The Center for Energy Research, University of California San Diego, is seeking one highly qualified researcher to join the center as postdoctoral scholar starting July 17, 2017. The 12-month position is part of Department of Energy (DOE) and California Energy Commission (CEC) funded projects to facilitate solar energy grid integration through solar energy forecasting and power grid modeling, and optimization. The goal is to reduce integration costs of high penetration of solar photovoltaic (PV) systems into the electric grid. 13 PhD students and one postdoc presently conduct their research in the lab. The postdoctoral scholar will have access to unique solar resource databases and observatories such as 1-sec inverter-level output from the 48 MW Copper Mountain Photovoltaic plant, a solar resource network at UC San Diego, validated OpenDSS models of 15 real distribution feeders, and forecast data from sky imager deployments around the country.
The successful candidate will work with colleagues at NREL, Sandia National Lab, utilities, and industry on joint projects and present at national and international conferences such as IEEE Power Engineering Society and North American Power Symposium. The postdoctoral scholar will also have the opportunity to participate in writing research proposals to the funding agencies such as CEC and DOE, serve as the principal advisor to the PhD students, and to co-author their conference and journal papers. Collaborative opportunities exist with the School of Global Policy and Strategy (Dr. David Victor), and the research groups of Profs. Carlos Coimbra, Sonia Martinez and Raymond de-Callafon.
Applicants should hold a PhD degree in a relevant discipline (e.g. electrical or mechanical engineering) that was conferred no more than 4 years ago or have submitted their thesis at the time of appointment. Solid knowledge of power systems analysis and optimization as well as strong programming and data analysis skills are required. Perfect written and oral communication skills are necessary.
Research experience on one or several of the following would be beneficial:
- frequency control and voltage regulation
- reliability theory and its application in power systems
- planning and optimal operation of microgrids with electric vehicles, energy storage systems and demand response
- market participation of renewable energy resources
- distribution system state estimation
To apply, please submit a short description of qualifications, CV including list of peer-reviewed publications, and list of three references to Prof. Jan Kleissl (firstname.lastname@example.org). Review of applications will start immediately and will continue until the position is filled. Further information about the position can be obtained from Prof. Kleissl.
One of our most recent works with Prof. Raymond A. de Callafon was accepted at 2016 IEEE International Systems Conference. The title of the paper is “Reliability of Dynamic Load Scheduling with Solar Forecast Scenarios” and can be found at: Link
This is a joint project with Prof. Raymond A. de Callafon,.
Paper abstract: In this paper an algorithm is developed to solve the on/off scheduling of (non-linear) dynamics electric loads based on predictions of the power delivery of a (standalone) solar power source. Knowledge of variations in the solar power output is used to optimally select the timing and the combinations of a set of given electric loads, where each load has a desired dynamic power profile. The algorithm exploits the desired power profiles of the electric loads in terms of dynamic power ramp up/down and minimum time on/off of each load to track a finite number of load switching combinations over a moving finite prediction horizon. Subsequently, evaluation of a user-specified optimization function with possible power constraints is evaluated over the finite number of combinations to allow for real-time computation of the optimal timing and switching of loads. The approach is illustrated on electric loads with varying first order dynamics for on/off switching and solar data obtained from the Solar Resource Assessment & Forecasting Laboratory at UC San Diego.
1. Abdulelah H Habib, Jan Kleissl and Raymond A. de Callafon, “Model Predictive Load Scheduling Using Solar Power Forecasting,” submitted to The American Control Conference 2016, Boston, USA. [link]
Solar developers are scrambling to meet Puerto Rico utility company PREPA’s new ramp-rate requirement. Any new utility-scale power plant operator must commit to limit changes in output (“ramps”) to 10% per minute — a tall order for PV, as a single PV panel could fluctuate over 70% per second.
What if solar developers could predict how passing clouds affect fluctuations in power output — and plan their plants accordingly?
Read full article.
Solar power shortfalls due to elevation in the Twin Peaks neighborhood of San Francisco for one year.
Topographic Effects on Solar Power Production Web App
A Google Earth map of shadows generated from sky imagery. The cloud and shadow layers are superimposed on Google Earth along with weather station and PV output data. Combined with cloud motion tracking, this information will next be used to make solar irradiance forecasts in the sky imager coverage area.
Shadow Mapping Movie 1
Shadow Mapping Movie 2
by Bryan Urquhart
Using the Google Earth file, one can easily determine the optimum tilt and azimuth angles for any site in California, as well as the average annual increase in radiation at the optimum tilt and azimuth versus horizontally flat. These maps were created using the SUNY 10km Gridded Dataset and an algorithm described by J. Page (in Practical Handbook of Photovoltaics: Fundamentals and Applications) for transforming horizontal global and diffuse radiation into radiation on an inclined surface.
California Maps in Google Earth
USA Maps in Google Earth
by Matt Lave
Google Earth KMZ download…
This color map illustrates the Mean Global Horizontal Solar Energy Density across the state of California, USA during a typical meteorological year (TMY). This is the energy that a horizontally oriented solar panel would receive in one year. The data for this map comes from the corrected National Solar Radiation Database, SUNY 10km Gridded Dataset.
Reference: Nottrott and Kleissl, 2010
by Anders Nottrott