S}_j=E_{dj}-E_{hj} \mathrm{and} E_{hj}<E_{dj} \mathrm{and} SO{C}_j=20\%$$

(12)

$$ENS=\sum _{j=1}^{8760}EN{S}_j$$

(13)

where $EN{S}_j$ is the energy not supplied at hour $j$ and $ENS$ is the annual energy not supplied.

3.2.4. Optimization of the Number of PV Modules

To determine an estimation of the optimal number of PV modules, an iterative process involving varying the number of PV modules and accordingly computing the energy surplus was performed; the necessary battery size and the energy not supplied was used.

4. Results and Discussion

The results of the approximate optimization process are depicted in Figure 3, where it is shown how the battery size and the energy surplus vary with the number of PV modules, considering only the configurations with $ENS=0$. It can be inferred that the quantity of modules directly impacts the size of the battery and the surplus of energy generated. Indeed, the battery size decreases with the number of panels, contrary to the surplus, which increases. However, the magnitude of this influence also depends on the location.

Moreover, as shown in Figure 3, for certain locations it is the surplus that varies more with the number of modules and for others it is the battery size that will have the bigger variation. The slope of each curve shows how big the influence on the number of PV modules is. Moreover, it can be noticed that from a certain number of modules on, the effects become negligible. These results show that the energy surplus is the most influenced metric in Australia and Canada, while it is the battery size in Congo.

The optimal number of modules is the one that minimizes both the energy surplus and the battery size, represented by the intersection of the curves on the graphs. A summary of the results is offered in

Table 3. Approximated optimal configuration of the system for each location, considering ENS = 0.

Title 1	Congo	Australia	Canada
Number of modules	43	38	74
Battery size (kWh)	1500	2900	7900
Annual energy surplus (kWh)	1643	2350	8014

.

The number of modules can be associated with the irradiation during the year in the different regions. Australia has the highest irradiation and therefore requires fewer modules than Canada or Congo. However, it can be noticed that the number of modules needed in Congo is close to the one in Australia, even though there is much less irradiation (1782 against 2854 kWh/m²). One reason could be that there is certain limit where more sun irradiation becomes useless for the amount of energy demanded.

The storage system is oversized because the energy not supplied was fixed to be zero. The price of it would be difficult to justify. Batteries are used to store energy when the energy production is bigger than the consumption and then supply this stored energy when needed. Therefore, the size of the battery can be directly correlated with the number of days when no energy is produced, i.e., when there is no irradiation at all. Congo and Australia do not have any days without irradiation and therefore need a smaller battery than Canada, which has 24 days without it. Moreover, those 24 days are consecutive, increasing the size of the battery needed.

The lowest surplus is observed in Congo, then in Australia, and finally in Canada. It was found that Congo, Australia, and Canada produce, respectively, 4.88%, 6.98%, and 25.06% (of the energy consumption, assumed equal for the three locations) surplus energy. Moreover, that excess could have been lowered with an increase in the battery size, but then the price would be higher.

As mentioned before, the battery system is oversized because ENS = 0 was imposed. It is interesting to see what the results are, accepting a non-zero ENS, as it is usual in off-grid RES-powered systems and increasing the energy production, through the increase of the number of modules. Based on Figure 3, the new number of PV modules chosen would equal an amount that no longer influences the battery size (50 for Congo, 55 for Australia, and 100 for Canada, not to increase too much the unique Canadian case). The results obtained with those configurations are summarized in

Table 4. Configurations of the RES-powered off-grid system, accepting ENS ≠ 0 (red numbers correspond to the optimal configuration).

Congo (50 PV modules)
Battery (kWh)	Surplus (kWh)	ENS (kWh)	ENS (%)	Time ENS (h)
250	6181	0	0	0
225	6206	790	2.35	18
200	6252	1500	4.46	39
50	8960	15,481	46.00	1822
Australia (55 PV modules)
Battery (kWh)	Surplus (kWh)	ENS (kWh)	ENS (%)	Time ENS (h)
250	17,655	0	0	0
150	17,768	777	2.31	29
100	18,260	2806	8.34	169
50	26,387	9970	29.62	1860
Canada (100 PV modules)
Battery (kWh)	Surplus (kWh)	ENS (kWh)	ENS (%)	Time ENS (h)
7000	19,130	0	0	0
6750	19,180	25,559	75.94	19
6500	19,230	102,409	304.28	79

.

It can be seen that for the locations in Congo and in Australia, increasing the number of modules can considerably decrease the battery size, making the configuration more economically viable. Moreover, the number of hours with ENS is very small. Based on several projects existing today, the maximum value of the ENS accepted is 3% of the total energy demand. This value depends on the location and is commonly increased to up to 10% for locations such as Canada. Two new configurations for Congo and Australia can be considered. With 50 PV modules, a storage of 225 kWh is enough for Congo, reducing the battery size by 85%. For Australia, a community equipped with 55 PV modules could decrease its battery size by nearly 95%, reaching a capacity of 150 kWh. In the case of Canada, the number of PV modules seems not to influence the battery size and no interesting configurations were found.

In conclusion, the configuration of the off-grid REC supply system (including the energy production and storage) varies with the location. The locations in Congo and Australia need relatively low numbers of PV modules and storage systems. Indeed, the two configurations used in those regions seem to have a potential to help a community to be energy self-sufficient. Furthermore, an increase in the number of modules drastically decreases the battery size, making the configuration more economically viable.

The case of Canada is more difficult given the lack of irradiance during consecutive days. The number of modules is reasonable, but the surplus of energy and the size of storage systems are too big. An alternative source of energy such as biomass could be interesting in the targeted region, as it does not depend on the meteorological conditions.

Moreover, it is important to keep in mind that several simplifications and assumptions have been made during the calculations. For example, the electrical consumption is not the same all over the globe, since the needs are different. Moreover, electrical consumption is only a part of the energy required to supply a community.

Nearly all the microgrids existing in the world are based on hybrid PV and battery systems. These systems have been successfully deployed in various off-grid and remote locations. While many of these microgrids include diesel generators as a backup, this is primarily for contingency purposes and not the primary source of power. The Energy Not Supplied (ENS) calculation accounts for instances when the batteries are depleted and there is insufficient solar energy available, thus highlighting the system’s potential shortfalls.

We acknowledge that, while simplified, this approach is common in initial feasibility studies, where the goal is to provide a first estimate of the system’s viability. In real-world applications, further refinements—such as incorporating backup generators or hybrid systems—may be necessary depending on the specific location and energy requirements. Nonetheless, the results presented offer valuable insights into the potential for solar PV deployment in off-grid communities, particularly when paired with battery storage to address intermittency.

5. Conclusions

The aim of the paper was to explore different approaches and technologies that enable the renewable energy community to meet its energy requirements without relying on traditional power grids or non-renewable energy sources.

The literature review provided valuable insights into the prevailing trends concerning energy sources and storage systems. Solar and wind energies emerged as the primary RESs used to meet the electricity requirements of renewable energy communities. Among the various storage options, lithium-ion batteries were found to be extensively utilized.

Based on these findings, the optimal system configuration was determined to be a combination of PV modules and lithium-ion batteries. To assess the sizing requirements, an optimization model and several simulations were conducted in three distinct locations with different irradiation profiles: Congo, Australia, and Canada. It was assumed that the energy needs of the communities remained consistent across all locations, with the supplied energy solely dedicated to fulfilling the electricity demand, excluding heating and cooling services.

In the case of Congo and Australia, it was observed that a relatively small number of PV modules and storage systems could sufficiently meet the energy needs of the community. These configurations hold significant potential for enabling energy self-sufficiency within these regions. Moreover, increasing the number of panels resulted in a substantial reduction in battery size, enhancing the economic viability of the system. However, the situation in Canada poses challenges due to the lack of solar irradiation over consecutive days. Although the number of modules required was reasonable, the storage system size was found to be excessively large. In such cases, exploring alternative energy sources, such as biomass, becomes compelling as it is not dependent on meteorological conditions. Despite the challenges faced in certain locations, the findings highlight the feasibility of achieving energy independence through renewables and emphasize the progress made in renewable energy technologies.

The findings of this study confirm that while renewable energy systems can meet the energy demands of remote communities, their successful implementation is contingent upon the local context. For instance, while solar energy proved effective in Congo and Australia, Canada’s long winters highlighted the need for hybrid systems incorporating wind or hydropower. These results suggest that future research should explore region-specific hybrid energy solutions, with a focus on enhancing system resilience and integrating energy storage technologies. Moreover, while our study has primarily focused on the technological feasibility, a detailed cost analysis is crucial to fully assess the economic viability of these systems. This includes evaluating capital expenditures, operational costs, and long-term financial sustainability to provide a more holistic view of the feasibility of renewable energy solutions for off-grid communities.