Off-grid Renewable Energy Systems Research Articles

Techno-Economic Evaluation of Hybrid Off-Grid Renewable Energy System

Techno-Economic Evaluation of Hybrid Off-Grid Renewable Energy System

Effect of Renewable Energy Consumption on Economic Growth in Kenya

One of the significant economic growth problems in Kenya is the income inequality and poverty rate which has made it impossible for among the households to afford clean energy. While the country has experienced some economic growth over the years, it is not yet clear whether the adoption of renewable energy has been the cause of this growth. There have been mixed results on the studies done on renewable energy use and their effect on economic growth and therefore calls for further research. The general objective of the study was to determine the effect of renewable energy consumption on economic growth in the Kenyan economy. Specifically, the study specific objectives were to determine the effect of solar energy consumption, geothermal energy consumption, and hydroelectric energy consumption and wind power energy consumption on economic growth in Kenya. This was made possible through an analysis of 37 years’ data from 1986 to 2022 on an annual time series basis. This study employed the endogenous growth theory as the main theory of the study and triple bottom line theory as the supplementary theory. The study employed an explanatory research design and analyzed data using the Autoregressive Distributed Lag model to establish whether renewable energy consumption had any effect on economic growth of the Kenyan economy. Findings of diagnostic test demonstrated that there was no multicollinearity among the independent variables, residuals were homoscedastic, and there was no autocorrelation among the residuals. The results of the Jarque bera normality test showed that the study's variables were normally distributed. The Augmented dickey fuller unit root test both showed that there was no unit root and that the variables had a short run relationship. Additionally, the model's stability over time was confirmed by the CUSUM test. Findings of the study were: the relationship between hydroelectric energy consumption and economic growth was positive and significant, wind power energy consumption and economic growth was positive and significant and between geo-thermal energy consumption and economic growth was positive and significant. The study concluded that geo-thermal energy consumption, hydroelectric energy consumption and wind power energy consumption significantly affect economic growth positively. The study therefore recommends that the Kenyan government should set ambitious targets for renewable energy consumption such as a specific percentage of electricity generation or overall energy consumption. The Kenyan government should enhance and enforce regulatory frameworks specific to renewable energy in order to provide a stable and predictable investment environment. Finally, the Kenyan government should focus on expanding access to renewable energy solutions in rural areas, where energy access is limited. Implementing off-grid renewable energy systems, such as solar home systems and mini-grids, can provide clean and reliable electricity to rural communities.

Optimization of off-grid renewable energy systems using a hybrid version of golden search algorithm

Currently, the development of HRESs (hybrid renewable energy systems) in remote areas is of great importance and popularity. However, measuring and optimizing the capacity of these systems faces a difficult challenge. Multiple works had been reported in the literature to optimize such systems, all of which aim to achieve an optimal configuration with minimum annual net cost. Therefore, the significance of providing off-grid electrification to remote areas through HRESs can be highlighted as a crucial case for sustainable growth. Accordingly, the study proposes a modified metaheuristic approach, known as the Hybrid Golden Search Algorithm (HGSA), for long-term application planning and optimization of the off-grid HRES. The aim of this algorithm is to minimize the amount of net cost which is used annually; to reduce the probability of power supply interruption. In order to assess the effectiveness of the proposed algorithm, a simulation study over a long period on a remote area was conducted. From the results, increasing the reliability level from 1 % to 3 % causes a decrease in the total net annual cost by around 7.3 % under the proposed HGSA and also results in a reduction in component size (around 6 % and 21 % reductions for the wind turbine area and storage tanks, respectively). Further, the HGSA technique obtains the lowest value of fitness function for the hybrid system at a reliability level of 3 %, which is 31,539,810$. This result demonstrates that the efficiency of HGSA outperforms Fuzzy Logic and Optimization, Artificial Bee Colony (ABC), and GSA techniques. Based on this, the proposed HGSA could lead to more promising results than the other comparative algorithms. Hence, the proposed HGSA can be a valuable tool for long-term application planning and optimization of off-grid HRES, which can contribute significantly to achieving sustainable development goals.

Investigating the role of subsistence renewables in alleviating power poverty within Nigeria’s energy-mix strategy

Nigeria, a country rich in renewable sources still heavily relies on nonrenewable energy, of which the government’s on-grid supply remains inadequate, and generally of poor services. This study, therefore, investigated subsistence renewables by citizens able to contribute to the country’s energy-mix plan and help ameliorate power poverty challenges. Qualitative primary data were sourced using the Delphi experts’ method from energy experts and practitioners possessing relevant knowledge bases regarding the energy-mix strategy with an understanding of the power poverty scenarios. The findings show that the effect size in the very high-level option for solar potentials was relatively more than wind and bio sources. The study reveals that citizens could mostly afford low-capacity solar installations, with considerable interest in the energy-mix strategy. Medium-capacity solar was found to be quite unaffordable, leading to a perceptually very low potential for solar. A showstopper in incorporating solar effectively into Nigeria’s energy-mix scenario was the citizens’ non-affordability of accessing high-capacity systems. It is, however, deduced that affordability and recognition of values are congruous. Conclusively, the experts are optimistic regarding subsistence renewables potentials in the energy-mix strategy. Solar is, however, considered the most crucial, surpassing wind and bio sources. The study suggests that the government could incentivize renewable energy systems, particularly solar, to promote subsistence solutions through affordability by citizens, adoption, and sustainability within the energy-mix strategy. It is further recommended that citizens’ income levels should be raised to favor the affordability of off-grid renewable energy systems.

Optimization of a hybrid solar/wind/storage system with bio-generator for a household by emerging metaheuristic optimization algorithm

The ever-increasing need for electricity in off-grid areas requires a safe and effective energy supply system. Considering the development of a sustainable energy system and the reduction of environmental pollution and energy cost per unit, this study focuses on the techno-economic study and optimal sizing of the solar, wind, bio-diesel generator, and energy storage structure. The emerging metaheuristic optimization method is used to estimate the techno-economic benefits of the suggested structure to provide the required electricity for a residential household. The optimal design is done with the harmony search (HS) method to validate the results obtained with the suggested algorithm. Minimizing the total annual cost (TAC) is a main objective for the design optimization of the system while satisfying the reliability and techno-enviro-economic constraints. The impact of the investment cost of biodiesel, biofuel cost, and reliability is investigated on the system sizing. The results show that the proposed algorithm has low simulation time (by 4.1 %) and the operating cost of the proposed algorithm is 1.12 % less than the existing HS algorithm based on the best TAC value. The TAC is received as $24,259 with the suggested method, which is the best optimal result compared to the result given by the HS algorithm. The proposed off-grid renewable energy system based on a bio-diesel generator is a viable and scalable solution to provide the required electricity for a residential household in all periods. The sensitivity analysis show that if the biofuel cost is reduced by 50.8 %, TAC will decrease by about 36.6 %. On the other hand, if the bio-diesel generator cost is reduced by 17 %, TAC will be decreased by 2.7 %. Finally, if the limit value of reliability is reduced from 2 to 0 %, TAC will increase by 13.2 %.

Designing off-grid renewable energy systems for reliable and resilient operation under stochastic power supply outages

The power supply from solar and wind generators is not only inherently variable but also prone to failure due to rare-weather related events, i.e., hailstorms, icing. Current system sizing strategies often consider system reliability or resilience but rarely consider them simultaneously. Here, we proposed a sizing approach for an off-grid power system to supply a minimum power threshold (Lth) during power disruption events. The Lth concept ensures blackout avoidance and enough dispatchable stored energy during power outages. We developed several scenarios with 4- to 24-hour simulated outage events occurring multiple times per year. After sizing a system, designs are tested by simulating the systems’ operation based on data containing stochastic outage events. The resulting lost load is recorded to assess system reliability and resilience. Results showed that, regardless of outage frequency, total annual stochastic outage durations up to 32 h did not affect the optimal capacities of system components while ensuring the same reliability level. However, system capacities increased by up to 90% when the annual outage duration increased to 144 h. Meanwhile, introducing a minimum power threshold, Lth = 0.97, further increased the renewables generation and storage capacity up to 50% and 7%, respectively. Systems’ resilience tests showed an 80% chance of a system designed with the Lth approach to withstand the prolonged stochastic power disruptions, while this value is only 25% for the systems designed using the conventional approach.

Technical and economic design of a novel hybrid system photovoltaic/wind/hydrokinetic to supply a group of sustainable buildings in the shape of airplanes

This research analyzes the impact of a hybrid off-grid renewable energy system consisting of wind turbines, solar photovoltaic, hydrokinetic turbines and battery-backed to provide a group of novel airplane-shaped buildings, generates development in nearby towns that sit on a city vantage point from Cuenca in Ecuador. This is an innovative proposal that, in addition to using renewable energy in the complex of buildings, generates development in nearby towns. Three sources of renewable energy under energy control, load cycle and load monitoring are used to determine new patterns in the behavior of the sources with respect to the demand for electricity. Above all, it reduces carbon. With the support of HOMER Pro, the generation sources are optimized to cover the electrical demand patterns of the group of buildings in the form of airplanes. The results show that the systems that include solar panels, wind and hydrokinetic generators have a higher cost but there is more guarantee by maintaining their charge levels in the batteries above 40%. The proposed methodology and design can be widely adapted to places with similar characteristics worldwide, creating a novel solution for this type of buildings powered by renewable energy. The annual energy required by the set of buildings is 234.86 MW h/year. When projecting the renewable energy system for 25 years, an NPC of $37,600 and a COE of $0.386/kWh are achieved.

Impact Assessment on Kenyan Minimum Energy Performance Standards and Energy Labels for Cold Chain Appliances

In addressing climate change and meeting countries National Determined Contribution (NDC) and national cooling action plan (NCAP) on energy ambitious targets in the year 2030, Kenya made several strides in drafting the appliances energy performance and labelling regulations of 2016. Energy efficiency enables distributed off-grid renewable energy systems, from solar home systems to renewable micro-grids, to deliver energy services that otherwise might be economically or technically infeasible in resource-constrained settings. This paper reviews specific multivariate econometric models over the period of 2016 to 2022 and summarizes the impact of minimum energy performance (MEPs) and the energy labels policies and regulations enacted. This paper assesses the energy efficiency label for cold chain appliances and identifies issues, potential improvements, and challenges for successfully nudging consumers towards highly energy efficiency cold chain appliances.

Optimal Design of a Hybrid Off-Grid Renewable Energy System Using Techno-Economic and Sensitivity Analysis for a Rural Remote Location

Due to the lack of grid power availability in rural areas, hybrid renewable energy sources are integrated with microgrids to distribute reliable power to remote locations. This optimal hybrid system is created using a solar photovoltaic system, wind turbine, diesel generator, battery storage system, converter, electrolyzer and hydrogen tank to provide uninterrupted power and meet different load demands of different communities in Doddipalli village, Chittoor, Andhra Pradesh, India. Optimization and techno-economic analysis are performed to design the proposed system using HOMER Software. Various configurations are obtained from the software among which the best four combinations are considered for case studies. This research article aims to design the optimal hybrid renewable energy system, wherein the design consists of PV/BS (1476 kW-solar PV, 417 batteries, electrolyser-200 kW, hydrogen tank-20 kg and 59.6 kW-converter) by comparing the minimum net present cost (NPC: $7.01 M), levelized cost of energy (LCOE: 0.244 $/kWh), and the high renewable fraction (RF: 84.1%). In this research, the proposed system would be more economical when solar energy becomes the primary source and is integrated with the battery. This research also presents a sensitivity analysis of the off-grid HRES system with various electrical load demands, project lifetime, and derating factors.

Techno-economic comparison of different hybrid energy storage systems for off-grid renewable energy applications based on a novel probabilistic reliability index

The application of energy storage technologies is crucial to the extensive exploitation of renewable energy for power generation in off-grid areas because energy storage can mitigate the intermittency of renewables and balance the supply–demand mismatch. A hybrid energy storage system (HESS) with two or more heterogenous and supplementary energy storages outperforms single energy storage from the perspective of reliability and cost-effectiveness, but how to select the optimal HESS configuration is still unknown. To this end, this paper investigates the techno-economic comparison of ten HESSs in off-grid renewable energy system applications, including all pairwise combinations of thermal energy storage, pumped hydro storage, hydrogen storage, battery, and supercapacitor. Moreover, a novel probabilistic reliability index called loss of power supply probability with probability of exceedance (LPSP-PoE) is proposed to quantitatively handle the long-term uncertainties of renewable energy resources. The rule-based coordinated energy management strategies considering the charging/discharging priority and optimized operating thresholds are established for different HESSs, and then the multi-objective optimization models aiming to minimize net present cost and LPSP-PoE are developed to search the optimal components sizing and operating thresholds simultaneously. Finally, the results of case studies show that: (1) the optimal net present cost of thermal energy storage-battery at the highest reliability level is 3.3472 billion USD, which is 6.98 %∼69.85 % lower than the figure for other HESSs, indicating that thermal energy storage-battery is the most cost-effective HESS configuration; (2) The optimal charging/discharging priority can lead to 29.6 %∼75.2 % cost reduction, and the optimal operating thresholds can always achieve the lowest LPSP, illustrating the superiority of the proposed operation strategies; (3) the techno-economic performance of all HESSs varies significantly with the PoE level and load profile, while their rankings remain basically unchanged, showing the robustness of the comparison results.

Scenario analysis, management, and optimization of a new Vehicle-to-Micro-Grid (V2μG) network based on off-grid renewable building energy systems

To fully exploit the potential of decarburization in the transport sector (e.g., electric vehicles (EV)) and energy sector (e.g., building energy system), this paper proposes a new concept of ‘Vehicle-to-Micro-Grid (V2μG) network’ that incorporates the off-grid building energy system with flexible power storage/supply provided by battery EVs (BEVs) and fuel cell EVs (FCEVs). The work is conducted with three main contributions: 1) a rule-based energy management strategy is proposed to study the impact of the V2μG interactions on the EV battery degradation; 2) a scenario analysis based on four working modes is conducted to evaluate the energy efficiency, costing, environmental impacts, and component ageing of the proposed V2μG network; 3) the optimum settings for system configuration, capacity, and operation strategy of the V2μG network are obtained by the NSGA-II algorithm. The study suggested that the degradation of lithium-ion batteries in BEV can be reduced by 13% compared to the network without FCEVs. In a community with 160 households and 200 EVs, the optimal V2μG network can reduce carbon dioxide emissions by 515.56 tons annually compared to the conventional off-grid building energy system powered by internal combustion engines.

Deep Learning Approaches for Long-Term Global Horizontal Irradiance Forecasting for Microgrids Planning

Providing sustainable energy to rural communities is considered in Sustainable Development Goal 7. Off-grid renewable energy systems arise as an affordable solution due to their portability and the availability of renewable sources for rural communities. In this work, to deal with the uncertainties of solar resources, we employ two deep learning models (feed forward and recurrent neural networks) to predict renewable sources in a long-term horizon. To this aim, the approach presented takes into account the necessity of a high enough resolution in the forecasting output. As a case study, we employ open source data for a location in Michoacan, Mexico as well as open source programming frameworks to ensure the replicability of the numerical experiments. The results show that our prediction model performs excellently with respect to the baseline methods (ARIMA, exponential smoothing, and seasonal naive) in terms of the evaluation metrics MASE (18.5% of reduction with respect to seasonal naive), RMSE (24.7%), WAPE (13.1%), MAE (12.9%), and APB (8.9%).

Solar–Biogas Microgrid: A Strategy for the Sustainable Development of Rural Communities in Pakistan

Access to uninterrupted power is not a luxury but a basic need. Rural communities living far from the national grid, particularly those in the southern region of Pakistan’s Khyber Pakhtunkhwa province, have limited access to a reliable power supply. In order to provide sustainable electricity, small-scale off-grid renewable energy systems are increasingly used for rural electrification. These systems are commonly known as stand-alone home systems or community micro-grids. This paper proposes an off-grid solar–biogas micro-grid for rural communities in the Lakki Marwat district of Khyber Pakhtunkhwa, Pakistan. The area is mainly dependent upon income from the agricultural and livestock sectors. HOMER was used to simulate the electric power system, while RET-Screen was used to analyze the economics of the system. The optimized system’s results demonstrate that the most economically and technically possible system, which produces 515 kWh and 338.50 m3 biogas daily, is made up of a 30-kW photovoltaic system coupled with a 37-kW biomass hybrid system, a 64-kWh battery storage capacity, and a 20-kW invertor. The system will meet the cooking and power needs of 900 individuals who reside in 100 homes. In addition to household users, the system will provide fixed-priced electricity to productive buildings, and free electricity to community buildings. The system will generate 1300 kg of organic fertilizer each day, which will be sold to local farmers for 50% less than what it would cost on the open market. The proposed approach is techno-economically viable based on the payback period and internal rate of return.

Selection of the best dispatch strategy considering techno-economic and system stability analysis with optimal sizing

In this paper, optimized hybrid off-grid renewable energy systems (HRES) have been designed for two divisional locations in Dhaka and Khulna in Bangladesh. An analysis is conducted using five different load dispatch strategies to find the best dispatch strategy for a cost-effective and technically feasible Islanded hybrid microgrid that will support the growing power demand. A comparative analysis among the various dispatch strategies is also presented to find out the best and worst dispatch strategies for the proposed HRESs. The two HRESs consist of solar PV, a backup diesel generator, wind turbine, load demand of 23.31 kW, and a battery storage system. HOMER predictive strategy, Generator Order, Load Following, Combined Dispatch, and Cycle Charging dispatch strategy has been adopted for evaluation. The two HRESs are optimized for CO2 emission, Levelized Cost of Energy, and Net Present Cost minimization along with a feasible and stable voltage-frequency response on basis of the five dispatch techniques. The techno-economic analysis of the two HRESs is conducted using the HOMER Pro software platform. For the power system response analysis, MATLAB/Simulink is used. This study provides a complete guideline for determining the optimum component sizing ensuring optimum operation and possible costing estimation for the optimized performance of the two HRESs for different dispatch techniques. Also, a comparative study among the designed HRESs, other hybrid systems, and conventional power plants is conducted to prove the significance of this research work.

Operational optimization for off-grid renewable building energy system using deep reinforcement learning

With the application of renewable energy in single office buildings, an increasing number of power grids require building systems coupled with renewable energy to realize off-grid operation. However, the uncertainty of renewable energy sources and the safety of the corresponding energy storage equipment have become major challenges for these systems. Reinforcement learning has made considerable progress in the field of building control as an advanced control algorithm; however, research on its application to the off-grid operation of renewable energy systems, particularly the specific reward function design is limited.Therefore, this study considered the off-grid operation of a renewable building energy system and the safety (prevention of deterioration) of its battery as optimization goals. This study is based on a real building energy system, through the operation control of generators, solar photovoltaics, and batteries to achieve optimization purposes. Aiming at these two optimization goals, this study introduces a detailed reward function design and complete reinforcement learning workflow through a Gaussian distribution. Two deep reinforcement learning (DRL) algorithms were trained and verified by offline reinforcement learning based on the measured data-sets of actual existing buildings in Japan. The results show that the proposed reinforcement learning design can better achieve the two optimization goals of off-grid operation and battery safety under ordinary and extreme conditions. On off-grid operation tasks, the best DRL algorithm can achieve a mean hourly grid power purchase error of less than 2 kWh for the entire optimization window. Furthermore, the proposed deep reinforcement learning algorithms can simultaneously maintain a maximum average 7.72 h of battery unsafe state over the entire 168-hour optimization window.

Quantitative evaluation of renewable-energy-based remote microgrids: curtailment, load shifting, and reliability

Achieving carbon-free electricity for all can be facilitated by setting up small to medium-scale off-grid renewable energy systems (RES); however, the variability of renewable energy sources challenges system reliability. In this paper, we optimize a range of RES configurations considering different combinations of solar, wind, battery and pumped hydro storage for a specific location under diverse flexibility scenarios: one of load shifting (flexibility of power demand by changing consumption patterns by 0–30%), one of small load loss/shedding (0–5%), and one of both. Next, we quantitatively assess the impact of the flexibility options, renewable source and energy storage type impact on the microgrids' optimal energy management, cost, curtailment, and reliability level. The results show that RES utilizes 40–45% of added flexibility during particular day hours. The period of these critical hours is significantly affected by the RES type: namely the morning hours for battery storage-based RES while the evening hours for pumped hydro storage-based RES. The load shifting decreases the RES cost by 4–9%, but it does not reduce the overall system curtailment due to the substantial decrease in the energy storage size compared to the RE generator size when the load deferment is allowed. However, load loss scenarios not only reduced the RES cost by 30–50% but they also lessened the system curtailment by 40–51%. The off-grid RES cost can be reduced up to 60–67% by increasing the system flexibility. Moreover, the type of RE input source substantially impacts the system’ self-sufficiency, energy served to energy generated ratio, and capacity factor, whose values vary in ranging 0.42–0.72, 0.18–0.5, and 0.13–0.23, respectively.

Socio-techno-economic assessment to design an appropriate renewable energy system for remote agricultural communities in developing countries

Access to clean energy for communities living in remote areas where grid extension is considered unfeasible can be provided by off-grid electrification systems using renewable energy (RE). Especially in developing countries, ensuring the appropriateness of such systems is crucial because it will determine the system's sustainability despite its limited resources. This study demonstrates the design process of an appropriate system by assessing the potential of three RE sources: solar, wind, and biomass in an oil palm and rubber-tree plantation village in South Kalimantan Province, Indonesia. A social assessment is done to avoid sustainability issues of the previously introduced technology intervention by identifying correlations between residents' attributes and satisfaction levels on a selection of social values through multiple correspondence analysis (MCA) and nonparametric methods. The techno-economic assessment and sensitivity analysis uses local data processed with the Hybrid Optimization of Multiple Energy Resources (HOMER) software. Results identified the need for a more appropriate clean energy supply for cooking, the potential role of modern technology, and access to information and communication in income generation, among other needs and opportunities that can be linked with the energy system design. The technical assessment showed that a centralized solar power plant paired with a diesel engine could provide power to the village. However, the cost of electricity (COE) is much higher than the price cap introduced by the national electricity company. This study urges a clear mechanism and a guarantee for the delivery of feed-in-tariffs (FIT) and a price cap exemption for off-grid RE systems. Furthermore, a people-centered public–private partnership business model and a remote capacity-building intervention are also needed. An appropriate energy system design must be supported by an enabling environment and supporting policies to be feasible.

Design, dynamic simulation, and optimal size selection of a hybrid solar/wind and battery-based system for off-grid energy supply

Remote energy systems are applicable, especially in critical circumstances like earthquakes, floods, etc. Besides, renewable energy systems are suitable energy sources in remote areas. Therefore, Due to high solar and wind potential and its location as an earthquake-prone, a solar-wind energy system for a remote application on a Conex is presented in this study. Considered wind turbines have power production of 1 kW and 3 kW. PV panels have an efficiency of about 20%. This Conex has 12 m2 of the area and resides three to five people and supplies 520 W power consumption of equipment except for the HVAC unit. A considered lifetime of the Conex is 20 years. The considered capacity of batteries is about 100–200 Ah. Six cities are selected as representatives of the six major climate types in Iran. The studied system is optimized for each city. The heating and cooling demand of Conex in cities are specified using TRNSYS software. The heat pump's electricity requirement as a cooling and heating unit with COP of four, lighting (20 W), laptops (140 W), and refrigerators (360 W) is calculated for Conex in each city. Mixed-integer linear programming is employed to optimize an off-grid renewable energy system to supply this electricity demand. The system's total cost, which includes the initial and O&M cost of PV panels and wind turbines, and the capital and replacement cost of the batteries together with the loss of power supply probability during a year, are considered two objective functions. The interest rate is assumed to be 15%. Achieved results show that Iran's solar and wind potential can balance the required load of the Conex with a probability of power outage as low as 1% of a year. However, there is a variance in the cost of the required renewable energy system for supplying Conex power consumption for different climate zones: in the Mediterranean region, the system costs 500–5000 $ as the most expensive one, and in the hot coastal dry region, it costs 900 $-2000 $ as the most cost-efficient region. Finally, results show that wind turbines can be cost-effective energy suppliers for most considered climates, especially considering O&M costs. This study can fill the gap of requiring off-grid temporary portable residences in different climates after disasters, which destroy buildings. Therefore, the lack of these temporary residences can be supplied without adding greenhouse gases to the environment, which reduces greenhouse emissions and electrical grid dependence of the building sector.

Optimal sizing of different configuration of photovoltaic, fuel cell, and biomass-based hybrid energy system.

The need for power is rising on a daily basis all across the world. Due to the finite supply of fossil fuels, it is critical to develop innovative non-renewable energy systems that can reduce reliance on conventional energy sources. A hybrid off-grid renewable energy system might be utilized to reduce reliance on traditional energy supplies, and to enhance the reliability of the renewable energy system. The process of selecting the appropriate combinations of components and their costs in order to produce an affordable, dependable, and effective alternative energy supply is known as hybrid system optimization. Hybrid energy technology can meet the energy needs of community very effectively. The goal of improving hybrid energy system control, size, and component selection is to offer society with a cost-effective electric power solution. The main aim of this paper is to use the proposed algorithm, i.e., hybrid chaotic particle swarm optimization and slime mould algorithm (HCPSOSMA), and Hybrid Optimization Model for Electric Renewable (HOMER) Software (Version 3.14.0) to minimize the levelized cost of energy (LCOE) supply and annualized cost system (ACS). The findings show that the proposed algorithm has an excellent convergence characteristic and the capacity to provide high-quality output. According to the simulation findings, the suggested off-grid-connected hybrid (solar PV/biomass/FC) power system is the most suitable and cost-effective option for the selected location, Patiala in Indian sub-continent.

Techno-economic study of off-grid renewable energy system in Darma valley, Uttarakhand, India

Techno-economic study of off-grid renewable energy system in Darma valley, Uttarakhand, India