publications

2026

1. AAE

   Grid integration of AI data centers: A critical review of energy storage solutions

   Sina Mohammadi, Wayne Wang, Marcus Chen I Wada, and 6 more authors

   Advances in Applied Energy, Jul 2026

   Abs DOI Bib HTML

   Artificial intelligence (AI) is driving unprecedented growth in data center (DC) scale and power demand. AI workloads impose highly dynamic, difficult-to-forecast power profiles on the utility grid, creating reliability and stability challenges that conventional DC architectures are not designed to address. This paper provides a critical review of energy storage systems (ESSs) as the key enabling technology for reliable grid integration of AI DCs. We organize the review around a four-layer hierarchical taxonomy, namely chip-level buffering, rack/server-level ESSs, facility-level uninterruptible power supply (UPS) systems, and grid-scale battery energy storage systems (BESSs), supplemented by non-battery technologies including fuel cells (FCs) and thermal energy storage (TES). Each layer is analyzed with respect to response timescale, power and energy ratings, operational role, integration challenges, and coordination requirements. Grid-interactive UPS (GiUPS) systems are examined in depth as a critical evolution of passive backup infrastructure into active grid-support assets capable of frequency regulation, fast frequency response, and voltage ride-through. Grid-scale BESSs are reviewed for their roles in peak shaving, renewable integration, and ancillary service provision. Second-life battery energy storage systems (SLBESSs) are evaluated as a cost-effective alternative for large-scale deployment. Key findings include: (i) AI DC load profiles differ fundamentally from traditional loads in their sub-second variability, making conventional ESS dispatch strategies insufficient; (ii) hierarchical, coordinated ESS deployment across all layers is necessary for effective load smoothing and grid support; and (iii) significant gaps remain in simulation tools, degradation modeling, load forecasting, and optimal multi-layer sizing. This review identifies open research challenges and future directions at the intersection of AI computing infrastructure and power system integration.

   @article{mohammadi2026gridai,
     title = {Grid integration of AI data centers: A critical review of energy storage solutions},
     author = {Mohammadi, Sina and Wang, Wayne and Wada, Marcus Chen I and Haghighi, Rouzbeh and Hassan, Ali and Liu, Hualong and Bhatnagar, Archit and Chen, Ang and Su, Wencong},
     journal = {Advances in Applied Energy},
     volume = {23},
     pages = {100291},
     year = {2026},
     month = jul,
     publisher = {Elsevier},
     doi = {10.1016/j.adapen.2026.100291},
   }

2. NAPS

   A Stackelberg–Bayesian Capacity-Market Game of Carbon Regulation and Second-Life Battery Investment under AI Data-Center Load Growth

   Rouzbeh Haghighi, Ali Hassan, Sina Mohammadi, and 2 more authors

   In 2026 North American Power Symposium (NAPS), Jul 2026

   Accepted for presentation

   Abs arXiv Bib

   Artificial intelligence (AI) data centers are driving rapid electricity load growth across all U.S. ISO/RTO regions, raising both system costs and carbon exposure. This study develops a three-level Stackelberg–Bayesian game in which a regulator (leader) sets carbon penalties and subsidies, a single ISO capacity market clears against an energy balance modeled as a classical generation-expansion problem, and technology-specific investors (followers) decide capacity and operation under incomplete information, yielding a Bayesian Nash equilibrium. The AI impact is captured parsimoniously as an additional load-growth factor on a greenfield-incremental expansion, isolating how much new capacity the growth pulls in and which technology fills it. Within this framework, we consider second-life battery (SLB) storage competing against new/first-life storage for capacity-market revenue. We quantify how a carbon tax, a renewable subsidy, and an SLB subsidy reshape the equilibrium investment mix, carbon emissions, and profit. Different scenarios are compared at the end based on cost-effectiveness and reduced carbon emissions.

   @inproceedings{haghighi2026stackelberg,
     title = {A Stackelberg--Bayesian Capacity-Market Game of Carbon Regulation and Second-Life Battery Investment under AI Data-Center Load Growth},
     author = {Haghighi, Rouzbeh and Hassan, Ali and Mohammadi, Sina and Wada, Marcus Chen I and Su, Wencong},
     booktitle = {2026 North American Power Symposium (NAPS)},
     year = {2026},
     note = {Accepted for presentation},
   }

2024

1. Poster

   A Comprehensive Python Package for Forecasting Power Outages

   Marcus Chen I Wada, Wencong Su, and LaRico Andres

   Sep 2024

   Abs DOI Bib PDF

   Michigan has the second-highest number of weather-related power outages in the U.S., and outages carry real public-health and economic costs. This poster presents a Python package that streamlines the outage-forecasting workflow: data collection (web scraping, API calls, relational database), data engineering (standardization, geographic processing, correlation tests), and forecasting with LSTM and generative probabilistic models. The case study uses DTE service-area outage data (2M+ customers, 62%+ of outages weather-related) joined with county-level weather data collected over 2022.

   @misc{wada2024sureposter,
     title = {A Comprehensive Python Package for Forecasting Power Outages},
     author = {Wada, Marcus Chen I and Su, Wencong and Andres, LaRico},
     year = {2024},
     month = sep,
     howpublished = {Poster, Summer Undergraduate Research Experience (SURE) Program, University of Michigan--Dearborn},
     doi = {10.7302/24853},
   }