Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting

Lulu Wen, Kaile Zhou, Shanlin Yang and Xinhui Lu

Energy, 2019, vol. 171, issue C, 1053-1065

Abstract: A deep recurrent neural network with long short-term memory units (DRNN-LSTM) model is developed to forecast aggregated power load and the photovoltaic (PV) power output in community microgrid. Meanwhile, an optimal load dispatch model for grid-connected community microgrid which includes residential power load, PV arrays, electric vehicles (EVs), and energy storage system (ESS), is established under three different scheduling scenarios. To promote the supply-demand balance, the uncertainties of both residential power load and PV power output are considered in the model by integrating the forecasting results. Two real-world data sets are used to test the proposed forecasting model, and the results show that the DRNN-LSTM model performs better than multi-layer perception (MLP) network and support vector machine (SVM). Finally, particle swarm optimization (PSO) algorithm is used to optimize the load dispatch of grid-connected community microgrid. The results show that EES and the coordinated charging mode of EVs can promote peak load shifting and reduce 8.97% of the daily costs. This study contributes to the optimal load dispatch of community microgrid with load and renewable energy forecasting. The optimal load dispatch of community microgrid with deep learning based solar power and load forecasting achieves total costs reduction and system reliability improvement.

Keywords: Optimal load dispatch; Community microgrid; Load forecasting; Solar power; Deep learning (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (57)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:171:y:2019:i:c:p:1053-1065

DOI: 10.1016/j.energy.2019.01.075

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