| Niazi, A., & B. Shaffer | Do gasoline prices influence electric vehicle usage? Evidence from the U.S | N/A | Revise & Resubmit | A 1% increase in gasoline prices raises EV driving by 0.35%, driven by households shifting from ICE vehicles, especially where charging infrastructure is strong. The findings show carbon pricing can boost EV use, but its impact depends on access to charging. |
| Darkwah, A, Shaffer, B. & Hastings-Simon, S | Layering Incentives: The Impact of Local Top-Ups on Solar Adoption and Welfare | Energy Policy | 2026 | The paper explores the impact of adding local “top-up” incentives to existing solar subsidies. |
| Armantalab, O., Ghosh, R., & Hawkins, J. | Potential for electric vehicle adoption in midwest US states: A stated preference and two-stage MRP study | Journal of Choice Modelling | 2026 | This study uses advanced forecasting methods to examine electric vehicle adoption in the U.S. Midwest, with a focus on pickup trucks. It identifies charging time as the key barrier to adoption and highlights the need for improved charging infrastructure and stronger incentives. |
| Parvar, S. S., Amjady, N., & Zareipour, H. | Behaviorally aware pricing of energy storage as a service platform: A prospect theory-based bi-level framework. | Energies | 2026 | This study proposes an enhanced Energy Storage as a Service (ESaaS) framework that aggregates distributed energy storage systems to participate in multiple electricity market services. By incorporating behavioral decision-making through prospect theory, the framework improves pricing strategies, market participation, and profitability for both energy storage owners and the ESaaS operator. |
| Vykhodtsev, A. V., ShakeriHosseinabad, F., Jang, D., Wang, Q., Rosehart, W., & Zareipour, H. | Artificial intelligence-assisted physics-based model of lithium-ion battery for power systems operation research | Journal of Energy Storage | 2026 | This work proposes a machine learning-based optimization model for lithium-ion battery energy storage systems that better represents battery degradation than conventional methods. The model delivers more realistic dispatch strategies, longer battery life, and higher energy arbitrage revenues. |
| Al-Shafei, A., Amjady, N., Zareipour, H., & Cao, Y. | Power system transition planning: A planner-oriented optimization model | Energies | 2026 | This paper presents a long-term power system planning model that integrates generation, transmission, storage, and grid-enhancing technologies under uncertainty. Using parallel Stochastic Dual Dynamic Programming, it enables computationally tractable planning for power system transition. |
| Khajehdehi, O., Kahou, M. E., Hastings, A., & Hastings-Simon, S | Modelling the ‘S curve’: transition dynamics in EV adoption | Environmental Research Communications, 7(12), 121012 | 2025 | The study finds that EV adoption is driven not just by affordability but also by factors like model availability, charging infrastructure, and consumer trust in battery technology, with hybrid vehicles playing a key transitional role. It shows that effective policies must combine financial incentives with measures that improve technology confidence and expand options to accelerate the shift from ICE vehicles. |
| Bailey, M. R., Brown, D. P., Shaffer, B., & Wolak, F. A. | Show me the money! A field experiment on electric vehicle charge timing | American Economic Journal: Economic Policy, 17(2), 259-284 | 2025 | The study finds that financial incentives reduce peak-time EV charging by 49%, with users shifting strongly to off-peak hours, while informational nudges have no effect. It shows that EV charging is highly flexible but behavior changes are not persistent without incentives, highlighting the importance of price-based policies. |
| Brown, D. P., Olmstead, D. E., & Shaffer, B | Electricity market design with increasing renewable generation: Lessons from Alberta. | The Electricity Journal, 107484 | 2025 | The study finds that Alberta’s current electricity market design faces growing challenges with higher wind and solar penetration, particularly in handling system reliability and operational constraints. It shows that adopting more integrated market designs that reflect physical grid realities can improve efficiency, reliability, and cost outcomes as renewable generation expands in Alberta. |
| Bailey, M. R., Brown, D. P., Shaffer, B. C., & Wolak, F. A. | Take the Load Off: Time and Technology as Determinants of Electricity Demand Response (No. w33836) | National Bureau of Economic Research | 2025 | The study finds that fully automated demand response reduces household electricity use during peak events five times more than programs requiring any level of active participation, even with smart technologies. It shows that human effort—not technology availability—is the main barrier to demand flexibility, highlighting the importance of automation in managing grids with high renewable penetration. |
| Rapson, D., & Shaffer, B | Smooth Operator? Managing Electric Vehicle Integration in Constrained Distribution Networks (No. 2540) | Federal Reserve Bank of Dallas | 2025 | The paper shows that rising EV adoption can strain local electricity distribution networks by reducing load diversity, creating capacity challenges at the neighborhood level. It highlights demand-based tariffs and managed charging as cost-effective solutions to ease grid pressure, though their success depends on consumer participation and well-designed incentives. |
| Zougheib, S., Hoecherl, M., Alqahtani, H., Mai, P. M., Hoteit, H., Vahrenkamp, V., Ezekiel, J., & Finkbeiner, T. | Towards SDG 13: Turning CO2 from a problem to a solution using the Earth’s natural heat | Frontiers for Young Minds | 2025 | This unique outreach article introduces CO₂-plume geothermal technology, showing how captured CO₂ can be stored underground while generating clean geothermal energy. It highlights an innovative approach to reducing emissions and advancing renewable energy. |
| Baghkarvasef, M., Bidram, A., Farrokhabadi, M., Sohail, I., Khargonekar, P. P., Zareipour, H., & Parvania, M. | Leveraging Artificial Intelligence for Enhancing Power Grid Resilience to Extreme Weather Events: Applications and Challenges | IEEE Energy Sustainability Magazine | 2025 | This article explores how data management and artificial intelligence can enhance power grid resilience against extreme weather events by supporting preventive and restorative actions across generation, transmission, and distribution systems. It also identifies key research and technology gaps in improving grid resilience. |
| Zamudio López, M., & Zareipour, H. | Modeling the Duration of Electricity Price Spikes Using Survival Analysis | Energies | 2025 | This paper introduces a survival analysis framework to model the duration of electricity price spikes using historical market data. The approach provides a simple, data-driven method to better understand the persistence of price spikes across electricity markets. |
| Sharma, S., Yao, H., Farrokhabadi, M., Zareipour, H., & Musilek, P. | A Dynamic Retail Market Model to Investigate Sustainability of Retail Contracts in DERs-Penetrated Markets | IEEE Open Access Journal of Power and Energy | 2025 | This paper presents a dynamic retail market model to evaluate the long-term impacts of increasing distributed energy resources on retailer sustainability. It proposes subscription-based business models that improve the financial viability of retailers while maintaining economic benefits for customers. |
| Zamudio López, M., Ioannou, Y., & Zareipour, H. | Forecasting Electricity Prices with Deep Learning and Dynamic Sparse Training | Sustainable Energy, Grids and Networks | 2025 | This paper investigates dynamic sparse training for deep learning-based electricity price forecasting, demonstrating that sparse models can achieve state-of-the-art accuracy while reducing computational complexity. It also shows that sparse networks effectively capture the underlying patterns of electricity market behavior. |
| Egharevba, G., Dankers, A., & Zareipour, H. | Forecasting Transmission Line Loss Using a Cluster-Based Refinement Framework and Scheduled Outage Data | IEEE Access | 2025 | This paper presents a transmission line loss forecasting framework that combines scheduled outage information with NLP and cluster-based machine learning to improve prediction accuracy. The approach outperforms existing methods by incorporating qualitative operational data into the forecasting process. |
| Ahmadi, A., Zareipour, H., & Leung, H. | Globalization for Scalable Short-term Load Forecasting | arXiv | 2025 | This paper investigates global load forecasting models for power transmission networks, demonstrating that cross-learning and clustering techniques improve forecasting accuracy, scalability, and robustness under data drift. The proposed framework outperforms traditional local forecasting models, particularly for large and evolving power systems. |
| Qu, J., Sun, Q., Qian, Z., Zareipour, H., & Dong, Z. Y. | A Transferable Framework of PV Power Forecasting for Cross-Regional Distributed PV Systems Using Domain Adversarial Temporal Network | IEEE Transactions on Industrial Informatics | 2025 | This paper proposes a transferable deep learning framework for photovoltaic power forecasting that uses transfer learning and domain adaptation to improve predictions across regions with limited historical data. The approach consistently outperforms existing methods, particularly for newly deployed distributed PV systems. |
| Egharevba, G., Dankers, A., & Zareipour, H. | Behind-the-Fence Generation Forecasting: A Batched Decomposition Framework | IEEE Access | 2025 | This paper presents a batched decomposition framework for behind-the-fence generation forecasting that improves prediction accuracy while eliminating information leakage common in traditional decomposition methods. The approach outperforms existing forecasting models when tested on real-world data from Alberta and Quebec. |
| Ahmadi, A., Zareipour, H., & Leung, H. | Similarity-Based Clustering for Identification and Segmentation of Responsive Electricity Customers | IEEE Access | 2025 | This paper proposes a similarity-based time series clustering method to identify and segment electricity customers based on their responsiveness to demand response signals. The approach improves clustering of non-stationary consumption patterns, enabling more effective identification of responsive customers. |
| Yao, H., Hakimian, V., Farrokhabadi, M., & Zareipour, H. | Emission-Aware Operation of Electrical Energy Storage Systems | arXiv | 2025 | This paper proposes a framework that enables energy storage systems to participate in carbon markets by tracking real-time operational emissions and generating emission performance credits. The approach optimizes storage dispatch to reduce emissions while creating new revenue opportunities through carbon credit trading. |
| Bahman, S., & Zareipour, H. | Long-term Multi-resolution Probabilistic Load Forecasting Using Temporal Hierarchies | Energies | 2025 | This paper presents a multi-resolution probabilistic load forecasting framework that produces consistent long-term forecasts across hourly to yearly time scales using temporal hierarchies. By integrating climate and economic data, the approach improves forecast accuracy and supports long-term energy planning and decision-making. |
| Al-Shafei, A., Amjady, N., Zareipour, H., & Cao, Y. | Power System Transition Planning: An Industry-Aligned Framework for Long-Term Optimization | arXiv | 2025 | This paper introduces a power system transition planning framework that integrates generation, transmission, storage, and grid-enhancing technologies into a single long-term optimization model. Using high-performance computing and Stochastic Dual Dynamic Programming, it enables practical, large-scale planning for the transition to low-emission power systems. |
| Tavakolian, M., & Zareipour, H. | Weather Feature Selection for Robust and Optimized Energy Load Prediction | Journal of Engineering Advances and Technologies for Sustainable Development | 2025 | This paper evaluates weather feature selection methods to improve short- and medium-term electricity load forecasting using hybrid deep learning models. The results show that incorporating multiple weather variables and location-specific feature selection enhances forecasting accuracy across different prediction horizons. |
| Fried, A., Shaffer, B., & Hastings-Simon, S | Sufficiency of level 1 charging to meet electric vehicle charging requirements | Environmental Research: Infrastructure and Sustainability, 4(2), 021001 | 2024 | Using real-world data from Calgary, the study finds that 29% of EVs can rely entirely on Level 1 charging, while 53% need only occasional fast charging, with most charging demand (median 99%) shiftable to Level 1. These results challenge the need for widespread Level 2 infrastructure and suggest a cost-effective way to expand charging access while reducing grid upgrade requirements. |
| Collins, G., Dahl, C. A., Fleming, M., Tanner, M., Martin, W. C., Nadkarni, K., Hastings-Simon, S., & Bazilian, M | Projecting demand for mineral-based critical materials in the energy transition for electricity | Mineral Economics, 37(2), 245-263 | 2024 | The study projects that the clean energy transition will drive a 294% increase in total material demand by 2050, with especially large growth for minerals like lithium (up to ~1300%) alongside major increases in steel and aluminum. It highlights significant variation across materials, underscoring the need for strategic planning in supply chains and resource management to support decarbonization. |
| Bailey, M. R., Brown, D. P., Myers, E., Shaffer, B. C., & Wolak, F. A | Electric vehicles and the energy transition: Unintended consequences of a common retail rate design (No. w32886) | National Bureau of Economic Research | 2024 | The study finds that time-of-use pricing shifts EV charging to off-peak periods but creates new “shadow peaks” that can strain local grid capacity and accelerate infrastructure upgrades. In contrast, managed charging effectively coordinates demand, reducing grid stress while maintaining strong consumer acceptance. |
| Ezekiel, J., Vahrenkamp, V., Hoteit, H. A., Finkbeiner, T., & Mai, P. M. | Techno-economic assessment of large-scale sedimentary basin stored–CO2 geothermal power generation | Applied Energy | 2024 | This paper demonstrates the techno-economic potential of using stored CO₂ to generate geothermal energy while permanently sequestering carbon. The integrated approach supports cost-effective clean electricity generation and carbon capture for net-zero energy systems. |
| Benson, A. L., Clarkson, C. R., & Zeinabady, D. | Analysis of enhanced geothermal system flowback and circulation test data for fracture and reservoir characterization. | Geoenergy Science and Engineerin | 2024 | This study introduces analytical models to estimate reservoir and fracture properties in enhanced geothermal systems using production and flowback data. Results from simulated cases and the Utah FORGE site highlight their value for improving geothermal reservoir characterization and development planning. |
| Zeinabady, D., & Clarkson, C. R. | Reservoir and fracture characterization for enhanced geothermal systems: A case study using multifractured wells at the Utah Frontier Observatory for research in geothermal energy site. | SPE Journal | 2024 | This study develops a post-fracture pressure decay analysis method to estimate fracture and reservoir properties for enhanced geothermal systems. The approach, validated using data from the Utah FORGE site, provides a practical and cost-effective tool for optimizing hydraulic fracture design and geothermal reservoir development. |
| Pearson, K. M., & Hastings-Simon, S | The mid-transition in the electricity sector: impacts of growing wind and solar electricity on generation costs and natural gas generation in Alberta | Environmental Research: Infrastructure and Sustainability, 3(4), 045007 | 2023 | The study finds that as variable renewables expand in Alberta, fossil fuel plants operate fewer hours but remain cost-effective, supporting emissions reductions of up to ~28 million tonnes annually while maintaining system reliability. It shows that even high renewable penetration can be achieved at reasonable costs but requires market reforms to incentivize flexible generation needed to balance variability. |
| Brown, D. P., Eckert, A., & Shaffer, B | Evaluating the impact of divestitures on competition: Evidence from Alberta’s wholesale electricity market | International Journal of Industrial Organization, 89, 102953 | 2023 | The study finds that after Alberta’s asset divestiture arrangements expired in 2020, peak electricity prices rose by 120%, with nearly two-thirds of the increase driven by greater market power among large suppliers. It shows that when divested assets are concentrated among large strategic firms, market power intensifies, raising concerns about competition and the need for regulatory oversight. |
| Hastings-Simon, S., Leach, A., Shaffer, B., & Weis, T | Alberta's Renewable Electricity Program: Design, results, and lessons learned | Energy Policy, 171, 113266 | 2022 | Alberta’s Renewable Electricity Program successfully procured over 1,300 MW of wind at low costs (CA$30–43/MWh), generating a financial surplus and attracting new market entrants, including projects with Indigenous equity participation. It also stimulated private renewable development, though the study finds that greater exposure to market prices could have improved long-term efficiency and better rewarded high-value generation. |
| Yang, S., Hastings-Simon, S., & Ravikumar, A. P | Global liquefied natural gas expansion exceeds demand for coal-to-gas switching in Paris compliant pathways | Environmental Research Letters, 17(6), 064048 | 2022 | The study finds that while LNG expansion can reduce emissions in the near term through coal-to-gas switching, long-term expansion exceeds what is needed in Paris-aligned scenarios, creating risks of stranded assets. It concludes that LNG is only climate-compatible under limited conditions, with its long-term role constrained by declining coal use and the need for low methane leakage and carbon capture. |
| Yang, S., Hastings-Simon, S., & Ravikumar, A. P. | Pipeline availability limits on the feasibility of global coal-to-gas switching in the power sector | Environmental science & technology, 56(20), 14734-14742 | 2022 | The study finds that only about 43% of global coal capacity is close enough to gas pipelines for feasible coal-to-gas switching, with even lower access in regions like India, limiting its scalability. It concludes that infrastructure constraints and fuel access mean coal-to-gas transitions cannot serve as a universal solution for global emissions reductions. |
| Shaffer, B., Samaras, C., & Auffhammer, M. | Make Electric Vehicles Lighter for Climate and Safety Benefits | Nature | 2021 | |
| Tabari, M., & Shaffer, B | Paying for performance: The role of policy in energy storage deployment | Energy Economics, 92, 104949 | 2020 | The study finds that FERC Order 755 in the US increased the likelihood of energy storage projects providing frequency regulation services by about 37% by better compensating fast, accurate resources. It shows that improved market rules can significantly boost storage deployment, even as cost barriers persist. |
| Leach, A., Rivers, N., & Shaffer, B | Canadian electricity markets during the COVID-19 pandemic: An initial assessment. | Canadian Public Policy, 46(S2), S145-S159 | 2020 | The study finds that during COVID-19 pandemic, electricity demand fell by ~10% in Ontario and ~5% in Alberta, British Columbia, and New Brunswick, alongside shifts in generation and increased exports from Ontario. It highlights implications for electricity rates, the value of power data as a real-time economic indicator, and the need for better data transparency across provinces. |
| Shaffer, B | Misunderstanding nonlinear prices: Evidence from a natural experiment on residential electricity demand | American Economic Journal: Economic Policy, 12(3), 433-461 | 2020 | Using a natural experiment in British Columbia, the study finds some households misinterpret nonlinear electricity pricing, responding as if marginal prices apply to all usage, which leads to exaggerated behavioral changes. Although few in number, these households significantly affect aggregate demand and incur welfare losses of about 10% of annual electricity spending. |
| Rivers, N., & Shaffer, B | Stretching the duck: How rising temperatures will change the level and shape of future electricity consumption | The Energy Journal, 41(5), 55-88 | 2020 | The study finds that rising temperatures and increased air conditioning adoption will significantly boost electricity demand during hot periods, shifting peak demand from winter to summer, especially in colder regions. It also shows that greater intraday variability will expand demand fluctuations, increasing the need for flexible electricity supply alongside growing renewable generation. |
| Shaffer, B | Location matters: Daylight saving time and electricity demand | Canadian Journal of Economics, 52(4), 1374-1400 | 2019 | The study finds that the impact of daylight saving time on electricity demand varies by region, driven by differences in sunrise times and societal waking patterns. In particular, DST increases electricity demand in areas with late sunrises and early waking hours, challenging the assumption that it universally saves energy. |
| Santos, G., & Shaffer, B | Preliminary results of the London congestion charging scheme | Public Works Management & Policy, 9(2), 164-181 | 2004 | The London Congestion Charging Scheme reduced congestion by 30% and traffic by 16% in its first year, while increasing travel speeds and improving bus reliability. It generated net economic benefits of £50 million and £68 million in revenues, demonstrating the effectiveness of congestion pricing in managing urban traffic. |
