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Citation: Schmidt O and Staffell I. Monetizing Energy Storage: A Toolkit to Assess Future Cost and Value (Oxford: Oxford University Press, 2023)
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Navigating the complex and fast-growing energy storage industry requires precise, clear insights. Energy Storage Ninja equips you with a versatile set of tools to assess the cost and value of electricity storage technologies. Interact with our intuitive models through the cards below, paving your way to stronger business decisions in this dynamic market.
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As pro-user you can conduct more robust analyses, export data for further processing and produce client-ready graphs.
- Download model results for further processing
- Produce high-resolution graphs and remove Ninja logo
- Assess impact of different discount rates
- Save custom data when exploring cost and value
- Model additional markets and years in arbitrage
- Model additional markets to assess storage's system value
Dr Oliver Schmidt
Oliver Schmidt is an entrepreneur and management consultant in the clean energy sector. He has previous experience as project manager at the strategy and financial transaction advisory firm Apricum, where he supported top management with strategic advice in the energy storage, solar PV, and hydrogen industries. Oliver also worked as an energy analyst at the International Energy Agency and as a management consultant at E.ON. He has a PhD on the future cost and value of energy storage from Imperial College London and developed the online platform www.EnergyStorage.ninja. His background is in engineering and renewable energy, which he studied at Imperial and the Swiss Federal Institute of Technology (ETH). He currently lives in Berlin.
Dr Iain Staffell
Iain lives in London with his wife and three children. He is an Associate Professor at Imperial College London, where he teaches energy economics and policy and leads a sustainable energy research group.
He holds degrees in Physics, Chemical Engineering, and Economics from the University of Birmingham. His research has won the Baker Medal and President's Award for Excellence in Research, and has featured in over 120 national and international media articles. Iain is passionate about making energy research transparent and openly available to all. He is a developer of the www.Renewables.ninja platform for modelling renewable energy supply and demand, and the www.EnergyStorage.ninja platform which accompanies this book.
This website is the digital partner to the “Monetizing Energy Storage” book. You can read the digital version of this book for free to get a deeper understanding of the cost and value of energy storage. The book also provides worked examples that guide you through using the tools on this website.
The data available on this website are licensed as Creative Commons CC BY-NC 4.0, meaning you are free to use, copy, redistribute and adapt them for non-commercial purposes, provided you give appropriate credit. If you wish to use the data for commercial purposes, please contact us.
"Essential for me as an investor to navigate this complex, fast-paced energy storage industry.”
Gerard Reid, Alexa Capital
“A must-read for industry and policy professionals.”
Julia Souder, Long Duration Storage Council
“Ground-breaking - an essential read”
Professor Dan Kammen, UC Berkeley
“The go-to resource … exemplary in terms of academic rigour set in a real-world context”
Professor Jim Skea, IPCC
Lifetime Storage
Application-specific lifetime costs account for all relevant cost and performance parameters of an energy storage technology in a particular application. This tab enables you to model lifetime cost, its variability and project it into the future.
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Electricity storage technologies should be compared using application-specific lifetime cost as these account for all technical and economic parameters affecting the cost of delivering stored electricity There are two forms of lifetime cost which matter:
Levelized cost of storage: Quantifies the discounted cost per unit of discharged electricity (USD/MWh) and thus describes the minimum revenue required for each unit of discharged energy for the project to have a net present value of zero
Annuitized capacity cost: Quantifies the discounted cost per unit of power capacity provided (USD/kW-year) and thereby describes the minimum revenue required for each unit of power provided for the project to have a net present value of zero
In this tab, you can include your assumptions on technology cost and application requirements and model the resulting lifetime cost components, variability and future projection
How to use this model
- Choose technology and application
- Click 'Tech values' or 'App values' for each to obtain respective input parameters
- Manually refine parameters based on your own insights if needed
- Click 'Calculate' to determine levelised cost of storage and annuitised capacity cost
How to use this model
- Choose standard deviation for each input parameter (parameters have been defined in 'Cost components')
- Click 'Calculate' to run Monte-Carlo analysis and determine possible variation of LCOS and ACC based on given variation for input parameters
How to use this model
- Choose relative annual change in cost and performance parameters (parameters have been defined in 'Cost components', parameter variation has been defined in 'Cost variation')
- Click 'Calculate' to determine LCOS and ACC for future project based on given change in input parameters
Tech Competitiveness
- Technology cost and performance as well as application parameters may be uncertain and therefore vary around a defined input
- This tab allocates a normal distribution to most input parameters, then calculates lifetime cost for each technology 1,000 times and determines the probability for lowest lifetime cost by comparing the ranges of lifetime cost results of all technologies
- Include your assumptions on technology cost and performance for all technologies, choose an application, and model which technologies are most competitive in that application
How to use this model
- Choose technology
- Click 'Load values' to obtain respective input parameters
- Manually refine parameters based on your own insights if needed and click 'Save values'
- Repeat 1-3 for each technology - Choose '9999' as cost input for those technologies not to be considered
- Click 'Calculate' to identify how likely the modelled technologies are to be most cost-competitive in the chosen application - the black line shows the lifetime cost of the one with the highest probability to be most cost-competitive
Competitive Landscape
- Electricity storage technologies will be most cost-competitive for different applications
- This tab allows you to choose technology cost and performance parameters for a wide range of technologies and then model across the full spectrum of discharge duration and cycle frequencies any application could have
- which technologies are most cost-efficient
- what the lifetime cost of the most cost-efficient technologies have
How to use this model
- Choose technology
- Click 'Load values' to obtain respective input parameters
- Manually refine parameters based on your own insights if needed and click 'Save values'
- Repeat 1-3 for each technology - Choose '9999' as cost input for those technologies not to be considered
- Click 'Best technologies!' to identify the most cost-competitive technology across the application landscape
- Click 'Lifetime cost!' to identify the lifetime cost of the most competitive technologies across the application landscape
Investment Cost
- Investment cost can change for technologies relative to their market growth (deployment) and experience rate (cost reduction)
- This tab allows you to
- Market growth rate: Choose a market and adjust the growth parameters to match the expected technology deployment
- Technology experience rate: Choose a technology and adjust the parameters to match the expected cost reduction
- Investment cost: Observe the impact of market growth and technology experience rate on future investment cost
How to use this model
-
Choose market growth rate
- Select predefined market
- Adjust parameters manually
-
Choose technology experience rate
- Select predefined rate
- Adjust parameters manually
- Observe future investment cost reduction
- Historic data displayed for reference only
Assessing finances of an energy storage project
- The analysis of the financial performance of energy storage is required to take an investment decision
- This tab enables you to determine the key metrics to assess financial performance based on your technology cost and performance inputs, and the application and financing of the storage system
- If the system application is arbitrage, the revenue inputs can be determined on the Arbitrage tab
How to use this model
- Choose project design parameters
- Choose technology performance and cost parameters
- Choose finance parameters
- Press Calculate!
Dispatching Storage to Maximise Arbitrage Profit
- Arbitrage of differences in wholesale power prices may be the largest application for electricity storage
- In this tab you can model the dispatch schedule and profit generated through arbitrage by
- Select market and year to choose the underlying price series
- Selecting storage size, round-trip efficiency and marginal operation cost (accounting for degradation effects)
How to use this model
How much storage do we need?
- The amount of energy storage needed to integrate different shares of intermittent renewable electricity is difficult to determine
- This tab enables you to identify a range of energy storage capacity needed for the power system of your choice based on its peak power demand, annual electricity demand and the share of intermittent renewable electricity
- The various data points represent findings from studies on the need for electricity storage in various power systems with very different characteristics - they form the basis to provide you with a range of energy storage capacity needs for your power system
How to use this model
- Enter peak power demand for your power system
- Enter annual electricity demand for your power system
- Enter penetration of intermittent renewable generators in your power system
Power System Under Investigation
Storage power capacity
Storage energy capacity
Sizing storage to integrate renewable energy
- There are trade-offs between deploying efficient, but expensive or inefficient, but low-cost electricity storage technologies
- This tab enables you to assess these trade-offs by choosing
- A power system for demand and weather data, the ratio between wind:solar generation and their share in total generation
- Size and efficiency of two distinct energy storage technologies in the power system
- Cost and lifetime for wind, solar and the two respective energy storage technologies
How to use this model
- Choose country and set wind:solar ratio
- Select storage size and efficiency
- Choose level of RE integration at which system cost should be evaluated
- Set technology cost, lifetime and discount rate
- Press 'Calculate'