Storage Sizing and Placement Tool in Distribution Grids
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The MDT is a decision support software that can provide the information needed to identify optimal microgrid designs in the early stages of the design process. MDT searches the trade space of alternative microgrid designs in terms of user-defined objectives, such as performance, reliability, and cost. It produces a Pareto frontier of solutions embodying the efficient tradeoffs amongst multiple user-defined objectives.
The Ko’olauloa community resilience hub has affordable microgrid options to meet its resilience requirements. Renewables and battery are insufficient without very high cost. Either diesel generation or a hydrogen battery are needed to assure availability and keep cost down.
Paper showing microgrid and energy efficiency tradeoffs for a case study for the Ko'olauloa Community resilience hub using HOMER and MDT.
The Storage Sizing and Placement Simulation (SSIM) application allows a user to define the possible sizes and locations of energy storage elements on an existing grid model defined in OpenDSS. Given these possibilities, the software will automatically search through them and attempt to determine which configurations result in the best overall grid performance. This quick-start guide will go through, in detail, the creation of an SSIM model based on a modified version of the IEEE 34 bus test feeder system. There are two primary parts of this document. The first is a complete list of instructions with little-to-no explanation of the meanings of the actions requested. The second is a detailed description of each input and action stating the intent and effect of each. There are links between the two sections.
The Energy Resilience for Mission Assurance (ERMA) project—a Department of Energy Grid Modernization Lab Consortium effort carried out via a partnership among five national laboratories— seeks to develop metrics to quantify how improvements to energy system resilience translate to improved Department of Defense (DoD) mission assurance (MA) during wide-scale, long-duration outages of the bulk power system. DoD missions are integral to national security and highly dependent on electric power. However, energy system planners—both civilian and military—lack a clear and quantifiable mapping between electric power system resilience and MA, leaving a gap in their ability to understand and consider national security outcomes within their planning efforts. The ERMA project seeks to fill this gap, providing stakeholders with new capabilities to understand the impact of electric power system resilience on MA during hazard scenarios.
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers.
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a users manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user’s manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
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.
This simple Microgrid Design Toolkit (MDT) use case will provide you an example of performing microgrid sizing by identifying the types and quantities of technology to be purchased for use in a microgrid. It will introduce basic principles of using the MDT microgrid sizing capability by comparing the results of two microgrids in two different markets. Please reference the MDT User Guide (SAND2020-4550) for detailed instructions on how to use the tool.
Abstract not provided.
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
Under its Grid Modernization Initiative, the U.S. Department of Energy (DOE), in collaboration with energy industry stakeholders developed a multi-year research plan to support modernizing the electric grid. One of the foundational projects for accelerating modernization efforts is information and communications technology interoperability. A key element of this project has been the development of a methodology for engaging ecosystems related to grid integration to create roadmaps that advance the ease of integration of related smart technology. This document is the product of activities undertaken in 2017 through 2019. It provides a Cybersecurity Plan describing the technology to be adopted in the project with details as per the GMLC Call document.
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).
Under its Grid Modernization Initiative, the U.S. Department of Energy(DOE),in collaboration with energy industry stakeholders developed a multi-year research plan to support modernizing the electric grid. One of the foundational projects for accelerating modernization efforts is information and communications technology interoperability. A key element of this project has been the development of a methodology for engaging ecosystems related to grid integration to create roadmaps that advance the ease of integration of related smart technology. This document is the product of activities undertaken in 2017 through 2019.It provides a Cybersecurity Plan describing the technology to be adopted in the project with details as per the GMLC Call document.