论文标题
使用神经网络的模型预测控制杂货店的灵活性管理
Grocery Store Flexibility Management Using Model Predictive Control With Neural Networks
论文作者
论文摘要
随着可再生能源（RES）产生越来越多的能源，平衡生产和消费的挑战正在转移到消费者而不是电网上。这需要在单个建筑物和地区层面上进行新的和聪明的灵活性管理方式。为此，本文介绍了一种基于模型的最佳控制（MPC）算法，该算法嵌入了深度神经网络，用于日前消费和生产预测。该算法用于优化位于芬兰的中型杂货店能源消耗。使用杂货店的现实生活实力测量值在模拟工具中测试了系统。我们报告每日峰值负载的$ 8.4 \％$ $ $ $ $ $ $ kWh电池的灵活性。另一方面，尝试优化能源现货价格时没有看到重大好处。我们得出的结论是，我们的方法能够显着减少杂货店中的峰值负载，而无需额外的运营成本。
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.
