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<p>The paper reviews several energy management system (EMS) approaches designed to optimize energy use within microgrids, taking into account uncertainties during day-ahead and real-time periods. Energy systems engineering covers residential areas, businesses, virtual power plants, electric cars and multi-carrier microgrids, as unpredictability is common in each. The document summarizes the best techniques used in managing energy for microgrids. They seek to schedule energy use efficiently, while dealing with all sorts of random events and changes in schedules. The text points out that there are several uncertainties associated with using microgrids such as errors in forecasts, uneven demand, wear out of equipment and asset lifespan. Such uncertain conditions can influence how well and how reliably the microgrid operates. Some different techniques are studied to account for uncertainty in microgrid applications. These models are called probabilistic, possibilistic, information gap theory and deterministic models. Every method gives different advice on managing uncertainties in setting up energy schedules. Factors like the modeling used, data resources, application traits, the platform used in real time and the timing of the optimization process decide which method to use. Various techniques in optimization may prove better for dealing with specific challenges and rules you have. The guide is meant to serve researchers by helping them pick the best optimization approach considering the unique concerns and particulars of energy scheduling. Researchers rely on using and choosing workable techniques because they consider the strengths and weaknesses of each. It talks about how microgrid operations can be upgraded, even in the face of uncertainty and also explains new developments affecting the sector. Some examples are improved prediction through analytics, machine learning, effective control across many locations and making use of new technologies to handle random problems and enhance how microgrids run. It points researchers and practitioners to best practices for increasing the reliability, efficiency and resilience of microgrids in changing and unpredictable situations.</p>

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A Comparative Study of Various Optimization Techniques for Enhancing Energy Management in Hybrid Microgrids

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • S. Kaliappan,
  • R. Krishna Priya,
  • Rabinarayan Sethi,
  • Abdulrajak Buradi,
  • Bibhu Prasad Ganthia

摘要

The paper reviews several energy management system (EMS) approaches designed to optimize energy use within microgrids, taking into account uncertainties during day-ahead and real-time periods. Energy systems engineering covers residential areas, businesses, virtual power plants, electric cars and multi-carrier microgrids, as unpredictability is common in each. The document summarizes the best techniques used in managing energy for microgrids. They seek to schedule energy use efficiently, while dealing with all sorts of random events and changes in schedules. The text points out that there are several uncertainties associated with using microgrids such as errors in forecasts, uneven demand, wear out of equipment and asset lifespan. Such uncertain conditions can influence how well and how reliably the microgrid operates. Some different techniques are studied to account for uncertainty in microgrid applications. These models are called probabilistic, possibilistic, information gap theory and deterministic models. Every method gives different advice on managing uncertainties in setting up energy schedules. Factors like the modeling used, data resources, application traits, the platform used in real time and the timing of the optimization process decide which method to use. Various techniques in optimization may prove better for dealing with specific challenges and rules you have. The guide is meant to serve researchers by helping them pick the best optimization approach considering the unique concerns and particulars of energy scheduling. Researchers rely on using and choosing workable techniques because they consider the strengths and weaknesses of each. It talks about how microgrid operations can be upgraded, even in the face of uncertainty and also explains new developments affecting the sector. Some examples are improved prediction through analytics, machine learning, effective control across many locations and making use of new technologies to handle random problems and enhance how microgrids run. It points researchers and practitioners to best practices for increasing the reliability, efficiency and resilience of microgrids in changing and unpredictable situations.