Collaborative Research: Crosslayer Optimization of Energy and Cost through Unified Modeling of User Behavior and Storage in Multiple Buildings

协作研究：通过对多个建筑物中的用户行为和存储进行统一建模来实现能源和成本的跨层优化

• 批准号: 1936494
• 负责人: Cristinel Ababei
• 金额: $ 16.67万
• 依托单位: Marquette University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936494&HistoricalAwards=false
• 关键词: Collaborative; Research; Crosslayer; Optimization; Energy

The building sector is the largest energy consumer in the world, and in the United States it accounts for more than 40 percent of the total energy consumption and greenhouse gas emissions. Therefore, it is economically, socially, and environmentally important to reduce the energy consumption of this sector. The goal of this collaborative proposal is to develop novel machine learning based algorithms to address the problem of energy optimization at the building and district levels. These algorithms are integrated within a simulation framework that combines user behavior with the collaboration between buildings equipped with photovoltaic arrays, energy storage systems, and smart grid meters. The proposed research is expected to lay the foundation for robust multi-objective optimization for next generation district level distribution systems. The proposed research is closely integrated with a broad and diverse education and outreach plan aimed at inspiring young women to pursue careers in STEM through summer programs for middle school. Additionally, the project will train the next generation of engineers and researchers by involving graduate and undergraduate students through the proposed research as well as through classes taught by the PIs encompassing the proposed research methodologies. Overall, the outcomes of this proposal are expected to significantly advance the areas of energy optimization, electric power systems, and smart grid design, as well as to have a positive impact on the academic and industrial communities and society.The project proposes and integrates, within the same software tool, novel machine learning models for complex user behavior at the individual building level, for energy load prediction and energy storage systems scheduling at the district level, and for cost reduction via energy peak spreading. These models are used to formulate and construct algorithmic solutions based on reinforcement learning, recurrent and deep neural networks, and deep reinforcement learning suitable for implementation in the future generation Virtual Power Plants. The methodologies employed for energy reduction and cost minimization include: 1) alter user behavior through personalized recommendations regarding changes in the appliance states (e.g., heating and air conditioning settings), 2) district-level scheduling of energy storage systems among buildings equipped with photovoltaic arrays and smart grid meters, and 3) building-level scheduling of energy consumption events for smart appliances equipped with smart Internet-of-Things controllers to take benefit of different energy prices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

建筑业是世界上最大的能源消费者，在美国，它占能源消耗和温室气体排放总量的40%以上。因此，降低该部门的能源消耗具有经济、社会和环境意义。这项合作提案的目标是开发新的基于机器学习的算法，以解决建筑和地区层面的能源优化问题。这些算法集成在一个模拟框架内，该框架将用户行为与配备光伏阵列、储能系统和智能电网仪表的建筑物之间的协作相结合。该研究为下一代地区级配电系统的鲁棒多目标优化奠定了基础。拟议的研究与广泛多样的教育和推广计划紧密结合，旨在激励年轻女性通过中学暑期课程追求STEM职业。此外，该项目将通过研究生和本科生参与拟议的研究以及通过PI教授的课程，包括拟议的研究方法，来培养下一代工程师和研究人员。总体而言，该项目的成果有望显著推动能源优化、电力系统和智能电网设计领域的发展，并对学术界、工业界和社会产生积极影响。该项目提出并集成了针对单个建筑物层面复杂用户行为的新型机器学习模型，用于在地区级的能量负荷预测和能量存储系统调度，以及用于通过能量峰值扩散来降低成本。这些模型用于制定和构建基于强化学习、递归神经网络和深度神经网络的算法解决方案，以及适合在下一代虚拟电厂中实施的深度强化学习。用于能量减少和成本最小化的方法包括：1）通过关于电器状态（例如，加热和空调设置），2）配备有光伏阵列和智能电网仪表的建筑物之间的能量存储系统的地区级调度，以及3）配备智能物联网的智能电器的建筑级能耗事件调度该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。