Data-Driven Energy Management System With Gaussian Process Forecasting and MPC for Interconnected Microgrids
Data-Driven Energy Management System With Gaussian Process Forecasting and MPC for Interconnected Microgrids
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DOI：
10.1109/tste.2020.3017224
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发表时间：
2021-01
影响因子：
8.8
通讯作者：
Leong Kit Gan;Pengfei Zhang;Jaehwa Lee;Michael A. Osborne;D. Howey
中科院分区：
文献类型：
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作者：
Leong Kit Gan;Pengfei Zhang;Jaehwa Lee;Michael A. Osborne;D. Howey
人们对预测和优化具有高比例可变可再生能源发电的微电网运行的兴趣正在增长。在本文中，我们研究和实验分析的高斯过程回归预测和模型预测控制算法在互联微电网的背景下的性能。该计划在六小时的时间范围内运行，取得了上级的结果，与假设完美预见的离线计算的最佳运行只有很小的偏差。我们还证明，虽然较长的地平线提供了一个更好的解决方案，在较低的电力成本，电池循环率也较高。最后，我们通过在微电网之间共享信息来展示可再生能源和负荷预测的改进。
Interest in predicting and optimising microgrid operation with a high proportion of variable renewable energy generation is growing. In this paper, we study and experimentally analyse the performance of a Gaussian-process regression forecasting and model predictive control algorithm in the context of interconnected microgrids. The scheme, which operated at six hours time horizon, achieved superior results with only a small deviation from the optimal operation calculated offline assuming perfect foresight. We also demonstrate that whilst a longer horizon provides a better solution in terms of lower cost of electricity, the battery cycling rate is also higher. Finally, we demonstrate improvements in renewable and load forecasts by sharing information between the microgrids.