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Exploring energy storage for grid innovation

The electrical grid is transforming as older, less efficient fossil fuel generation plants are closing and more wind and solar farms are added to the system. These changes, however, can introduce additional complexity to a system that requires new tools to help the grid transition reliably. At the same time, companies are attempting to reach net zero emissions by reducing carbon emissions from their operations and supply chains, removing greenhouse gases from the air, and partnering with others on specific emissions reduction projects. Energy storage has the potential to support both of these objectives, by improving grid reliability and helping reduce emissions from the electricity system.

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The global energy storage market could hit one terawatt-hour by 2030, according to the latest forecast from research company BloombergNEF. Energy storage has been deployed in the United States mostly to help balance the electricity system when there are short-term fluctuations in supply and demand. However, these energy storage projects are currently incentivized to maximize revenue, which at times can inadvertently increase system emissions. 

In 2022, Meta, along with our partner Broad Reach Power, launched a pilot to test how energy storage projects such as large-scale batteries could reduce greenhouse gas emissions while continuing to help preserve a reliable electrical grid. Meta is working with industry leaders to study how emerging technologies like energy storage can support grid reliability and reduce emissions as we continue to build some of the most efficient and innovative data center facilities in the world. 

The yearlong pilot consists of 9.9 MW energy storage projects at three sites in Texas, each of which is one hour in duration, and located in different sites to study whether, if incentivized correctly under different grid conditions, a storage project can reduce system wide emissions, increase overall revenue, and still contribute to preserving the reliability of the system.

The sites are in Odessa, Angleton, and Lopeño, and all three projects were operating prior to Meta’s participation. Odessa is in a renewable energy–rich area of the state that is subject to high curtailment, meaning not all the renewable energy is able to be used by electricity consumers when it’s produced. Angleton is located in a pocket of the grid near Houston with high electrical demand, and Lopeño is located in South Texas near coastal wind projects. Both Angleton and Lopeño have adjacent projects that continue to be solely optimized for price, and these serve as control projects for the pilot. 

 

Being able to store renewable, carbon free energy in areas where there is a surplus means that those energy systems can draw on that stored energy during high demand times before having to dip into energy produced by fossil fuels that generate more greenhouse gases. Consequently, energy storage can reduce demand for the highest-emitting generation plants and use lower emissions energy to meet the same demand, reducing overall grid emissions.

 Meta will compensate the project developer based on the number of metric tons of emissions reductions they create. One of the hypotheses of the pilot is that pricing emissions reductions provides a clear and simple signal that aligns the parties' incentives and enables a more detailed study of how carbon price signals alter storage dispatch. 

“Broad Reach Power is excited to partner with Meta and demonstrate how standalone energy storage can reduce GHG emission by following marginal emission signals,” says Yizhou Jing, manager of trading and origination at Broad Reach Power. “As more solar and wind projects are interconnected into the power grid, increasing power supply intermittency, it is critical for our customers to quantify the environmental value and grid reliability that standalone energy storage can bring.”

Meta will also partner with third-party verifiers through the Energy Storage Solutions Consortium, a new consortium led by Meta, REsurety and Broad Reach Power. The consortium will create an open-source, third-party-verified methodology to quantify the greenhouse gas benefits of certain grid-connected energy storage projects, like the pilot program in Texas. The coalition is made up of a broad cross-section of leading technology companies, emissions data providers, investors, storage developers and service providers, and non-governmental organizations.  

In order to calculate the greenhouse gas benefits of large-scale energy storage facilities, the consortium will leverage locational marginal emissions information. Locational marginal emissions can be used to calculate the metric tons of GHG emissions displaced through the charging and discharging of energy storage facilities on the grid at a specific location and point in time. 

Or put differently, the overall impact of the operation of an energy storage project in a given time interval can be calculated as a product of the electricity either charged (i.e. taken from the grid) or discharged (i.e. injected in the grid) by the project and the marginal emissions rate at its location in the same time interval. The net emissions impact of the energy storage project can be calculated over a selected time period as the sum of the emissions impacts in each time interval.

“Storage can be a game-changer in accelerating grid decarbonization,” says Adam Reeve, SVP of software solutions at REsurety. “In order to realize that potential, though, we need credible and transparent means of evaluating and then maximizing its avoided carbon impact. We’re excited to work with Meta and Broad Reach Power in this pilot and to bring a data-driven approach to reducing grid emissions.”

In order to show how this methodology could potentially reduce carbon emissions, the consortium conducted an interim case study on an operating energy storage system (ESS) using sample data to demonstrate the emission reduction potential. The ESS is located in Texas, USA, and is connected to the ERCOT power grid. The calculations are based on one month of operational data and then scaled to a one year period by holding the emissions constant in every month. However, we expect that real-life conditions on the grid, including supply and demand constraints, reliability requirements, and wind and solar conditions would impact the actual results. 

There are currently ~1,540 MW of standalone storage projects already operational in Texas’s ERCOT power grid that could be eligible under this methodology. The volume of emissions reductions that can be generated under this methodology will vary based on project location and market conditions. Based on preliminary estimations, these 1,540 MW could reduce emissions by about 97,020 tCO2e annually. There are approximately 38.2 GW of standalone storage projects in the interconnection queue in the ERCOT market alone, which could result in emission reduction for around 2.4 million tCO2e if scaled linearly. 

While the case study projected potential reductions in carbon emissions on paper, once the pilot in Odessa, Angleton and Lopeño wraps up in June 2023, Meta and its partners plan to evaluate the actual dispatch and revenue information. They will then publish the results of the pilot to share how energy storage projects behaved with the addition of a carbon price signal, how different grid conditions altered the emissions reduction impact of the projects, and help other organizations to understand how to deploy energy storage to drive decarbonization and reliability. 

“Energy storage has tremendous potential to fight climate change, but it’s not a given. Storage only reduces emissions if it charges at specific times—times of surplus renewables or other clean energy, known as low 'marginal emissions' at that location. That’s why we’re thrilled to see the Energy Storage Solutions Consortium adopting this simple, powerful technique. The results of this pilot and coalition will accelerate decarbonization efforts everywhere, and we’ve been delighted to support as advisors,” says Gavin McCormick, founder and executive director of WattTime. 

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