Electricity Model

[Reginald Hanfy]

TheGeothermal Technologies Office (GTO) uses the Geothermal Electricity Technology Evaluation Model (GETEM) to understand the performance and cost of energy technologies GTO seeks to improve. The model helps GTO determine which proposed research, development, and deployment (RD&D) programs and projects might offer the most efficient improvement when based on taxpayer funding.

Electrical power generation is the sole geothermal use considered by GETEM. The model does not provide assessment capabilities for geothermal heating and cooling technologies. It can evaluate a hydrothermal or an enhanced geothermal system resource type, and then either a flash-steam or air-cooled binary power plant based on specific resource parameters.

The 2016 version is an update of the 2012 Beta Version of the GETEM tool, which focuses on the use of an enhanced geothermal resource with an air-cooled binary generation plant. The 2012 version has not been subjected to any rigorous check or validation. Do not use the 2012 Beta Version without accessing the GETEM manuals and revision notes. A new version of GETEM, integrated into the free techno-economic System Advisor Model (SAM), is planned for release in the near future. Keep an eye out for more information about this update.

This model and its documentation were prepared as required work under a subcontract from the National Renewable Energy Laboratory, a U.S. Department of Energy (DOE) national laboratory, to Princeton Energy Resources International, Rockville, Maryland. The estimates and correlations for the performance and costs of geothermal electric power systems are intended for use in analysis of government policies, and should not be construed or represented as "official DOE estimates of performance and/or cost of any real geothermal power system or any of its components."

Further, neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its usefulness would not infringe privately owned rights.

The primary purpose of HEM is to gain insights into how structural electricity system changes impact an interconnected energy ecosystem both as a whole and from the perspectives of different stakeholders.

Electricity system planning has traditionally been conducted from the perspective of a single central planner, with limited consideration for other stakeholders, such as suppliers, regulators, and consumers. Recent and ongoing technological, market and policy innovations, however, have created a growing and diverse landscape of electricity systems throughout the United States and the world. Because of these developments, questions have been raised regarding the structure of future electricity systems. For instance, how decentralized will generation and other grid services become? And to what degree might customers or communities choose total or partial self-supply?

HEM answers these questions with a decentralized, stakeholder-centric approach that incorporates multiple perspectives to ensure that all members of an electricity system are considered as the structure evolves.

The HEM framework is initially being applied in a case study of distributed photovoltaics (DPV) adoption in a bulk power system composed of natural gas and utility-scale photovoltaic generators. Researchers are exploring the interactions of regulatory structure, retail rate design, and DPV compensation, as well as their impacts on utility-scale investments, customer investments, retail rates, and net surpluses.

HEM is designed to address structural questions in an integrated way that is open to the inclusion of new types of stakeholders, outcome metrics, and decision-making processes. The framework can be applied to multiple jurisdictions and is extensible for many different timeframes, technologies, and use cases.

The HEM team has partnered with a diverse technical review committee that reflects many of the stakeholder voices factored into the modeling itself. Although these representatives have offered input throughout the project, any results and findings do not necessarily reflect their opinions or the opinions of their institutions.

The committee includes representatives from Renewable Energy Buyers Alliance, Fort Collins Utilities, the World Resources Institute, Electron, the California Office of Ratepayer Advocates, Southern Company, New Buildings Institute, WSP, ISO New England, Kevala Inc., the University of Wisconsin, the University of Maryland, Johns Hopkins University, Loughborough University, Penn State University, Carnegie Mellon University, University College Dublin, and American University.

Open-source HEM software is being developed with plans to eventually release it on Github. The Holistic Electricity Modeling framework is associated with the Scalable Integrated Infrastructure Planning (SIIP) initiative.

While searching for new information on Utility Network, I came across (for the first time) this very interesting and informative website about the Communications Utility Network Foundation Data Model:

Especially the detailed equipment models at the bottom of the page are very helpful. I would now be interested to know if there are similar sites for the other Esri Foundation data models, but specifically for electricity.

Thanks for the hint to the data dictionary and the other information. I will look at the data dictionary in detail. Actually, I was hoping for a clearer, more visual representation of the Electric Foundation data model.

This paper, a companion to NI WP 14-12, describes the structure of, and data sources for, the electricity component of the Dynamic Integrated Economy/Energy/Emissions Model (DIEM), which was developed at the Nicholas Institute for Environmental Policy Solutions at Duke University. The DIEM model includes a macroeconomic, or computable general equilibrium (CGE), component and an electricity component that gives a detailed representation of U.S. regional electricity markets. The electricity model (DIEM-Electricity) discussed in thus paper can be run as a stand-alone model or can be linked to the DIEM-CGE macroeconomic model to incorporate feedbacks among economy-wide energy policies and electricity generation decisions and interactions between electricity-sector policies and the rest of the U.S and global economies. Broadly, DIEM-Electricity is a dynamic linear-programming model of U.S. wholesale electricity markets that represents intermediate- to long-run decisions about generation, capacity planning, and dispatch of units. It provides results for generation, capacity, investment, and retirement by type of plant. It also determines wholesale electricity prices, production costs, fuel use, and CO2 emissions. Currently, the model can consider, at a national policy level, renewable portfolio standards, clean energy standards, caps on electricity-sector CO2 emissions, and carbon taxes.

A model more accurately predicts how much summertime electricity and water use could increase due to climate change between 2030 and 2052, when global warming is expected to increase by 1.5 degrees Celsius. These projections only consider the effects of climate; not other factors such as population growth or technological shifts. (Purdue University infographic/Greg Simmons)

If the city generated any less electricity, it would be risking a power shortage that may require drastic measures to avoid rolling blackouts, according to projections from a model designed by Purdue University researchers.

That estimated increase is larger than previous projections because it takes into account how consumers use electricity and water at the same time. The model also considers a wider range of climate features that affect this mixed use, such as humidity and wind speed, making predictions more accurate.

In a study published Thursday (March 5) in the journal Climatic Change, the collaborative team applied this model to five other cities in the U.S. Midwest: Cleveland; Columbus, Ohio; Indianapolis; Madison, Wisconsin; and Minneapolis.

This means that for Chicago, the best-case scenario is that electricity use increases by 12% and water use increases by 4% if global warming crosses a 1.5 C threshold. But if a 2.0 C threshold is reached, then the worst-case scenario is a 20% increase in electricity use and a 6% increase in water use.

SWITCH (Solar and wind energy integrated with transmission and conventional sources) is a linear programming modeling platform used to examine least cost energy systems designed to meet specific reliability, performance and environmental quality standards.

SWITCH is a capacity expansion model that invests in new generation and transmission assets as well as in end-use and demand-side management options (including electrified vehicles and storage) with a high-resolution assessment and planning package to explore the system performance resting from different scenarios.

SWITCH was initially developed for California, but has been expanded and refined to explore energy choices across the US West (the WECC, Chile, Nicaragua, China), with future plans to cover the East African Power Pool (EAPP) and India.

The water/hose analogy for electricity is useful for explaining voltage, current, and power. In general terms, charge is water, voltage is the pressure of water, current is the flow of the water. Power is the total amount of water flowing in given time. You can have a small pipe with high pressure, or a large pipe with low pressure each passing the same amount of water. Increasing the height of the water reservoir increases the potential energy of the water (voltage). Resistance can be explained as the roughness of the width of the pipe.

Researchers are developing materials with low resistance for energy efficient electronics. The flow of electrons in a solar cell can be optimized by using a grid of highly conductive metal as a collector. The exact design of this grid depends on an understanding of sheet resistance.

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