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Subject Area

Computer Science

Article Type

Original Study

Abstract

This paper presents the simulation and optimization of hybrid energy systems integrating renewable energy and storage technologies. Linear Programming (LP) is utilized to minimize operational costs, while a Hidden Markov Model (HMM) improves reliability by accurately predicting energy flow. Hardware-in-the-loop simulation in Proteus demonstrates efficient energy trading, significantly reducing dependence on conventional sources. The results from a 24-hour analysis show that the system achieves an 87.14% load factor, indicating high efficiency in utilizing the system's rated capacity, 26.23 system reliability, and a 20% improvement in cost efficiency and enhanced sustainability, providing a robust and practical framework for hybrid energy solutions applicable to real-world systems. The study also emphasizes how crucial it is to combine predictive modeling and optimization techniques in order to manage uncertainties in renewable generating, such as variations in wind and solar power.  The proposed system efficiently balances supply and demand by using storage technologies, guaranteeing service continuity even under unpredictable circumstances.  By bridging the gap between theoretical design and actual deployment, hardware-in-the-loop simulation confirms the model's practical applicability.  According to the findings, these hybrid frameworks may be expanded to bigger energy infrastructures or microgrids, allowing nations to satisfy their expanding energy needs while lessening their environmental effect.  All things considered, the study advances affordable, dependable, and sustainable hybrid energy systems that aid in the shift to greener energy futures.

Keywords

Hybrid Energy Systems, LP, HMM, Energy Trading Strategies, Energy Storage Management.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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