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A hybrid renewable energy system combining solar photovoltaic generation, battery storage, and grid interaction was designed and evaluated for a small hotel. Using MATLAB simulations, the Arctic Puffin optimisation algorithm was applied to determine the optimal sizes for the solar array and battery storage, alongside grid electricity transactions, with the objective of minimising the levelised cost of energy. The optimised configuration removed the requirement for diesel generators and achieved a 100 percent renewable energy fraction. The resulting levelised cost of energy was $0.1236 per kilowatt-hour, with a total net present cost of approximately $1.16 million and an annualised system cost of $91,494. Additionally, the setup produced surplus electricity, exporting 351,600 kilowatt-hours back to the grid while drawing 99,440 kilowatt-hours, confirming the rapid convergence and operational efficiency of the optimisation model.
Commercial hospitality facilities often rely heavily on expensive, polluting diesel generators to handle grid instability or power demands. Demonstrating that an algorithmically optimised mix of solar arrays, battery storage, and grid exchange can reach a complete renewable fraction at a defined cost provides commercial property owners with a clear technical benchmark for cutting operational expenses and reducing carbon emissions.
This research is relevant to engineering consultants, renewable energy developers, and small commercial operators seeking optimal equipment sizing for microgrids. Because the study relies entirely on MATLAB computer simulations and algorithmic modelling, it remains at an early computational stage. Commercial adoption would require applied physical testing, site-specific electrical engineering, and compliance with local utility interconnection frameworks.
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In an era where sustainable energy solutions are critical, this research work presents and introduces the optimal design of a hybrid renewable energy system (HRES) for a small hotel by integrating solar power, battery storage, and grid interactions. MATLAB was used for the simulations, and the Arctic Puffin optimization (APO) algorithm was employed to minimize the levelized cost of energy (LCOE). The optimization considered the PV power, battery size, and grid purchases. Results show that the system effectively utilizes solar energy and battery storage, thus reducing reliance on the grid and eliminating the need for diesel generators. The APO algorithm converged rapidly, thus validating its efficiency. The outcomes of the optimization process show an LCOE of $0.1236 per kWh and a total net present cost (TNPC) of $1.1567 × 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup>. The annualized cost of the system (ACS) was determined to be $91,494. Notably, the system sold 351,600 kWh back to the grid while purchasing 99,440 kWh, achieving a renewable energy fraction (REF) of 100%. This optimized hybrid renewable energy system offers reliable, cost-effective, and sustainable energy, promoting the adoption of clean energy technologies.
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DOI: 10.1109/mepcon63025.2024.10850154
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