Conference Agenda
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Thematic Session: Demand Response in an Electrifying World: Empirical Evidence on Prices, Technology, and Household Behavior
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This session brings together four papers across different markets and experimental settings that study how pricing, incentives, and automation affect electricity demand, with implications for system costs and grid management in power systems with high renewable penetration. The focus is on household and electric vehicle demand response, and on the roles of technology, effort, and behavioral frictions.
Bailey et al. show in a field experiment that fully automated demand response delivers substantially larger peak-load reductions than programs requiring any active household participation, even when smart technologies are provided. Metcalfe et al. evaluate a large-scale AI-managed EV charging tariff and find substantial peak demand reductions through automated load shifting, with low override rates and sizable consumer and system benefits. Ovaere and Vergouwen show that dynamic retail pricing can increase peak demand by concentrating consumption in low-price hours, while peak demand charges offset these effects by internalizing capacity constraints, with responses driven by EV owners.
Together, the papers document how demand flexibility depends not only on price incentives, but also on automation, effort costs, and infrastructure, and how these factors interact with electricity market design in systems with growing renewable generation. | ||
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Take the Load Off: Effort and Technology as Determinants of Electricity Demand Response 1University of Calgary; 2University of Alberta; 3Stanford University As electricity systems transition to greater shares of renewable energy, flexible demand becomes vital for grid stability. We test whether smart technologies can unlock this flexibility or if human behavior remains the key barrier. Our field experiment randomizes peak event timing across households enrolled in one of three demand response programs: fully automated, app-enabled, and manual. Households with automated systems reduced consumption five times more than those requiring any action. Simply providing smart technology made no difference when effort was still required. Our results suggest that time and attention—not technology—are the main constraints to realizing greater demand-side flexibility. AI in Charge: Large-Scale Experimental Evidence on Electric Vehicle Charging Demand 1Columbia University; 2Centre for Net Zero; 3University College London; 4NBER One of the promising opportunities offered by AI to support the decarbonization of electricity grids is to align demand with low-carbon supply. We evaluated the effects of one of the world’s largest AI managed EV charging tariffs (a retail electricity pricing plan) using a large-scale natural field experiment. The tariff dynamically controlled vehicle charging to follow real-time wholesale electricity prices and coordinate and optimize charging for the grid and the consumer through AI. We randomized financial incentives to encourage enrollment onto the tariff. Over more than a year, we found that the tariff led to a 42\% reduction in household electricity demand during peak hours, with 100\% of this demand shifted to lower-cost and lower-carbon-intensity periods. The tariff generated substantial consumer savings, while demonstrating potential to lower producer costs, energy system costs, and carbon emissions through significant load shifting. Overrides of the AI algorithm were low, suggesting that this tariff was likely more efficient than a real-time-pricing tariff without AI, given our theoretical framework. We found similar plug-in and override behavior in several markets, including the UK, US, Germany, and Spain, implying the potential for comparable demand and welfare effects. Our findings highlight the potential for scalable AI managed charging and its substantial welfare gains for the electricity system and society. We also show that experimental estimates differed meaningfully from those obtained via non-randomized difference-in-differences analysis, due to differences in the samples in the two evaluation strategies, although we can reconcile the estimates with observables. Capacity Constraints and Allocative Efficiency: Evidence from Residential Electricity Pricing University of Gent, Belgium Widespread electrification introduces large, shiftable residential electricity demand, raising new questions about allocative efficiency and capacity constraints in electric grids. We study how peak demand charges can alleviate capacity constraints, induced by the increased prevalence of electric vehicles and potentially exacerbated by dynamic pricing schemes. Using high-frequency electricity consumption data, we estimate that peak demand charges reduce peaks by 3 percent. The effects are driven by electric vehicle owners, who shift charging to nighttime. In contrast, responses among households without large shiftable loads dissipate over time. Household-level effects aggregate to lower local grid demand peaks, reducing costly, capital-intensive grid investment. | ||