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What is Round Trip Efficiency?

Energy storage systems function by taking in electricity, storing it, and subsequently returning it to the grid. The round trip efficiency (RTE), also known as AC/AC efficiency, refers to the ratio between the energy supplied to the storage system (measured in MWh) and the energy retrieved from it (also measured in MWh). This efficiency is expressed as a percentage (%).

The round trip efficiency is a crucial factor in determining the effectiveness of storage technology. A higher RTE indicates that there is less energy loss during the storage process, resulting in a more efficient overall system. Grid systems engineers strive for energy storage systems to achieve an 80% RTE whenever feasible, as it signifies a desirable level of efficiency and minimizes energy losses.

What Factors Can Affect the Round Trip Efficiency of an Energy Storage System?

The RTE of an energy storage system can be influenced by various factors, including:

1. Technology: Different storage technologies have varying round-trip efficiencies. For example, hydro storage typically ranges from 65% in older installations to 75-80% in modern deployments, while flywheels have efficiencies of about 80% to 90%. Some battery technologies can have round-trip efficiencies ranging from 75% to 90%.

2. Storage duration: Some technologies may experience leakage or energy loss over long-term storage, which can affect round-trip efficiency. It is important to consider the specific characteristics and limitations of the storage technology when evaluating its efficiency.

3. Age and condition of the system: Older storage systems may have lower round-trip efficiencies compared to newer ones. Factors such as wear and tear, component degradation, and maintenance practices can impact the overall efficiency of the system.

4. Charging and discharging rates: The speed at which energy is charged into and discharged from the storage system can affect its efficiency. Certain technologies may have lower efficiencies at high charging or discharging rates.

5. System design and control: The design and control strategies implemented in the energy storage system can influence its round-trip efficiency. Optimal system design, efficient power electronics, and effective control algorithms can improve the overall efficiency of the system.

6. Temperature: Temperature can have an impact on the performance and efficiency of energy storage systems. Extreme temperatures can affect the efficiency of certain storage technologies, such as batteries, leading to lower round-trip efficiencies.

Considering these factors is crucial when evaluating the round-trip efficiency of an energy storage system, as they can significantly affect its performance and effectiveness in storing and retrieving energy.

Must Read: What is Power Conversion Efficiency?

Elliot is a passionate environmentalist and blogger who has dedicated his life to spreading awareness about conservation, green energy, and renewable energy. With a background in environmental science, he has a deep understanding of the issues facing our planet and is committed to educating others on how they can make a difference.

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Utility-scale batteries and pumped storage return about 80% of the electricity they store

Electric energy storage is becoming more important to the energy industry as the share of intermittent generating technologies, such as wind and solar, in the electricity mix increases. Electric energy storage helps to meet fluctuating demand, which is why it is often paired with intermittent sources. Storage technologies include batteries and pumped-storage hydropower , which capture energy and store it for later use. Storage metrics can help us understand the value of the technology. Round-trip efficiency is the percentage of electricity put into storage that is later retrieved. The higher the round-trip efficiency, the less energy is lost in the storage process. According to data from the U.S. Energy Information Administration (EIA), in 2019, the U.S. utility-scale battery fleet operated with an average monthly round-trip efficiency of 82%, and pumped-storage facilities operated with an average monthly round-trip efficiency of 79%.

EIA’s Power Plant Operations Report provides data on utility-scale energy storage, including the monthly electricity consumption and gross electric generation of energy storage assets, which can be used to calculate round-trip efficiency. The metrics reviewed here use the finalized data from the Power Plant Operations Report for 2019—the most recent year for which a full set of storage data is available.

Pumped-storage facilities are the largest energy storage resource in the United States. The facilities collectively account for 21.9 gigawatts (GW) of capacity and for 92% of the country’s total energy storage capacity as of November 2020.

In recent years, utility-scale battery capacity has grown rapidly as battery costs have decreased. As batteries have been increasingly paired with renewables , they have become the second-largest source of electricity storage. As of November 20, 2020, utility-scale battery capacity had 1.4 GW of operational capacity. Another 4.0 GW of battery capacity is scheduled to come online in 2021, according to EIA’s Preliminary Electric Generator Inventory .

Although battery storage has slightly higher round-trip efficiency than pumped storage, pumped-storage facilities typically operate at utilization factors that are currently twice as high as batteries. Increasing durations among battery applications could shift battery operations toward services that reward longer output periods. For example, in 2015, the weighted average battery duration was a little more than 46 minutes, but by 2019, weighted average battery durations had doubled to 1.5 hours. The role of batteries and their capability to provide high levels of round-trip efficiency may become more important as batteries continue to be deployed and as the intermittent renewables share of the electricity mix grows.

Tags: storage , electricity

Empowering Innovations: The Bright Future of Round Trip Efficiency of Battery

Table of Contents

Round Trip Efficiency of Battery

The concept of round trip efficiency of battery is pivotal in energy storage technologies. This section will provide an extensive introduction to what round trip efficiency means in the context of batteries. We'll explore its importance in various applications, ranging from small-scale electronics to large-scale energy systems. Understanding the round trip efficiency of battery is essential for assessing the performance and sustainability of these energy storage devices.

The Science Behind Round Trip Efficiency

Delving deeper into the technicalities, this part will explain how the round trip efficiency of battery is determined. It will cover the fundamental principles of battery operation, including charge and discharge cycles, energy losses during these cycles, and how they affect overall efficiency. Factors like temperature, charge rate, and battery age, which significantly impact round trip efficiency, will be discussed in detail.

Components Affecting Round Trip Efficiency

In this subsection, we will explore the various components of batteries, such as electrodes, electrolytes, separators, and casings, and how each contributes to or detracts from the round trip efficiency. The material composition of these components, their engineering, and how they interact with each other play a critical role in the efficiency of the battery.

The Role of Battery Design

This part will discuss how the physical and chemical design of a battery influences its round trip efficiency. Topics like battery size, shape, internal architecture, and the arrangement of cells within a battery pack will be covered. The section will also explore how innovative design strategies are being employed to enhance efficiency.

Types of Batteries and Their Round Trip Efficiency

This section will provide a comparative analysis of different battery types, such as lithium-ion, lead-acid, and nickel-metal hydride, focusing on their round trip efficiencies. Each battery type's unique characteristics, advantages, and limitations in terms of efficiency will be discussed.

Lithium-Ion Batteries and Efficiency

Focusing on lithium-ion batteries, this subsection will delve into why they are widely regarded for their high round trip efficiency. We will examine the factors that contribute to this efficiency and the challenges that still exist. The latest advancements in lithium-ion technology aimed at improving efficiency will also be highlighted.

Other Battery Technologies

This part will look at alternative battery technologies, comparing their round trip efficiencies with that of lithium-ion batteries. It will cover emerging technologies like solid-state batteries, flow batteries, and others, discussing their potential to rival or surpass the efficiency of traditional battery types

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Improving the Round Trip Efficiency of Battery

This section will explore the various strategies and technological advancements aimed at enhancing the round trip efficiency of battery. It will cover research and development efforts in new materials, battery chemistry, and manufacturing techniques. Discussion will include how these advancements could potentially increase efficiency, reduce costs, and extend the life of batteries.

Innovations in Battery Materials

Delving into the realm of materials science, this subsection will explore new and innovative materials being developed to increase the round trip efficiency of battery. This includes advancements in electrode materials, electrolyte formulations, and separator technologies. We'll look at how these new materials can reduce energy losses during charging and discharging, thereby improving overall efficiency.

The Future of Battery Efficiency

In this part, we will explore the cutting-edge research and future directions aimed at pushing the boundaries of round trip efficiency in battery technology. This will include a discussion on potential breakthroughs, the challenges researchers face, and the implications these advancements could have on the global energy landscape.

Applications and Importance of High Round Trip Efficiency

Discussing the wide range of applications where high round trip efficiency in batteries is critical, this section will cover areas like electric vehicles, renewable energy storage, grid management, and consumer electronics. We'll explore how improvements in battery efficiency can impact these fields, leading to more sustainable and efficient energy usage.

Impact on Electric Vehicles

Focusing on electric vehicles (EVs), this subsection will discuss how the round trip efficiency of battery affects the performance, range, and cost-effectiveness of EVs. We'll explore current challenges, the importance of efficiency improvements in the context of EV adoption, and how advancements could shape the future of transportation.

Renewable Energy Storage Systems

In this part, the role of battery efficiency in the effectiveness and viability of renewable energy storage systems will be examined. We'll discuss how higher round trip efficiency can enhance the storage and release of energy from sources like solar and wind, making renewable energy more reliable and accessible.

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Battery Round Trip Efficiency Definition: Understanding the Concept

The definition of battery round trip efficiency is a fundamental concept in the realm of battery technology and energy storage. This section aims to elucidate the battery round trip efficiency definition and its relevance in practical applications.

Exploring the Battery Round Trip Efficiency Definition

Battery round trip efficiency is defined as the ratio of the energy output of a battery to the energy input required to recharge it. This definition provides a quantitative measure of how effectively a battery stores and then releases the energy put into it. It's a critical parameter for evaluating the performance of a battery, as it directly influences the efficiency and cost-effectiveness of the battery in its practical application.

Implications of Battery Round Trip Efficiency in Energy Systems

Understanding the battery round trip efficiency definition is vital for anyone involved in the design, manufacture, or use of battery systems. This efficiency metric is particularly important in applications where energy conservation and efficiency are paramount, such as in electric vehicles, renewable energy systems, and portable electronic devices. A higher round trip efficiency means more of the stored energy is available for use, which is crucial for the overall efficiency and sustainability of these systems.

Environmental Impact of Battery Efficiency

This new section will explore the environmental implications of the round trip efficiency of battery. It will discuss how increased efficiency can lead to reduced energy waste and lower carbon footprints. This part will also cover the lifecycle of batteries, including manufacturing and recycling processes, and how efficiency plays a role in minimizing environmental impact.

Economic Aspects of Battery Efficiency

In this section, we'll delve into the economic aspects of round trip efficiency in batteries. It will cover how higher efficiency can lead to cost savings for consumers and businesses, and its impact on the overall economy. This part will also discuss the investment in research and development for more efficient batteries and how this drives innovation in the battery industry.

Safety and Reliability Concerns

This part will address how the round trip efficiency of battery relates to their safety and reliability. We'll explore the challenges that arise when trying to balance high efficiency with safety, especially in high-demand applications like electric vehicles and energy storage systems. This section will also discuss the measures taken to ensure that efficiency improvements do not compromise the safety and longevity of batteries.

Regulatory and Policy Frameworks

This new section will examine the role of regulatory and policy frameworks in shaping the development and adoption of efficient battery technologies. It will cover current regulations and standards related to battery efficiency, and how these policies impact the industry. Additionally, this part will discuss potential future policies that could encourage or mandate improvements in battery efficiency.

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BatteryRound Trip Efficiency Calculation: Methods and Importance

The calculation of battery round trip efficiency is a critical aspect in assessing the performance of battery systems. This section delves into the methodologies and significance of accurately performing batteryround trip efficiency calculation.

Understanding the Basics of Battery Round Trip Efficiency Calculation

To begin with, battery round trip efficiency calculation involves determining the ratio of the energy outputted by the battery to the energy inputted into it during charging. This calculation is crucial for understanding how much energy is lost in the process of charging and discharging a battery. These energy losses typically occur due to factors like internal resistance, heat generation, and the inefficiencies in the battery's chemical processes.

Step-by-Step Process of Battery Round Trip Efficiency Calculation

To calculate battery round trip efficiency, one must first measure the amount of energy inputted into the battery during the charging process. This is typically done in watt-hours (Wh) or kilowatt-hours (kWh). Following this, the energy outputted by the battery during discharge is measured. The round trip efficiency is then calculated by dividing the energy outputted by the energy inputted and multiplying the result by 100 to obtain a percentage. A higher percentage indicates a more efficient battery with less energy loss.

The Significance of Accurate Calculation

Accurate battery round trip efficiency calculation is crucial for several reasons. Firstly, it allows for the comparison of different battery technologies on a uniform basis, aiding in the selection of the most efficient and suitable battery for a specific application. Secondly, understanding the efficiency of a battery helps in estimating its operational costs and its impact on the overall efficiency of the system it powers, such as an electric vehicle or a renewable energy storage system.

Battery Storage Round Trip Efficiency: Key Aspects and Evaluation

The concept of battery storage round trip efficiency is crucial in the context of energy storage systems. This section focuses on defining and understanding the nuances of battery storage round trip efficiency and its impact on energy storage solutions.

Defining Battery Storage Round Trip Efficiency

Battery storage round trip efficiency is a measure that indicates how efficiently a battery can store and then release the energy it has been charged with. This efficiency is calculated by comparing the amount of energy input into the battery during charging to the amount of usable energy output during discharge. A higher battery storage round trip efficiency signifies that a larger portion of the input energy is available for use, making the battery more effective and economical for energy storage purposes.

Importance of Battery Storage Round Trip Efficiency in Energy Systems

In the field of energy storage, especially in systems like grid storage or electric vehicles, battery storage round trip efficiency plays a pivotal role. It directly affects the viability and performance of the storage system. High-efficiency levels mean more energy is available for use from each charging cycle, which is crucial for the overall energy efficiency and operational cost of the system. As such, battery storage round trip efficiency is a key parameter in the selection and design of battery systems for various applications.

Round Trip Efficiency Battery Storage: A Brief Overview

The term roundtrip efficiency in battery storage is a vital metric in the energy sector. This section provides a succinct overview of what round trip efficiency battery storage entails and its significance.

Understanding RoundTrip Efficiency Battery Storage

Round trip efficiency in battery storage refers to the measure of how effectively a battery can store and then return the energy that is put into it. It is a crucial indicator of a battery's performance, affecting the viability and efficiency of energy storage systems. This efficiency is especially important in applications where energy conservation and effective storage are key, such as in renewable energy systems and electric vehicles.

The Role of Round Trip Efficiency in Battery Storage Systems

The significance of roundtrip efficiency battery storage cannot be overstated. It directly influences how much stored energy is actually usable, impacting the overall effectiveness and cost-efficiency of the storage system. High round trip efficiency battery storage means more energy is available for use, reducing waste and improving the sustainability of the system.

Round Trip Efficiency of Battery Formula: Essential Calculation

The round trip efficiency of battery formula is a fundamental equation in battery technology. This section is dedicated to explaining the round trip efficiency of battery formula and its application in measuring battery performance.

The Basic RoundTrip Efficiency of Battery Formula

At its core, the roundtrip efficiency of battery formula involves a simple calculation: dividing the energy output of the battery (measured in watt-hours or kilowatt-hours) by the energy input required to charge the battery, and then multiplying by 100 to express it as a percentage. This formula is critical for determining how much energy a battery can effectively use out of the total energy it consumes during the charging process.

Practical Applications of the Formula

In practical terms, the round trip efficiency of battery formula is used extensively by engineers and technicians to assess the performance of different types of batteries. This formula helps in comparing the efficiency of various battery technologies and designs, playing a crucial role in battery research and development. A higher percentage obtained from this formula indicates a more efficient battery, with less energy lost during charging and discharging cycles.

The round trip efficiency of battery formula is not just a theoretical tool; it has significant practical implications in the development and selection of batteries for various applications, from small electronics to large-scale energy storage systems.

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Tesla Battery Round Trip Efficiency: Insights into Performance

Tesla battery round trip efficiency is a key metric that highlights the effectiveness of Tesla's battery technology. This section aims to shed light on the specifics of Tesla battery round trip efficiency and its implications.

Understanding Tesla Battery Round Trip Efficiency

Tesla battery round trip efficiency refers to the efficiency with which Tesla's batteries can store and then release energy. This efficiency is a critical aspect of Tesla's battery technology, reflecting how much energy is retained and available for use after charging. The higher the round trip efficiency, the more effective the battery is at minimizing energy losses during charge and discharge cycles.

Significance of Tesla Battery Round Trip Efficiency in Electric Vehicles

Tesla battery round trip efficiency is particularly important in the context of their electric vehicles (EVs). High round trip efficiency means that more of the energy stored in the vehicle's battery is available for driving, enhancing the vehicle's range and overall performance. Tesla's focus on optimizing battery round trip efficiency has been a significant factor in their EVs' success, as it directly impacts driving range, charging times, and the overall user experience.

Frequently Asked Questions (FAQs) About Battery Efficiency

What is Round Trip Efficiency of Battery?

Round trip efficiency of a battery refers to the measure of how effectively a battery can store and then release the energy that is put into it during charging. It is calculated by dividing the energy output during discharge by the energy input during charging, then multiplying by 100 to get a percentage. A higher value indicates that the battery is more efficient, losing less energy in the process of charging and discharging.

How Does Temperature Affect Battery Efficiency?

Temperature can significantly impact the efficiency of a battery. Extreme temperatures, both hot and cold, can affect the chemical reactions within a battery, thereby impacting its ability to store and release energy efficiently. Typically, high temperatures can accelerate degradation, while low temperatures can reduce the battery's effective capacity.

Can the Round Trip Efficiency of a Battery Improve Over Time?

Generally, the round trip efficiency of a battery decreases over time as the battery undergoes wear and tear from repeated charging and discharging cycles. However, advancements in battery technology, materials, and management systems can lead to improvements in newer batteries. Ongoing research is focused on developing batteries with longer lifespans and better efficiency retention over time.

What Factors Influence the Round Trip Efficiency of Electric Vehicle Batteries?

Several factors influence the round trip efficiency of electric vehicle (EV) batteries. These include the battery's chemical composition, design, the efficiency of the battery management system, and operational conditions such as temperature and charging habits. Additionally, the way the vehicle is driven and the efficiency of other vehicle systems can also impact the overall round trip efficiency of the battery.

Conclusion: The Future of Round Trip Efficiency in Battery

This concluding section will summarize the critical importance of round trip efficiency in batteries, reflecting on the discussed topics. It will envision the future of battery technology with a focus on efficiency, considering the potential impacts on various industries, the environment, and society at large.

Round Trip Efficiency

Energy Storage System Efficiency

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Don’t Neglect Round-Trip Efficiency and Cost of Charging When Considering Levelized Cost of Storage

The world is moving toward renewable sources for electricity generation in an attempt to reduce fossil-fuel reliance. But wind and solar can’t provide a consistent flow of power 24/7, and grid operators have realized that new electricity generation needs to be paired with storage to manage periods with no sun or wind.

The decreasing cost of lithium-ion batteries has made battery energy storage systems (BESS) more affordable; however, the cost of battery storage systems represents only 20%-25% of any project’s lifetime cost. Power equipment, land, site work, cabling, project design and management, grid integration, transportation, and other related up-front costs represent another 25%.

So, what makes up the other ~50%? Operations and maintenance, otherwise known as O&M, represent a few percentage points. O&M generally includes expenses associated with maintaining, repairing, and operating energy storage systems over their lifespan. The rest comes from the cost of electricity to charge the system, which is significantly affected by the system’s overall round-trip efficiency (RTE).

Why RTE and Cost of Energy Matter

Levelized cost of storage (LCOS) is a metric used to determine the cost per unit of energy discharged from an energy storage system. The calculation is usually expressed in dollars per megawatt hour (MWh) and includes initial costs plus operating costs divided by the energy discharged over the asset’s service life.

There are dozens of potential variables that may be used to determine the true levelized cost of storage, and different vendors will add, omit, or adjust different ones to put their products in the best light. This is why it’s so important to understand the role of RTE and cost of energy in a storage system, because they often have the biggest impact. These are also components that vendors with low-RTE technologies will most often discount (or omit altogether).

Round-trip efficiency is a measure of the amount of energy put into a system compared to the amount dispatched, and is expressed as a percentage. A system with a high RTE (75%+) is able to dispatch most of the energy fed into it. A low RTE indicates that the system loses a considerable amount of energy, often to heat arising from irreversible side reactions or high internal cell resistance. Many long-duration energy storage systems have RTEs below 50%, creating a significant amount of energy waste.

For example, lithium-ion batteries generally have RTEs of 90%+. In contrast, lead-acid batteries have lower RTEs of around 70%, meaning that approximately 30% of charge energy is lost. RTEs for flow batteries can range from 50%–75%, while metal-air batteries could have RTEs as low as 40%.

If the electricity used to charge low-RTE batteries was free, efficiency might not matter much. But electricity always comes with a cost. Some might argue that during periods where supply exceeds demand, renewables could be used to charge batteries when they would otherwise be curtailed. There’s a logic to that, but curtailment periods can’t always be predicted.

Even if you’re using electricity that would otherwise be curtailed, you have to assign a monetary value. If a turbine is spinning or a solar panel is generating electricity and a battery system is storing that electricity, every component in the system is subject to normal wear and tear plus maintenance and replacement protocols—all of which have costs associated. Factors at play include:

Technology lifespan and degradation rate. An energy storage system’s service life is determined by technology and cycles. All energy storage systems deteriorate over time, making them less efficient at storing and discharging energy. The same goes for generation sources. From solar to wind to flow batteries to lithium-ion, the more the components are used, the shorter the lifespan and the sooner the need for repair, replacement or augmentation.

Maintenance costs. Solar panels, wind turbines, battery systems, transmission lines, and power equipment all have to be maintained. The more they’re used, the more often components need to be serviced or replaced.

Long-Duration Doesn’t Always Mean Lower LCOS

The latest buzzy term in the energy space is “long-duration energy storage,” or LDES for short. While there’s no single definition of what the term means, the term has generally come to describe a non-lithium storage technology that can provide energy for anywhere from 8 to 160 hours at a lower installed cost per MW than lithium-ion batteries or a standard natural gas turbine.

LDES isn’t confined to battery storage; non-battery technologies include compressed air, latent heat, flywheels, and more. In fact, pumped hydro currently accounts for the vast majority of all LDES capacity in the US, and will likely remain in that position for an extended time. Battery technologies being positioned for LDES use include flow batteries, zinc-based chemistries, metal air, nickel hydrogen, and more.

These technologies all work well and are generally safer than lithium-ion batteries, but they come with trade-offs. Many have high up-front costs and must be amortized over 30–40-year periods to be cost competitive. Some have very low energy densities, requiring significant amounts of land for installations above a few megawatt hours. Some are rate-limited and can’t discharge as quickly as needed for specific applications. Some have very restricted siting requirements. And maybe most importantly, many have RTEs below 60%, with a few at 40% or lower.

So, what does this all mean? The race is on to build a better storage system, and with no universal standard for calculating LCOS, every vendor is using a model that plays to the strength of their own technology. If you’re investigating a new storage technology, be sure to ask a few questions when LCOS numbers come up, such as:

How many years are they calculating when it comes to system life? Lithium-ion batteries usually have to be augmented or replaced somewhere between 10 and 15 years of use; vendors with low densities or high installed costs may calculate over 30–40 years to lower their LCOS while factoring in two or more replacement cycles for lithium-ion.

What are they using for the cost of electricity to charge the system, and how does that compare with your actual costs? Even if you’re only planning to charge the system during periods you’d normally be curtailing renewables, remember that there’s still a cost to running those systems. A system with a low RTE may end up having a much higher LCOS even when you’re paying very little for electricity.

Are they including the cost of land in their calculations? If you’re installing a storage facility in a rural area where land is cheap, this may not matter so much. But if you need to place storage in or near a high-cost-of-living area, cost of land (and availability) could be one of your primary concerns and should definitely play a role in the LCOS calculation.

Are they including installation tax credits (ITCs) or production tax credits (PTCs) in their calculations? If so, be sure that the numbers are correct for your projects, and that the same are being applied to any other technologies you’re evaluating.

— Mukesh Chatter is the CEO of Alsym Energy , a technology company developing a low-cost, high-performance rechargeable battery chemistry that is free of lithium and cobalt.

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Eric Parker, Hydrogen and Fuel Cell Technologies Office: Hello everyone, and welcome to March's H2IQ hour, part of our monthly educational webinar series that highlights research and development activities funded by the U.S. Department of Energy's Hydrogen and Fuel Cell Technologies Office, or HFTO, within the Office of Energy Efficiency and Renewable Energy. My name's Eric Parker, and I'm the HFTO webinar lead. As always, we'll be announcing more of these topics on a rolling basis. So, please keep an eye on your email for next month's.

This WebEx call is being recorded, and we will be posting the full recording and the slides on the DOE website. All attendees will be on mute throughout the webinar, so please submit your questions via the Q&A box you should see in the bottom right of your WebEx panel. We will cover those questions during the Q&A portion at the end of today's presentation. Without further ado, though, I'd like to turn it over to our DOE host, Neha Rustagi, who will tee-up today's topic and speaker. Thanks, Neha.

Neha Rustagi, Hydrogen and Fuel Cell Technologies Office: Thank you, Eric, and thank you everyone for joining. So, today, we have Michael Penev, from the National Renewable Energy Lab, providing an overview of work they've been leading in long duration energy storage. So, characterizing the role of many different technologies, including hydrogen and fuel cells. Then, also, a model that they've developed to allow for any user to do their own analysis of this kind. I really want to acknowledge my colleague, Sam Baldwin, from the DOE Strategic Analysis Team. He's been leading this effort out of DOE. On that, I'm going to kick it over to Michael.

Michael Penev, National Renewable Energy Laboratory: Thank you, Neha. Can you guys hear me okay?

Neha: Yep, you're good.

Michael: Okay. I would also like to acknowledge that this has been an extensive team effort. We've had input from the team members listed here, and ranging from detailed grid modeling to extensive research on costs of various technologies. So, for our talk today, I'm going to go over our methodology of our analysis, some preliminary results, and then I'm going to introduce a model that we use for this. It's called the StoreFAST Model. I'm going to describe it as well as provide a quick demo of how the model operates.

So, long duration energy storage within our efforts to analyze nearly 100 percent renewable grids, we're finding that short duration storage is not the cost optimal way of getting to nearly 100 percent renewable grid. As grids exceed approximately 80 percent renewables, the variability on the grids from those resources from the point of the supply as well as from demand induces the need for long duration energy storage. So, when we talk about long duration energy storage, we're talking about technologies that provide multiple days of storage, definitely above 12 hours, but on the order of 5 days if where we've been focusing for this analysis. What's new about this analysis is that we're not only considering today's technology costs, but we're also considering how the technology costs may decrease over time as we have learning curves, as well as volume manufacturing for each technology type.

For this analysis, we're using levelized costs of energy as the key metric. That is defined as the total cost for operating the systems divided by the total kilowatt hours provided by the system. For the analysis, we're using the StoreFAST model and we're basing the analysis at roughly 100 megawatts of storage capacity for each of the systems. We're analyzing systems between 12 hours of storage duration rating up to 7 days of storage duration rating. One thing I should point out is when we talk about duration rating, we mean that if the system were to be completely fully, how long does it take to completely discharge to a minimal state of charge while providing full rated power to the grid.

Our storage systems are broken out into three distinct components. We consider separately the charged components of the system. So, for example, in a hydrogen system, that would entail the rectifier to take AC power and to produce DC power for an electrolyzer, the electrolyzer itself, as well as a subsequent compressor. All these capital costs and other items are characterized based off of the charging of the system. One thing to point out is when we talk about charging costs, those are based off of input power. So, when we say dollars per kilowatt of charging, that means based off of the rate of power of charging. When we talk about electrolyzers, typically the electrolyzer can be a very different capacity rating than a fuel cell. So, we've disaggregated charging power from discharging power.

The second component that we model is the storage component. This component is strictly the storage. So, for example, if we are talking about hydrogen storage, that would be the salt cavern itself. It would be able to receive hydrogen, store it, and then return the hydrogen to a power generation equipment. If there's a discharging component, again, for hydrogen systems, this would entail fuel cell or combustion turbine. If there are any power electronics associated with it, that will be part of the system.

In addition to energy storage systems, we also model flexible power generators. When the grids need additional power, it is agnostic whether that power comes from stored energy from prior overgeneration or if it comes from a flexible power generator. So, in terms of a green source of flexible power, we can, for example, consider biologically the right molecules, such as ethanol, being run through a combined cycle plant to produce power flexibly in a renewable fashion. This, by the way, is a technology that has been demonstrated to about nearly 100 megawatts of capacity.

In order to understand how the systems would be operated in a highly-renewable grid, we took analysis results from detailed PLEXOS modeling. This is a grid model that considers the optimal way of dispatching various power generation equipment on the grid. When we go to 85 percent renewables, we're seeing that the grid would be optimally operated having energy storage systems that exceed at least five days' worth of storage. On the upper right corner is an example, a graphic of how the grid would operate such a system. We see that along the X axis, we have time of the year. On the Y axis, we have hour of the day. A long duration energy storage systems would be operating at the diurnal fashion. So, during the day, when solar power is widely available, the system will be charging. Then it will be discharging its capacity during the evening hours.

During the spring months, we're seeing an excess of renewables and not an excess of demand for electricity. So, during this timeframe, on the net, the system would be increasing in state of charge until about mid-summer, when we see temperatures increase, people likely having more air conditioning loads, and, at the same time, the renewables, spring runoff from hydro reduces and we're seeing that the net state of charge decreases over time. So, this is the typical operation profile for long duration energy storage.

The grid model simulated different round-trip efficiency systems and characterized for us how a 40-percent efficient system would operate, a 60-percent round-trip efficiency system would operate, all the way up to 80 percent. What is important for us is the capacity factor for charging and discharging. On the lower left, you're going to see charging and discharging capacity factors for different round trip efficiencies. Within our modeling framework, that's the take away that we use to model the tech economics of these systems. For flexible power generators, we're queuing from the variable operating expenses to produce an additional kilowatt hour from the various technologies that we've modeled as flexible power generators.

I'm going to go over some results of our modeling. First, I'm going to look at current costs. So, if we have the technologies, not improved but rather have the current costs, how will they operate within that framework of different capacity factors and sizing.

So, on this slide, we can see an example of two technologies. On top, we have pumped hydro storage. On the bottom, we have heavy-duty fuel cells using salt caverns to store hydrogen. On the right, we have two graphs. Along the X axis is duration rating for different systems that we consider, and along the Y axis is the levelized cost of energy.

First thing I want to point out if the bottom wedge, which is the operating expense associated with charging the systems. One thing we've noticed that PHS, having a relatively high round-trip efficiency and purchasing electricity at two cents per kilowatt hour, that's the basis that we're using for utility scale renewable electricity. That operating expense adds up to a relatively shallow wedge. On the bottom, we see that having a low round-trip efficiency, a fuel cell system requires a substantial amount of more electricity in order to provide peak power generation.

Another item that is fairly different from those two systems is the storage component. This is the capital associated with storage. For pumped hydro, that is a substantial expense. If you want to have a higher duration rating system, you have to purchase more capital. You have to pay additional financing expenses and increasing maintenance expenses. That profile for hydrogen systems is fairly flat. The marginal cost of storing additional kilogram of hydrogen or additional kilowatt hour of electricity is fairly small. So, we see a low sensitivity to duration of storage.

Now, in subsequent slides, we're going to look at just a total levelized cost of energy for different technologies. On this slide, we're focusing at the 12-hour duration rating, and we see that within our modeling framework, we do get similar results to literature in terms of predicting the types of technologies that are going to be more competitive. In this case, we're seeing a baseline of natural gas-combined cycle on the bottom, and then the storage technologies such as pumped hydro, thermal energy storage, VRB's in here as well as compressed air energy storage systems. Those show as being competitive.

When we look further to the right, we're seeing that those technologies that are competitive at the shorter durations, they tend to have more expensive energy storage cost component and become less competitive at longer durations. So, for example, the VRB and pumped hydro become private or expensive at this range, and technologies such as natural gas-combined cycle with CCS as well as diabatic CAES and hydrogen technologies, they're less sensitive to duration of storage. At this level, they're the most competitive option.

Now, to look at learning curves, we assumed an additional capacity of 200 gigawatts being installed and how that might impact the cost of capital for both power and energy for each of these systems. I'm not going to belabor the details of all the detailed research that we have done on learning curves, but the next slides regard to look at slightly learned out technology set.

So, again, at 12 hours, we're seeing technologies such as PHS, pumped hydro, vanadium redox, adiabatic CAES, as well as hydrogen storage with PEM fuel cells being competitive. One thing we did take out in this slide is technologies that are carbon emitters, just because we're trying to conform to our highly renewable scenario. So, combined cycle plant with natural gas is not listed in this technology set.

When we look farther to the right, we'll see that learning has benefitted both electrolysis portion of hydrogen storage systems as well as the capital for fuel cells, and those technologies become substantially less expensive. We did not assume any learning of salt caverns. So, that cost component remains insensitive to duration of storage.

Within our analysis, we perform Monte Carlo simulations on components that either have variability or uncertainty. This is an example of the results for current technologies as well as future technologies. The way to read them is that each one of those is essentially a histogram. The leading edge on the left is the first percentile. The thickest portion of the violin plots is the most likely value. Then the tail-end is the 99th percentile of the Monte Carlo analysis runs.

A couple of observations from here is that there is a lot of overlap from technologies, meaning that, depending on local conditions such as cost of renewables, or cost of electricity, or cost of capital for PHS, for example, if different storage technologies are available, one – or the technology going to be preferable. So, we don't expect one technology would necessarily win out against all the other technologies. Another thing I want to point out is that when we consider hydrogen technologies, there is the opportunity for the electrolyzer not just to charge the storage system but also it can produce hydrogen which can be stored, staged, and sold as retail hydrogen for various uses; commercial or industrial uses. So, we have overlaid here in orange is cases where we sell hydrogen for as much as $3.00 per kilogram of hydrogen. If you're familiar with those type of costs, that is a fairly competitive price point for hydrogen.

Next, I'm going to demonstrate the StoreFAST model. I'm going to go through an overview as well as provide a quick demo. So, the StoreFAST model is intended to provide a consistent framework for utility scale power and it will analyze both energy storage systems as well as flexible power generation systems on side-by-side basis. So, it can provide either information for a single technology or multiple technologies and it will give you simultaneous results for all of them. The information will also be cast and interpreted by results from grid models. So, if you have technologies that have different roundtrip efficiency or if they have variable operating expenses for flexible power generators, those will calibrate the capacity factors that we would expect to see for these types of systems.

The model will also provide risk analysis for uncertainty or [audio skips – inaudible] – couple [audio skips – inaudible] this model _____ the strategy analysis. So, shorter duration systems _____ proficiency ancillary services to be _____ of their revenue stream. When we look at long duration energy storage, we anticipate that those would primarily derive their revenue from energy generation and ancillary services are not factored into that market. Additionally, the depth of the ancillary market is fairly shallow and we're evaluating the performance in terms of energy storage, which is a deeper market.

The model is currently calibrated with grids of models of 85 percent renewable scenario. Lastly, I want to caveat that currently the model of those amortization evading refurbishments. So, for example, if you want to look at components that are replaced on a ten-year schedule, those additional replacements, in the future, would have to be amortized and added to the maintenance expense, a variable maintenance expense for each of the components.

The StoreFAST model is based off another model called H2-FAST which is a very rigorous financial model. It takes its input items such as capital, maintenance, system usage, energy usage, energy prices, as well as financial parameters such as types of depreciation, types of capital or capital structure that you have available for financing. The model uses generally-accepted accounting principles as well – also known as GAP. They compute for each of the systems a forecast of input statements, cashflow statements, as well as balance sheets. In terms of outputs, we have levelized cost of energy as well as some key parameters such as internal rate of return, payback period, et cetera. All of these are available in graphical format in both individual as well as multi car the type of analysis.

So, going through the model, the first thing that she would have to specify is global parameters. Those parameters are duration of rating - does she want to benchmark each of the systems – cost of electricity, if it's going to be available to the systems, if natural gas is one of the feed stocks or ethanol, or if you're co-producing carbon dioxide, which would be sequestered at certain costs. Those would be specified on a global basis.  In terms of individual inputs for the systems, the first thing that the user specifies is capital costs. So, we would have charging, storage costs, as well as discharging. Each one of those can be specified on a flat basis, or if there is a variability of costs versus size, the user can provide that scaling factors for each of the technologies.

I'm going to reiterate one item is that charging costs are based off of capacity of charging, not capacity of discharging. Some of literature out there bases the entire cost of the system on power generation and not charging basis. So, if you're specifying charging component cost, they would need to be on a power input rating basis.

The next thing that the user has to specify is maintenance. The model accounts for fixed and variable costs for charging, storage, and discharging.

Feedstock costs are specified on per unit output basis. So, for example, if we look at electricity required for charging, that would be how much electricity is required to charge the system to produce a kilowatt hour of power. For example, if we have a system for hydrogen energy storage that has a roundtrip efficiency of 35 percent of so, the amount of electricity required to produce a kilowatt hour of energy output would be the inverse of that. So, one over to roundtrip efficiency would be the value that you will specify for amount of electricity feedstock required.

Here, we can see as an example, natural gas for flexible power generators, this is the heat rate in terms of MMBTUs per kilowatt hour electricity. If you have CO2 co-production, that would be specified, again, on a per unit of energy output. One example I want to point out is that PHS has significantly less electricity requirement for power generation. That is due to its much higher roundtrip efficiency.

Another item to specify in case you're interested to model hydrogen co-production, that would be the amount of electricity required to produce a kilogram of hydrogen. In this case, we have 56 kilowatt hours of energy to produce each kilogram of hydrogen that is co-produced. Also, we specify a percentage of time that the system would be idle. So, for example, if you're producing hydrogen for charging purposes 30 percent of the time, and you're producing power 10 percent of the time, that remaining idle time, we still want to preserve 5 percent on top of any co-production time. Electricity for co-producing would be expected to be more expensive since we're not just taking off-peak electricity, but we're taking a larger set of the electricity hours and prices throughout the day. So, we have a separate input for electricity as well as we have a value that one can specify for co-produced hydrogen.

In terms of sensitivity parameters, the user can specify the parameters listed here. So, for example, storage duration, the range specified here, would be able to inform two things. One is a sensitivity analysis as a tornado chart, which I'll demonstrate later, informing the minimum and maximum around the baseline so the system will be tested for 72-hour duration rating all the way up to 168 for 7 days' worth of storage. The other parameters – so, for example, cost of electricity, the user can specify ranges. The other way that we use these ranges is for Monte Carlo analysis. Those are assigned triangular distributions. The Monte Carlo analysis would test the model against each of those ranges.

In terms of output, the model provides visuals for three parameters. So, the first one is duration rating sensitivity to levelized cost of energy. Here, we can see that we have all the technologies that the user has decided to test, and their total levelized cost of energy is displayed on a side-by-side basis. The other output that is available is a breakdown of each of the technologies in terms of what items sum up to arrive at the levelized cost of energy. Here we can see a breakdown, for example, of electricity, charging, storage, so on and so forth.

The last output before we get into Monte Carlo analysis is the sensitivity analysis of individual parameters. So, for this particular system, cost of charging was the most sensitive going from one to three cents per kilowatt hour. We can see that 1 cent per kilowatt hour, that corresponds to $336.00 per megawatt hour. At 2 cents, we're at 365. At 3 cents, we're at the right side of the tornado chart. So, the next most important parameter is roundtrip efficiency, system life, so on and so forth. That's the way to read these likes of charts.

The user can select which chart they would like to focus on. So, for example, if the select HDV-PEM, the model will populate the pertinent graphics for it.

If the user is interested in spending an hour or letting the computer crunch for about an hour, they can generate Monte Carlo results, which are violin charts, as we described earlier in the talk.

The model, itself, is self-documented. Just downloading the model will provide you with a brief walk-through of how to use it as well as each of the inputs for row headings or column headings of texts which described what the inputs are expected.

With this, I'm going to go and do a quick demonstration of the model. So, this is the model Rook approximately has downloaded. We might have a few more updates to it. The first step that you're going to see is a description of it. Within the description, the one thing that's important is our color legend. Anything in yellow expects an input from the user. Anything in blue, do not modify it. Those contain our formulas for the algorithms of the model. Some of the key results are in green.

The other important thing is a walk-through. There is a description of the tabs as well as a line-by-line walk-through, which I'm going to spare you. Lastly, the model has technologies populating in terms of capital costs, performance, as well as methodology. We do provide reference links for all of these and those references can be found in the description tab. One thing I would like to note is that here is a publication that is in the works that would have a much more detailed description of how we arrived at the various costs and performances.

The technology specification tab is the main interaction point for users. In the upper left corner are the global inputs. We have, in the center section of this, the users can provide information for various technologies side-by-side. In here, we're provided these sets of technologies. We have capital cost specifications, operation and maintenance specifications, feedstock usage, and how the system is being used, as well as we have contingency for hydrogen co-production if that's an interest. We have descriptions, references for issues of technologies. If the users would like to look at where we got any of the technology costs, those references are down here. Sensitivity parameter tab that we describe. These are the inputs for uncertainties and variabilities that you would like to have tested.

Capacity factor definitions, these are extracts from detailed grid models. These are coefficients derived from 85 percent renewable scenario, and they inform capacity factors that are being used within the model.

When the user specifies a particular technology, they can also look at the detailed capital costs for it. So, for example, the user has specified HDV-PEM with salt and we can see the charging capital in terms of dollars, discharging capital, electricity consumption. Any financial assumptions are also in here so we can see the tax rate, the type of depreciation, the depreciation periods, capital structures, so on and so forth. Report tables would inform the user on annual basis what their income statements would look like, cashflow statements, balance sheets. All those parameters are provided. Lastly, the overwrite tab allows users to provide, for example, detailed electricity price structure of price profiles. For example, if you would like to look at annual energy outlook prices, you can provide those, or you can use a flat input such as two cents per kilowatt hour associated with an escalation rate.

So, with that, as an example of the model, what I would like to do is add a system with hydrogen co-production. So, what we will do is we will take the inputs that we have for HDV-PEM with salt. We can strictly copy the entire column. So, we take all these inputs. Sorry, on full screen apparently doesn't let you _____. Okay, so we populated that. We'd like to differentiate it. We put a dash-two here for the name. The way we would like to differentiate this case is to say that we can have hydrogen co-production. That's signified with putting a one as a key here. We want to sell that hydrogen for, let's say, $3.00 per kilogram. Once we do this, we would run the update. These calculations take about two minutes. They would go through each of the technologies and compute the base case scenario as well as for the tornado chart that is updated that will compute the performance for each of the technologies of the extremes of the variability set that we've provided.

While that is running, I want to point out a couple of things. In the model, as would be published, we have a certain number of technologies that would pre-specified. So, we have hydrogen technologies. We have vanadium redox batteries, pumped hydro, adiabatic-CAES, thermal storage. But for the users' imagination, you can consider ammonia as energy storage, for example. Ammonia can be produced by electrolysis of renewables using air and hydrogen to produce ammonia, and that can be cheaply stored in cryogenic settings and then returned to power with various technologies. There are other technologies such as gravity energy storage, liquid air energy storage, batteries of various chemistries. What the user would need to do is capture the characteristics for charging, for storage, and for discharging, then can populate the model with that set of information.

In terms of flexible power generators, we have turbines. So, combined cycle turbines with carbon capture and sequestration or without capture and sequestration. We have compressed air energy storage as well as ethanol turbines. But, again, if the user sees a potential for nuclear being able to capture dynamics required for flexible power generators, they can provide those characteristics in terms of fuel consumption into the model and they can model that. Same thing with geothermal or maybe even conventional hydro. Currently, conventional hydro mostly produces power as baseloads, but we can see that that can be potentially have some of the dynamics in there that would allow the systems to provide flexible power generation.

So, when the model is running in the lower left corner, you can see an update. It just happened to have finish the analysis that we just ran. We can look at some of the results. If we use the drop-down menu in the upper left corner, we can see that now we have a system called HDV-PEM Salt-2. When we click on it, we'll see that that system is highlighted in here as well as we can see the breakdown of levelized cost of energy and sensitivity of the system for the various inputs.

One thing that I would like to point out is that in this particular scenario, the hydrogen sales happen to be of substantial contribution to the levelized cost of energy. Actually, in this case, they happen to dominate the revenue stream hence the capital cost cut attribution and operating expenses for producing electricity are greatly subsidized by that whole product of hydrogen. Here we can see how this levelized cost of energy relates to the other technologies. Lastly, the breakdown of that technology in terms of sensitivity to each of the input parameters.

I'm going to switch here back to PEM with salt. The one thing I would like to demonstrate is that it's really hard to see the intercept points between the selected technologies and all the other technologies. If the user scrolls down, they will see they are precalculated for them. So, for example, we know that this technology and this technology – so the highlighted technology – they intercept at a duration rating of zero-point-seven hours. With closed-loop pump hydro, they intercept at 16 hours. So, this would be the point of indifference, whether one technology or the other technology, they would have the similar levelized cost of energy. So, we see the intercepts here as well as the levelized cost of energy in terms of dollars per megawatt at 120 hours. Those baseline values are computed here. So, that's just for users' convenience.

If the user has time, they can also click this button, and in an hour, it will calculate the levelized cost of energy violins for all the technologies including that additional technology that was added.

In case you download the model and you would like to restore the defaults, I have provided two buttons. One is for restoring current technologies as well as future technologies. If you're testing a relatively unique system of your own, I would suggest not using these buttons, because they will override any inputs that you put into the technology specification tabs. With that, I would like to open to questions.

Neha: All right, thank you, Michael. So, we've got a lot of questions in the chat box. So, I'm going to start with, "Can you speak to how you came about the assumptions for electricity price since they seem lower than what is the case mainstream today?"

Michael: Right. So, the modeling framework that we use is PLEXOS. It actually calculates variable operating expense and does not calculate total cost of electricity from the grid. So, as such, it's not a useful way of informing the levelized cost of energy that's generated from renewables. We turned to long-term PPAs from renewables such as solar and wind, which are currently approaching two cents per kilowatt hour. If that is the prevailing power generation equipment on a future 85 percent renewable grid, we would the utility scale electricity would have that price profile as well.

Neha: All right, thank you. Can you speak to the role of pipes in hydrogen storage? I think there were questions just around that concept because it isn't as mainstream.

Michael: Right. You said pipes?

Neha: Right. Yeah, underground pipes.

Michael: Yeah. So, there are a number of technologies being looked at out there for storing hydrogen at low cost. One of them is burying pipes underground in order to get some benefits of isostatic pressure in order to reduce the amount of steel that's required for storing that hydrogen. So, we looked at research and systems analysis in that area, and we captured the expected capital costs as well as maintenance, and we cast that as one of the potential case studies for hydrogen. With that said, it is more geography agnostic. You're not necessarily relying on geologic storage, either a salt cavern or hard rock cavern to store hydrogen, but the costs are significantly higher. So, in order to characterize the expected applicability of that technology for hydrogen energy storage, we added that technology storage characteristic to the model set.

Neha: Thank you. Then, there were a few, and I just want to note specifically the storage cost that were assumed were an output of other work that we have ongoing at Argonne National Lab that's also very near publication. So, hopefully, once that's published, you can get more information about what was assumed there from that work. Also, as there were questions around how you – can you speak a little bit further to the learning rates and how you assumed improvements in technology and then, also, how that relates to the relatively low hydrogen prices that we had, since I know those were all related?

Michael: Yeah. So, for learning rates, we did extensive literature search in terms of what are the current capacity of each of the technologies, how much that technology has been produced in terms of power bases, how many gigawatts have been deployed of that technology, and we looked at literature in terms of what learning rates would be applicable for each of the technologies. So, if you have a doubling of a particular technology, how much learning can you have per doubling of installed capacity? So, that informed our projection for 200 gigawatts in terms of current costs, and for each doubling, how the learning rate would be expected to reduce price profile for power as well as for energy. So, the technologies have either had the substantial amounts of existing installations and they – so, for example, if we look at combined cycle plants, there are a lot of combined cycle plants out there. They still are expected to have a learning curve on top of that. But due to the fewer number of doublings that we would expect that price changed between current costs and future costs of 200 gigawatts is expected to be roughly the same.

Other technologies – so, for example, if we look at salt caverns, we expect to have very little learning. The technology – the literature does not expect substantial learning on how salt caverns are formed and we see a flatter profile on some of these technologies. Other technologies have – so for example, vanadium redox batteries, there aren't very many of them out there, but according to literature, there is a material cost that, even due to learning, would still be present in the total cost of storage. So, that also informs the potential learning for each of the technologies. Lastly, I want to say that we're expecting to have a publication on this, which goes into the full depth of analysis on how the computations are done as well as how the learning rates and what individual sources for data were used for each of the learning rates.

Neha: Thank you. Can you also clarify, when we're speaking to a given duration of storage, like 120 hours or any different number, are we referring to continuous output for that period of time, or is it 12 hours over 10 days?

Michael: Yeah, so when we say 120 hours duration storage, that means if the system were fully charged, and then it start – if it were to start producing power, at rate of power at the full 100 megawatts, it would take five days before that system is completely discharged. Obviously, that's not how the systems are operated. The systems are, instead, operated throughout the year with partial discharges. So, throughout the year, they'll typically have one full cycle of charge and discharge cycles. So, one deep cycle and a lot of shallow cycles. But the rating of the system is basically it's ability to discharge for a long duration from full to empty.

Neha: Thank you. Can you speak to the difference between adiabatic and diabatic storage?

Michael: Yeah. So, there are two types of compressed air energy storage. Let me start with diabatic compressed air energy storage. That's a system that has been demonstrated. In both systems, air is compressed using a compressor into a storage. The compression energy is exhibited in two ways. One, it induces high temperature and compressed air. That heat from compression is storage in thermal energy storage. Actually, hang on. For diabatic air, we just compress the air and store it on the ground. On the way back, when we want to discharge the storage, the air is fed through a turbine and heated with combustion of natural gas, and then it's run through a turbine to generate power and run a synchronous generator.

Adiabatic air is different. Instead of using natural gas to reheat the air before the turbine, compression energy is stored in thermal energy storage. When the system is discharged, the air is reheated through that thermal energy storage before it goes into a turbine and the generator. So, basically, diabatic compressed air energy storage uses natural gas and adiabatic energy storage uses compressed – it uses thermal energy storage for the thermal portion of the cycle.

Neha: Got it. Thank you. Can you speak to whether we included pumped hydro from existing reservoirs or only looked at brand-new reservoir development?

Michael: Right, very good question. For current capacity, we estimated the global installed capacity of pumped hydro. For future capacity, we added additional 200 gigawatts of storage. That storage would, obviously, have to be new capacity.

Neha: All right. Thank you. Can you speak to kind of the minimum level of load that cell batteries can tolerate? So, what's like a minimum level of cycling baked into these assumptions?

Michael: Yeah. Details about turn-down are not captured within the PLEXOS models. The PLEXOS models are – they're fairly very detailed models, but they do have to do some simplifying operating assumptions. So, in terms of turn-down, I would actually – if you email me that message, I can put you in contact with the folks that actually run the PLEXOS models. If they have a method for capturing turn-down, they will speak more intelligently to that than me.

Neha: Sounds good. Thank you. There's lots of questions about sensitivity of this analysis to renewable energy penetration. So, I know we were leveraging existing work in that space. If you want to add anything else to that about how sensitive this is to that _____ _____ penetration.

Michael: Yeah. There is very large sensitivity at lower penetration rates. So, up to about 50 percent the amount of long duration energy storage is fairly minimal that you would need. At higher penetration rates, at 80 percent plus, you would have a different profile. Possibly as future work, I would suggest, as we get a larger portfolio of penetration analyses and how storage and flexible generators are used within those scenarios, we could probably provide a set of calibration factors here to – so that the user can select, "I want to test my system in a 50 percent scenario," "In 100 percent scenario," so on and so forth. But at least for right now, we have provided the calibrating coefficients for 85 percent scenario. We will expect, as you go further up, that you would need more and more long duration energy storage. I know that some scenarios are starting to come out with 100 percent renewables. As those come off of peer review, we can see about informing the model with those results.

One other thing I would like to point out is when you run PLEXOS grid models, before that, there is a capacity expansion modeling done with a model like REEDS. That would be informed by the capital costs, variable operating expenses, so on and so forth of each of the technologies. As part of us going through these analyses, we capture the characteristics for each of the technologies in order to inform REEDS and then from REEDS and PLEXOS, we get performance characteristics. So, we will be going back and forth and recalibrating each other in terms of how much penetration we would expect for each of the technologies, how they would maybe use them in the future.

Neha: Thank you. So, there were lots of questions about how to use the model. So, the model link is on the slide, then, definitely, we can share it with folks after this call. But one of the questions that came up was, "Does the model have baked in constraints such that if somebody makes an unrealistic assumption of one that doesn't make sense, will it flag that?"

Michael: Yeah. One thing – I have certainly done that is accidentally put in the wrong parameters. Since we have so much visualizations coming from the model, you will have lots of clues if the levelized cost of energy looks odd to you or if you see that the model has obviously errors and LCUE, if it says, "Not a number," or something is divided by zero. I've not provided substantial error capturing just because, typically, the errors become fairly obvious once you run the model. The runtime is fairly short. So, the cycle between providing inputs and getting feedback is fairly short. But if we find that the users are running into issues where it's not clear if the results that they're getting are valid, we can add some additional error handling. This is a new model. I would expect to have tweaks as we roll it out.

Neha: Thank you. Then, I think this is going to be our last question. Can you speak to why liquid-based storage wasn't included in all of our pathways for gaseous-based storage, hydrogen storage?

Michael: Yeah, cryogenic air storage is very interesting. We would have liked to have it. Generally, we pick technologies that are high TRL and have a lot of literature that we can tap into to inform capital costs, storage costs, new operating expenses. Liquid air was not one of the technologies that we had high confidence in the parameters that are seen in those technologies. So, yeah, we're providing the flexibility of users to test that technology by issuing this model so that users can test the costs that they have on hand.

Neha: Got it. Thank you. I think there were also questions about liquid hydrogen. The reason why that was left out was just because it would bring down the roundtrip efficiency of the hydrogen-based process and offset it is _____ viable in the near term than a gaseous-based path.

Michael: Yeah. We actually have good information for liquid hydrogen. At one point, we tested up to about 200 different variations with salt oxide fuel cells and alkaline electrolyzers, and storing liquid, storing gaseous, storing some other hydrogen some other ways. There are a lot of different permutations. But absolutely correct, as Neha said, one thing that we found with that system is, yes, it does set a very low cost of storage, but there are a couple of items to be concerned about. One is boil off if you're storing on a seasonal basis. The boil off could be used for your day-to-day energy source, but it is something to contend with as well as it does reduce the roundtrip efficiency. It does take quite a bit to liquify hydrogen and the capacity factor would be even lower for liquid storage over other technologies.

Neha: All right. Thank you, Michael. So, I guess just the last thing I'll note is that the link to the model is available here. These slides and an audio recording will be posted probably within about a week. Then, this model is in beta testing, so any feedback that we get, we really appreciate. So, if you do use the model and you have any feedback for us, please feel free to relay either to Michael, himself, or to anyone of us on the DOE end. With that, Eric, do you have any other closing comments?

Eric: Yeah. Thank you, Neha, and Michael, for the presentation today. Thank you everyone for joining. Michael, if you could advance to the final slide at the very end. As Neha mentioned, I'll remind everyone that – one more, please – the recording and slides will be available online at the DOE.gov website. So, please, check back there soon as well as future topics. So, with that, I'll wish everyone a great rest of their week, and goodbye.

Let ’ s Get Technical

A blog about codes, standards, and best practices for solar, energy storage, and microgrids, let's get technical.

Energy Storage 101

Michael Morse

Over the last year, we have seen an increasing number of solar PV design projects that integrate energy storage systems (ESS). Industry forecasts show this trend continuing— speeding up even more, in fact . Whether residential, commercial or utility-scale, the solar industry is quickly becoming the solar-plus-storage industry. In this, and future, blog posts, we will explain the ins and outs of energy storage as it relates to solar PV. To start, let’s build a foundation through some important ESS terminology abd common applications.

Solar-Plus-Storage Terminology

While ESS technology is advancing quickly, the core energy storage lexicon is consistent. Among the most ubiquitous and important terms are the following:

Rated power and usable energy 

Power is instantaneous. A 4 kW battery/inverter ESS package, for example, is capable of providing 4 kW of power at that very moment . Energy is a measure of power over time . If that same ESS is capable of delivering 4 kW of power for three straight hours when fully charged, its usable energy capacity is 12 kWh (4 kilowatts X 3 hours = 12 kilowatt-hours).

Power and energy are analogous to a bucket of water with a spigot at the bottom: Power describes the size of the spigot, while energy describes the amount of water the bucket can hold. We need to know both measurements to have an idea of how much water we can get out of the bucket, and for how long.

Depth of discharge (DOD)

Rated energy capacity, as discussed above, is not wholly reflective of the actual capacity that should be extracted from the ESS. DOD accounts for this, and describes the percentage of available energy that system designers should aim to use in a given charge-discharge cycle. 

In a perfect world, an ESS could be completely discharged from 100% to 0% (without sacrificing the battery’s longevity), representing a 100% depth of discharge. In reality, this is not the case. Each system will have a specific ideal maximum DOD. For some chemistries, like lead-acid, recommended maximum DOD is at or below 80%. For others, like lithium-ion, DOD can be much higher—on the order of 95%.

State of charge (SOC)

Expressed as a percentage, the SOC represents the current level of charge and ranges from fully charged to fully depleted. Tracking SOC is critical in determining when and how quickly to discharge an ESS.

Round-trip efficiency  

Round-trip efficiency describes the fraction of energy required to charge the battery (in kWh) compared to the amount of energy that can be retrieved from it (also in kWh). Higher efficiencies reduce the energy lost during the charge and discharge processes. 

Thanks to the laws of physics, energy in does not perfectly match energy out in real-world conditions. Current leaks and heat loss, among other factors, contribute to the inefficiency of an ESS. Luckily, most modern systems have fairly high round-trip efficiencies—on the order of 80%, per EIA . Lithium-ion batteries are particularly efficient (about 95% on average) while lead-acid batteries tend to fall in the 75-80% range.

Underneath their shiny metallic exterior, rechargeable batteries are nothing more than controlled electrochemical reactions that take place repeatedly and in both directions. During discharge, ions move through the electrolyte and electrons from the anode to the cathode. While charging, external voltage is applied to the system to reverse the process and shift ions and electrons back to their original places. Thus completes one cycle. 

The cycle life is the number of charge-discharge cycles the ESS is able to support before its capacity falls under 80% of its original capacity. Cycle life varies quite a bit based on the ESS chemistry and manufacturer. Some lithium-ion batteries have lifespans exceeding 15,000 cycles, while lead-acid batteries may be rated for just a few hundred cycles. 

Solar-Plus-Storage Applications

Pairing a solar PV array with onsite energy storage has become commonplace for residential and utility-scale systems, and new technologies are making medium-level storage financially palatable for commercial applications as well. What are the most common use cases for storage at each project scale? Let’s take a look.

Residential applications

Since its early days, energy storage has been sold as a means for residential buildings to operate off-grid or keep the lights on when the power goes out. As natural disasters grow stronger and more frequent , the need for energy resiliency has never been more apparent. What’s more, utilities around the country are self-inducing power shut-offs to minimize fire risks. To avoid the dangers and inconveniences of increasingly common outages, residential consumers are turning to at-home energy storage options.

While solar PV has an impactful ROI for homeowners through reduced monthly electricity bills, residential energy storage has a limited financial payback. Motivation for installing ESS is driven predominantly by resiliency, with the exception of the state of California and a few smaller regions around the country whose electricity providers offer time-of-use rate schedules that pay homeowners a premium for electricity discharged to the grid during times of high demand. 

Commercial and industrial (C&I) applications

Commercial-level energy storage is poised for growth. While residential and utility-scale systems have taken off, the diversity of commercial building sizes, layouts and electrical loads presents a major challenge. To this point, commercial ESS applications tend to fall within two main buckets: energy resiliency and demand-charge reduction.

Much like residential systems, commercial building owners want to keep cold storage units, computer servers and other critical infrastructure running even when the grid goes down. The building owner must determine exactly which loads need to be backed up and for how long. When sized properly, an ESS can keep a business functioning for hours, days or even weeks without relying on the grid.

While backup power is popular and pragmatic, demand-charge reduction is perhaps the most common commercial ESS application. Commercial utility rates are priced largely on a per-kWh basis but step up with demand. Demand charges are a portion of the overall electricity bill that are based on a customer’s peak level of demand occurring over a defined time period (usually around 15 minutes). An ESS, in concert with smart energy management, can be discharged at times of high onsite power usage to reduce average demand to levels below the utility’s highest price tier. This is especially valuable for industrial use, where large, power-intensive equipment is more common.

Utility-scale applications

Use cases for energy storage at the utility scale are much different than for smaller residential and commercial systems. From a grid operator perspective, the central value of energy storage lies in smoothing out electricity supply and demand to ease the strain on generation sources and transmission lines. As we have mentioned in previous blog posts , the duck curve is a serious problem for electrical utilities. Demand spikes in the early morning and later in the evening force utilities to quickly (and expensively) ramp up generation. 

To flatten the duck curve, energy storage may be used to control both electricity supply and demand on the grid. For example, imagine a utility-scale solar PV farm operating outside a major metro area. Without onsite storage, the PV generation may be curtailed during times of low demand, which, coincidentally, tend to take place during peak sun hours. But with integrated ESS, no power is wasted when loads are low but generation is high—any excess electricity is stored locally to be discharged to meet demand later in the day.

Alternatively, utilities may leverage distributed residential ESS through a virtual power plant system. Here, the utility takes control of some number of energy storage units in its jurisdiction with customer approval. Then, instead of spinning up generators to meet peak demand, the utility can draw on its network of stored energy throughout the grid. As more homeowners install solar PV and battery-backup systems, virtual power plants will be an increasingly viable method of demand control for electrical utilities. 

Energy storage is here to stay. As solar-plus-storage installations spread nationwide and around the world, it’s imperative that our industry stays up to date with technology trends, code updates and safety standards. For additional information on energy storage, we encourage you to check out our other articles and courses linked below.

• Article: AC vs DC Coupling Solar-Plus-Storage
• Article: California Building Codes Updates for Solar-Plus-Storage
• HeatSpring Course: NFPA and Fire Codes for Solar-Plus-Storage
• HeatSpring Course: NEC 2017 and 2020 Requirements for Solar-Plus-Storage

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Ac vs. dc coupling energy storage systems.

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Pumped Storage Hydropower

Pumped storage hydropower does not calculate LCOE or LCOS, so do not use financial assumptions. Therefore all parameters are the same for the R&D and Markets & Policies Financials cases.

2023 ATB data for pumped storage hydropower (PSH) are shown above. Base Year capital costs and resource characterizations are taken from a national closed-loop PSH resource assessment completed under the U.S. Department of Energy (DOE) HydroWIRES Project D1: Improving Hydropower and PSH Representations in Capacity Expansion Models. Resource assessment and cost assumptions are documented by  (Rosenlieb et al., 2022)  and subsequent updates are described on NREL's resource data web page: " Closed-Loop Pumped Storage Hydropower Supply Curves ." The ATB considers only closed-loop systems due to their lower environmental impacts: open-loop and other configurations are not included in these estimates. Operation and maintenance (O&M) costs and round-trip efficiency are based on estimates for a 1,000-MW system reported in the 2020 DOE " Grid Energy Storage Technology Cost and Performance Assessment ."  (Mongird et al., 2020) . Projected changes in capital costs are based on the DOE Hydropower Vision study  (DOE, 2016)  and assume different degrees of technology improvement and technological learning. 

The three scenarios for technology innovation are:

• Conservative Technology Innovation Scenario (Conservative Scenario): no change from baseline CAPEX and O&M costs through 2050
• Moderate Technology Innovation Scenario (Moderate Scenario): no change from baseline CAPEX and O&M  costs through 2050, consistent with the Reference case in the DOE Hydropower Vision study  (DOE, 2016)
• Advanced Technology Innovation Scenario (Advanced Scenario): CAPEX reductions of 12% by 2050 based on improved process and design improvements along with advanced manufacturing, new materials, and other technology improvements, consistent with Advanced Technology in the DOE Hydropower Vision study  (DOE, 2016) ; no changes to O&M.

Resource Categorization

Resource categorization from a national closed-loop PSH resource assessment is described in detail by  (Rosenlieb et al., 2022)  with subsequent updates described on NREL's resource data web page: " Closed-Loop Pumped Storage Hydropower Supply Curves ." Individual sites are identified using geospatial algorithms to delineate potential reservoir boundaries, exclude reservoirs that violate technical potential criteria (e.g., protected land, critical habitat), find all possible reservoir pairings, and then eliminate overlapping reservoirs to produce the least-cost set of nonoverlapping reservoir pairs. This procedure is done for alternative storage durations of 8, 10, and 12 hours. Underlying data are site-specific, but for the ATB, resource classes are binned by capital cost such that each class contains a roughly equal amount of total national PSH capacity potential. Binning is done at the national level for the data tables below, and other representations use region-specific cost bins to better represent the distribution of site characteristics in each region. Physical characteristics and capital cost statistics for each ATB class and a 10-hour storage duration are included in the table below. 

Resource Class Capacity and Capital Costs

Resource Class Design Values

Scenario Descriptions

Cost reductions in the Advanced Scenario reflect various types of technology innovations that could be applied to PSH facilities. These potential innovations, which are discussed in the DOE Hydropower Vision Roadmap  (DOE, 2016) , are largely similar to technology pathways for hydropower without pumping.

Summary of Technology Innovation: Advanced Scenario

Scenario Assumptions

No explicit deployment assumptions or learning rates are used to define the Advanced Technology Innovation Scenario for PSH. All cost reductions are attributed to improved technology, processes, designs, and contracting along with advanced materials and improved construction practices. Deployed PSH capacity is 23 GW in the base year (2021), and the rate of cost reduction is 0.6 %/yr through 2035 and 0.2%/yr from 2035 to 2050.

Representative Technology

The resource assessment procedure requires several design specifications to be defined up front, and for the resource included in the ATB, these include hydraulic heads of 200 m–750 m, a maximum reservoir distance of 12 times the head height, and dam heights of 40 m, 60 m, 80 m, or 100 m  (Rosenlieb et al., 2022)  and " Closed-Loop Pumped Storage Hydropower Supply Curves " (NREL). Upper and lower reservoir volumes are also assumed to be within 10% of each other. Given the resulting technical specifications of each reservoir pair, the powerhouse (turbine, generator, and electrical equipment) can be sized flexibly for a given reservoir pair, and here data are included for a powerhouse sized to result in 8, 10, or 12 hours of storage duration (i.e., the maximum number of hours generating at rated capacity). 

Methodology

This section describes the methodology to develop assumptions for CAPEX, O&M, and round-trip efficiency. 

Capital Expenditures (CAPEX)

Capital costs are first calculated for each site using the PSH cost model from Australia National University  (Blakers et al., 2019) , adjusted to use a 33% project contingency factor instead of the base 20% assumption to better align with other technologies and U.S. industry practice. The cost model uses reservoir and powerhouse characteristics as inputs to generalized equations for PSH overnight capital cost. These raw costs are then further calibrated to more closely match hydropower industry expectations by multiplying site costs by a factor equal to the ratio of the central CAPEX estimate in  (Mongird et al., 2020)  for a 1,000-MW, 10-hour facility to the median CAPEX of all sites in the capacity range of 900–1,100 MW  (Mongird et al., 2020) . This factor is equal to 1.51, and due to the limited amount of available cost data, this factor is applied uniformly to all sites. Grid connection costs are then added based on the distance from the powerhouse location (assumed at the lower reservoir) to the nearest high-voltage transmission line node   (Maclaurin et al., 2021) . Cost assessment is described in detail by  (Rosenlieb et al., 2022) .

The maps below plot the median CAPEX in each state for each of the 15 resource classes when individual sites are binned by cost separately for each state. Some states have zero sites identified, largely due to insufficient elevation differences to meet the 200-m minimum head height criteria. The ratio of water conveyance length between reservoirs to head height (L/H ratio) is also shown for individual sites. The display also includes links to a bar chart and a tabular display. The bar chart shows more granular data for each balancing area defined in the Regional Energy Deployment System ( ReEDS ) capacity expansion model  (Ho et al., 2021)  along with the state average PSH capital cost. The table allows the data to be filtered by class and balancing area to view region- or class-specific data.

Regional PSH Capital Cost by Class

Operation and Maintenance (O&M) Costs

(Mongird et al., 2020)  characterize PSH O&M costs using a literature review of recently published sources of PSH cost and performance data. For the 2023 ATB, we use cost estimates for a 1,000-MW plant, which has lower labor costs per power output capacity compared to a smaller facility. O&M costs also include component costs for standard maintenance, refurbishment, and repair. O&M cost reductions are not projected for future years because the relevant technical components are assumed to be mature, so they are constant and identical across all scenarios.

Round-Trip Efficiency

Round-trip efficiency is also based on a literature review by  (Mongird et al., 2020) , who report a range of 70%–87% across several sources. The value of 80% is taken as a central estimate, and no improvements are projected either in  (Mongird et al., 2020)  or here because the relevant technical components are assumed to be mature. Thus, round-trip efficiency is constant and identical across all scenarios. 

The following references are specific to this page; for all references in this ATB, see References .

Rosenlieb, Evan, Donna Heimiller, and Stuart Cohen. “Closed-Loop Pumped Storage Hydropower Resource Assessment for the United States.” Golden, CO: National Renewable Energy Laboratory, 2022. https://doi.org/10.2172/1870821 .

Mongird, Kendall, Vilayanur Viswanathan, Jan Alam, Charlie Vartanian, Vincent Sprenkle, and Richard Baxter. “2020 Grid Energy Storage Technology Cost and Performance Assessment.” Washington, D.C.: U.S. Department of Energy, December 2020. https://www.energy.gov/energy-storage-grand-challenge/downloads/2020-grid-energy-storage-technology-cost-and-performance .

DOE. “Hydropower Vision: A New Chapter for America’s Renewable Electricity Source.” Washington, D.C.: U.S. Department of Energy, 2016. https://doi.org/10.2172/1502612 .

Blakers, Andrew, Matthew Stocks, Bin Lu, Kirsten Anderson, and Anna Nadolny. “Global Pumped Hydro Atlas.” Australian National University, 2019. http://re100.eng.anu.edu.au/research/phes /.

Maclaurin, Galen, Nicholas Grue, Anthony Lopez, Donna Heimiller, Michael Rossol, Grant Buster, and Travis Williams. “The Renewable Energy Potential (ReV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling.” Golden, CO: National Renewable Energy Laboratory, 2021. https://doi.org/10.2172/1563140 .

Ho, Jonathan, Jonathon Becker, Maxwell Brown, Patrick Brown, Ilya (ORCID:0000000284917814) Chernyakhovskiy, Stuart Cohen, Wesley (ORCID:000000029194065X) Cole, et al. “Regional Energy Deployment System (ReEDS) Model Documentation: Version 2020.” Golden, CO: National Renewable Energy Laboratory, June 9, 2021. https://doi.org/10.2172/1788425 .

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• Published: 11 March 2019

Highly efficient reversible protonic ceramic electrochemical cells for power generation and fuel production

• Chuancheng Duan   ORCID: orcid.org/0000-0002-1826-1415 1 ,
• Robert Kee 1 ,
• Huayang Zhu 1 ,
• Neal Sullivan 1 ,
• Liangzhu Zhu 1 ,
• Liuzhen Bian 1 ,
• Dylan Jennings 1 &
• Ryan O’Hayre   ORCID: orcid.org/0000-0003-3762-3052 1  

Nature Energy volume  4 ,  pages 230–240 ( 2019 ) Cite this article

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• Electrocatalysis
• Hydrogen fuel

An Author Correction to this article was published on 22 July 2020

This article has been updated

Reversible fuel cells based on both proton exchange membrane fuel cell and solid oxide fuel cell technologies have been proposed to address energy storage and conversion challenges and to provide versatile pathways for renewable fuels production. Both technologies suffer challenges associated with cost, durability, low round-trip efficiency and the need to separate H 2 O from the product fuel. Here, we present a reversible protonic ceramic electrochemical cell based on an yttrium and ytterbium co-doped barium cerate–zirconate electrolyte and a triple-conducting oxide air/steam (reversible) electrode that addresses many of these issues. Our reversible protonic ceramic electrochemical cell achieves a high Faradaic efficiency (90–98%) and can operate endothermically with a >97% overall electric-to-hydrogen energy conversion efficiency (based on the lower heating value of H 2 ) at a current density of −1,000 mA cm −2 . Even higher efficiencies are obtained for H 2 O electrolysis with co-fed CO 2 to produce CO and CH 4 . We demonstrate a repeatable round-trip (electricity-to-hydrogen-to-electricity) efficiency of >75% and stable operation, with a degradation rate of <30 mV over 1,000 h.

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The data that support the findings of this study are available from the corresponding authors on reasonable request. See Author contributions for specific datasets.

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22 july 2020.

A Correction to this paper has been published: https://doi.org/10.1038/s41560-020-0669-7

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Acknowledgements

This work was supported by the Advanced Research Projects Agency–Energy (ARPA-E) through the REFUEL (award DE-AR0000808) and REBELS programmes (award DE-AR0000493). Additional support was provided by the Army Research Office under grant number W911NF-17-1-0051, the Office of Naval Research via grant N00014-16-1-2780, the National Science Foundation via grant DMR156375, the Colorado School of Mines Foundation via the Angel Research Fund and the Colorado Office of Economic Development and International Trade (COEDIT) under their Advanced Industries Proof-of-Concept Grant programme. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of ARPA-E, the Department of Energy, the Army Research Office or the US Government.

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Chuancheng Duan, Robert Kee, Huayang Zhu, Neal Sullivan, Liangzhu Zhu, Liuzhen Bian, Dylan Jennings & Ryan O’Hayre

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C.D. and R.O. developed the intellectual concept, designed the experiments, analysed the data and led the manuscript writing. H.Z. and R.K. developed the PCFC and PCEC model comparisons and contributed to the discussion and analysis. D.J. performed the transmission electron microscopy. N.S., L.Z. and L.B. provided suggestions on the experiments, data interpretation and manuscript refinement.

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Correspondence to Chuancheng Duan or Ryan O’Hayre .

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Supplementary Figures 1–16, Supplementary Note 1, Supplementary Tables 1–5, Supplementary references.

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Duan, C., Kee, R., Zhu, H. et al. Highly efficient reversible protonic ceramic electrochemical cells for power generation and fuel production. Nat Energy 4 , 230–240 (2019). https://doi.org/10.1038/s41560-019-0333-2

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Received : 20 July 2018

Accepted : 18 January 2019

Published : 11 March 2019

Issue Date : March 2019

DOI : https://doi.org/10.1038/s41560-019-0333-2

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