Improved TLBO algorithm for optimal energy management in a hybrid microgrid with support vector machine-based forecasting of uncertain parameters

Krishna, Raji; Hemamalini, S.

doi:10.1016/j.rineng.2024.102992

Results in Engineering

2024

DOI: 10.1016/j.rineng.2024.102992

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Improved TLBO algorithm for optimal energy management in a hybrid microgrid with support vector machine-based forecasting of uncertain parameters

Raji Krishna,

S. Hemamalini

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Cited by 3 publications

References 33 publications

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An efficient and resilient energy management strategy for hybrid microgrids inspired by the honey badger's behavior

Shaier,

Elymany,

Enany

et al. 2024

Results in Engineering

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An efficient and resilient energy management strategy for hybrid microgrids inspired by the honey badger's behavior

Shaier,

Elymany,

Enany

et al. 2024

Results in Engineering

View full text Add to dashboard Cite

Enhanced wombat optimization algorithm for multi-objective optimal power flow in renewable energy and electric vehicle integrated systems

Nagarajan,

Rajagopalan,

Bajaj

et al. 2025

Results in Engineering

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A Metaheuristic Approach to Analyze the Techno-Economical Impact of Energy Storage Systems on Grid-Connected Microgrid Systems Adapting Load-Shifting Policies

Dey,

Krishnamurthy,

Fose

et al. 2024

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Battery energy storage systems (BESSs) and plug-in hybrid electric vehicles (PHEVs) are essential for microgrid operations to be financially viable. PHEVs can serve as mobile storage devices, storing excess energy during times of low demand and delivering it during times of high demand. By offering reliable on-site energy storage, BESSs improve cost efficiency by allowing the microgrid to store cheap, off-peak electricity and release it when prices increase. To minimize generation costs and alleviate grid stress during periods of high demand, load-shifting policies shift inelastic loads to off-peak hours when energy prices are lower. When combined, these tactics support dependable, affordable, and effective microgrid management. A recently developed RIME algorithm is used as the optimization tool to reduce the total operating cost (TOC) of an MG system for three cases and three situations. The cases emphasize a modified load demand style influenced by the optimal load-shifting method (OLSM) and order characteristics load-shifting policy (OCLSP), whereas the situations refer to the inclusion of ESS in the MG system. The TOC decreased from $2624 without ESS to $2611 and $2331 with PHEVs and BESSs, respectively. These costs were further reduced to $1192, $1162, and $1147, respectively, when OLSM was implemented to restructure the base load demand. Additionally, a balance between a minimal TOC and carbon emission was obtained when an OLSM-based load demand model was used with BESSs. The RIME algorithm outperformed many recently developed algorithms and is consistent and robust, yielding better quality solutions.

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Improved TLBO algorithm for optimal energy management in a hybrid microgrid with support vector machine-based forecasting of uncertain parameters

Cited by 3 publications

References 33 publications

An efficient and resilient energy management strategy for hybrid microgrids inspired by the honey badger's behavior

An efficient and resilient energy management strategy for hybrid microgrids inspired by the honey badger's behavior

Enhanced wombat optimization algorithm for multi-objective optimal power flow in renewable energy and electric vehicle integrated systems

A Metaheuristic Approach to Analyze the Techno-Economical Impact of Energy Storage Systems on Grid-Connected Microgrid Systems Adapting Load-Shifting Policies

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