建筑能源系统需求动态感知与分布式节能优化方法研究

Due to the large demand of buildings and its flexibility, buildings have a significant energy saving potential. However, building energy management may face the challenges such as diversity and high uncertainties in the demand, networked structure, and high computational complexity. In this proposal, to address the challenges mentioned above, we focus on the sensing of personalized thermal comfort of occupants and the optimization problem of building energy systems. From the view of cyber-physical system (CPS)’s point, we will first develop a dynamic sensing model of personalized thermal comfort of occupants with considering the uncertainties. With this model, we will propose an algorithm to effectively reduce the policy space based on the optimization of building network topology. Finally, we will develop a distributed optimization method to efficiently solve the building energy management problem that contains large-scale of mixed integer decision variables, multiple time scales, complicated networked structure and constraints for energy capacity limitation of energy devices. The performance of the developed methods will be demonstrated based on a real building at Xi’an Jiaotong University. This project will contribute to establishing theoretic foundations and developing practical methods for energy management of cyber-physical energy systems.

建筑能源系统能耗巨大且需求柔性强，具有巨大的节能优化潜力。本项目旨在从建筑能源系统需求侧优化所面临的需求多样性、系统高不确定性、复杂网络化结构及计算复杂性等重大挑战出发，研究人员个性化舒适需求动态感知建模和以此为基础的建筑能源系统需求侧节能优化等问题。从信息物理融合的角度，研究人员舒适需求多样性和不确定性的动态感知建模、建筑能源系统需求侧动态网络拓扑结构优化和策略空间精简方法、以及分布式算法邻域信息异步交换机制等关键科学问题，建立基于策略结构精简的建筑能源系统需求侧优化的系统化模型，提出包含大规模混合决策变量、多尺度时空耦合、复杂网络结构、能耗设备容量限制约束的动态随机优化问题的分布式高效求解方法。并基于西安交通大学彭康楼实际建筑能源系统，验证项目研究成果。研究成果为信息物理深度融合的能源系统节能优化运行做出基础理论和方法上的贡献，具有良好的前瞻性。

建筑能源系统能耗巨大且需求柔性强，具有巨大的节能优化潜力。本项目旨在从建筑能源系统需求侧优化所面临的需求多样性、系统高不确定性、复杂网络化结构及计算复杂性等重大挑战出发，研究人员个性化舒适需求动态感知建模和以此为基础的建筑能源系统需求侧节能优化等问题。从信息物理融合的角度出发，通过研究人员舒适需求多样性和不确定性的动态感知建模、建筑能源系统需求侧动态网络拓扑结构优化和策略空间精简方法、以及分布式算法邻域信息异步交换机制等关键科学问题，建立了基于策略结构精简的建筑能源系统需求侧优化的系统化模型，提出了包含大规模混合决策变量、多尺度时空耦合、复杂网络结构、能耗设备容量限制约束的动态随机优化问题的分布式高效求解方法，并在实际建筑能源系统中验证项目研究成果。项目研究期间，课题组共发表学术论文16篇，其中在IEEE Transactions on Automation Science and Engineering、 IEEE Transactions on Smart Grid、 Applied Energy、 Energy and Buildings等能源系统和建筑节能领域的著名SCI收录期刊发表论文7篇。项目负责人获2020年教育部自然科学二等奖（第二完成人）。通过本项目研究，课题组建立了数据种类齐全的驻在人员行为和建筑室内环境共享数据库，并向全球学者免费开放，已被UC Berkeley、清华大学等十余家国内外研究机构采用。项目研究成果应用在北京冬奥会延庆冬奥村智能人居国家示范工程，为建成具有国际领先水平的奥运村智能人居系统提供了系统化解决方案；成果还在西安交通大学、清华大学、UC Berkeley、同方股份、江苏晟能科技有限公司、江苏三意楼宇科技有限公司等国内外高校和企事业单位进行实际测试与落地转化，取得了显著的节能效果和社会经济效益。项目研究成果为信息物理深度融合的建筑能源系统节能优化运行提供基础理论和关键技术支撑。