Empowering Communities Through Transparent and Interpretable Energy Modeling Platforms

Written by Rachel Gray, Benedict Vergara, Saniya LeBlanc, and Payman Dehghanian, The George Washington University

The design and operation of energy systems build the foundation for district energy systems to meet environmental, security and financial goals. Therefore, developing transparent and interpretable energy modeling platforms, while sharing development plans and experiences with the surrounding communities, will accelerate the transition to clean and interconnected Smart Cities.

Energy Modeling in a Smart City of the Future

Energy modeling platforms offer community leaders, who might lack technical knowledge, the ability to consider all possible technologies, both the physical hardware and the software analytics involved in the generation and management of resources for district energy systems. Collaboration between institutions within large cities empowers community leaders, who might lack financial support, by connecting them to “living lab” examples of installations and retrofitting plans within similar space, environmental and legal restrictions.

Both efforts have been initiated by The George Washington University. Our research team is developing an energy management tool as well as opening our doors to surrounding communities within our nation’s capital to learn from our experience as we transition our campus into an exemplary institution in a future Smart City, Washington D.C.

Challenges and Opportunities

There are excellent open-source and well-developed tools available for energy modeling, but there is no unified platform to consider both hardware and software integration into the energy system. Community leaders and city planners need a unified platform to consider possible energy technologies, sensors, controls, and prediction modules, all accompanied with explanations and educational resources. Such resources are imperative for decision makers who lack the technical understanding of these technologies. Furthermore, city planners are increasingly concerned with the security and resilience of their energy systems in the face of more frequent extreme weather events. Energy modeling tools drive the development of smart cities by providing realistic insight on how a redesigned or retrofitted energy system would perform in the future.

Energy management transition frameworks have been standardized, at a high level, by the International Organization for Standardization, through ISO 50001. This standard highlights the general strategy but lacks information on how regional specifics, such as weather, population tendencies, or regulation, affect the choices made by community leaders.

Leveraging Emerging Technologies

Energy system modernization (e.g., centralized vs. decentralized solutions, controllable vs. renewable generation, or storage options) allow cities to find the right balance between financial, environmental and energy security goals. Operating Combined Heat and Power (CHP) units within the energy mix adds to the complexity of the real-time energy scheduling problem. During recent years, there have been many developments in the algorithms provided by commercial solvers that allow them to solve effectively in terms of solution time and quality. These solvers allow for larger numbers of energy resources within a Smart City to be dispatched conjointly to meet a mutual objective: day-to-day cost efficiency or service continuity during emergencies. Advances in artificial intelligence improve prediction efforts and identify system anomalies, which result in the ability to foresee demand shift, equipment failure or utility price fluctuations as a result of externalities.

A next-generation energy management system (as depicted below) will leverage all technological advancements mentioned to operate under a Model Predictive Control (MPC) or “rolling horizon” framework which predicts, operates, and responds to unforeseen changes.

energy management system artificial intelligence and optimization technologies

Figure 1: A next generation Energy Management System (EMS) integrates a variety of evolving artificial intelligence (AI) and optimization technologies.


renewable storage technologies model predictive control framework

Figure 2: A next generation EMS leverages renewable and storage technologies to operate under a Model Predictive Control (MPC) framework.


Broader Community Impacts

Developing transparent and interpretable energy modeling platforms enables community development groups to understand city energy use, plan for future energy improvements, and project the results of potential energy system changes. Sharing regional experiences on energy transitions with the surrounding communities within a large city will empower community leaders to take the right steps and develop interconnected Smart Cities.

Our research team at The George Washington University is building an energy management platform focused on model transparency and result interpretability so that those without a technical background can still visualize and learn about how all Smart City technologies fit into a modern energy system. Understanding how ubiquitous sensors and remote controls combined with energy resources can impact the performance of the city, measured in terms of cost, reliability, resilience, and vulnerability, is critical for making future decisions. Our research team will be hosting “living lab” events on our campus to showcase how these different upgrades have made their way into the system operation. The aim is to provide a resource for our region to learn from our experiences as a role model. Through clear models and interpretable results, city planners and community leaders are offered enhanced energy literacy to make informed decisions for the betterment of their citizens.





This article was edited by Aris Gkoulalas-Divanis.

To view all articles in this issue, please go to February 2022 eNewsletter. For a downloadable copy, please visit the IEEE Smart Cities Resource Center.

Rachel Gray Headshot
Rachel Gray is a second year doctoral student studying mechanical engineering with a focus on fluid mechanics, thermal sciences and energy at The George Washington University. Her research goals are to model urban combined heat and power plants with integrated renewables and energy storage. Rachel obtained her Bachelor's of Science in mechanical engineering at The George Washington University, where she was named a Clare Boothe Luce Research Scholar, a Duke Energy Fellow, and an NSF Nanotechnology Fellow. She conducted research at the LeBlanc Lab, and The National Renewable Energy Laboratory, during her undergraduate career. Her research focused on characterizing advanced materials for next-generation energy technologies, namely thermoelectric generators and hydrogen fuel cells.
Benedict Vergara HeadShot
Benedict Vergara received his B.Sc. and M.Sc. in Electrical Engineering at The George Washington University, both with concentrations in Energy and Power Systems. Since graduation, Ben worked at the engineering consulting firm Strategic Analysis, Inc. as a staff engineer aiding the development of techno-economic analyses for clean energy technologies. Ben has returned to GW as a doctoral student in Electrical Engineering with a focus on the applications of machine learning and blockchain technologies to the operation of a smart power grid. His research goals are to develop control and analysis techniques to improve reliability, resiliency, and efficiency for urban energy systems.


Saniya LeBlanc Headshot
Saniya LeBlanc is an Associate Professor in the Department of Mechanical & Aerospace Engineering at The George Washington University.  Her research goals are to create next-generation energy systems with advanced materials and manufacturing techniques. Previously, she was a research scientist at a startup company where she created research, development, and manufacturing characterization solutions for thermoelectric technologies and evaluated the potential of new power generation materials. Dr. LeBlanc obtained a PhD in mechanical engineering with a minor in materials science at Stanford University. She was a Churchill Scholar at University of Cambridge where she received an MPhil in engineering, and she has a BS in mechanical engineering from Georgia Institute of Technology.
Payman Dehghanian Headshot
Payman Dehghanian received the B.Sc., M.Sc., and Ph.D. degrees all in electrical engineering from the University of Tehran, Tehran, in 2009, Sharif University of Technology, Tehran, in 2011, and Texas A&M University, College Station, TX, USA, in 2017, respectively. He is currently an Assistant Professor with the Department of Electrical and Computer Engineering, George Washington University, Washington, D.C., USA. His research interests include power system protection and control, power system reliability and resiliency, asset management, and smart electricity grid applications. Dr. Dehghanian was the recipient of the 2013 IEEE Iran Section Best M.Sc. Thesis Award in Electrical Engineering, the 2014 and 2015 IEEE Region 5 Outstanding Professional Achievement Awards, and the 2015 IEEE-HKN Outstanding Young Professional Award.

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