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1. ENENGY MANAGEMENT OF SMART COMMUNITY WITH EV CHARGING USING DISTRIBUTED MODEL PREDICTIVE CONTROL

   Fenglin Zhou, Yaoyu Li (October 2018, Proceedings of ASME 2018 Dynamic Systems and Control Conference)

   Human activities in buildings are connected by various transportation measures. For the emerging Smart and Connected Communities (S&CC), it is possible to synergize the energy management of smart buildings with the vehicle operation/travel information available from transportation infrastructure, e.g. the intelligent transportation systems (ITS). Such information enables the prediction of upcoming building occupancy and upcoming charging load of electrified vehicles. This paper presents a predictive energy management strategy for smart community with a distributed model predictive control framework, in which the upcoming building occupancy and charging load are assumed to be predictable to certain extent based on the ITS information. An illustrative example of smart community is used for simulation study based on a Modelica simulation model, in which a chilled-water plant sustains the ventilation and air conditioning of three buildings, and each building is assumed to host a number of charging stations. Simulation study is performed to validate the proposed strategy. 

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2. Urban scale vehicle-to-building-to-grid integration leveraging human mobility modeling for enhanced grid flexibility

   https://doi.org/10.1007/s12273-025-1346-3  

   Liu, Yapan; Dong, Bing (November 2025, Building Simulation)

   Abstract Across the U.S., the increasing demand for electric vehicle (EV) charging infrastructure is placing new demands on the power grid, challenging its stability and efficiency. To address these challenges, this study proposes a Vehicle-to-Building-to-Grid (V2B2G) framework that incorporates urban-scale human mobility modeling to optimize EV charging and discharging. Using anonymized GPS traces from the study community, we extracted individual mobility patterns and applied kernel-density estimation to predict departure and arrival times for each user. The framework was tested in a mixed-use community in Phoenix, Arizona that includes both residential and commercial buildings. A comprehensive decentralized model predictive control (MPC) framework is implemented to minimize energy costs and enhance grid flexibility through demand-side management while maintaining occupant comfort. Four different control strategies were designed and evaluated, the strategy which balances both user and grid benefits demonstrated the best performance, achieving: (1) a flattened grid net load curve, with a 56% reduction in on-peak demand and a 56% decrease in peak load rebound; (2) a 37.96% reduction in grid net load compared to the baseline control; and (3) a 68.05% performance improvement when considering six flexibility factors: cost savings, total-energy reduction, on-peak demand reduction, off-peak demand reduction, load shifting from peak to non-peak hours, and peak-load-rebound reduction. These findings enhance our understanding of the impacts of urban mobility and EV charging optimization on grid management. 

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3. A Global Building Occupant Behavior Database

   https://doi.org/10.1038/s41597-022-01475-3  

   Dong, Bing; Liu, Yapan; Mu, Wei; Jiang, Zixin; Pandey, Pratik; Hong, Tianzhen; Olesen, Bjarne; Lawrence, Thomas; O’Neil, Zheng; Andrews, Clinton; et al (December 2022, Scientific Data)

   Abstract This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting. 

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4. A Method For Co-Optimizing Electric Vehicle Scheduling And Microgrid Management In Freight Logistics

   https://doi.org/10.1177/03611981251356505  

   Enriquez-Contreras, Luis Fernando; Peng, Dongbo; Yao, Ruili; Barth, Matthew; Boriboonsomsin, Kanok (February 2026, Transportation Research Record: Journal of the Transportation Research Board)

   This paper presents an integrated approach to optimizing microgrid management and electric truck logistics for transportation research. The experiment involves a 100 kW solar photovoltaic system, a 500 kWh battery energy storage system, the electric demand of a commercial building, and a heavy-duty vehicle charging system. The study aims to demonstrate how synchronized optimization of a microgrid control algorithm and a truck route algorithm can reduce overall system costs. The microgrid management system is designed to meet the constraints and requirements of a commercial electric truck charging scheduler. This integrated approach is an improvement over previous systems as it uses the scheduler’s outputs—such as time frames and energy requirements—as constraints for microgrid management. The truck scheduling algorithm iteratively learned to optimize charging times, ensuring that charging occurs during low-cost periods or when renewable energy is available. The electric vehicle scheduler adjusted truck arrival times based on the availability of clean energy sources, creating a feedback loop that continuously improves cost efficiency. Results indicated significant cost savings, with electric utility costs for electric vehicle (EV) charging being only 0% to 20% of the original value while the transportation system is only 23% to 64%, compared with the baseline scenario without the co-optimization framework. These findings suggest that the proposed integrated approach can effectively reduce costs and improve the efficiency of microgrid and electric truck operations. Uncoordinated charging schedules leads to a higher power demand than a well-organized battery electric truck (BET) dispatching strategy. Optimizing truck charging times and energy needs based on microgrid conditions can significantly reduce electricity and transportation costs. 

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5. Modelling, Simulation and Control of Smart and Connected Communities

   Li, Zhaoxuan; Pipri, Ankur; Dong, Bing; Gatsis, Nikolaos; Taha, Ahmad; Yu, Nanpeng (August 2017, Building Simulation 2017)

   This study attempts to establish the need for a framework to assess the impact of connected buildings in a smart community. The contribution is a software framework designed to optimize buildings and grids at a district level. The following research products are developed: (1) An innovative method to model a cluster of buildings—with people’s behavior embedded in the cluster’s dynamics—and their controls so that they can be integrated with grid operation and services; (2) a novel optimization framework to solve complex, centralized control problems for large-scale systems, leveraging convex programming approaches; and (3) a methodology to assess the impacts of connected buildings in terms of (a) the grid’s operational stability and safety and (b) buildings’ optimized energy consumption. To test the proposed framework, a large-scale simulation of a subtransmission network with three power generating stations and serving over 300 artificial buildings is conducted. 

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