Bio

Research
I build state-of-the-art computational tools for energy and carbon management problems. Two currently active projects include
1) Building tools to track emissions in the US power system. See energy.stanford.edu/gridemissions
2) Experimenting with building energy systems on the Stanford campus in the context of the COOLER Research Program. COOLER’s goal is to make large, modern buildings more energy-efficient, low carbon and resilient using data, optimization, and control.

See https://jdechalendar.su.domains/ for more.

Teaching
ENERGY 104/204: This course explores the global transition to a sustainable global energy system. We will formulate and program simple models for future energy system pathways. We will explore the drivers of global energy demand and carbon emissions, as well as the technologies that can help us meet this demand sustainably. We will consider constraints on the large-scale deployment of technology and difficulties of a transition at large scales and over long time periods. Assignments will focus on building models of key aspects of the energy transition, including global, regional and sectoral energy demand and emissions as well as economics of change. Prerequisites: students should be comfortable with calculus and linear algebra (e.g. Math 20, Math 51) and be familiar with computer programming (e.g. CS106A, CS106B). We will use the Python programming language to build our models.

Academic Appointments

Boards, Advisory Committees, Professional Organizations

  • Research Assistant, Benson Lab (2014 - Present)

Professional Education

  • MS, XXIst century Energy

Contact

  • Academic
    jdechale@stanford.edu
    University - Other Teaching/Research Department: Energy Science & Engineering Position: Adjunct Professor

Additional Info

Links

Current Research and Scholarly Interests

Integrated Energy Systems

2022-23 Courses

All Publications

  • Distributional health impacts of electricity imports in the United States ENVIRONMENTAL RESEARCH LETTERS Hennessy, E. M., de Chalendar, J. A., Benson, S. M., Azevedo, I. L. 2022; 17 (6)

    View details for DOI 10.1088/1748-9326/ac6cfa

    View details for Web of Science ID 000797697500001

  • A physics-informed data reconciliation framework for real-time electricity and emissions tracking APPLIED ENERGY de Chalendar, J. A., Benson, S. M. 2021; 304

    View details for DOI 10.1016/j.apenergy.2021.117761

    View details for Web of Science ID 000703563100001

  • On incorporating forecasts into linear state space model Markov decision processes. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences de Chalendar, J. A., Glynn, P. W. 2021; 379 (2202): 20190430

    Abstract

    Weather forecast information will very likely find increasing application in the control of future energy systems. In this paper, we introduce an augmented state space model formulation with linear dynamics, within which one can incorporate forecast information that is dynamically revealed alongside the evolution of the underlying state variable. We use the martingale model for forecast evolution (MMFE) to enforce the necessary consistency properties that must govern the joint evolution of forecasts with the underlying state. The formulation also generates jointly Markovian dynamics that give rise to Markov decision processes (MDPs) that remain computationally tractable. This paper is the first to enforce MMFE consistency requirements within an MDP formulation that preserves tractability. This article is part of the theme issue 'The mathematics of energy systems'.

    View details for DOI 10.1098/rsta.2019.0430

    View details for PubMedID 34092099

  • Tracking emissions in the US electricity system. Proceedings of the National Academy of Sciences of the United States of America de Chalendar, J. A., Taggart, J., Benson, S. M. 2019

    Abstract

    Understanding electricity consumption and production patterns is a necessary first step toward reducing the health and climate impacts of associated emissions. In this work, the economic input-output model is adapted to track emissions flows through electric grids and quantify the pollution embodied in electricity production, exchanges, and, ultimately, consumption for the 66 continental US Balancing Authorities (BAs). The hourly and BA-level dataset we generate and release leverages multiple publicly available datasets for the year 2016. Our analysis demonstrates the importance of considering location and temporal effects as well as electricity exchanges in estimating emissions footprints. While increasing electricity exchanges makes the integration of renewable electricity easier, importing electricity may also run counter to climate-change goals, and citizens in regions exporting electricity from high-emission-generating sources bear a disproportionate air-pollution burden. For example, 40% of the carbon emissions related to electricity consumption in California's main BA were produced in a different region. From 30 to 50% of the sulfur dioxide and nitrogen oxides released in some of the coal-heavy Rocky Mountain regions were related to electricity produced that was then exported. Whether for policymakers designing energy efficiency and renewable programs, regulators enforcing emissions standards, or large electricity consumers greening their supply, greater resolution is needed for electric-sector emissions indices to evaluate progress against current and future goals.

    View details for DOI 10.1073/pnas.1912950116

    View details for PubMedID 31792173

  • Why 100% Renewable Energy Is Not Enough JOULE de Chalendar, J. A., Benson, S. M. 2019; 3 (6): 1389–93

    View details for DOI 10.1016/j.joule.2019.05.002

    View details for Web of Science ID 000472067900001

  • City-scale decarbonization experiments with integrated energy systems ENERGY & ENVIRONMENTAL SCIENCE de Chalendar, J. A., Glynn, P. W., Benson, S. M. 2019; 12 (5): 1695–1707

    View details for DOI 10.1039/c8ee03706j

    View details for Web of Science ID 000473083100018

  • Experimental Investigation of a Capacity-Based Demand Response Mechanism for District-Scale Applications de Chalendar, J. A., Glynn, P. W., Benson, S. M., Bui, T. X. HICSS. 2019: 3709-3718

    View details for Web of Science ID 000625294903091

  • Pore-scale modelling of Ostwald ripening JOURNAL OF FLUID MECHANICS de Chalendar, J. A., Garing, C., Benson, S. M. 2018; 835: 363–92

    View details for DOI 10.1017/jfm.2017.720

    View details for Web of Science ID 000416942900001

  • Pore-scale Considerations on Ostwald Ripening in Rocks Energy Procedia de Chalendar, J. A., Garing, C., Benson, S. M. 2017; 114: 4857-4864

    View details for DOI 10.1016/j.egypro.2017.03.1626

  • Pore-scale capillary pressure analysis using multi-scale X-ray micromotography Advances in Water Resources Garing, C., de Chalendar, J. A., Voltolini, M., Ajo-Franklin, J. B., Benson, S. M. 2017; 104: 223-241

    View details for DOI 10.1016/j.advwatres.2017.04.006

https://orcid.org/0000-0002-3586-199X