Using Historical Analysis to Envision a Sustainable Future
Funding support from the Alfred P. Sloan Foundation is gratefully acknowledged
Our project examines how 20th-century engineers and system operators developed the algorithms that currently provide optimization and control of the electric power grid and the ways in which these algorithms might frame and/or limit the cleaner grid of the future. Specifically, we propose to study:
the social context in which power system experts developed, disseminated, and adopted algorithmic solutions to network operating challenges
the ways in which those algorithms were and may still be determinative; and
whether past approaches to framing and solving problems will likely work for a future system architecture that integrates significantly more renewable generating technologies, storage capacity, and distributed generation.
University of Houston Center for Public History's Public Historians at Work podcast on the project featuring Julie, Dan, and Sairaj.
Article titled "Discovering Power in the Past: The Algorithms and Power Systems Architecture Project" highlighting different project elements and the recorded podcast.
Article titled "Looking Back to Prepare for the Future of the Power Grid" discussing various project elements.
Retrace the development of differing strategies to achieve system optimization and control, identifying when particular algorithms became formal or informal system standards.
Place the evolution of these algorithms within the context of social, political, & economic imperatives that shaped the grid; reveal the extent to which particular assumptions about power systems technology are contingent on social factors, and the ways in which control and optimization algorithms of the past may accelerate / limit widespread integration of renewables.
Capture the memories & experiences of individuals who were instrumental in the development of algorithms (locating the quieter voices, the voices of opposition, & the intentionally hidden voices amongst the storytellers) to examine how algorithms may still be determinative.
Leaders
Assistants