Rural Electric Systems Research Articles

Performance analysis of hybrid off-grid renewable energy systems for sustainable rural electrification

Delivering sustainable power to rural communities in sub-Saharan Africa, particularly in Somalia, where many areas are far from the electrical grid and suffer from energy shortages, necessitates the deployment of appropriate technologies. This study evaluates the techno-economic and environmental viability of a hybrid renewable energy system (HRES) comprising a 15 kWp photovoltaic (PV) generator, 10 kW wind turbine (WT), 25 kW diesel generator (DG), and a 72-kWh battery energy storage system (BESS). The HRES is proposed to supply sustainable power to a rural community in Somalia. The techno-economic assessment of the proposed HRES and a comparative analysis of other configurations—namely the PV/WT/BESS and PV/DG/BESS systems is conducted using HOMER and MATLAB software. The results indicate that the proposed PV/WT/DG/BESS HRES demonstrates superior techno-economic performance compared to the other configurations. The lowest net present cost of $96,899.16 and the levelized cost of energy (LCOE) of $0.090/kWh were achieved. This system attains 25 % excess electricity, maintains a 0 % unmet electric load, and demonstrates robust environmental performance with a renewable penetration rate of 91.8 %. Approximately 53.34 % of greenhouse gas emissions are reduced compared to the PV/DG/BESS configuration. The study confirmed the significant impacts of all sensitivity parameters on operational costs, LCOE, fuel prices, fuel intake, and renewable fraction. The overall results indicate superior performance in the optimal system case, offering a feasible and suitable choice for the sustainable electrification of rural communities.

Achieving universal energy access in remote locations using HOMER energy model: a techno-economic and environmental analysis of hybrid microgrid systems for rural electrification in northeast Nigeria

The developing world continues to face substantial obstacles to achieving affordable and dependable electricity access. This issue is especially pertinent for Nigeria, where diesel generators are widely relied upon in urban and rural regions because of an underdeveloped and unreliable national grid. The lack of grid reliability is worsened in Northeastern Nigeria, an area plagued by conflict, extreme poverty, and grid infrastructure deterioration. This study investigates the feasibility of implementing community-scale microgrids in rural areas without grid connection access. It focuses on assessing the technical, economic, and environmental aspects of utilizing these microgrids to deliver inexpensive and dependable electricity to underserved populations to increase energy access. A case study was conducted in Kabuiri, a village with an estimated population of 2,300 residents and an estimated load demand of 610 kWh per day. A hybrid microgrid system was designed and optimized to meet the community’s load demand using HOMER software, sized to produce 610 kWh/day of electricity with a renewable penetration of 99%. The optimal solar PV/battery/generator system had a levelized cost of electricity (LCOE) of $ 0.093 per kWh, a net present cost (NPC) of $266,709, and an annual operating cost of $9,110. The system contributed 1,624 kg CO2 eq/year of global warming potential and 56.81 kg O3 eq/year of smog formation during operation. Sensitivity analysis showed that the system could effectively react to or adapt to substantial increases in diesel prices, requiring only marginal increases in photovoltaic capacity and reduced generator usage to maintain the most cost-efficient operation. Additionally, the system model can be adapted based on the population of the remote community without substantially impacting the LCOE, however, the NPC increases with increase in population size. This research will aid in increasing energy access in remote locations by providing insights to stakeholders and energy access project developers.

AI learning-driven optimization of microgrid systems for rural electrification and economic empowerment

Artificial Intelligence (AI) and machine learning (ML) are transforming the landscape of rural electrification through their application in microgrid systems. Microgrids, localized networks that can operate independently or in conjunction with the main grid, offer a viable solution for delivering reliable electricity to rural areas. AI-driven optimization enhances the performance and efficiency of these microgrids, addressing the unique challenges faced in remote regions and contributing to economic empowerment. AI technologies, including machine learning algorithms and data analytics, play a critical role in optimizing microgrid systems. By employing predictive maintenance, AI can forecast equipment failures and schedule timely repairs, thereby minimizing downtime and reducing maintenance costs. Additionally, AI enhances energy management through precise load forecasting, optimizing energy distribution, and integrating renewable energy sources like solar and wind. This not only balances energy supply and demand but also mitigates the variability associated with renewable sources. The impact of AI-driven microgrid optimization on rural electrification is profound. Improved system reliability and efficiency result in more stable energy supplies, which is crucial for supporting local businesses, educational facilities, and healthcare services in remote areas. Economically, the reduction in energy costs and the creation of new job opportunities further empower rural communities. Socially, reliable electricity enhances the quality of life, enabling better educational outcomes and promoting economic development. Successful implementations of AI in microgrid systems have demonstrated significant benefits. For instance, AI-optimized microgrids in rural communities have shown improved performance and stability, leading to greater energy access and economic benefits. However, challenges remain, including technical complexities, financial constraints, and regulatory hurdles. Addressing these challenges through continued research, investment, and policy support is essential for scaling AI-driven solutions and achieving broader impacts. AI-driven optimization of microgrid systems offers a powerful tool for advancing rural electrification and economic empowerment. By leveraging AI technologies, we can enhance energy reliability, reduce costs, and foster economic growth in underserved regions, paving the way for a more equitable and sustainable future. Keywords: AI Learning-Driven, Microgrid Systems, Rural Electrification, Economic Empowerment.

A multiscale approach to optimize off-grid hybrid renewable energy systems for sustainable rural electrification: Economic evaluation and design

This article thoroughly investigates the design and optimization of an off-grid hybrid renewable energy system for a remote town in the province of Ankara, whose traditional power infrastructure is lacking. The goal is to provide an efficient and cost-effective energy system that can supply the village's power needs indefinitely. The optimization process entails selecting the best mix of renewable energy sources and sizing components to obtain the best economic and technical performance. To handle the complexity of the optimization problem, the Nelder-Mead simplex search method is used, taking into account the stochastic nature of renewable energy generation and the nonlinear features of RES-based power plants. The simulation results show that the suggested hybrid system outperforms traditional diesel power generators in terms of economic viability and environmental sustainability. The system assures consistent power supply by using reserve energy devices such as batteries, thereby minimizing the intermittent nature of renewable sources. With a competitive energy cost of €0.63/kWh, the optimized hybrid system ensures a dependable and continuous power supply, fulfilling the village's electrical requirement for an amazing 16 years.

Techno-economic analysis of a PV/WT/biomass off-grid hybrid power system for rural electrification in northern Morocco using HOMER

The increasing environmental concerns associated with fossil fuels have elevated the significance of sustainable energy sources such as solar, wind, and biomass. This study aims to design a hybrid renewable energy system capable of meeting the annual energy demand of residential areas in Zoumi's circle, estimated at 15545.13 kWh/day. Using HOMER Pro software, we analyze various combinations of renewable resources (solar, wind, biomass) and storage solutions (batteries) to assess their electricity generation, cost implications, and environmental impact. The study identifies an optimal hybrid configuration that includes solar, wind, biomass, batteries, and converters. This integrated system proves to be the most cost-effective option, achieving an energy cost of 0.125 $/kWh and a net present cost (NPC) of 8.29 $ million. It generates 11.14 GWh/year and achieves a 100 % renewable energy ratio, significantly reducing CO2 emissions by approximately 5900 tons/year compared to diesel generators.

Optimization and feasibility analysis of hybrid standalone renewable energy systems for rural electrification in Chamoli, Uttarakhand

Optimization and feasibility analysis of hybrid standalone renewable energy systems for rural electrification in Chamoli, Uttarakhand

Optimization and performance enhancement of renewable energy microgrid energy system using pheasant bird optimization algorithm

The main aim of this research work is to design and select an optimal renewable energy resource based microgrid (MG) system for rural area electrification of India. MG system consists of diesel generator (DG), battery bank (BB), wind turbine (WT), biomass generator (BMG) and solar photovoltaic (PV) modules with seven different configurations. The optimal system is selected based on technological, economic, environmental, social and reliability parameters. For this objective, a novel algorithm called pheasant bird optimization algorithm (PBOA) is presented and compared with particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), cuckoo search algorithm (CSA) and HOMER Pro. Results show that PV/WT/BMG/BB/DG is an optimal configuration with sizes of 1148 units of PV, 42 units of WT, 20 units of BMG, 36 units of BB and 1 unit of DG. PBOA has determined the optimal resource size for the MG system. In terms of social and reliability parameters for the PV/WT/BMG/BB/DG system, PBOA generated values of 93.58 %, for RF, 0.08754 for EGJC and 0.01 % for LPSP. For the optimal configuration, PBOA estimated NPC, COE, CE values are ₹66185779, ₹9.3 and 650901 kg/yrs respectively. PBOA reaches the minimal NPC of ₹66185779 in just 180 iterations, whereas PSO, ACO, GA, and CSA required 360, 200, 360 and 250 iterations respectively. PBOA also achieved minimal COE at a rate of 1.52, 1.11, 1.11 and 1.94 times faster than PSO, GA, CSA and ACO respectively. The contribution of PV and WT sources to the total generated electricity is approximately 50 % and 35 % respectively. PBOA exhibits faster convergence time and iteration compared to the PSO, ACO, GA and CSA algorithm. The comparison results indicate that the optimal PV/WT/BMG/BB/DG configuration select for rural areas. This research provides valuable information for policy makers and investors on the implementation of MG system in rural regions.

Novel integration and optimization of reliable photovoltaic and biomass integrated system for rural electrification

The novel concept of using hybrid renewable resources to provide clean energy helps to address the unique shortcomings of each renewable source. This study describes an innovative concept about the design of optimal grid PV/biomass 100 % renewable and fully reliable energy systems without considering energy storage devices. By leveraging community solar and biomass resources, the proposed system can power remote villages with a minimum cost of energy. Multi-Objective Genetic Algorithm (MOGA) is employed to perform an optimal procedure. The PV-biomass deployment project's economic viability is assessed using financial metrics such as net present cost (NPC) and cost of energy (COE). In order to install a hybrid system at the chosen site, the optimal configuration (PV 87 kW, biomass1 29 kW, and biomass2 125 kW) was examined. The NPC, COE, and total system cost, in this configuration, are $118,942, $0.02/kWh, and $892,892, respectively. The combined yearly consumption of the biomass1 and biomass2 generators is 704.81 tons of wheat straw. The total annual CO2 emissions of the PV, biomass1, and biomass2 generators in this system are avoided by 729.5 tons. The findings clearly demonstrate that the suggested approach can manage a reliable power flow with a suitable configuration. The solar PV and biomass system examined in this study will probably be a specialized solution in regions with abundant biomass resources; nevertheless, it offers a reliable starting point for the creation of larger-scale bioenergy value chains with the long-term objective of generating electricity from wheat straw biomass materials.

Integrated Load–Source Side Management for Techno-Economic-Environmental Performance Improvement of the Hybrid Renewable Energy System for Rural Electrification

Electricity produced by a hybrid renewable energy system (HRES) is reliable, economical, and environmentally benign. However, effective management of the load-source side is essential to enhance the reliability, affordability, and eco-friendliness of HRES. Therefore, an integrated load–source side management for techno-economic-environmental (TEE) performance improvement of HRES is developed in this paper. The factors considered are namely technical (renewable energy portion, excess energy factor, and unmet load), environmental (particulate matter and carbon emission), and economical (annualized cost of system, total net present cost, and cost of energy). To solve the HRES size optimization problem, a marine predators algorithm (MPA) is used for the first time. HRES is investigated without Load side management (LSM), with LSM, and with LSM integrated with source-side management (SSM). LSM is carried out using the peak shifting technique, which shifts time-movable loads (TMLs) to off-peak hours depending on excess energy availability, taking into account consumer comfort violations and customer behavior uncertainty. For SSM, a comparatively better energy management strategy (EMS) than the load following, cycle charging, and generator order is proposed, which improves the TEE performance of HRES. The integrated load–source side management saves 5% in NPC and 0.008 $/kWh in COE, respectively. To come up with a configuration that is more feasible, a sensitivity analysis for the HRES's component costs and macroeconomic variables is carried out. Furthermore, the optimal configuration COE is slightly higher than that of the most recent study in the literature. By contrasting the MPA result with the HRES PSO result, the accuracy of the MPA result is also confirmed.

Design and optimization of an off-grid integrated renewable energy system for remote rural electrification in India

Design and optimization of an off-grid integrated renewable energy system for remote rural electrification in India

A stochastic approach to determine the energy consumption and synthetic load profiles of different customer types of rural communities

The electricity demand is highly stochastic and unpredictable. This is due to the fact that it is significantly impacted by a number of factors, including the type of load, weather, time of day, seasonality, economic limitations, customers’ way of living, and other randomness factors. An accurate load model is one of the main inputs for the design of an economical and reliable renewable-based rural electrification system for rural communities and demand management systems. This paper presents a generic methodology for determining a rural community's energy consumption load profile, which is used to determine the most cost-effective size of renewable energy sources for rural electrification purposes. To determine the load profile parameters, such as the types of appliances used, their functioning times, functioning windows, and expected minimum and maximum cycle time, a field survey was conducted in four rural electrified Ethiopian villages. Since the survey findings will not fully explain the stochastic nature of the load profile, the load parameters are randomly generated, and a bottom-up approach is used to estimate the rural community's energy usage. In this study, household loads, public institution loads, business loads, and small industrial loads are the main consumer types taken into account. These loads are classified according to their energy usage as weekdays, weekends, and national and religious holidays. A MATLAB program is developed and implemented to obtain the load profiles of different customer groups. The results of this study are assessed in accordance with the multi-tier criterion and verified through the use of the well-known software HOMER Pro- and LoadProGen.

Optimal design and techno-economic analysis of hybrid renewable energy systems: A case study of Thala city, Tunisia

ABSTRACT This study explores the techno-economic feasibility of, both off-grid and on-grid, hybrid renewable energy systems for remote rural electrification in Thala City, located in the highest region of Tunisia, using wind and biomass resources. Employing Hybrid Optimization of Multiple Energy Resources based on different scenarios includes grid-connected and stand-alone configurations with pumped storage hydropower and lead acid battery storage while minimizing the levelized cost of energy, the net present cost, and greenhouse gas emissions. The optimal configuration wind/biomass/pumped-hydro storage/Converter grid-connected, minimizes the levelized cost of energy (LCOE) and net present cost (NPC), resulting in a cost of 501,540 US$ and LCOE of 0.042 US$/kWh. Notably, 7% of electricity is generated from olive mill waste, 69% from wind turbines, and 24% is purchased from the grid. This hybrid system emits 342 tons/year of CO2, 76% less than a grid-alone system, contributing to an annual CO2 reduction of 1000 tons.

A hybrid solar–biogas system for post-COVID-19 rural energy access

Abstract Solar home systems for rural electrification are often designed with a limited energy supply, which presents a drawback for the technology. Furthermore, uncontrolled livestock faeces in rural communities constitute environmental sanitation and health risks. Livestock excrement can be used through a biogas digester to supplement solar energy to provide adequate and sustainable electricity access to underserved rural communities while achieving waste management. Therefore, this study presents a hybrid solar–biogas system for a more dynamic energy supply and waste management for post-Covid recovery plans in rural communities. A parametric research approach that involves the use of the Integrated Environment Solution Virtual Environment software application and mathematical models to design the desired household load and the hybrid system sizing is used in the study. The findings show that the daily household energy consumption was 6.6 kWh, equivalent to 206.40 kWh/month. A 1.2-kWp and 1.2-m3 hybrid solar–biogas system was found to adequately power the house. Financially, the total initial investment cost of the system was $5777.20 with a net present value of $6566.78, net profit of $4443.6, a payback period of 14 years and 8 months, and a levelized cost of energy of $0.21/kWh; these include a 60% initial investment and maintenance costs subsidy. Energy performance contracting and energy-as-a-service were recommended to effectively run and operate the system. The study successfully revealed the design, specifications and upscaling mechanism of the proposed hybrid solar–biogas system. More research is required to unveil the efficacy of the system, the performance gap and the perception of the technology by the beneficiaries.

Optimization of a Hybrid Off-Grid Solar PV—Hydro Power Systems for Rural Electrification in Cameroon

The use of decentralized renewable energy systems will continue to play a significant role in electricity generation especially in developing countries where grid expansion to most remote areas is uneconomical. The income levels of these off-grid communities are often low, such that there is a need for the delivery of cost-effective energy solutions through optimum control and sizing of energy system components. This paper aims at minimizing the net present cost (NPC) and the levelised cost of energy (LCOE). The study presents a hybrid power system involving a hydroelectric, solar photovoltaic (PV), and battery system for a rural community in Cameroon. The optimization of the system was done using HOMER Pro and validated using a meta-heuristic algorithm known as genetic algorithm (GA). The GA approach was programmed using the MATLAB software. After the HOMER simulation, the optimal power capacity of 3 kW solar PV, 334.89 Ah battery, and 32.2 kW microhydropower was used to meet the load. The village load profile had a daily energy usage of 431.32 kWh/day and a peak power demand of 38.49 kW. The optimized results showed an NPC and LCOE of $90,469.16 and 0.0453 $/kWh, respectively. The system configuration was tested against an increase in hydropower capacity, and it was observed that increasing the hydropower capacity has the ability to significantly reduce the LCOE as well as the battery and solar PV size. A comparative analysis of the two approaches showed that the optimization using GA was more cost-effective than HOMER Pro with the least LCOE of 0.0344 $/kWh and NPC of $86,990.94 as well as a loss of power supply probability (LPSP) of 0.99%. In addition, the GA method gave more hydropower generation than HOMER Pro. This supports the fact that stochastic methods are more realistic and economically viable. They also accurately predict system operation than deterministic methods.

Evaluating the impact of industrial loads on the performance of solar PV/diesel hybrid renewable energy systems for rural electrification in Ghana

Access to reliable electricity remains a significant challenge in many rural communities worldwide. Off-grid solar PV hybrid renewable energy systems (HRES) have emerged as a viable option for rural electrification. However, rural communities' lack of productive load often limits their effectiveness. This study aimed to assess the impact of agro-processing productive loads on the performance of off-grid solar PV HRES for rural electrification. Hybrid Optimization Multiple Energy Resource (HOMER) software was used to perform a techno-economic analysis of a solar PV/diesel HRES. The study findings showed improvement in the rural community's load factor and solar load correlation with the integration of the productive load. Subsequently, increasing the renewable energy fraction in solar PV/diesel HRES reduces the levelized cost of energy (LCOE), making electricity generation more cost-effective for rural electrification in Ghana. Comparatively, the improved LCOE was found to be substantially higher than the End User Tariff of all residential consumers on the national grid, even under high PV penetration and full capital cost subsidy cases. The study provides valuable insights into the role of agro-based productive loads in enhancing the performance of rural off-grid solar HRES.

Assessing the feasibility of off-grid photovoltaic systems for rural electrification

In this investigation, the absence of an electricity grid in numerous locations, including military bases, tiny houses, and chalets, prompted the development of a model for providing electrical energy through an off-grid Photovoltaic (PV) system in Konya, Türkiye. The study delineates the daily energy consumption of a residential dwelling as 39,974 Wh/day, and the feasibility of satisfying this demand through the implementation of a 9.45 kWp PV system is scrutinized. The research encompasses the determination of optimal tilt and azimuth angles set at 35° and 0°, respectively. The maximum global effective irradiation intensity, recorded in August at 208.3 kWh/m², contrasts with the minimum intensity observed in December, registering at 106.2 kWh/m². Likewise, electricity production attained its zenith in August at 1,581.3 kWh, starkly contrasting its lowest level in December at 791 kWh. Modelling outcomes conclude that Solar Fraction (SF) values equate to unity during summer but fall below unity during winter. Furthermore, a surplus in electricity generation relative to demand is observed during the summer, resulting in the full charge of batteries. Evaluating the annual average SF, it is deduced that the modelled system fulfils 90.8% of the energy requirement. The Performance Ratio (PR), an additional pivotal parameter in PV systems, reaches its zenith at 0.865 in November and its nadir at 0.614 in August. This comprehensive study underscores the efficacy of the modelled off-grid PV system in meeting the energy demands of the selected residence, emphasizing the significance of seasonal variations and key performance metrics in assessing system performance.

Economic Analysis of a Hybrid Energy System for Rural Electrification for the Hasik Area, Oman

Renewable energy resources have become the best way to solve the issues created by traditional power resources in terms of environmental effects, energy cost reduction, and high-power demand. The hybrid-based system gives a realistic economic solution where grid extension is non-feasible especially for remote areas. The main objective of this study is to determine the optimum size of a hybrid-based system to fulfill the requirements of remote sites located in the Hasik area in the southern part of Oman. The work is carried out using HOMER Pro and MATLAB software. The proposed hybrid system in the Hasik network is a combination of PV systems with existing diesel generation. Real load data as well as solar radiations were utilized in the proposed model. HOMER selected an optimum solution for the hybrid system resulting in a combination of a 2,500 kW PV system and 3,100 kW diesel generation as well as providing a reduction in the cost of energy and CO2 emissions by 13% and 22%, respectively. The optimum PV sizing for the hybrid system was determined using a genetic algorithm tool in MATLAB. The optimization was based on reducing the system power losses, improving the voltage profile, and minimizing cost. Optimization verification was done using load flow analysis by MATLAB to determine the PV sizing effects on the system power losses and voltage profile. Based on the model results, the hybrid system is feasible in the Hasik area.

Feasibility investigation and economic analysis of photovoltaic, wind and biomass hybrid systems for rural electrification in Afghanistan

Feasibility investigation and economic analysis of photovoltaic, wind and biomass hybrid systems for rural electrification in Afghanistan

Economic Analysis of On-Grid Photovoltaic-Generator Hybrid Energy Systems for Rural Electrification in Indonesia

Economic Analysis of On-Grid Photovoltaic-Generator Hybrid Energy Systems for Rural Electrification in Indonesia

Design and Modeling of Hybrid Solar PV/Mini Hydro Micro-grid Systems for Rural Electrification: A Case of Gilgel Abay River, Ethiopia

Design and Modeling of Hybrid Solar PV/Mini Hydro Micro-grid Systems for Rural Electrification: A Case of Gilgel Abay River, Ethiopia