Holistic data‐driven method for optimal sizing and operation of an urban islanded microgrid
Abstract This study proposes a holistic data‐driven method for the optimal sizing and operation of a building‐level islanded microgrid with renewable energy resources in an urban setting. Firstly, various metres are integrated on an energy monitoring platform where field data are collected. A random...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Wiley
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/a87714ab4b9c4075b3550ee2688f0b95 |
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| Sumario: | Abstract This study proposes a holistic data‐driven method for the optimal sizing and operation of a building‐level islanded microgrid with renewable energy resources in an urban setting. Firstly, various metres are integrated on an energy monitoring platform where field data are collected. A randomised learning‐based forecasting model is designed for supply/demand prediction in the microgrid. Based on the forecasting results, data‐driven uncertainty modelling is used to characterise the uncertainties associated with renewable energy supply and loads. An optimal sizing approach is then proposed to determine the optimal sizes for energy storage systems (ESSs) and distributed generators with the overall aim of minimising the investment and maintenance costs. Based on the optimal sizing and uncertainty scenarios, a two‐stage coordinated energy management method is proposed to minimise the operating cost under uncertainties. To validate the proposed method, it is compared with a benchmark method. Simulation results show that the proposed method can reduce the system cost while preserving the ESS lifetime. The developed methods are packaged onto a real‐time platform for implementation. |
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