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1. Reversible Surface Energy Storage in Molecular-Scale Porous Materials

   https://doi.org/10.3390/molecules29030664  

   Bratko, Dusan (February 2024, Molecules)

   Forcible wetting of hydrophobic pores represents a viable method for energy storage in the form of interfacial energy. The energy used to fill the pores can be recovered as pressure–volume work upon decompression. For efficient recovery, the expulsion pressure should not be significantly lower than the pressure required for infiltration. Hysteresis of the wetting/drying cycle associated with the kinetic barrier to liquid expulsion results in energy dissipation and reduced storage efficiency. In the present work, we use open ensemble (Grand Canonical) Monte Carlo simulations to study the improvement of energy recovery with decreasing diameters of planar pores. Near-complete reversibility is achieved at pore widths barely accommodating a monolayer of the liquid, thus minimizing the area of the liquid/gas interface during the cavitation process. At the same time, these conditions lead to a steep increase in the infiltration pressure required to overcome steric wall/water repulsion in a tight confinement and a considerable reduction in the translational entropy of confined molecules. In principle, similar effects can be expected when increasing the size of the liquid particles without altering the absorbent porosity. While the latter approach is easier to follow in laboratory work, we discuss the advantages of reducing the pore diameter, which reduces the cycling hysteresis while simultaneously improving the stored-energy density in the material. 

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2. Thin-film CdTe photovoltaics – The technology for utility scale sustainable energy generation

   https://doi.org/10.1016/j.solener.2018.07.090  

   Munshi, Amit H.; Sasidharan, Nikhil; Pinkayan, Subin; Barth, Kurt L.; Sampath, W.S.; Ongsakul, Weerakorn (October 2018, Solar Energy)
3. An Examination of Power Converter Architectures for Utility-Scale Hybrid Solar Photovoltaic and Battery Energy Storage Systems: The Features of Several Power Conversion Architectures

   https://doi.org/10.1109/MIAS.2022.3214016  

   Gupta, Mahima (March 2023, IEEE Industry Applications Magazine)
4. Key Challenges for Grid‐Scale Lithium‐Ion Battery Energy Storage

   https://doi.org/10.1002/aenm.202202197  

   Huang, Yimeng; Li, Ju (November 2022, Advanced Energy Materials)

   Abstract A rapid transition in the energy infrastructure is crucial when irreversible damages are happening quickly in the next decade due to global climate change. It is believed that a practical strategy for decarbonization would be 8 h of lithium‐ion battery (LIB) electrical energy storage paired with wind/solar energy generation, and using existing fossil fuels facilities as backup. To reach the hundred terawatt‐hour scale LIB storage, it is argued that the key challenges are fire safety and recycling, instead of capital cost, battery cycle life, or mining/manufacturing challenges. A short overview of the ongoing innovations in these two directions is provided. 

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5. Scalable Optimal Power Management for Large-Scale Battery Energy Storage Systems

   https://doi.org/10.1109/TTE.2023.3331243  

   Farakhor, Amir; Wu, Di; Wang, Yebin; Fang, Huazhen (September 2024, IEEE Transactions on Transportation Electrification)

   Large-scale battery energy storage systems (BESS) play a pivotal role in advancing sustainability through their widespread applications in electrified transportation, power grids, and renewable energy systems. However, achieving optimal power management for these systems poses significant computational challenges. To address this, we propose a scalable approach that partitions the cells of a large-scale BESS into clusters based on state-of-charge (SoC), temperature, and internal resistance. Each cluster is represented by a model that approximates its collective SoC and temperature dynamics and overall power losses during charging and discharging. Using these clusters, we formulate a receding-horizon optimal power control problem to minimize power losses while promoting SoC and temperature balancing. The optimization determines a power quota for each cluster, which is then distributed among its constituent cells. This clustering approach drastically reduces computational costs by working with a smaller number of clusters instead of individual cells, enabling scalability for large-scale BESS. Simulations show a computational overhead reduction of over 60% for small-scale and 98% for large-scale BESS compared to conventional cell-level optimization. Experimental validation using a 20-cell prototype further underscores the approach's effectiveness and practical utility. 

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