CPS: Synergy: Collaborative Research: SMARTER - Smart Manager for Adaptive and Real-Time Decisions in Building ClustERs

1239257（吴）。 传统上，建筑物被视为纯粹的能源消费者;然而，随着新的电网基础设施和分布式能源的出现，建筑物不仅可以消耗能源，而且还可以输出能源。因此，该项目消除了同一集群中建筑物之间或集群与电网之间的传统界限，将单个智能建筑转变为网络支持工具支持的NetZero建筑集群。在这项研究中，建立了一个协同决策框架的时间，空间分布的建筑集群作为一个自适应和强大的系统在智能电网。 该框架包括用于建筑能源建模、智能数据融合、分散决策和自适应决策的创新算法和工具，以应对下一代建筑系统的理论和实践挑战。 该研究开发了用于需求侧负荷管理的网络物理工程工具，这已被能源行业确定为一项重大挑战。它从根本上将当前集中式和单向配电业务模式转变为分散式和多向电力共享和配电业务模式，降低整体能耗，并允许在不断变化的运营环境中做出最佳决策。教育和外联工作包括开发新的教育模块，在K-12各级传播，并通过ASEE eGFI储存库传播。通过与每个机构的多个本科生和研究生课程以及社区服务团体的整合，进一步产生教育影响。通过我们的行业合作，影响力还扩大到更广泛的能源行业以及医疗保健服务和城市交通系统的运营。http://swag.engineering.asu.edu/ 1239247（文）。 传统上，建筑物被视为纯粹的能源消费者;然而，随着新的电网基础设施和分布式能源的出现，建筑物不仅可以消耗能源，而且还可以输出能源。因此，该项目消除了同一集群中建筑物之间或集群与电网之间的传统界限，将单个智能建筑转变为网络支持工具支持的NetZero建筑集群。在这项研究中，建立了一个协同决策框架的时间，空间分布的建筑集群作为一个自适应和强大的系统在智能电网。该框架包括用于建筑能源建模、智能数据融合、分散决策和自适应决策的创新算法和工具，以应对下一代建筑系统的理论和实践挑战。该研究开发了用于需求侧负荷管理的网络物理工程工具，这已被能源行业确定为一项重大挑战。它从根本上将当前集中式和单向配电业务模式转变为分散式和多向电力共享和配电业务模式，降低整体能耗，并允许在不断变化的运营环境中做出最佳决策。教育和外展工作包括开发在K-12级别和通过ASEE eGFI存储库传播的新型教育模块。通过与每个机构的多个本科生和研究生课程以及社区服务团体的整合，进一步产生教育影响。通过我们的行业合作，影响力还扩大到更广泛的能源行业以及医疗保健服务和城市交通系统的运营。http://swag.engineering.asu.edu/ 1239093（刘易斯）。 传统上，建筑物被视为纯粹的能源消费者;然而，随着新的电网基础设施和分布式能源的出现，建筑物不仅可以消耗能源，而且还可以输出能源。因此，该项目消除了同一集群中建筑物之间或集群与电网之间的传统界限，将单个智能建筑转变为网络支持工具支持的NetZero建筑集群。在这项研究中，建立了一个协同决策框架的时间，空间分布的建筑集群作为一个自适应和强大的系统在智能电网。该框架包括用于建筑能源建模、智能数据融合、分散决策和自适应决策的创新算法和工具，以应对下一代建筑系统的理论和实践挑战。该研究开发了用于需求侧负荷管理的网络物理工程工具，这已被能源行业确定为一项重大挑战。它从根本上将当前集中式和单向配电业务模式转变为分散式和多向电力共享和配电业务模式，降低整体能耗，并允许在不断变化的运营环境中做出最佳决策。教育和外展工作包括开发在K-12级别和通过ASEE eGFI存储库传播的新型教育模块。通过与每个机构的多个本科生和研究生课程以及社区服务团体的整合，进一步产生教育影响。通过我们的行业合作，影响力还扩大到更广泛的能源行业以及医疗保健服务和城市交通系统的运营。

1239257 (Wu). Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations. http://swag.engineering.asu.edu/ 1239247 (Wen). Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations. http://swag.engineering.asu.edu/ 1239093 (Lewis). Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations.