Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm
Abstract An islanded hybrid AC‐DC microgrid interconnects renewable energy sources, distributed generators, and energy storage, primarily for remote areas without grid access. Its reliability depends on variable renewable output and load demand, while an energy management system optimizes power sche...
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13115 |
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| author | Raji Krishna Hemamalini S |
| author_facet | Raji Krishna Hemamalini S |
| author_sort | Raji Krishna |
| collection | DOAJ |
| description | Abstract An islanded hybrid AC‐DC microgrid interconnects renewable energy sources, distributed generators, and energy storage, primarily for remote areas without grid access. Its reliability depends on variable renewable output and load demand, while an energy management system optimizes power scheduling and reduces costs. In the first phase of this paper, uncertainty parameters like day‐ahead power from renewable energy sources (RES) and load demand (LD) are forecasted using the long short‐term memory (LSTM) deep learning algorithm. The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. In the second phase, the forecasted RES power and LD are used for optimal distributed generator (DG) scheduling using the improved grey wolf optimization (IGWO) algorithm. The objective of energy management in an islanded hybrid microgrid (HMG) is to minimize daily operating costs by considering load demand and the bidding costs of energy sources and storage devices. Two operational scenarios are evaluated to minimize the operating costs and optimize battery life. The proposed method, validated with IEEE standard test systems, is compared against several metaheuristic techniques. Results demonstrate that the improved grey wolf optimization (IGWO) algorithm is more effective at reducing costs and provides faster optimal solutions. |
| format | Article |
| id | doaj-art-7ec7e51589be4cc7976aa18ea8c8b78d |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-7ec7e51589be4cc7976aa18ea8c8b78d2025-01-30T12:15:53ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118163640365810.1049/rpg2.13115Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithmRaji Krishna0Hemamalini S1School of Electrical Engineering Vellore Institute of Technology Chennai Tamil Nadu IndiaSchool of Electrical Engineering Vellore Institute of Technology Chennai Tamil Nadu IndiaAbstract An islanded hybrid AC‐DC microgrid interconnects renewable energy sources, distributed generators, and energy storage, primarily for remote areas without grid access. Its reliability depends on variable renewable output and load demand, while an energy management system optimizes power scheduling and reduces costs. In the first phase of this paper, uncertainty parameters like day‐ahead power from renewable energy sources (RES) and load demand (LD) are forecasted using the long short‐term memory (LSTM) deep learning algorithm. The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. In the second phase, the forecasted RES power and LD are used for optimal distributed generator (DG) scheduling using the improved grey wolf optimization (IGWO) algorithm. The objective of energy management in an islanded hybrid microgrid (HMG) is to minimize daily operating costs by considering load demand and the bidding costs of energy sources and storage devices. Two operational scenarios are evaluated to minimize the operating costs and optimize battery life. The proposed method, validated with IEEE standard test systems, is compared against several metaheuristic techniques. Results demonstrate that the improved grey wolf optimization (IGWO) algorithm is more effective at reducing costs and provides faster optimal solutions.https://doi.org/10.1049/rpg2.13115distributed power generationenergy management systemsenergy storagehybrid renewable energy systemsload forecastingoptimisation |
| spellingShingle | Raji Krishna Hemamalini S Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm IET Renewable Power Generation distributed power generation energy management systems energy storage hybrid renewable energy systems load forecasting optimisation |
| title | Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm |
| title_full | Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm |
| title_fullStr | Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm |
| title_full_unstemmed | Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm |
| title_short | Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm |
| title_sort | long short term memory based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm |
| topic | distributed power generation energy management systems energy storage hybrid renewable energy systems load forecasting optimisation |
| url | https://doi.org/10.1049/rpg2.13115 |
| work_keys_str_mv | AT rajikrishna longshorttermmemorybasedforecastingofuncertainparametersinanislandedhybridmicrogridanditsenergymanagementusingimprovedgreywolfoptimizationalgorithm AT hemamalinis longshorttermmemorybasedforecastingofuncertainparametersinanislandedhybridmicrogridanditsenergymanagementusingimprovedgreywolfoptimizationalgorithm |