基于神经网络模型预测控制的杂货店柔性管理
Grocery Store Flexibility Management Using Model Predictive Control With Neural Networks
摘要：
随着越来越多的能源由可再生能源（RES）生产，平衡生产和消费的挑战正在转移到消费者而不是电网。这就需要在个别建筑物和地区层面上采用新的、智能的灵活性管理方法。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，用于日前的消费和生产预测。该算法用于优化位于芬兰的一个中型杂货店的能源消耗。系统在模拟工具中进行了测试，利用现实生活中的电力测量从杂货店。我们报告了一个$8.4\%$减少日常峰值负荷的灵活性提供了一个$20$千瓦时电池。另一方面，在试图优化能源现货价格方面没有看到明显的好处。我们的结论是，我们的方法是能够显着降低在杂货店的峰值负荷，而无需额外的运营成本。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
