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This paper describes the economically optimal adoption and operation of distributed energy resources (DER) by a hypothetical California microgrid consisting of a group of commercial buildings over an historic test year, 1999. The optimisation is conducted using a customer adoption model (DER-CAM) developed at Berkeley Lab and implemented in the General Algebraic Modeling System (GAMS). A microgrid is a semiautonomous grouping of electricity and heat loads interconnected to the existing utility grid (macrogrid) but able to island from it. The microgrid minimises the cost of meeting its energy requirements (consisting of both electricity and heat loads) by optimising the installation and operation of DER technologies while purchasing residual energy from the local combined natural gas and electricity utility. The available DER technologies are small-scale generators (< 500 kW), such as reciprocating engines, microturbines, and fuel cells, with or without combined heat and power (CHP) equipment, such as water and space heating and/or absorption cooling. By introducing a tax on carbon emissions, it is shown that if the microgrid is allowed to install CHP-enabled DER technologies, its carbon emissions are mitigated more than without CHP, demonstrating the potential benefits of small-scale CHP technology for climate change mitigation. Reciprocating engines with heat recovery and/or absorption cooling tend to be attractive technologies for the mild southern California climate, but the carbon mitigation tends to be modest compared to purchasing utility electricity because of the predominance of relatively clean central station generation in California.

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The technical and financial feasibility and desirability of microgrids has been shown in simulation and on paper. In order to demonstrate them in practice, an energy manager (EM) for control of microgrid equipment is needed. An all-knowing EM is not possible, an extremely information rich and intelligent but expensive EM might not be the optimal choice, and yet an EM that is too simple threatens several perceived benefits of DER. Both art and science will be required to develop the optimal EM and microgrid for a given site. This report serves to introduce the science. Further work should serve to develop this science and to gain experience on actual systems from which the art will emerge.iii Energy Manager Design for Microgrids Tables List of Tables AcknowledgmentsWe would like to thank program managers Mark Rawson, Bernard Treanton, and Laurie ten-Hope. Special thanks to Ron Hofmann and Joe Eto for their valuable comments on an early draft of this report.This work builds on prior efforts supported by DOE through program manager Philip Overholt.Finally, this work benefited from the work and insight of many current and prior members of the DER team at the Berkeley Lab. We would like to thank Executive SummaryOn-site energy production, known as distributed energy resources (DER), offers consumers many benefits, such as bill savings and predictability, improved system efficiency, improved reliability, control over power quality, and in many cases, greener electricity. Additionally, DER systems can benefit electric utilities by reducing congestion on the grid, reducing the need for new generation and transmission capacity, and offering ancillary services such as voltage support and emergency demand response.Local aggregations of distributed energy resources (DER) that may include active control of on-site end-use energy devices can be called microgrids. Microgrids require control to ensure safe operation and to make dispatch decisions that achieve system objectives such as cost minimization, reliability, efficiency and emissions requirements, while abiding by system constraints and regulatory rules. This control is performed by an energy manager (EM). Preferably, an EM will achieve operation reasonably close to the attainable optimum, it will do this by means robust to deviations from expected conditions, and it will not itself incur insupportable capital or operation and maintenance costs.Also, microgrids can include supervision over end-uses, such as curtailing or rescheduling certain loads. By viewing a unified microgrid as a system of supply and demand, rather than simply a system of on-site generation devices, the benefits of integrated supply and demand control can be exploited, such as economic savings and improved system energy efficiency.While the EM can provide supervision and suggest near-optimal operation of the microgrid, lower-level controllers, local to DER equipment and loads, may provide the actual control, upon receiving suggestions from the EM. This structure must facilitate interoperability of...

The deployment of small (< 1-2 MW) clusters of generators, heat and electrical storage, efficiency investments, and combined heat and power (CHP) applications (particularly involving heat activated cooling) in commercial buildings promises significant benefits but poses many technical and financial challenges, both in system choice and its operation; if successful, such systems may be precursors to widespread microgrid deployment. The presented optimization approach to choosing such systems and their operating schedules uses Berkeley Lab's Distributed Energy Resources Customer Adoption Model [DER-CAM], extended to incorporate electrical storage options. DER-CAM chooses annual energy bill minimizing systems in a fully technology-neutral manner. An illustrative example for a San Francisco hotel is reported. The chosen system includes two engines and an absorption chiller, providing an estimated 11% cost savings and 10% carbon emission reductions, under idealized circumstances.