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.

中文翻译：

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具有高斯过程预测和MPC的数据驱动型能源管理系统，用于互联微电网

预测和优化具有可变比例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了互连微电网背景下的高斯过程回归预测和模型预测控制算法的性能。该方案在六个小时的时间范围内运行，取得了优异的结果，并且与假设理想的远见的离线计算的最佳运行只有很小的偏差。我们还证明了，虽然更长的使用寿命在降低电力成本方面提供了更好的解决方案，但电池循环速率也更高。最后，我们通过在微电网之间共享信息展示了可再生能源和负荷预测的改进。