AlbertPainter: Fix miscopy in Timeseries section

2023-04-10T18:41:59Z

Fix miscopy in Timeseries section

← Older revision		Revision as of 14:41, 10 April 2023
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	Time-series modeling is generally required for storage value estimation because the state of charge (SOC) of an ESS couples all moments in time, meaning the model must track the SOC over time as the storage system operates and restrict the SOC from going out of bounds. Performing a service that charges or discharges the storage system at one point in time can impact the storage system’s ability to provide another service in the future. This means that, unlike in many power system models, each time step cannot be considered independent.		Time-series modeling is generally required for storage value estimation because the state of charge (SOC) of an ESS couples all moments in time, meaning the model must track the SOC over time as the storage system operates and restrict the SOC from going out of bounds. Performing a service that charges or discharges the storage system at one point in time can impact the storage system’s ability to provide another service in the future. This means that, unlike in many power system models, each time step cannot be considered independent.

	Tools that incorporate time-series modeling arrive at different answers to the question, “how many hours/days/weeks/months/years should be included in ~~the model. In other cases,~~ this is less important (usually when making a decision where the error is balanced on both sides). Sizing vs Dispatch-OnlyAll tools in this document fit can be divided into two categories. Either the tool sizes a storage system automatically or it does not. Tools that do not automatically size the storage system could be considered ‘value calculators’ for storage and can still be used to inform sizing analysis by repeatedly calculating the value of systems with different sizes. Tools that automatically size the storage system may have included the power and energy capacity of the storage in their optimization algorithm or rule set directly or may execute repeated fixed-size value analysis behind the scenes. These tools will output the power and energy capacity that results in the highest project net value (or another financial metric). The repeated fixed-size analysis has the advantage of giving the user visibility into the sensitivity of the results to storage power and energy capacity but may not arrive at a globally optimum solution if the range of power and energy capacities is not wide enough or if it performs too few value calculations to have a high likelihood of getting near the optimal solution. Still, it is good to have a wider understanding of the solution space around the optimum so a more robust solution can be found. If the optimum solution’s financial results are only slightly better than another solution and that other solution is less risky (e.g. indicates a smaller system size/cost or relies less on unpredictable events), then the less “optimal” solution might be the better solution. This is to say that all elements of a decision made by a human may not be included in every tool, so it is a good practice to interrogate the results and use several results to inform a robust decision. Microgrid vs DERA microgrid is a group of interconnected loads and distributed energy resources (DER) within clearly defined electrical boundaries that act as a single controllable entity with respect to the grid. A microgrid can connect and disconnect from the grid to enable it to operate in both grid-connected and islanded mode. Some tools are specifically designed to handle always-islanded microgrids, some are specifically meant to handle normal operation of grid-connected DERs, and some are meant to model grid-connected and islanded operation together. The objectives and types of decisions captured in these tools can differ, which may result in different results. For example, a tool designed for always-islanded microgrids that tries to minimize the cost of a microgrid to cover a given load may not be suitable for a grid-connected customer that is going to use a solar plus storage system to cover a fraction of their load during infrequent grid outages and to reduce their electricity bill. Time-Series, Instantaneous, or Design Day Time-series modeling is generally required for storage value estimation because the state of charge (SOC) of an ESS couples all moments in time, meaning the model must track the SOC over time as the storage system operates and restrict the SOC from going out of bounds. Performing a service that charges or discharges the storage system at one point in time can impact the storage system’s ability to provide another service in the future. This means that, unlike in many power system models, each time step cannot be considered independent. 11826632		Tools that incorporate time-series modeling arrive at different answers to the question, “how many hours/days/weeks/months/years should be included in this time series analysis?” depending on the objectives of the tool. Design days are an approach adopted by tools like DER-CAM that divide a year into a few representative days of time-series data per month and use those days to perform the analysis. This approach can be well-validated for some of the decisions DER-CAM focuses on but would break down when trying to model other cases, like those that include long duration energy storage or uncommon loads. Other tools, like EPRI’s DER-VET, incorporate at least one year of time series data into every analysis. This comes at the expense of computational intensity but allows for more flexibility.
	~~1-3 Tools that incorporate time-series modeling arrive at different answers to the question, “how many hours/days/weeks/months/years should be included in this~~ time series analysis?” depending on the objectives of the tool. Design days are an approach adopted by tools like DER-CAM that divide a year into a few representative days of time-series data per month and use those days to perform the analysis. This approach can be well-validated for some of the decisions DER-CAM focuses on but would break down when trying to model other cases, like those that include long duration energy storage or uncommon loads. Other tools, like EPRI’s DER-VET, incorporate at least one year of time series data into every analysis. This comes at the expense of computational intensity but allows for more flexibility.

	There are a series of challenges for storage system valuation models around the interdependence of each time step. For example, how likely is it that a bad solar day will align with a high load day for a solar plus storage microgrid? How likely is it that a market service will deplete the state of charge of the storage system and make it unable to meet other objectives in the future? Tools with different focuses will adopt different answers to these questions.		There are a series of challenges for storage system valuation models around the interdependence of each time step. For example, how likely is it that a bad solar day will align with a high load day for a solar plus storage microgrid? How likely is it that a market service will deplete the state of charge of the storage system and make it unable to meet other objectives in the future? Tools with different focuses will adopt different answers to these questions.

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2022-01-21T18:59:08Z

Created page with "{{DISPLAYTITLE: {{SUBPAGENAME}}}} = Introduction = Large-scale advanced energy storage systems (ESS) have a short track record relative to other electric power technologies...."

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Energy Storage Valuation/Characteristics - Revision history

AlbertPainter: Fix miscopy in Timeseries section

Mevans: Created page with "{{DISPLAYTITLE: {{SUBPAGENAME}}}} = Introduction = Large-scale advanced energy storage systems (ESS) have a short track record relative to other electric power technologies...."