Harnessing Batteries and Carbon Contracts to Accelerate Grid Decarbonization, authored by Tierra Climate in partnership with REsurety
This paper examines the economic carbon impact of compensating batteries for carbon reduction using detailed electricity emissions data and a carbon contract. Carbon contracts with grid-scale batteries might provide corporations with an elegant solution to meet sustainability targets and decarbonize the electricity grid, which cannot be accomplished through renewable energy purchases alone.
REsurety uses Locational Marginal Emissions (LMEs) data to analyze the effectiveness of the three carbon accounting methods proposed for compliance with new production tax credits available for clean hydrogen under the Inflation Reduction Act (IRA). This analysis considers 32 electrolyzer-renewable project pairs across 3 different grid regions (ERCOT, PJM, and CAISO) using hourly emissions and generation data from 2022. Seen in Table 1 below, the results show that, due to the difference in carbon intensities on the grid based on location and timing, determining “clean” hydrogen using Annual Energy Matching often results in significant increases in emissions despite the procurement of an equivalent quantity of energy from offsite clean energy to match the electrolyzer’s consumption. Further, Table 1 shows that while Local Hourly Energy Matching can help reduce net emissions in some locations, the impact of local transmission constraints often results in significant increases in net emissions even after energy is “matched” by hour. Finally, the Annual Carbon Matching method, using LME data, can ensure low or zero net emissions and qualification for the clean hydrogen production tax credit. The Annual Carbon Matching method also helps to incentivize development of electrolyzers in locations with cleaner grids with lower existing marginal emissions and the procurement of renewable energy in locations with dirtier grids and higher existing marginal emissions, therefore maximizing the ‘greening of the grid’ impact of the IRA legislation.
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Global climate change has pushed carbon emissions to the forefront of public scrutiny and scientific inquiry. Striving to reduce their net carbon footprint, large energy consumers have increasingly turned to renewable energy resources. These energy consumers have pioneered different approaches toward clean energy procurement, such as the RE100 initiative, Google’s 24/7 Carbon-Free Energy, Microsoft’s 100/100/0 vision, and the Emissions First partnership led by Meta and Amazon. This white paper examines different clean energy procurement strategies in terms of overall cost and effectiveness in carbon emissions reduction.
Using locational marginal emission rate (LMERs), we quantify the cost and carbon emissions impact of clean energy procurement strategies for corporate energy consumers with varying load shapes and within a variety of balancing authorities. We compare energy matching strategies against a strategy that directly accounts for carbon emissions, which we call carbon matching, for two different types of large electricity consumers in 5 different balancing authorities. Balancing authorities ranged from large ISO/RTOs (PJM and CAISO) to vertically integrated utility regions covering a regional (Duke Energy Carolinas) or municipal area (Los Angeles Department of Water and Power and Portland General Electric).
The results show the following:
Carbon matching, a strategy that directly accounts for carbon emissions using LMERs and ensures that avoided emissions are equal or greater than emissions attributable to load, is more cost-effective than any of energy matching strategies analyzed;
Energy matching does not guarantee reaching carbon neutrality;
Localized energy matching decreases carbon displacement efficiency;
Local energy matching may not be practical in certain regions, which could deter participation;
Hourly energy matching is the least efficient strategy at displacing carbon emissions, and its cost varies greatly depending on location
If you’d like to learn more about REsurety’s Location Marginal Emissions (LMEs) offerings, please contact us.
“LME is an important tool in assessing individual projects because seemingly identical renewable energy projects can have drastically different impacts on avoided carbon emissions.”
CarbonCount is a decision tool that evaluates investments in U.S.-based renewable energy, energy efficiency, and climate resilience projects to determine how efficiently they reduce CO2 equivalent (CO2e) emissions per $1,000 of investment. CarbonCount produces a quantitative impact assessment for investments’ carbon avoidance by integrating forward-looking project assumptions, emissions factors, and capital investment.
This white paper explains why CarbonCount matters, why it’s being updated, the methodology behind it, and use cases. REsurety’s Locational Marginal Emissions (LME) data is also featured in the paper.
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EXCERPT: Corporations are increasingly focused on reducing their carbon footprints by decarbonizing the electric grid. While solar and wind energy development have rightly been a mainstay of these efforts, there is growing consensus that producing more clean energy alone isn’t enough. To maximize grid decarbonization, clean generation needs to occur at times and locations where its output displaces the highest-emitting resources. Consumption timing and location should be adjusted to minimize its carbon emissions via siting decisions, demand flexibility measures, and energy efficiency. And energy storage is needed to manage grid congestion and mismatches between clean supply and demand.
Effective carbon accounting frameworks can help coordinate these complex mitigation strategies by allocating emissions among the entities responsible for producing them. These accounting frameworks attempt to ensure that activities with more impact on actual emissions have more impact on carbon accounts. Given the large and increasing interest of investors, customers, regulators, and governments in corporate decarbonization initiatives, effective carbon accounting frameworks can encourage corporations to maximize their actual carbon reductions.
Authored by Jennifer Newman, Vice President of Atmospheric Science Research, REsurety
White Paper Executive Summary
An “8760” (sometimes known as a “typical meteorological year,” or “TMY”) is a year-long hourly time series of expected generation for a wind or solar project. As the name implies, an 8760 contains generation values for all 8,760 hours of a year (non-leap year) and captures the typical seasonal and diurnal generation patterns at the site. Despite their widespread use in the renewable energy industry, there are two particular use cases of 8760s that can lead to significant errors in revenue estimation: 1) the pairing of an 8760 with a non-concurrent price time series and 2) the use of an 8760 as an input to a forward-looking price model.
The first, pairing an 8760 with non-concurrent prices, misses the impact of hourly wind and solar generation on market price, which can be particularly significant in markets with high renewable penetration. For example, Figure 1 demonstrates that pairing an 8760 with non-concurrent ERCOT power prices results in annual wind project revenue overestimates that can exceed 30%.
The second use case, using an 8760 generation profile as an input to a pricing model, does allow the user to capture the impact of hourly renewable generation on market price, if modeled correctly; however, the resulting distribution of forecasted prices will only represent the impact of a single, “normal” weather year. In reality, renewable energy projects will experience a variety of weather conditions, with non-typical weather years having an asymmetric and sometimes extreme impact on the price of power.
In this paper, we use observed and modeled data to quantify the impact of using an 8760 for renewable energy project value estimation, with a primary focus on wind generation. We demonstrate that pairing an 8760 with non-concurrent prices results in consistent wind project value overestimates in markets with significant wind penetration. We also show that using an 8760 to drive a forward-looking price model leads to a condensed price distribution that misses extremes and is not representative of historical price distributions.
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A Force Multiplier for the Carbon Impact of Clean Energy Programs
EXCERPT: While the impressive growth in clean energy development is an encouraging signal that we can tackle the harms of greenhouse gases and climate change, we should remember that clean energy deployment itself is not the ultimate goal. Tracking environmental goals in traditional units of MWh of clean energy is an outdated and imprecise approach that does not measure the carbon emissions reductions actually achieved. For example, clean resources in locations where high-emitting fossil plants cannot be retired for reliability reasons have high carbon abatement value, as do clean resources whose output aligns with times of high emissions intensity on the grid. Batteries and hybrid resources that can shape clean energy injection to maximize carbon abatement can also provide decarbonization benefits that are ignored by traditional MWh-based accounting.
There is a better way to measure and incentivize clean energy resources. We propose that customers, markets, and policymakers embrace the concept of Locational Marginal Emissions (LMEs) as a force multiplier for directing their clean energy program dollars to maximize carbon impact.In our 2-year analysis of renewable energy projects across Texas, we found that directing clean energy deployment to the highest-value renewable projects has the potential to double the carbon impact as compared to a more traditional annual energy matching approach. Setting goals and measuring performance using carbon-based metrics can help organizations select generation technologies, make siting decisions, and operate resources to minimize their carbon footprint.
REsurety contributes a chapter on how to manage risk in virtual PPAs through Volume Firming Agreements in this new report by RE-Source, a joint platform of WindEurope, SolarPower Europe, the RE100, and the World Business Council for Sustainable Development.
Corporates have a variety of different drivers for looking to source power from renewables, but the possibility to lower and fix electricity costs is a major part of the rationale for these deals. A recent survey of 1,200 companies across six countries showed that, of those sourcing renewables, 92% of them are doing so in order to reduce energy costs. Although decarbonization commitments often provide the initial driver to consider renewable corporate sourcing, the ability for a PPA to reduce energy cost volatility and generate savings on energy bills over the long term is cited by most corporates as providing the main business case.
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REsurety and Energy GPS have joined forces again to empirically analyze how wind farms with P99 Hedges would have fared in ERCOT in the record-setting 2018-2019 period.
In the spring of 2018, REsurety and Energy GPS co-authored a white paper¹ describing the source and impact of a common error in forecasting hedged wind farm revenue. The error arises when valuing the revenue from a wind farm utilizing a fixed quantity energy price swap, commonly known as a “P99 Hedge”. In the time since publishing that original white paper, the ERCOT power market has experienced both record-breaking levels of wind generation and record-breaking power prices — both of which have significantly impacted the settlements of P99 Hedges.
Our original white paper used generation data from July 2011 through February 2018 (the “Original Period”); this update uses data from March 2018 through September 2019 (the “Update Period”), and considers where, how, and why P99 Hedge performance during the Update Period differed from the Original Period.
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For C&I Buyers looking to mitigate risks in their PPAs, CohnReznick provides new accounting guidance on Volume Firming Agreements and Settlement Guarantee Agreements in a whitepaper co-published with REsurety.
An important consideration for Buyers is how these contracts, or group of contracts, will be assessed for accounting purposes. Like a traditional vPPA, contracts like the SGA and VFA can require complex accounting analysis. The application of the appropriate financial accounting requires not only a clear understanding of the nature of the transaction and the rights and obligations of the parties to such agreements, but also the ability to appropriately navigate through the various Topics, Subtopics, Sections, and Subsections of the Financial Accounting Standards Board’s Accounting Standards Codification (“ASC”).
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Orrick, Microsoft, and REsurety describe the Proxy Generation PPA: what it is and why it represents a necessary evolution of the corporate clean energy buyer’s contract.
Beginning as novelty transactions dominated by socially conscious “tech” companies, corporate & industrial (C&I) renewable energy purchases now exert tremendous pull in the electricity market. Since 2013 and in the United States alone, C&I buyers have contracted for approximately 14,000 MW of renewable energy, continuing to make headlines with every purchase.
C&I buyers’ appetites for renewable energy have unleashed tremendous creativity in structuring new products. As a result, C&I buyers benefit from state-of-the-art offerings, including: direct purchases of renewable energy by C&I buyers; “green tariffs”; and intermediated deals allowing C&I buyers with smaller purchasing requirements to piggy back onto larger deals originated by financial institutions or by other C&I buyers.
This paper turns a lens onto direct purchases, the predominant form of renewable energy transaction. And, this paper further narrows its focus onto the preferred structuring tool for those direct purchases—the long-term power purchase agreement (PPA)—by exploring methods for re-tooling the PPA (1) to simplify the contracting and negotiation process, (2) to better align the interests of green power buyers and power sellers, and (3) to empower C&I buyers to use the latest risk management tools being made available to them from insurance and commodity markets.
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REsurety and Energy GPS partnered to bring you The “P99 Hedge” That Wasn’t, an empirical analysis of how wind farms with fixed volume swaps (also known as P99 Hedges or “Bank Hedges”) may be underestimating the impact of hourly mismatches in their financial model. Our analysis demonstrates that the commonly used modeling method (which ignores the hourly relationship between wind generation and power prices) results in dramatic over-estimations of project revenue.
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