Renewable energy solutions are appropriate for on-grid and off-grid applications, acting as a supporter for the utility network or rural locations without the need to develop or extend costly and difficult grid infrastructure. As a result, hybrid renewable energy sources have become a popular option for grid-connected or standalone systems. This paper examines hybrid renewable energy power production systems with a focus on energy sustainability, reliability due to irregularities, techno-economic feasibility, and being environmentally friendly. In attaining a reliable, clean, and cost-effective system, sizing optimal hybrid renewable energy sources (HRES) is a crucial challenge. The presenters went further to outline the best sizing approach that can be used in HRES, taking into consideration the key components, parameters, methods, and data. Moreover, the goal functions, constraints from design, system components, optimization software tools, and meta-heuristic algorithm methodologies were highlighted for the available studies in this timely synopsis of the state of the art. Additionally, current issues resulting from scaling HRES were also identified and discussed. The latest trends and advances in planning problems were thoroughly addressed. Finally, this paper provides suggestions for further research into the appropriate component sizing in HRES.
Access to inexpensive, clean energy is a key factor in a country’s ability to grow sustainably The production of electricity using fossil fuels contributes significantly to global warming and is becoming less and less profitable nowadays. This work therefore proposes to study the different possible scenarios for the replacement of light fuel oil (LFO) thermal power plants connected to the electrical network in northern Cameroon by renewable energy plants. Several scenarios such as the combination of solar photovoltaic (PV) with a pumped hydro storage system (PHSS), Wind and PHSS and PV-Wind-PHSS have been studied. The selected scenarios are evaluated based on two factors such as the system’s total cost (TC) and the loss of load probability (LOLP). To achieve the results, metaheuristics such the non-dominated sorting whale optimization algorithm (NSWOA) and non-dominated sorting genetic algorithm-II (NSGA-II) have been applied under MATLAB software. The optimal sizing of the components was done using hourly meteorological data and the hourly power generated by the thermal power plants connected to the electrical grid. Both algorithms provided satisfactory results. However, the total cost in the PV-PHSS, Wind-PHSS, and PV-Wind-PHSS scenarios with NSWOA is, respectively, 1%, 6%, and 0.2% lower than with NSGA-II. According to NSWOA results, the total cost for the PV-Wind-PHSS scenario at LOLP 0% is 4.6% and 17% less than the Wind-PHS and PV-PHSS scenarios, respectively. The profitability study of all three scenarios showed that the project is profitable regardless of the scenario considered.
Renewable energies are clean alternatives to the highly polluting fossil fuels that are still used in the power generation sector. The goal of this research was to look into replacing a Heavy Fuel Oil (HFO) thermal power plant in Limbe, southwest Cameroon, with a hybrid photovoltaic (PV) and wind power plant combined with a storage system. Lithium batteries and hydrogen associated with fuel cells make up this storage system. The total cost (TC) of the project over its lifetime was minimized in order to achieve the optimal sizing of the hybrid power plant components. To ensure the reliability of the new hybrid power plant, a criterion measuring the loss of power supply probability (LPSP) was implemented as a constraint. Moth Flame Optimization (MFO), Improved Grey Wolf Optimizer (I-GWO), Multi-Verse Optimizer (MVO), and African Vulture Optimization Algorithm (AVOA) were used to solve this single-objective optimization problem. The optimization techniques entailed the development of mathematical models of the components, with hourly weather data for the selected site and the output of the replaced thermal power plant serving as input data. All four algorithms produced acceptable and reasonably comparable results. However, in terms of proportion, the total cost obtained with the MFO algorithm was 0.32%, 0.40%, and 0.63% lower than the total costs obtained with the I-GWO, MVO, and AVOA algorithms, respectively. Finally, the effect of the type of storage coupled to the PV and wind systems on the overall project cost was assessed. The MFO meta-heuristic was used to compare the results for the PV–Wind–Hydrogen–Lithium Battery, PV–Wind–Hydrogen, and PV–Wind–Lithium Battery scenarios. The scenario of the PV–Wind–Hydrogen–Lithium Battery had the lowest total cost. This scenario’s total cost was 2.40% and 18% lower than the PV–Wind–Hydrogen and PV–Wind–Lithium Battery scenarios.
This study demonstrates how to use grid-connected hybrid PV and biogas energy with a SMES-PHES storage system in a nation with frequent grid outages. The primary goal of this work is to enhance the HRES’s capacity to favorably influence the HRES’s economic viability, reliability, and environmental impact. The net present cost (NPC), greenhouse gas (GHG) emissions, and the likelihood of a power outage are among the variables that are examined. A mixed solution involves using a variety of methodologies to compromise aspects of the economy, reliability, and the environment. Metaheuristic optimization techniques such as non-dominated sorting whale optimization algorithm (NSWOA), multi-objective grey wolf optimization (MOGWO), and multi-objective particle swarm optimization (MOPSO) are used to find the best size for hybrid systems based on evaluation parameters for financial stability, reliability, and GHG emissions and have been evaluated using MATLAB. A thorough comparison between NSWOA, MOGWO, and MOPSO and the system parameters at 150 iterations has been presented. The outcomes demonstrated NSWOA’s superiority in achieving the best optimum value of the predefined multi-objective function, with MOGWO and MOPSO coming in second and third, respectively. The comparison study has focused on NSWOA’s ability to produce the best NPC, LPSP, and GHG emissions values, which are EUR 6.997 × 106, 0.0085, and 7.3679 × 106 Kg reduced, respectively. Additionally, the simulation results demonstrated that the NSWOA technique outperforms other optimization techniques in its ability to solve the optimization problem. Furthermore, the outcomes show that the designed system has acceptable NPC, LPSP, and GHG emissions values under various operating conditions.
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