Conference Agenda

As power markets decarbonize, market participants face increasingly complex decisions, from investing in renewable generation to optimizing procurement and shifting load. Yet the evolving and interdependent dynamics of electricity prices and grid carbon intensities complicate these decisions. To disentangle this complexity and support energy decision-making, we develop a hybrid analytical framework that integrates parametric regression with machine learning capabilities to jointly analyze day-ahead electricity prices and carbon intensities. In doing so, we uncover multiple “market regimes” determined by different combinations of energy prices, economic activity, and weather factors. We reveal how price–emissions dynamics differ across regimes and can, at times, diverge. Using a series of exercises, we illustrate how these regimes manifest and shape financial and emissions outcomes. We discuss the market-wide implications of targeted supply-side changes and demand-side shifts, pointing to how cost- versus emissions-saving objectives reshape market dynamics.

Nuclear and intermittent renewables (wind and solar) are generally regarded as the only scalable technologies producing low-carbon electricity. However, the extent to which these technologies can co-exist in a reliable power system depends on whether nuclear units can adjust their operations to renewable output fluctuations. Using hourly data from the French power system, we find that nuclear units are operated quite flexibly, and that the foregone energy production due to ''load following'' actions (relative to the counterfactual of operating at full capacity during load following events) is currently limited. However, we find that an additional load following event is associated with a slightly higher likelihood of a unit failure. We also find that unit-level minimum output constraints are binding more frequently as system-wide renewable generation increases, especially so for units most exposed to solar generation. In 2024, hours during which available nuclear flexibility was exhausted are associated with non-positive hourly day-ahead prices.

While zero-marginal-cost renewable energy has reduced wholesale electricity prices, its effect on retail electricity prices remains uncertain. A common concern is that costly investments in renewable integration, such as transmission expansion, generate system-level costs that may offset or even exceed savings from lower generation costs. This paper tests this conjecture empirically using annual sales data from the EIA 861 and electricity generation data from the EIA 923 for the majority of the United States between 2004 and 2023. To address the inherent simultaneity between prices and energy generation, I leverage spatial variation in wind potential and time-varying levelized costs of wind energy to construct a Bartik-style instrument to predict renewable penetration. I find that a one percentage point increase in renewable penetration at the balancing authority level reduces real utility level yearly average retail prices by 0.3%, implying a 3.5% decrease in real retail prices at the mean change in renewable penetration. Crucially, OLS estimates that do not account for endogeneity are positively biased and insignificant. Even under alternate instruments and sample restrictions, the estimated effect is tightly bounded between - 0.7% and 0.7, suggesting that increased renewable penetration has had, at most, a modest impact on energy affordability.

The energy transition requires significant investment in intermittent renewable energy sources, such as solar and wind power. New generation capacities are generally procured through fixed price contracts, such as power purchase agreements and contracts for difference, or feed-in tariffs. With these designs, renewable technologies are selected based on their generation, regardless of their adequacy with demand and supply by other technologies. We show that fixed-price contracts implement the optimal portfolio of renewable technologies if the price is adjusted with a technology-specific bonus-malus system that depends on the correlation between renewable energy production and the wholesale electricity price. We estimate the bonus-malus for solar and wind power in California, France, Germany, and Spain and decompose it to identify the key market factors driving the adjustment. We argue that the bonus-malus measures the cost of integrating intermittent generation into the energy mix. Therefore, it should be added to the levelized cost of energy (LCOE) to obtain the cost of generating an additional megawatt-hour with a specific renewable technology.