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Reaching universal access to electricity by 2030 requires a massive deployment of mini-grids in rural areas of developing countries. Among the many challenges hindering this process, there are the high uncertainties in assessing demand patterns in rural communities, the costs of field survey campaigns, and the absence of ample and reliable datasets...
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... Lorenzoni et al. [43] developed a database of load profiles from sixty-one mini-grid projects in DCs. A clustering identified archetypal profiles, followed by graphical analysis to explore factors influencing load profile shapes. ...
... Household wealth proxies such as "monthly expenditure", "dwelling quality index", and "motorized vehicle ownership" positively impact appliance presence, aligning with findings from Rao and Ummel, Dominguez et al., and Richmond et al. [26,28,40]. [28,[42][43][44]. Higher "education of household head" also correlates with greater appliance ownership, possibly reflecting better electricity awareness or higher income, as suggested by Rao and Ummel [26], though contrary to Kurata et al. [27]. ...
To grant reliable and affordable electricity provision to non-electrified communities, proper system sizing, based on accurate demand estimation is crucial. However, the absence of historical data, and scarce, scattered, and often unreliable pre-electrification surveys, make this process particularly prone to errors. Acquiring data, especially with high quality and detail, is difficult, time-consuming and expensive. Even though, in a few site-specific cases the limited data collected has allowed researchers to develop methodologies to generate synthetic demand profiles based on variegated site-specific socioeconomic information and appliance adoption patterns. However, given the lack of comprehensive datasets of such information, the use of synthetic meth-odologies has been circumscribed to limited regional and socioeconomic scopes. This research proposes the development of a data-driven machine-learning framework for estimating appliance adoption patterns with a subset of relevant socioeconomic indicators, identified throughout a comprehensive literature analysis and data collection across various sources. To successfully train the model, a novel open-access database has been created and populated with socioeconomic information combined with appliance data collected from public and private sources. Finally, a structured logistic regression analysis has been performed, not only to capture the nexus of socioeconomic factors with appliance adoption but also to estimate the most relevant ones. The methodology calibrated with the proposed open-access database has shown 71.7 % accuracy, which represents an important achievement in the field. The study's findings lay the foundations for simplifying the estimation of appliance adoption, which can facilitate the demand estimation for sizing rural energy systems and rural electrification approaches.
... Load profiles are important when modeling, simulating, and optimizing energy systems because they offer insights into energy consumption patterns [124][125][126]. These profiles inform decisions on system sizing, resource allocation, and operational strategies by detailing variations in energy demand over time. ...
... The importance of load profiles cannot be over-emphasized. However, the development of load profiles, particularly in the global south, faces significant challenges due to limited data collection and sharing, high survey costs, and socioeconomic barriers [124][125][126]. To navigate these challenges, studies typically include small sample sizes, focus on specific components, and rely on average profiles, as summarized in Table 3. Small sample sizes limit the generalizability of the findings to the broader population, while profiles focusing on limited components may not capture the full range of energy consumption behaviours and needs. ...
... The energy consumption is then broken down by explicitly defining the number of people and households in a community, and the components that constitute the off-grid loads such as household devices, health clinic, school, retail shop, and industrial/agricultural energy consumption. These load profiles can then be scaled using the archetypical profiles developed by Lorenzoni et al. [125], as illustrated in Fig. 9 [see supplementary materials 1 and 2 (excel-based load calculation tool)]. ...
... Finally, powering new consumers requires a design phase of the grid, especially in remote areas, where the national grid is absent, making building mini-grids necessary. Therefore, accurate information on the users' patterns and behaviours is of great importance to estimate, for example, the initial load, which is a critical task [32]. In fact, overestimating the load profile will eventually jeopardise the profitability of the project due to extra costs, while underestimating the demand will lead to issues regarding the reliability of provided services and leaves the customers dissatisfied [32][33][34][35]. ...
... Therefore, accurate information on the users' patterns and behaviours is of great importance to estimate, for example, the initial load, which is a critical task [32]. In fact, overestimating the load profile will eventually jeopardise the profitability of the project due to extra costs, while underestimating the demand will lead to issues regarding the reliability of provided services and leaves the customers dissatisfied [32][33][34][35]. ...
In the West African Monetary and Economic Union (UEMOA), information on the characteristics of the users and patterns of electricity end-uses remains hard to find. This study aims to contribute to reducing the gap in research on domestic electricity consumption in the region by unveiling the ownership rates, patterns of use and electricity consumption of domestic appliances in urban households through a city-wide survey. Three categories of urban users were investigated including high, medium and low consumers. Findings demonstrated various ownership rates for appliances, ranging from 100% for lighting fixtures to 0% for washing machines depending on user category. Domestic electricity demonstrated patterns consisting of three peak demand periods, with the main ones occurring in the evening (19:00 to 20:00) and the night (22:00). Other demand characteristics include an average daily electricity use ranging from 0.50 to 6.42 kWh per household, a maximum power demand of between 0.19 and 0.70 kW and a daily load factor between 35 and 58%. Finally, the appliances contributing the most to domestic electricity use include air-conditioners, fans, fridges and freezers, televisions and lighting fixtures, with contributions differing from one category of user to another. Policy implications including review of the appliances’ importations framework and policies, and incentives for purchasing efficient appliances, design of more tailored policies, considering the different backgrounds of the users, education enhancement on energy behaviours for increasing energy efficiency/conservation, and implementation of DSM programs including load levelling, load shifting and load reducing depending on the type of appliance for energy conservation in the domestic buildings were derived. Overall, a large range of stakeholders of the electricity sector, not only in the West African Economic and Monetary Union (UEMOA), but also in other regions and countries sharing common characteristics should be interested in the results of this study.
... This can be achieved through long-or short-term measurements or predictions (Serrano-Guerrero et al., 2018). Previous studies present models for energy profiles prediction based on consumer parameters, or employing surveys, regression analysis, decision trees, and ANN (Abarkan et al., 2013;Blodgett et al., 2017;Lorenzoni et al., 2020;Serrano-Guerrero et al., 2018;Tso & Yau, 2007). ANN has been successful in forecasting household electric energy consumption and load profiles (Rodrigues et al., 2014). ...
This paper presents an approach for sizing a hybrid photovoltaic system for a small-scale peanut oil processing company (Yaye Aissatou, Passy) in rural Senegal using a synthetic load profile. In this study, a predictive model of the electrical load of a service-based plant oil processing company was developed through a diagnosis, to evaluate the extraction process. The mass and energy balance were measured, and the process was implemented into MATLAB Simulink. The simulated load profile was implemented in HOMER Pro and the characteristics of the most profitable hybrid systems were identified. The results showed that the lowest net present cost over 25 years was found with a PV/battery/grid-system with 18.6 kW p solar panels, 16 kWh of storage, and an initial investment of 20,019 €. Compared to a grid-only scenario, this solution reduces the net present cost from an initial 72,163 € to 31,603 €, the operating cost from 3675 € per year to 590 € per year, and the cost of energy from 0.29 to 0.13 €/kWh. The renewable fraction of the proposed system is 90.0 % while the expected payback period is 6.2 years. The study demonstrates the economic feasibility of using solar energy for plant oil processing.
... In many cases, data on electricity demand are not available or not reliable since they are rarely collected, and not reported systematically [4]. The lack of databases with high-resolution data on energy usage from rural areas results in limited historical data to draw from [5]. The most common method for estimating electricity usage currently adopted is through appliance ownership and use data collected via interviews [6]. ...
... Solar PV is the only renewable energy source included in our analysis, as previous work highlighted the abundant potential of solar PV in Kenya [40] and represents the most common primary renewable electricity source in Kenyan mini-grids; thereby allowing a transfer of the results [41]. As input data for a time series of PV irradiation in hourly resolution, we chose the MERRA-2 dataset with the reference year 2019 [42] at the location of latitude −3 • 22 00 S longitude 39 • 57 25 E. Data was accessed via [43]. ...
... We demonstrate the assessment in a case study in rural Kenya, applying a bottom-up approach to design the specific energy systems delivering clean hydrogen-based cooking. As the two essential assumptions of the electricity demand profile and cooking demand patterns match with typical profiles reported in the previous literature (see [41] for electricity profiles, [30] for hydrogen cooking fuel demand, and [51] for e-cooking profiles), we expect the findings of the study, including sensitivity analysis, to be transferable to a wide spectrum of rural settings in East Africa with similar cooking preferences. ...
Hydrogen has recently been proposed as a versatile energy carrier to contribute to archiving universal access to clean cooking. In hard-to-reach rural settings, decentralized produced hydrogen may be utilized (i) as a clean fuel via direct combustion in pure gaseous form or blended with Liquid Petroleum Gas (LPG), or (ii) via power-to-hydrogen-to-power (P2H2P) to serve electric cooking (e-cooking) appliances. Here, we present the first techno-economic evaluation of hydrogen-based cooking solutions. We apply mathematical optimization via energy system modeling to assess the minimal cost configuration of each respective energy system on technical and economic measures under present and future parameters. We further compare the potential costs of cooking for the end user with the costs of cooking with traditional fuels. Today, P2H2P-based e-cooking and production of hydrogen for utilization via combustion integrated into the electricity supply system have almost equal energy system costs to simultaneously satisfy the cooking and electricity needs of the isolated rural Kenyan village studied. P2H2P-based e-cooking might become advantageous in the near future when improving the energy efficiency of e-cooking appliances. The economic efficiency of producing hydrogen for utilization by end users via combustion benefits from integrating the water electrolysis into the electricity supply system. More efficient and cheaper hydrogen technologies expected by 2050 may improve the economic performance of integrated hydrogen production and utilization via combustion to be competitive with P2H2P-based e-cooking. The monthly costs of cooking per household may be lower than the traditional use of firewood and charcoal even today when applying the current lifeline tariff for the electricity consumed or utilizing hydrogen via combustion. Driven by likely future technological improvements and the expected increase in traditional and fossil fuel prices, any hydrogen-based cooking pathway may be cheaper for end users than using charcoal and firewood by 2030, and LPG by 2040. The results suggest that providing clean cooking in rural villages could economically and environmentally benefit from utilizing hydrogen. However, facing the complexity of clean cooking projects, we emphasize the importance of embedding the results of our techno-economic analysis in holistic energy delivery models. We propose useful starting points for future aspects to be investigated in the discussion section, including business and financing models.
... First, the size of the large dataset was reduced. Then, using Linkage Ward's method, the hierarchical clustering algorithm was employed to reduce the dataset to representative cluster centroids [60,61]. In particular, for each household, three different clusters were assumed in each of the three different time intervals. ...
Historically, the combination of generous subsidies along with extreme climate has led to unsustainable domestic electricity consumption in Saudi Arabia. The residential sector constitutes a significant portion of this consumption. Amid the economic challenges, the country enforced a new electricity tariff for residential consumers in 2018. This study thus leverages change in 2018–2020 by collecting and analyzing the electricity consumption data of 73 households in the Eastern Province of Saudi Arabia. The energy consumption is modeled based on the households’ attributes (e.g., dwelling type, ownership, number of residents, rooms, ventilation type, etc.) and applied tariffs using a machine learning technique. The extreme learning machine (ELM) is employed in solving the overfitting problem due to low-volume data. The correlation matrix is also constructed to determine the relationship between the household attributes. The ELM model developed in this study extracts the correlation between the input variables in determining energy consumption and also predicts the energy consumption related to low consumption data. The findings indicated that the electricity consumption between the pre-revised tariff year and the revised tariff enforcement year saw a reduction which was consistent in the subsequent years. This was also validated by the paired sample t-test, which showed a significant decrease in electricity consumption for the study period. The analysis also revealed that several household attributes had a relatively high impact on the reduction in the electricity consumption level following the revised tariffs, whereas the majority of the attributes had a moderate impact. In addition to these key findings, the demonstrated pathway adopted in this study is itself a methodological contribution that provides critical information about the sensitivity of the impacts of tariffs on energy consumption with respect to different household attributes. Economic factors being the critical stress need to be blended with existing energy consciousness for positive changes in favor of energy-saving behavior of the household members. The study does not attempt to represent the population of concern, but demonstrates a methodology that would help unleash inherent energy consciousness in favor of sustainable and energy-efficient behavior.
... Proper modelling of the tariff scheme should therefore not neglect tariff payment frequency. On a different note, [35] created a database of load profiles coming from sixty-one mini-grid projects located in DCs. A clustering algorithm was applied to the load curves, identifying a set of archetypal profiles; subsequently, an explorative graphical analysis of the data was performed, to point out the influence of some potential factors on load profile shape. ...
... [45] performed a similar analysis to that of [35], although adopting a customer-based point of view, rather than a system-based one. 821 customers were segmented, using a k-means clustering algorithm, in terms of their mean normalized load curves and mean daily electricity consumption. ...
... Measurement_age, suggesting that the likelihood of owning an appliance grows over time once electricity is provided. Analogous conclusions were derived in [26,[34][35][36]. ...
To grant a reliable and affordable electricity provision to non-electrified communities, proper system sizing, based on accurate demand estimation is crucial. However, the absence of historical data, coupled with scarce, scattered and often unreliable pre-electrification surveys, makes this process particularly prone to errors. Acquiring data, especially with high quality and detail, is often difficult, time consuming and expensive. Even though, in a few site-specific cases the limited data collected have allowed researchers to develop methodologies to generate synthetic demand profiles based on variegated site-specific socio-economic information and appliance adoption patterns, among other parameters. However, given the lack of comprehensive datasets of such information, the use of synthetic methodologies has been circumscribed to limited regional and socio-economic scopes. In this study, by means of a data-driven approach, we propose a machine learning methodology to estimate the appliance adoption in rural areas of developing countries, supported by a review of the drivers of load demand and appliance adoption identified in literature and an extensive data collection to populate the data-driven technique. These data are subsequently harmonized into a database, which consists of 60 drivers for a total of 16252 users in Sub-Saharan Africa. The database, released in open access, has been used to calibrate logistic regression models to estimate the ownership of 8 key appliances, whose accuracy is about 71.7% when most features are used. The output can assist future players in the development of short, yet reliable surveys, ensuring an accurate demand estimation while also limiting time and operational costs.
... Socio-economic Appliance Aggregated Demand Time-series Granularity Consuming unit AEGE [28] no no no no Geometries no no GEP [9] yes no yes yes Raster (>0.008 deg) no no EAE [8] yes yes yes yes Raster (various) no yes This study yes yes yes yes Geometries Village, Household, Appliance yes adoption and use at household level, and present their information only at aggregated level, distributed over geospatial rasters. As outlined in [29], the collection of data at different granularity is quite complex, and requires to explore different sources, often not harmonized, as confirmed by the about 20 references needed to collect 61 load profiles of rural microgrids. Data on social behaviors and local economics are sometimes available as results of field surveying campaigns, conducted by International Bodies, such as the Multi-Tier framework by ESMAP [30] or National Statics Bodies [31]. ...
... Also data at country scale are rarely publicly available worldwide and even IEA lacks some data for some countries [56]. Final energy uses and data at local scale are even more scarce [29]. A recent paper [29] proposed a first classification of load demand for about 60 isolated microgrids in developing countries and released the data publicly. ...
... Final energy uses and data at local scale are even more scarce [29]. A recent paper [29] proposed a first classification of load demand for about 60 isolated microgrids in developing countries and released the data publicly. However, even in that case, load information was often limited to few representative daily curves and rarely multi-year dynamics, which are critical for investments, were found in the literature. ...
Energy Access is a pivotal need for socio-economic growth. Proven to be a key enabler of development and progress, access to electricity has been prioritized by governments using grid extension actions and off-grid solutions, namely microgrids and home systems technologies, fed by renewable sources. However, achieving universal access to energy is still highly challenging, given the lack of resources and the large population currently unserved. The lack of adequate socio-economic data at granular scale and of a good understanding of demand uptake led by economic growth is a barrier for efficient energy planning. Access to cojoint demand and socio-economic data at local level is crucial, yet hard to obtain: often such data are unavailable or very difficult to collect, and current data platforms often lack the ability to conjointly store variegated socio-economic and time series data. For these reasons, in this paper, we present a comprehensive methodology that, based on an extensive literature review, draws guidelines for developing data-sharing platforms in energy access, develops a proposed architecture to support the data collection of conjoint socio-economic and time-series data, and proposes a prototype of the final application. The methodology leverages on a novel extensive literature review to identify the major determinants of demand uptake and the corresponding consuming entities: villages, households and appliances. The proposed architecture is able to capture numeric, categorical and time series information for all consuming entities, based on state-of-the-art NoSQL databases. Finally, a prototype implementation with a web-based interface developed with Angular and Spring is proposed and discussed.
... Based on these facts, metaheuristic algorithms are extensively used as the best choice compared to the other methods. Several metaheuristic optimization algorithms had applied during the last decade that has covered the approach of microgrid size [26]. Few optimization methods such as GA, PSO, DE, etc., were used by both computer scientists and many scientists in different areas. ...
In this paper, the optimization and multiple-criteria decision analysis (MCDA) of a stand-alone photovoltaic and battery energy system (PV-BES) has been used to supply power to a desalination plant in the United Arab Emirates (UAE). To provide a continuous power supply, different types of battery technologies have been used as a renewable energy storage system in this study as Nickel Iron (NiFe), Lithium Iron Phosphate (LiFePO4), and Lead Acid (PbSO4) with three different depths of discharges. Six different configurations of the PV-BES were modeled. In total, nine metaheuristic optimization algorithms were used in the MATLAB environment to provide an optimal sizing of the PV-BES. The mayfly optimization algorithm has provided the best optimal Annual Levelized Cost (ALC) values compared with the remaining algorithms. The mayfly algorithm has more robustness and faster convergence in providing the optimal global best values. Furthermore, three different approaches of MCDA and weights methods were used. The inputs and the results for the optimization process in addition to the sustainable development goal (SDGs) from the united nation (UN) were used as criteria for MCDA. The PV-Li-ION at 50 % depth of discharge (DOD) was the best option among all cases based on the six configurations and nine optimization algorithms.