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Therefore, the main concern of this paper is to investigate the impact of micro-grid''s topologies on developing an efficient micro-grid''s EMS and an optimization model using …
This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability.
In this paper, we use the modified whale algorithm to solve the microgrid optimization problem. First, we set the economic cost and environmental cost as two modeling …
The GWO algorithm, as discussed in Sect. Optimization technique: Grey Wolf optimization GWO, is implemented in MATLAB software to get the optimal solution of the developed optimization problem ...
The remainder of the paper is structured as follows: Section II provides a brief overview of the structure of the studied microgrid, while Section III describes the proposed framework for the …
An enumeration-based iterative optimization algorithm (EBIOA) was used by Bhuiyan et al. to address the optimal sizing of an islanded microgrid, ensuring a minimized …
At the same time, due to the standard moth-flame optimization algorithm having low optimization accuracy and are easy to fall into local optimal solution, an improved …
Intelligent algorithms, notably Spider Monkey Optimization and Firefly Algorithm, have demonstrated efficacy in solving optimization problems within radial distribution networks and microgrid energy scheduling. Leveraging the advantages of these algorithms, the proposed hybrid approach aims to enhance optimization capabilities further.
This paper presents a model for microgrid optimal scheduling considering multi-period islanding constraints. The objective of the problem is to minimize the microgrid …
In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with …
The conceptual framework of the algorithm utilized for optimization of the IGDT-based MG in the present research is the direction of flow algorithm (FDA). ϕ is within the …
This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial …
Therefore, this paper proposes a surrogate model particle swarm optimization algorithm based on the global-local search mechanism. Firstly, aiming at the problem that the statistical information ...
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.
A chaos sparrow search algorithm based on Bernoulli chaotic mapping, dynamic adaptive weighting, Cauchy mutation, and reverse learning is proposed, and different types of test …
The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative …
Research investigates methods to optimize the capacities and locations of storage systems in order to improve the resilience and flexibility of microgrids. Optimization Techniques: …
In addition to the algorithms mentioned before, other algorithms for resource optimization of microgrids have also been used in some studies, such as GWO, moth flame algorithm, ant colony algorithm, etc. These algorithms also have their own advantages in the resource optimization problem.
Several studies in the literature show that the optimization of a microgrid can be solved by various algorithms. The most frequently used algorithm type is a genetic algorithm (GA) [ 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95 ].
1. Introduction. Microgrid (MG) is a cluster of distributed energy resources (DER) that brings a friendly approach to fulfill energy demands in a reliable and efficient way in …
In order to avoid the defect that the traditional particle swarm optimization algorithm is easy to fall into the local optimal solution, this paper uses the combination of …
Then, we summarize the optimization framework for microgrid operation, which contains the optimization objective, decision variables and constraints. Next, we systematically review the optimization algorithms for microgrid operations, of which genetic algorithms and simulated annealing algorithms are the most commonly used.
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