Optimal economic sizing of stand-alone hybrid renewable energy system (HRES) suiting to the community in Kurukshetra, India

Saraswat, Ravita; Suhag, Sathans

doi:10.12785/ijcds/120166

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By handling non-convex optimization problems and having the potential to find and reach global optima, which is unusual when using traditional techniques, metaheuristic methods have an edge over traditional optimization techniques. According to the literature, many metaheuristic algorithms have been used, including the whale optimization algorithm (WOA) [84], genetic algorithm (NSGA-II) [85], particle swarm optimization (PSO) [86], teaching-learning-based optimization (TLBO) [87], chameleon swarm algorithm (CSA) [88], grey wolf optimization (GWO) [89], and seagull optimization algorithm (SOA) [90]. However, only one metaheuristic approach was used in those studies for the best sizing because metaheuristic approaches are stochastic, which is essential to assess and contrast how well these approaches perform in terms of optimal sizing.…”

Section: Introductionmentioning

confidence: 99%

Techno-Economic Analysis and Optimization of Hybrid Renewable Energy System with Energy Storage under Two Operational Modes

et al. 2023

View full text Add to dashboard Cite

Access to cheap, clean energy has a significant impact on a country’s ability to develop sustainably. Fossil fuels have a major impact on global warming and are currently becoming less and less profitable when used to generate power. In order to replace the diesel generators that are connected to the university of Debre Markos’ electrical distribution network with hybrid renewable energy sources, this study presents optimization and techno-economic feasibility analyses of proposed hybrid renewable systems and their overall cost impact in stand-alone and grid-connected modes of operation. Metaheuristic optimization techniques such as enhanced whale optimization algorithm (EWOA), whale optimization algorithm (WOA), and African vultures’ optimization algorithm (AVOA) are used for the optimal sizing of the hybrid renewable energy sources according to financial and reliability evaluation parameters. After developing a MATLAB program to size hybrid systems, the total current cost (TCC) was calculated using the aforementioned metaheuristic optimization techniques (i.e., EWOA, WOA, and AVOA). In the grid-connected mode of operation, the TCC was 4.507 × 106 EUR, 4.515 × 106 EUR, and 4.538 × 106 EUR, respectively, whereas in stand-alone mode, the TCC was 4.817 × 106 EUR, 4.868 × 106 EUR, and 4.885 × 106 EUR, respectively. In the grid-connected mode of operation, EWOA outcomes lowered the TCC by 0.18% using WOA and 0.69% using AVOA, and by 1.05% using WOA and 1.39% using AVOA in stand-alone operational mode. In addition, when compared with different financial evaluation parameters such as net present cost (NPC) (EUR), cost of energy (COE) (EUR/kWh), and levelized cost of energy (LCOE) (EUR/kWh), and reliability parameters such as expected energy not supplied (EENS), loss of power supply probability (LPSP), reliability index (IR), loss of load probability (LOLP), and loss of load expectation (LOLE), EWOA efficiently reduced the overall current cost while fulfilling the constraints imposed by the objective function. According to the result comparison, EWOA outperformed the competition in terms of total current costs with reliability improvements.

show abstract