Evaluation of Microgrid Revenues from Ancillary Services Market Participation in Different US Markets

This project models microgrids with different characteristics and configurations (generation, load, scale, location, etc.) in various US energy markets. It studies their potential for Ancillary Services (A/S) market participation, using LBNL’s DER-CAM decision support tool for decentralized energy systems and Argonne’s Conventional Hydroelectric and Environmental Resource Systems (CHEERS). After exhaustively reviewing the existing A/S products in each market, we model A/S products in DER-CAM and CHEERS, and conduct studies to estimate potential revenue streams from A/S market participation for microgrids. We then use the revenue estimations to determine the most beneficial A/S product for the considered set of microgrids.

In detail,

  • We are providing a primer on ancillary services with a comprehensive overview and description of the various services offered in the different markets and regions in the U.S.
  • We are researching publically available information sources to summarize historical price trends of ancillary services by product type and market and provide summary statistical analyses for some U.S. markets with particular emphasis on the PJM energy market.
  • After determining the A/S markets and characteristics we model them in the Investment and Planning version of DER-CAM.
  • We also develop the DER-CAM data models for the test microgrid.
  • Furthermore, we select test microgrids with different characteristics in terms of generation portfolio, load type, microgrid size and scale, location, etc.

In the long term we extend the analysis to look at optimal microgrid operations and revenue maximization with ancillary services prices projected under assumptions for different regional resource mixes, particularly under larger shares of variable renewable energy resources in the future PJM mix. We use DER-CAM, CHEERS, and System Capacity Expansion Models developed for DOE’s Wind and Water Power Program that develop future optimal capacity mixes under high shares of renewables and derive prices for energy and ancillary services under various renewable futures.