CS22: Energy Economics and Modeling

Motivation: Electricity prices are highly volatile and uncertain due to increasing use of renewable energy and market differences, making accurate electricity price forecasting (EPF) crucial for investors, regulators, and utilities. However, the existing literature remains divided, context-biased, and lacks an inclusive comparison of forecasting approaches. Therefore, this study aimed to identify and evaluate the most common approaches used, price horizons studied, strengths and weaknesses of the models, and key research gaps in the literature. 
Methods: The study adopted a systematic literature review approach, reviewing 71 peer-reviewed papers published between 2015 and 2024, selected from major academic databases. 
Results: The findings indicate that most studies focus primarily on deregulated markets (liberalized) and in Europe, emphasizing short term prices and point forecasts, and predominantly using artificial intelligence and hybrid approaches for price forecasting. In contrast, medium-and long term price forecasting and probabilistic (interval) forecasts, receive limited attention, especially in regulated and developing countries. Therefore, the study recommends that future research should prioritize context-specific forecasting models for regulated markets, exploring medium- and long-term price forecasting, and advancing probabilistic (interval-based) approaches to better support risk management, investment planning, and energy policies. 

Liquefied natural gas (LNG) remains a critical component of global energy security; however, its lifecycle carbon emissions present a major challenge for decarbonization. This study presents an integrated techno-economic evaluation of a carbon-neutral, transoceanic LNG–CO₂ energy system linking the United States and Europe. In the proposed configuration, LNG is exported from the United States to European markets for power generation, while the resulting carbon dioxide (CO₂) is captured, liquefied, transported back across the Atlantic, and permanently stored in offshore geological formations in the United States.
The analysis adopts an incremental evaluation framework that focuses on the additional costs and benefits associated with integrating CO₂ transport and storage into an existing LNG export supply chain, rather than re-evaluating baseline LNG export revenues. A stoichiometric carbon accounting model is used to quantify CO₂ generation from LNG combustion and define corresponding transport and storage requirements. Maritime logistics and geological sequestration are evaluated using standardized vessel capacities, representative operating cycles, and field-scale injection constraints.
The techno-economic assessment incorporates additional capital and operating expenditures for dual-purpose maritime transport, CO₂ conditioning, terminal modifications, and offshore storage development. Revenue enhancements arise from improved vessel utilization through the elimination of ballast return voyages, CO₂ transport and storage service fees, and carbon-market incentives. Sensitivity analyses examine the effects of carbon price levels, storage development costs, vessel utilization rates, and regulatory conditions on breakeven carbon management costs.
Research findings demonstrate that bidirectional LNG–CO₂ logistics enhance capital efficiency and significantly reduce the net cost of achieving carbon-neutral LNG operations through integrated CO₂ transport and storage. The proposed framework provides a quantitative basis for assessing the incremental investments required to achieve carbon neutrality within established LNG export systems, while supporting long-term energy security.

Cross-border energy infrastructure projects, such as large-scale hydropower and international interconnections, are essential for global decarbonization goals but frequently encounter significant implementation barriers due to the inherent difficulty in quantifying and allocating benefits across multiple nations. Traditional evaluations in international literature often lack operational granularity or fail to capture the systemic value of flexibility and environmental externalities. This study addresses how high-granularity modeling can overcome these hurdles, using a USD 10 billion investment in Central Asia's Rogun (3,600 MW) and Kambarata-1 (1,860 MW) hydropower projects as a case study. These mega-projects are macroeconomically transformative, but require external financing for their development, as they represent approximately 50% and 30% of Tajikistan's and Kyrgyzstan's national GDPs, respectively.


The methodology evaluates the integrated expansion of generation and interconnections at an hourly scale, applying state-of-the-art techniques such as the Stochastic Dual Dynamic Programming (SDDP) algorithm for optimal system dispatch with temporal coupling. It advances standard practices by integrating detailed cascade modeling for the Vakhsh and Naryn rivers with thermal Unit Commitment constraints and the stochasticity of over 300 renewable locations modeled with spatial-temporal correlation. Furthermore, the framework incorporates Dynamic Probabilistic Reserves for variable renewable integration and applies individualized shadow carbon costs to quantify the economic displacement of polluting thermal generation.


Findings demonstrate that systemic benefits outweigh capital expenditures through coal displacement and enhanced regional resilience. Beyond resolving chronic energy deficits, these reservoirs act as "natural batteries" that enable renewable expansion and provide substantial systemic benefits to neighboring nations, underscoring the positive impact of regional partnerships in facilitating project financing through shared economic value. The study concludes that this transparent mathematical framework provides a replicable blueprint for regional cooperation treaties and infrastructure investments, aligning with the global challenge of bridging energy developments across borders to fuel progress.

Corporate energy scenarios are influential tools that shape expectations about the future of global energy systems. This study presents a longitudinal, multi-method analysis of BP's Energy Outlook (2011–2024), applying Latent Dirichlet Allocation (LDA) topic modeling, sentiment analysis, and a novel bias diagnostic framework across four key dimensions: market framing, policy adaptation, technological assumptions, and geopolitical considerations.
The results reveal persistent structural biases-fossil fuel path dependency, regulatory lag, and short-term reactivity-despite the gradual integration of net-zero narratives and emerging technologies. While BP's scenario vocabulary has diversified over time, the scenarios remain strategically conservative, continuing to emphasize natural gas and incremental transition logics. These findings suggest a pattern of incremental institutional adaptation, shaped by evolving reputational, market, and policy considerations, rather than wholesale strategic realignment.

This work examines strategic decision-making in the Argentine natural gas system, focusing on the optimal sizing of LNG liquefaction facilities and firm export volumes to neighboring countries under simulated production and market conditions. The analysis explicitly accounts for infrastructure capacity constraints and domestic consumption across two seasonal periods (winter and summer).
To this end, we propose a methodology that integrates mathematical programming models with real-world data, including production scenarios for the Vaca Muerta basin, node-level natural gas demand, import and regasification capacities, and simulated market conditions for pipeline gas and LNG exports. The objective of the methodology is to quantify the impact of export and infrastructure decisions on LNG plant sizing, pipeline export volumes, import requirements for system balancing, and the resulting effects on the country's net trade balance.
As an experimental exercise, the model is applied under two alternative regulatory frameworks for Argentina: a self-sufficiency-based regime, in which only full-year surplus production may be exported, and a market-oriented framework that allows unrestricted trade of natural gas and LNG. These frameworks represent, respectively, the natural gas policy regime that Argentina implemented until 2023 and the reform proposals currently under discussion and, in particular cases, implementation. The analyzed network configurations include scenarios with gas pipeline capacity expansions, thereby formalizing the decision-making process and providing quantitative support based on data-driven, prescriptive analytics.
The results indicate that relaxing regulatory constraints on gas and LNG trade leads to larger optimal sizes for LNG facilities and higher levels of year-round exports for a given level of gas production. Furthermore, for these optimal configurations, price-pair simulations for gas imports and exports suggest that net trade gains may persist even under unfavorable market conditions in which import prices exceed export prices.