Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
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DOI：
10.3390/en14123453
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发表时间：
2021-06
期刊：
影响因子：
3.2
通讯作者：
Eugenio Borghini;C. Giannetti;J. Flynn;G. Todeschini
中科院分区：
文献类型：
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作者：
Eugenio Borghini;C. Giannetti;J. Flynn;G. Todeschini
分散式可再生能源发电（如太阳能光伏电池板和风力涡轮机）和低碳技术的日益普及，将在不久的将来增加配电网络的压力。在这种情况下，能源存储正成为传统昂贵的网络基础设施加固的关键替代方案，因为它具有灵活性、降低成本和快速部署能力。在这项工作中，提出了一种端到端的数据驱动解决方案，以优化设计电池单元的控制，旨在降低峰值电力需求。该解决方案使用最先进的机器学习方法来预测电力需求和光伏发电，并结合优化策略，最大限度地利用光伏能源为储能单元充电。为此，在英国普利茅斯附近的Stentaway一次变电站和其他六个地点收集的历史需求，天气和太阳能发电数据被采用。
The growing adoption of decentralised renewable energy generation (such as solar photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain experienced by the distribution networks in the near future. In such a scenario, energy storage is becoming a key alternative to traditional expensive reinforcements to network infrastructure, due to its flexibility, decreasing costs and fast deployment capabilities. In this work, an end-to-end data-driven solution to optimally design the control of a battery unit with the aim of reducing the peak electricity demand is presented. The proposed solution uses state-of-the-art machine learning methods for forecasting electricity demand and PV generation, combined with an optimisation strategy to maximise the use of photovoltaic energy to charge the energy storage unit. To this end, historical demand, weather, and solar energy generation data collected at the Stentaway Primary substation near Plymouth, UK, and at other six locations were employed.