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Reports by Devan Samant, Abraham Silverman & Zachary A. Wendling • May 30, 2024
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The majority of US states use a renewable portfolio standard (RPS) to achieve clean energy targets. RPS programs typically set annual clean energy production levels, but they ignore the significant variations in greenhouse gas (GHG) emissions intensity of the grid at different times of the day and at different locations. Newly available locational marginal emissions (LME) data, which are collected at thousands of physical locations and updated every five minutes, provide insights into where and when the electricity sector produces the most and least GHG emissions. Incorporating LMEs into RPSs would allow states to identify and reward “high impact” clean energy production: that which replaces the dirtiest generation.
This report examines the impact that incentivizing clean energy production at high LME times and locations could have on reducing emissions in RPS programs. In five scenarios based on data from four states in the PJM grid (Illinois, New Jersey, Pennsylvania, and Virginia), the authors examine hypothetical shifts in energy production from times and geographic areas with differing clean or dirty generation mixes. The proof-of-concept exercises the authors ran found that shifting clean energy production into the three dirtiest hours of the day resulted in approximately 10% less emissions than the baseline case. Geographically shifting production to displace energy at a dirtier locale resulted in 9%–20% less emissions, depending on the LME makeup at the given location versus the baseline.
States can leverage the following LME trends to improve the effectiveness of their compliance programs:
About one in four American households experience some form of energy insecurity. Within this group, Black, Indigenous, Latine, low- and moderate-income (LMI), and other disadvantaged communities face a disproportionately higher burden.
The rapid expansion of artificial intelligence (AI), especially Large Language Models (LLMs) such as GPT-3 and Gemini on which the now well-known ChatGPT AI and Gemina assistant systems...
Full report
Reports by Devan Samant, Abraham Silverman & Zachary A. Wendling • May 30, 2024