As more and more energy is produced from renewable energy sources (RES), the challenge for balancing production and consumption is being shifted to consumers instead of the power grid. This requires new and intelligent ways of flexibility management at individual building and district levels. To this end, this paper presents a model based optimal control (MPC) algorithm embedded with deep neural network for day-ahead consumption and production forecasting. The algorithm is used to optimize a medium-sized grocery store energy consumption located in Finland. System was tested in a simulation tool utilising real-life power measurements from the grocery store. We report a $8.4\%$ reduction in daily peak loads with flexibility provided by a $20$ kWh battery. On the other hand, a significant benefit was not seen in trying to optimize with respect to the energy spot price. We conclude that our approach is able to significantly reduce peak loads in a grocery store without additional operational costs.

中文翻译：

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使用神经网络模型预测控制的杂货店灵活性管理

随着越来越多的能源来自可再生能源 (RES)，平衡生产和消费的挑战正在转移到消费者而不是电网。这需要在单个建筑和地区层面采用新的智能方式进行灵活性管理。为此，本文提出了一种嵌入深度神经网络的基于模型的最优控制 (MPC) 算法，用于日前消费和生产预测。该算法用于优化位于芬兰的一家中型杂货店的能源消耗。系统在仿真工具中使用杂货店的真实功率测量进行了测试。我们报告说，通过 20 美元 kWh 的电池提供的灵活性，每日峰值负载减少了 8.4 美元\%。另一方面，试图优化能源现货价格并没有看到显着的好处。我们得出的结论是，我们的方法能够显着降低杂货店的峰值负载，而无需额外的运营成本。