Senior Research Associate, Electric Power Systems Laboratory
Clean energy systems, Grid integration of renewable energy resources, Energy solutions for the developing world, Multi-domain system modeling & simulation, Microgrids & islanded power systems, stochastic behavior and related controls, program management
Daniel Zimmerle is a Senior Research Associate in the Energy Institute @ Colorado State. Zimmerle leads research programs for remote community microgrids and the integration of distributed generation into power systems. Additionally, Zimmerle has been (or is) PI on four major studies of methane emissions in the natural gas supply chain, and leads the CSU METEC test facility for the ARPA-E MONITOR program. Prior to CSU, he served as the Chief Operating Officer at Spirae, Inc. and worked 20 years at Hewlett Packard and Agilent Technologies including experience as both a division general manager and R&D manager. He has lead organizations in several business areas, including computer systems, test systems, and consumer products. Organizations included personnel in the US, Ireland, Singapore and other countries.
He holds a BSME and MSME from North Dakota State University.
Click here for a complete list of publications
Use of short-term forecasting to improve microgrid performance
Control algorithms that utilize short-term cloud forecasting to maximize the penetration of photovoltaic systems in off-grid diesel systems.
Control development and system integration to support micro-utilities in rural areas of the developing world. Taking a holistic approach that combines electrification with social sciences, economic development, water systems and health improvement.
Improved system models for electro-mechanical systems
Development and verification of improved engine models for off-grid and industrial applications, including mining, drilling and hydraulic fracturing. Includes model development, experimental verification of models, and improved modeling methods.
Three large projects that include field measurements and statistical modeling of emissions. Producing a ground-breaking update of emission rates and utilizing more robust stochastic modeling methods than previously utilized. Additional project seeks to reconcile bottom-up and top-down estimates of emissions from an O&G production basin.