ERMA is leveraging Sandia’s Microgrid Design Toolkit (MDT) [1] and adding significant new features to it. Development of the MDT was primarily funded by the Department of Energy, Office of Electricity Microgrid Program with some significant support coming from the U.S. Marine Corps. The MDT is a software program that runs on a Microsoft Windows PC. It is an amalgamation of several other software capabilities developed at Sandia and subsequently specialized for the purpose of microgrid design. The software capabilities include the Technology Management Optimization (TMO) application for optimal trade-space exploration, the Microgrid Performance and Reliability Model (PRM) for simulation of microgrid operations, and the Microgrid Sizing Capability (MSC) for preliminary sizing studies of distributed energy resources in a microgrid.
This simple Microgrid Design Toolkit (MDT) use case will provide you an example of a basic microgrid design. It will introduce basic principles of using the MDT islanded mode optimization by modifying a baseline microgrid design and performing an analysis of the results. Please reference the MDT User Guide (SAND2020-4550) for detailed instructions on how to use the tool.
Power systems in rural Alaska villages face a unique combination of challenges that can increase the cost of energy and lowers energy supply reliability. In the case of the remote village of Shungnak, diesel and heating fuel is either shipped in by barge or flown in by aircraft. This report presents a technical analysis of several energy infrastructure upgrade and modification options to reduce the amount of fuel consumed by the community of Shungnak. Reducing fuel usage saves money and makes the village more resilient to disruptions in fuel supply. The analysis considers demand side options, such as energy efficiency, alongside the installation of wind and solar power generation options. Some novel approaches are also considered including battery energy storage and the use of electrical home heating stoves powered by renewable generation that would otherwise be spilled and wasted. This report concludes with specific recommendations for Shungnak based on economic factors, and fuel price sensitivity. General conclusions are also included to support future work analyzing similar energy challenges in remote arctic regions.
The Microgrid Design Toolkit (MDT) supports decision analysis for new ("greenfield") microgrid designs as well as microgrids with existing infrastructure. The current version of MDT includes two main capabilities. The first capability, the Microgrid Sizing Capability (MSC), is used to determine the size and composition of a new, grid connected microgrid in the early stages of the design process. MSC is focused on developing a microgrid that is economically viable when connected to the grid. The second capability is focused on designing a microgrid for operation in islanded mode. This second capability relies on two models: the Technology Management Optimization (TMO) model and Performance Reliability Model (PRM).
Microgrids are a focus of localized energy production that support resiliency, security, local con- trol, and increased access to renewable resources (among other potential benefits). The Smart Power Infrastructure Demonstration for Energy Reliability and Security (SPIDERS) Joint Capa- bility Technology Demonstration (JCTD) program between the Department of Defense (DOD), Department of Energy (DOE), and Department of Homeland Security (DHS) resulted in the pre- liminary design and deployment of three microgrids at military installations. This paper is focused on the analysis process and supporting software used to determine optimal designs for energy surety microgrids (ESMs) in the SPIDERS project. There are two key pieces of software, an ex- isting software application developed by Sandia National Laboratories (SNL) called Technology Management Optimization (TMO) and a new simulation developed for SPIDERS called the per- formance reliability model (PRM). TMO is a decision support tool that performs multi-objective optimization over a mixed discrete/continuous search space for which the performance measures are unrestricted in form. The PRM is able to statistically quantify the performance and reliability of a microgrid operating in islanded mode (disconnected from any utility power source). Together, these two software applications were used as part of the ESM process to generate the preliminary designs presented by SNL-led DOE team to the DOD. Acknowledgements Sandia National Laboratories and the SPIDERS technical team would like to acknowledge the following for help in the project: * Mike Hightower, who has been the key driving force for Energy Surety Microgrids * Juan Torres and Abbas Akhil, who developed the concept of microgrids for military instal- lations * Merrill Smith, U.S. Department of Energy SPIDERS Program Manager * Ross Roley and Rich Trundy from U.S. Pacific Command * Bill Waugaman and Bill Beary from U.S. Northern Command * Tarek Abdallah, Melanie Johnson, and Harold Sanborn of the U.S. Army Corps of Engineers Construction Engineering Research Laboratory * Colleagues from Sandia National Laboratories (SNL) for their reviews, suggestions, and participation in the work.
Many critical loads rely on simple backup generation to provide electricity in the event of a power outage. An Energy Surety Microgrid TM can protect against outages caused by single generator failures to improve reliability. An ESM will also provide a host of other benefits, including integration of renewable energy, fuel optimization, and maximizing the value of energy storage. The ESM concept includes a categorization for microgrid value proposi- tions, and quantifies how the investment can be justified during either grid-connected or utility outage conditions. In contrast with many approaches, the ESM approach explic- itly sets requirements based on unlikely extreme conditions, including the need to protect against determined cyber adversaries. During the United States (US) Department of Defense (DOD)/Department of Energy (DOE) Smart Power Infrastructure Demonstration for Energy Reliability and Security (SPIDERS) effort, the ESM methodology was successfully used to develop the preliminary designs, which direct supported the contracting, construction, and testing for three military bases. Acknowledgements Sandia National Laboratories and the SPIDERS technical team would like to acknowledge the following for help in the project: * Mike Hightower, who has been the key driving force for Energy Surety Microgrids * Juan Torres and Abbas Akhil, who developed the concept of microgrids for military installations * Merrill Smith, U.S. Department of Energy SPIDERS Program Manager * Ross Roley and Rich Trundy from U.S. Pacific Command * Bill Waugaman and Bill Beary from U.S. Northern Command * Melanie Johnson and Harold Sanborn of the U.S. Army Corps of Engineers Construc- tion Engineering Research Laboratory * Experts from the National Renewable Energy Laboratory, Idaho National Laboratory, Oak Ridge National Laboratory, and Pacific Northwest National Laboratory
The Microgrid Design Toolkit (MDT) is a decision support software tool for microgrid designers to use during the microgrid design process. The models that support the two main capabilities in MDT are described. The first capability, the Microgrid Sizing Capability (MSC), is used to determine the size and composition of a new microgrid in the early stages of the design process. MSC is a mixed-integer linear program that is focused on developing a microgrid that is economically viable when connected to the grid. The second capability is focused on refining a microgrid design for operation in islanded mode. This second capability relies on two models: the Technology Management Optimization (TMO) model and Performance Reliability Model (PRM). TMO uses a genetic algorithm to create and refine a collection of candidate microgrid designs. It uses PRM, a simulation based reliability model, to assess the performance of these designs. TMO produces a collection of microgrid designs that perform well with respect to one or more performance metrics.