Volume 19, Issue 4 (December 2023)                   IJEEE 2023, 19(4): 151-170 | Back to browse issues page


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VAISH J, Tiwari A K, K. S. Cost Optimization and Battery Sizing of Grid-Connected Microgrid with Distributed Energy Resources Using Random Forest Technique. IJEEE 2023; 19 (4) :151-170
URL: http://ijeee.iust.ac.ir/article-1-2924-en.html
Abstract:   (817 Views)
In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.
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Type of Study: Research Paper | Subject: Artificial Intelligence Techniques
Received: 2023/06/06 | Revised: 2024/04/20 | Accepted: 2023/12/13

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

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.