This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficienc.
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Microgrids play a crucial role in modern energy systems by integrating diverse energy sources and enhancing grid resilience. This study addresses the optimization of microgrids through the deployment of high
Microgrids can be designed through (dc) or An efficient method in optimizing a multicarrier energy microgrid structure is proposed in Reference 93, where, the term microgrid structure is
The first challenge in regulated DC microgrids is constant power loads. 17 The second challenge stems from the pulsed power load problem that commonly occurs in indoor microgrids. The pulsed loads in the microgrid limit
Review of optimization techniques used in microgrid energy management systems. Mixed integer linear program is the most used optimization technique. Multi-agent systems are most ideal for solving unit commitment and demand management. State-of-the-art machine learning algorithms are used for forecasting applications.
Robust optimization techniques can help microgrids mitigate the risks associated with over or under-estimating energy availability, ensuring a more reliable power supply and reducing costly backup generation [96, 102].
Therefore, an optimal energy management technique is required to achieve a high level of system reliability and operational efficiency. A state-of-the-art systematic review of the different optimization techniques used to address the energy management problems in microgrids is presented in this article.
Resilience enhancement Microgrid optimization promotes resilience by reducing the reliance on centralized power grids, which are vulnerable to outages, cyberattacks, and natural disasters.
An EMS is apt to optimize the operation of microgrids from several points of view. Optimal production planning, optimal demand-side management, fuel and emission constraints, the revenue of trading spinning and non-spinning reserve capacity can effectively be managed by EMS.
Energy Storage and Stochastic Optimization in Microgrids—Studies involving energy management, storage solutions, renewable energy integration, and stochastic optimization in multi-microgrid systems. Optimal Operation and Power Management using AI—Exploration of microgrid operation, power optimization, and scheduling using AI-based approaches.
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