Download PDF Summer Bridge on Engineering the Energy Transition June 26, 2023 Volume 53 Issue 2 This issue explores the energy transition needed to address the mounting threats of climate change. The articles are an excellent resource to help inform meaningful decisions and steps for energy-related contributions to reduce carbon emissions. Energy Storage: A Key Enabler for Renewable Energy Wednesday, June 7, 2023 Author: Jeremy Twitchell, Di Wu, and Vincent Sprenkle Energy storage is essential to a clean electricity grid, but aggressive decarbonization goals require development of long-duration energy storage technologies. The job of an electric grid operator is, succinctly put, to keep supply and demand in constant balance, as even minor imbalances between the two can damage equipment and cause outages. This balance is a highly complex undertaking that involves coordinating hundreds of generation units with the demands of millions of individual customers. Historically, this challenge was mitigated by predictability: the generation (supply) side had power plants that could be turned up or down as needed, while the load (demand) side had customers who generally had the same devices in their houses and used them in the same ways. Grid operators knew what was coming and could adjust production to accommodate it. Given recent changes in energy supply and demand, energy storage is of increasing interest to ensure reliable and sustainable provision. In this article we explain the current challenges to power supply and demand and then provide an overview of energy storage technologies. Following a summary of the modeling challenges associated with energy storage and recent advances in overcoming those challenges, we discuss systems and technologies needed to maintain a clean and reliable electric grid. Current Challenges to Power Supply and Demand Climate change and technological innovations that have made renewable generation financially competitive and increasingly accessible have fundamentally changed the nature of supply and demand. A rapidly increasing share of electricity comes from variable sources, distributed energy resources and electrical vehicles mean that generation can come from just about anywhere on the grid, and customer demands may vary widely. Fortunately, technical innovations have also delivered new forms of electrical energy storage that can keep generation and load in balance. To maintain that balance, grid operators call on flexible ancillary services to reconcile differences between electric supply and demand. But the services vary in both the size of the differences that they remedy and the duration over which they are employed. Figure 1 illustrates how various ancillary services are used to keep supply and demand in balance during a portion of a 24-hour period, based on a sample day-ahead demand forecast, actual demand, and generation. The figure shows how flexible resources such as energy storage can help to integrate variable sources of generation such as wind and solar. Moment-to-moment variability in the output of renewable resources requires frequency regulation to absorb peaks and fill in valleys to maintain generation and load balance. Longer-term variability in output (e.g., due to a cloudy day) requires activation of spinning reserves to replace the lost production. And finally, the variable output of distributed energy resources such as rooftop solar can vary the demands of individual customers, requiring energy imbalance resources to correct differences between forecast and actual demand. Understanding Current Energy Storage Technologies Energy storage devices are unique among grid assets because they can both withdraw energy from the grid during periods of excess generation and inject energy during periods of insufficient generation. These capabilities make storage an ideal source of both ancillary services and the grid flexibility necessary to incorporate variable energy resources such as wind and solar. However, determining how to optimally deploy energy storage is a challenge under traditional electric grid planning practices, and the rapidly changing grid is creating demand for new long-duration energy storage (LDES) technologies that have not yet been commercially proven. Energy storage is distinct from other electric grid assets in three important ways: Flexibility: Because energy storage technologies can act as either a load (when charging) or a generator (when discharging), they can provide a range of grid-balancing services. Scalability: Most energy storage technologies are modular, which allows them to be scaled down to a small device that supports the demands of a single customer or scaled up to a large project that supports the demands of thousands of customers. Duration: Unlike a power plant that can provide electricity as long as it is connected to its fuel source, energy storage technologies are energy-limited: they store their fuel in a tank and must recharge when that tank is empty. Because energy storage technologies have different durations, they also have different measurement scales than other grid assets. A generator’s capabilities are expressed in its maximum potential output, using kilowatts (kW) or megawatts (MW). But a storage asset’s capabilities are generally expressed in terms of its kW or MW output as well as its total energy content, expressed in kilowatt-hours (kWh) or megawatt-hours (MWh). Virtually all US energy storage projects constructed since 2013 have used lithium-ion batteries. How-ever, despite that growth, pumped storage hydropower accounts for the majority of installed energy storage in the United States.[1] Figure 2 summarizes current US energy storage deployments in both total installed capacity and total installed storage content as of the end of 2022. Modeling Challenges While energy storage can provide tremendous flexibility to integrate variable renewable generation in a distributed or centralized manner, it is challenging to model the optimal usage of an energy storage system (ESS) and fully capture its potential benefits from bundling services. Following are modeling challenges involved in identifying storage needs: Operational characteristics: The physical capability and operational characteristics of an ESS must be modeled so that it can be fairly evaluated against other resource options. Appropriate models are required to maintain a good balance between fidelity and simplicity. Degradation effects: An ESS is generally subject to degradation over time, which can affect its performance and reduce its lifespan. Models are needed to capture degradation impacts of different charging and discharging operation and inform the design of charging controls. Use cases and applications: The required modeling methods and formulation could vary by stakeholders with different objectives and use cases. The services to be evaluated, corresponding energy and power requirement, and reward/benefit calculation must be properly captured and represented. The problem becomes much more complicated when resilience and environmental benefits are considered in addition to economic benefits. Regions and systems: Modeling and valuing energy storage require a comprehensive understanding of factors such as the generation mix, grid infrastructure, market structures and rules, distribution system capacity, and load growth rate, which typically vary from one region/system to another. Operational uncertainties: These are associated with wind and solar generation, electric energy and ancillary service price, and load. Assumption of a perfect forecast may overestimate the benefits of energy storage, so it is important to model operational uncertainties when evaluating the benefits of and developing control strategies for energy storage. Failure to account for uncertainty may result in a model that undervalues the flexibility benefits of storage in adapting to those uncertainties (Sioshansi et al. 2021). Dispatch and control strategies: An informed control strategy is crucial for realizing the benefits of an ESS. Advanced dispatch and control methods are required to maximize stacked value streams considering various couplings and constraints, such as trade-offs among services, short- and midterm temporal interdependency, degradation effects, and operational uncertainties. Modeling is much more complicated when resilience and environmental benefits are considered in addition to economic benefits. There has been a significant effort to develop model-ing and optimization methods to tackle these challenges (Wu and Ma 2021). To model the physical capacity of an ESS, a scalar linear system is often used to simplify the dynamics of the energy state. This system is parameterized by constant efficiencies and static limits on charging and discharging power, energy, or state of charge. Nonlinear, high-fidelity models can provide a more accurate representation of ESS operations but at a cost of increasing complexity. Regarding degradation effects, models of varying complexity and accuracy range from fixed lifespan to loss-of-life-only and full models. Advances in Strategies, Algorithms, and Other Tools While the exact objective function and constraints typically vary from one storage project to another, modeling and valuation frameworks and problem formulations have been developed for most use cases and applications. Moreover, dispatch and control strategies and algorithms are available for co-optimization, rule-based control, mathematical programming, and hybrid control. There are also stochastic programming, risk-aware control, and learning-based methods to address uncertainties. In addition, modeling and valuation tools developed during the past few years help various stakeholders identify value streams and evaluate the economic benefits of ESS (Siberry et al. 2022). There exist numerous similarities and differences among these tools, and it is often not easy for users to differentiate among tools and select the most appropriate to meet their specific needs. To address this challenge, a model selection platform has been developed at Pacific Northwest National Laboratory to review and compare more than 60 energy storage modeling, valuation, and simulation tools developed by the US Department of Energy national laboratories and suggest the best-suited tools based on users’ needs and requirements.[2] Users can filter tools based on a few high-level attributes, view a side-by-side comparison table of all tools, or take a quiz to find the best match based on their desired specifications. These tools continue to evolve and improve as the energy storage industry grows and matures. Technology and Systems Needs Frequency Regulation Markets Before 2016, the average duration of utility-scale lithium-ion batteries installed in the United States was about 40 minutes (EIA 2022). At these shorter durations, frequency regulation markets were the only viable market for batteries. In fact, regional implementation (by the regional transmission organization PJM) of a frequency regulation market product designed to compensate batteries based on their unique characteristics played a key role in opening ancillary service markets to energy storage (Chen et al. 2017). However, frequency regulation markets are relatively shallow compared to other electricity markets, which means they can accommodate much lower levels of participation. Table 1 shows the sizes of frequency regulation markets in the seven US wholesale energy markets. As the table illustrates, the size of each region’s frequency regulation market relative to its overall energy market ranges from 0.6 to 4.5 percent. The size of these markets, coupled with competition from other energy resources that can provide frequency regulation, means that opportunities for energy storage to provide frequency regulation have declined in recent years. But at the same time, these changing grid needs, coupled with rapid cost declines, have caused battery storage technologies to evolve to support longer durations and their usage on the grid has changed, as explained in the next section. Utility-Scale Storage The Energy Information Administration (EIA) collects data on US utility-scale storage projects, including duration and planned uses. Figure 3 shows how reported uses changed for newly constructed storage projects from 2016 to 2021. Column percentages sum to more than 100 because most storage projects reported multiple uses. As figure 3 illustrates, the share of newly installed storage systems providing frequency regulation declined from 59 percent in 2016 to 39 percent in 2021, while the share of those providing spinning reserves remained relatively steady. The share of new projects providing peak shaving rose from 34 percent to 44 percent over the period, renewable energy increased from 38 percent to 56 percent, and arbitrage projects tripled from 10 percent to 30 percent. Both arbitrage and peak shaving involve discharging the battery during peak periods. The difference is that a peak shaving battery is built with multiple hours of duration to help the grid meet peak demands and earn additional revenue through capacity markets, whereas arbitrage involves a shorter-duration battery built purely for economics (charge during the lowest-cost hours and discharge during the highest-cost hours) and either operates in regions with no capacity market or accepts a derated capacity credit if available. While renewable integration is not a defined grid service, the EIA data capture storage projects that are colocated with renewable generation to help “firm” the renewable output or that charge from excess renewable energy. The data show that there is a positive relationship between variable renewable generation and storage deployments and that, as the uses of energy storage evolve, so does the average duration of new projects (from about 40 minutes in 2016 to about 2.6 hours in 2021). LDES Technologies for Variable Renewable Resources LDES technologies will significantly reduce the costs of operating a power system powered solely by variable renewable resources (Dowling et al. 2020). In the event of mismatches between when energy is generated by a fully renewable-power grid and when it is consumed, two classes of LDES would be required to reconcile the mismatches, one up to 20 hours in duration and one with weeks of duration (Twitchell et al. 2023). A review of several LDES studies identified a consensus that when an electric system reaches 80 percent variable generation, LDES of up to 100 hours would be required to maintain reliability, and a fully variable grid would require LDES of 1,000 hours or more (Albertus et al. 2020). Despite these recognized needs, however, a review of utility planning practices concluded that the common use of short time horizons prevents utilities from fully identifying the value of LDES (Sánchez-Pérez et al. 2022). Significant research and development are required to provide LDES technologies in the quantities needed for electric system decarbonization. Federal LDES R&D programs include ARPA-E’s Duration Addition to electricitY Storage (DAYS) program,[3] designed to support early-stage research into innovative technologies capable of providing 10–100 hours of energy, and the Department of Energy’s Long Duration Storage Shot,[4] supporting the development and deployment of commercial storage products with 10–100 hours of duration at competitive costs. On the commercialization side, iron-air battery developer Form Energy has signed deals with US utilities for three demonstration projects of its technology, which it claims will provide 100 hours of duration (Form Energy 2020, 2023). And ESS Inc., a US-based manufacturer of iron flow batteries with up to 12 hours of duration, has signed multiple agreements globally to deploy its technology.[5] Other technologies, including liquefied air and thermal storage, are also nearing commercial scale (LDES Council and McKinsey & Company 2021). Summary Energy storage is an enabling technology for rapid acceleration in renewable energy deployments. It enables flexibility to ensure reliable service to customers when generation fluctuates, whether over momentary periods through frequency regulation or over hours, by capturing renewable generation for use during periods of peak demand. Progress in the integration of renewable energy requires both significant increases in the amount of energy storage on the grid and the development of new types of energy storage that can ensure reliability over days and seasons. While there is cause for optimism on this front, continued investment in research, development, and deployment of LDES technologies is crucial to enable electric grid decarbonization. References Albertus P, Manser JS, Litzelman S. 2020. Long-duration electricity storage applications, economics, and technologies. Joule 4(1):21–32. Chen H, Baker S, Benner S, Berner A, Liu J. 2017. PJM integrates energy storage: Their technologies and wholesale products. IEEE Power & Energy 15(5):59–67. Dowling JA, Rinaldi KZ, Ruggles TH, Davis SJ, Yuan M, Tong F, Lewis NS, Caldeira K. 2020. Role of long-duration -energy storage systems in variable renewable electricity systems. Joule 4(9):1907–28. EIA [Energy Information Administration]. 2022. Form EIA-860: Annual Electric Generator Report. EIA. 2023. Form EIA-860M: Monthly Electric Generator Report for January 2023. Form Energy. 2020. Form Energy announces pilot with Great River Energy to enable the utility’s transition to an affordable, reliable, and renewable electricity grid. https://formenergy.com/form-energy-announces-pilot-with- great-river-energy-to-enable-the-utilitys-transition-to- an--affordable-reliable-and-renewable-electricity-grid/ . Form Energy. 2023. Form Energy partners with Xcel Energy on two multi-day energy storage projects. https://-formenergy.com/form-energy-partners-with-xcel- energy-on-two-multi-day-energy-storage-projects/. LDES Council, McKinsey & Company. 2021. Net-Zero -Power: Long-Duration Energy Storage for a Renewable Grid. http://www.ldescouncil.com/assets/pdf/LDES--brochure-F3- HighRes.pdf. Sánchez-Pérez PA, Staadecker M, Szinai J, Kurtz S, Hidalgo-Gonzalez P. 2022. Effect of modeled time horizon on quantifying the need for long-duration storage. Applied Energy 317:119022. Siberry V, Wu D, Wang D, Ma X. 2022. Energy Storage Valuation: A Review of Use Cases and Modeling Tools. Technical Report DOE/OE-0029. US Department of Energy. Sioshansi R, Denholm P, Arteaga J, Awara S, Bhattacharjee S, Botterud A, Cole W, Cortés A, de Queiroz A, DeCarolis J, & 13 others. 2021. Energy-storage modeling: State-of-the-art and future research directions. IEEE Transactions on Power Systems 37(2):860–75. Twitchell J, Desomber K, Bhatnagar D. 2023. Defining long-duration energy storage. Energy Storage 60:105787. Wu D, Ma X. 2021. Modeling and optimization methods for controlling and sizing grid-connected energy storage: A review. Current Sustainable/Renewable Energy Reports 8:123–30. [1] Pumped storage hydropower pumps water to a higher elevation and then releases it to run back down through a turbine to generate electricity when needed. [2] PNNL Model Selection Platform, https://msp.pnnl.gov/ [3] http://Arpa-e.energy.gov/technologies/programs/days [4] https://www.energy.gov/eere/long-duration-storage-shot [5] ESS Inc., https://essinc.com/news-events/ About the Author:Jeremy Twitchell is a senior energy analyst, Di Wu is a chief engineer, and Vincent Sprenkle is technical group manager, Electrochemical Materials and Systems Group, all at Pacific Northwest National Laboratory.