Energy Storage Valuation - Revision history

Mevans at 18:59, 21 January 2022

2022-01-21T18:59:39Z

← Older revision		Revision as of 14:59, 21 January 2022
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	Energy storage valuation tools can be used to make critical decision around energy storage, including where to locate energy storage, how big to size the best power and energy capacity for a storage system, what applications make the most sense for a particular system, which technical solution to select from a set of technology offerings, how to pair the storage system with other resources, etc. Energy storage is often characterized by high cost and high flexibility/value, so modeling the financial performance of the storage can be a sensitive, uncertain process. Unlike with other power technologies, modeling energy storage requires complicated approaches necessitated by its energy limit that increase uncertainty relative to an energy-unlimited resource. Users may rely on the results from a valuation tool to support decisions on which millions of dollars depend, so robust, case-specific valuation that internalizes these sources of uncertainty and allows for an understanding of cost, reward, and risk tradeoffs is a critical part of the storage decision making process.		Energy storage valuation tools can be used to make critical decision around energy storage, including where to locate energy storage, how big to size the best power and energy capacity for a storage system, what applications make the most sense for a particular system, which technical solution to select from a set of technology offerings, how to pair the storage system with other resources, etc. Energy storage is often characterized by high cost and high flexibility/value, so modeling the financial performance of the storage can be a sensitive, uncertain process. Unlike with other power technologies, modeling energy storage requires complicated approaches necessitated by its energy limit that increase uncertainty relative to an energy-unlimited resource. Users may rely on the results from a valuation tool to support decisions on which millions of dollars depend, so robust, case-specific valuation that internalizes these sources of uncertainty and allows for an understanding of cost, reward, and risk tradeoffs is a critical part of the storage decision making process.

	[[Characteristics\| Characteristics of Energy Storage Value Estimation Tools]]		[[Energy_Storage_Valuation/Characteristics\| Characteristics of Energy Storage Value Estimation Tools]]

	[[Tools\| Energy Storage Valuation Tools]]		[[Tools\| Energy Storage Valuation Tools]]

	[[Benchmarking\| Tool Comparison and Benchmarking]]		[[Benchmarking\| Tool Comparison and Benchmarking]]

Mevans at 18:42, 21 January 2022

2022-01-21T18:42:21Z

← Older revision		Revision as of 14:42, 21 January 2022
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	[[Characteristics\| Characteristics of Energy Storage Value Estimation Tools]]		[[Characteristics\| Characteristics of Energy Storage Value Estimation Tools]]

	[[Tools\| Energy Storage Valuation Tools]]		[[Tools\| Energy Storage Valuation Tools]]

	[[Benchmarking\| Tool Comparison and Benchmarking]]		[[Benchmarking\| Tool Comparison and Benchmarking]]

Mevans at 18:42, 21 January 2022

2022-01-21T18:42:02Z

← Older revision		Revision as of 14:42, 21 January 2022
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	Energy storage valuation tools can be used to make critical decision around energy storage, including where to locate energy storage, how big to size the best power and energy capacity for a storage system, what applications make the most sense for a particular system, which technical solution to select from a set of technology offerings, how to pair the storage system with other resources, etc. Energy storage is often characterized by high cost and high flexibility/value, so modeling the financial performance of the storage can be a sensitive, uncertain process. Unlike with other power technologies, modeling energy storage requires complicated approaches necessitated by its energy limit that increase uncertainty relative to an energy-unlimited resource. Users may rely on the results from a valuation tool to support decisions on which millions of dollars depend, so robust, case-specific valuation that internalizes these sources of uncertainty and allows for an understanding of cost, reward, and risk tradeoffs is a critical part of the storage decision making process.		Energy storage valuation tools can be used to make critical decision around energy storage, including where to locate energy storage, how big to size the best power and energy capacity for a storage system, what applications make the most sense for a particular system, which technical solution to select from a set of technology offerings, how to pair the storage system with other resources, etc. Energy storage is often characterized by high cost and high flexibility/value, so modeling the financial performance of the storage can be a sensitive, uncertain process. Unlike with other power technologies, modeling energy storage requires complicated approaches necessitated by its energy limit that increase uncertainty relative to an energy-unlimited resource. Users may rely on the results from a valuation tool to support decisions on which millions of dollars depend, so robust, case-specific valuation that internalizes these sources of uncertainty and allows for an understanding of cost, reward, and risk tradeoffs is a critical part of the storage decision making process.

			[[Characteristics\| Characteristics of Energy Storage Value Estimation Tools]]
			[[Tools\| Energy Storage Valuation Tools]]
			[[Benchmarking\| Tool Comparison and Benchmarking]]

Mevans at 18:37, 21 January 2022

2022-01-21T18:37:17Z

← Older revision		Revision as of 14:37, 21 January 2022
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	This wiki page identifies key gaps in the current field of energy storage modeling tools, characterizes key differences between energy storage modeling and valuation tools, and identifies where specific tools fall in this characterization. In addition, this report presents the beginnings of a quantitative benchmarking and comparison exercise between select tools meant to highlight their modeling differences and help the reader understand where certain tools are most applicable. This exercise will be expanded in future publications. The results show broad alignment between tools’ modeling approaches, and the key differences come out in scope, usability, and how the modeling approach (for example, perfect foresight dispatch optimization) is applied. These differences show up strongly in the quantitative benchmarking results, where there is less alignment despite similar modeling approaches owing to differences in implementation and applicability.		This wiki page identifies key gaps in the current field of energy storage modeling tools, characterizes key differences between energy storage modeling and valuation tools, and identifies where specific tools fall in this characterization. In addition, this report presents the beginnings of a quantitative benchmarking and comparison exercise between select tools meant to highlight their modeling differences and help the reader understand where certain tools are most applicable. This exercise will be expanded in future publications. The results show broad alignment between tools’ modeling approaches, and the key differences come out in scope, usability, and how the modeling approach (for example, perfect foresight dispatch optimization) is applied. These differences show up strongly in the quantitative benchmarking results, where there is less alignment despite similar modeling approaches owing to differences in implementation and applicability.

			* Broad similarity exists between the modeling approaches of currently available energy storage valuation tools (with some exceptions), but this similarity does not necessarily extend to quantitative results owing to differences in the implementation and applicability of the tools.
			* EPRI and others have made progress toward filling the gaps in valuation tool capability, but key gaps remain, and new gaps have opened as new needs have been highlighted.
			* In the future, broad-scope, standardized benchmarking of energy storage valuation tools will help bring the field into an alignment that does not always exist currently.

			Energy storage valuation tools can be used to make critical decision around energy storage, including where to locate energy storage, how big to size the best power and energy capacity for a storage system, what applications make the most sense for a particular system, which technical solution to select from a set of technology offerings, how to pair the storage system with other resources, etc. Energy storage is often characterized by high cost and high flexibility/value, so modeling the financial performance of the storage can be a sensitive, uncertain process. Unlike with other power technologies, modeling energy storage requires complicated approaches necessitated by its energy limit that increase uncertainty relative to an energy-unlimited resource. Users may rely on the results from a valuation tool to support decisions on which millions of dollars depend, so robust, case-specific valuation that internalizes these sources of uncertainty and allows for an understanding of cost, reward, and risk tradeoffs is a critical part of the storage decision making process.

Mevans at 18:35, 21 January 2022

2022-01-21T18:35:48Z

← Older revision		Revision as of 14:35, 21 January 2022
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	~~DER-VET is an open-source, optimization-based planning tool to aid~~ in the ~~design~~ of ~~microgrids and distributed~~ energy ~~resources (DER) deployments to maximize benefit to individual customers~~, ~~ratepayers~~, and ~~to society~~. ~~DER-VET provides a platform to model~~ the ~~operation and subsequent value of a set~~ of ~~DERs (“DER mix”), potentially configured in a microgrid, collectively providing~~ a ~~set of stacked services. DER-VET uses load~~ and ~~other site-specific data~~ to ~~optionally optimize~~ the ~~size of the DERs concurrently with its dispatch optimization~~. The ~~technologies modeled in DER-VET include various types of energy storage, intermittent renewable generation, fueled generation, controllable loads/electric vehicles~~, and hybrid resources like combined heat and power (CHP). These energy resources can be used in any combination to improve grid reliability, improve customer resilience by providing backup to local critical loads, decrease the electricity bill incurred by the ~~site, participate~~ in ~~wholesale energy or ancillary services markets~~, ~~provide demand response or resource adequacy~~, ~~or some allowable combination of these. DER-VET could be connected with~~ the ~~grid simulation tools~~ (for example ~~OpenDSS™~~, ~~DRIVE, etc.~~) ~~to allow for easy transitions between the models~~.		This wiki page identifies key gaps in the current field of energy storage modeling tools, characterizes key differences between energy storage modeling and valuation tools, and identifies where specific tools fall in this characterization. In addition, this report presents the beginnings of a quantitative benchmarking and comparison exercise between select tools meant to highlight their modeling differences and help the reader understand where certain tools are most applicable. This exercise will be expanded in future publications. The results show broad alignment between tools’ modeling approaches, and the key differences come out in scope, usability, and how the modeling approach (for example, perfect foresight dispatch optimization) is applied. These differences show up strongly in the quantitative benchmarking results, where there is less alignment despite similar modeling approaches owing to differences in implementation and applicability.
	~~Various practical microgrid case studies are tested using DER-VET and the study results are documented~~ in ~~this report. Microgrid applications are broadly grouped under three usecases and they are briefly discussed. This first part of the report series will focus on~~ the ~~first two usecases and sub-scenarios. DER-VET~~ results ~~and inferences are discussed~~ in ~~detail for each of the two usecases and identified sub-scenarios for each usecase. To make the study realistic, actual site data and utility service territory tariffs~~ and ~~market structure are used in this study~~.

Mevans: Created page with "DER-VET is an open-source, optimization-based planning tool to aid in the design of microgrids and distributed energy resources (DER) deployments to maximize benefit to indivi..."

2022-01-21T18:28:39Z

Created page with "DER-VET is an open-source, optimization-based planning tool to aid in the design of microgrids and distributed energy resources (DER) deployments to maximize benefit to indivi..."

New page

DER-VET is an open-source, optimization-based planning tool to aid in the design of microgrids and distributed energy resources (DER) deployments to maximize benefit to individual customers, ratepayers, and to society. DER-VET provides a platform to model the operation and subsequent value of a set of DERs (“DER mix”), potentially configured in a microgrid, collectively providing a set of stacked services. DER-VET uses load and other site-specific data to optionally optimize the size of the DERs concurrently with its dispatch optimization. The technologies modeled in DER-VET include various types of energy storage, intermittent renewable generation, fueled generation, controllable loads/electric vehicles, and hybrid resources like combined heat and power (CHP). These energy resources can be used in any combination to improve grid reliability, improve customer resilience by providing backup to local critical loads, decrease the electricity bill incurred by the site, participate in wholesale energy or ancillary services markets, provide demand response or resource adequacy, or some allowable combination of these. DER-VET could be connected with the grid simulation tools (for example OpenDSS™, DRIVE, etc.) to allow for easy transitions between the models.
Various practical microgrid case studies are tested using DER-VET and the study results are documented in this report. Microgrid applications are broadly grouped under three usecases and they are briefly discussed. This first part of the report series will focus on the first two usecases and sub-scenarios. DER-VET results and inferences are discussed in detail for each of the two usecases and identified sub-scenarios for each usecase. To make the study realistic, actual site data and utility service territory tariffs and market structure are used in this study.

← Older revision		Revision as of 14:42, 21 January 2022
Line 8:		Line 8:

	[[Characteristics\| Characteristics of Energy Storage Value Estimation Tools]]		[[Characteristics\| Characteristics of Energy Storage Value Estimation Tools]]

	[[Tools\| Energy Storage Valuation Tools]]		[[Tools\| Energy Storage Valuation Tools]]

	[[Benchmarking\| Tool Comparison and Benchmarking]]		[[Benchmarking\| Tool Comparison and Benchmarking]]