|Title||Modeling of customer adoption of distributed energy resources|
|Year of Publication||2001|
|Authors||Chris Marnay, Joseph S Chard, Kristina Hamachi LaCommare, Timothy Lipman, Mithra M Moezzi, Boubékeur Ouaglal, Afzal S Siddiqui|
|Keywords||DER-CAM, distributed energy resources (der)|
This report describes work completed for the California Energy Commission (CEC) on the continued development and application of the Distributed Energy Resources Customer Adoption Model (DER-CAM). This work was performed at Ernest Orlando Lawrence Berkeley National Laboratory (Berkeley Lab) between July 2000 and June 2001 under the Consortium for Electric Reliability Technology Solutions (CERTS) Distributed Energy Resources Integration (DERI) project. Our research on distributed energy resources (DER) builds on the concept of the microgrid (µGrid), a semiautonomous grouping of electricity-generating sources and end-use sinks that are placed and operated for the benefit of its members. Although a µGrid can operate independent of the macrogrid (the utility power network), the µGrid is usually interconnected, purchasing energy and ancillary services from the macrogrid. Groups of customers can be aggregated into µGrids by pooling their electrical and other loads, and the most cost-effective combination of generation resources for a particular µGrid can be found. In this study, DER-CAM, an economic model of customer DER adoption implemented in the General Algebraic Modeling System (GAMS) optimization software is used, to find the cost-minimizing combination of on-site generation customers (individual businesses and a µGrid) in a specified test year. DER-CAM s objective is to minimize the cost of supplying electricity to a specific customer by optimizing the installation of distributed generation and the self-generation of part or all of its electricity. Currently, the model only considers electrical loads, but combined heat and power (CHP) analysis capability is being developed under the second year of CEC funding. The key accomplishments of this year s work were the acquisition of increasingly accurate data on DER technologies, including the development of methods for forecasting cost reductions for these technologies, and the creation of a credible example California µGrid for use in this study and in future work. The work performed during this year demonstrates the viability of DER-CAM and of our approach to analyzing adoption of DER.
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