CS5: Energy Economics and Modeling

The rise of cloud computing, artificial intelligence, and digital services has led to data centers becoming one of the fastest growing sources of electricity demand in the U.S., yet the system-wide implications of this growth for electricity prices, emissions, and transmission congestion remain poorly quantified at a national level. In this study, we develop a framework for quantifying the economic and environmental impacts of data center electricity demand across the U.S. We use PyPSA-USA, an open-source power flow and capacity expansion model that jointly optimizes generation dispatch and capacity investment to minimize system cost subject to linearized transmission constraints across the continental U.S. The model considers inter-regional power flow, transmission congestion, and locational marginal pricing at a balancing area-level resolution (134 zones). We run counterfactual PyPSA-USA scenarios with and without data center load under current and future (2030 and 2035) grid conditions incorporating utility and ISO-level projections of data center load growth. We quantify the impacts of data center demand growth on wholesale prices, emissions, capacity investment, and transmission congestion at a nationwide scale. We further explore the role of data center flexibility by showing the extent to which temporal shifting of computational workloads can mitigate grid impacts.
Results show that meeting data center demand requires significant new generation investment in both renewables and fossil fuels. Regions with CO2 emission constraints rely on dispatchable fossil fuels with CCS to meet the baseload nature of data center demand. Without flexibility, the continuous nature of this demand substantially raises system-wide wholesale prices. The economic effects vary regionally due to local generation mix, transmission capacity, and concentration of data center load. These findings contribute to recent discussions around large load interconnection, data center siting, and the potential for flexible computing loads to lower system-wide impacts.

Energy system models have become essential tools for exploring possible long-term energy transition options to achieve net-zero emissions from regional to global levels, from daily to annual temporal resolution. Among multiple types of energy system models, bottom-up optimization models are particularly well suited for generating long-term energy transition normative scenario analysis. Modeling to Generate Alternatives (MGA) has been increasingly used with energy system optimization models to tackle uncertainty, non-modeled objectives, and other complexities of the real-world energy transition. However, MGA results are highly contingent on the subjectively assumed value of slack, which defines how much higher the total system costs can be with respect to the costs of the cost-optimal scenario. Using electricity system hindcasting in 31 European countries, this study quantifies deviations between costs of the cost-optimal scenario and the real-world transition represented in an optimization model and hence provides first-of-the-kind empirical evidence for slack. A slack of 10% – 20% that are commonly used in the MGA applications are strikingly lower than the median cost deviation of 44 – 60% that is obtained by hindcasting with single-year and multi-year electricity system optimization model D-EXPANSE, if modeling 30 years ahead and if accounting for costs during the modeled period only. Although this slack value highly varies between countries, time horizons of interest, and historical cost assumptions, it is barely ever around or below 10%. These results underscore the importance of using larger and country-specific slack values, estimated using hindcasting with each particular model or informed by the guiding values from this paper. These findings are essential for energy modeler and policy makers to design energy transition pathways that are more aligned with the real-world dynamics.

The global energy transition is often framed as a rapid substitution of fossil fuels by low-carbon sources. However, in emerging economies with growing energy demand, fiscal dependence on hydrocarbons, and structural development challenges, the sequencing of this transition becomes a central policy issue. This paper investigates how transition pathways can be designed to balance decarbonization, energy security, and economic resilience.
Using Brazil as a case study, we analyze medium- and long-term energy scenarios based on integrated energy system modeling and fiscal impact simulations. The research evaluates the role of natural gas as a transition fuel, its interaction with expanding renewable generation, and the macroeconomic implications of alternative production trajectories. Particular attention is given to the potential fiscal impacts associated with premature decline in upstream investments, including government revenues, social funds, and industrial competitiveness.
Our findings suggest that transition strategies that overlook sequencing effects may increase external dependence, reduce fiscal space, and compromise industrial decarbonization pathways. Conversely, coordinated planning that integrates natural gas infrastructure, renewable expansion, and industrial policy can enhance system resilience while maintaining alignment with climate commitments.
The paper contributes to the debate on differentiated transition pathways between developed and developing economies, highlighting the importance of institutional planning capacity, infrastructure integration, and policy coherence. It argues that bridging continents in the global energy transition requires acknowledging structural asymmetries and designing context-sensitive strategies that fuel progress without undermining economic stability.

We study how energy prices shape the decoupling in Latin America. We develop a multisector general equilibrium model to distinguish between two key channels: the technique effect (within-sector efficiency gains) and the composition effect (reallocation of economic activity). Using panel data for 21 Latin American countries from 1990 to 2021, we first employ a descriptive decomposition analysis and then test the model's predictions with a shift-share instrumental variable strategy. Our results reveal a clear dichotomy. Higher energy prices induce a persistent composition effect, shifting activity toward services. However, in manufacturing and transport-especially in developing and commodity-exporting economies-higher energy prices are linked to short-run increases in energy intensity, reflecting a perverse technique effect associated with scale expansion during booms. This tension highlights the need for policy approaches that address both structural and efficiency channels in resource-dependent economies.

Techno-economic cost optimization energy system models are widely used to inform long-term energy and climate decision-marking. However, real-world energy transitions are influenced by factors beyond cost optimization, including policy, institutional constraints, and sectoral dynamics, often leading to outcomes that diverge from model assumptions and reduce model's ability to represent real-world trends. Although recent modeling approaches attempt to capture this complexity, systematic evidence on how well cost-optimization models reproduce real-world transitions-particularly at the whole energy system level-remains limited. Key questions regarding the extent to which such models deviate from real-world transitions across countries and sectors, and how hindcasting insights can inform model design and policy interpretation remain largely unexplored.
To fill this gap, we conduct a large-scale hindcasting analysis of national whole energy system models in 31 European countries over 1990–2024 using a perfect-foresight cost-optimization framework. We develop a multi-sector, bottom-up, technology-rich model representing national energy systems from primary supply to end-use sectors. Historical data are used to generate cost-optimal pathways and compare them with real-world transitions represented within the same framework. We additionally test the influence of modeling four major historical policies individually and in combination: emissions trading system, carbon taxes, renewable energy targets, and sectoral policies. 
Results show that models reproduce total system costs with deviations of 2%–10% and sectoral costs with deviations of 1%–18% above cost-optimal pathways, with variation across sectors, countries, and technologies. Policy representation shows mixed effects on accuracy: emissions trading and renewable targets often improve alignment with real-world transitions, whereas strong sector-specific policies can increase deviations or mask other policy impacts. These findings provide large-scale empirical insight from hindcasting with national whole energy system models, informing improvements in model design and policy interpretation.