Results 131 to 140 of about 163,972 (287)

Stochastic Switching Games

open access: yes, 2018
We study nonzero-sum stochastic switching games. Two players compete for market dominance through controlling (via timing options) the discrete-state market regime $M$. Switching decisions are driven by a continuous stochastic factor $X$ that modulates instantaneous revenue rates and switching costs.
Li, Liangchen, Ludkovski, Michael
openaire   +2 more sources

Bottlenecks‐Breaking in Zinc‐Iodine Batteries Toward Practical Implementation: A Review and Perspective

open access: yesENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Aqueous zinc–iodine batteries (Zn–I2Bs) offer promise for grid storage due to safety and cost advantages yet face critical bottlenecks: severe self‐discharge (polyiodide shuttling and HER), limited energy density, sluggish kinetics, and zinc anode instability.
Jia‐Lin Yang   +3 more
wiley   +1 more source

The asymptotic value in finite stochastic games

open access: yes, 2012
We provide a direct, elementary proof for the existence of $\lim_{\lambda\to 0} v_{\lambda}$, where $v_{\lambda}$ is the value of a $\lambda$-discounted finite two-person zero-sum stochastic ...
Oliu-Barton, Miquel
core   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

open access: yesEpilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida   +5 more
wiley   +1 more source

Assessment of Voltage Unbalance and Power Losses in Distribution Network Integrated With PVs and EV Charging Stations

open access: yesEnergy Science &Engineering, EarlyView.
ABSTRACT Unequal load distribution in three‐phase power distribution networks leads to voltage unbalance and decreased network efficiency. Integration of photovoltaics (PVs) and electric vehicle charging stations (EVCSs) into such networks is a challenging task as their penetration affects the power quality of the system.
Maaz Ahmad   +4 more
wiley   +1 more source

High‐Fidelity Simulation‐Driven Control Framework for Robust Grid Integration of Renewable Energy Systems

open access: yesEnergy Science &Engineering, EarlyView.
This work proposes a high‐fidelity, simulation‐driven control framework for robust grid integration of hybrid PV–wind systems using a modular, hierarchical multi‐loop architecture with adaptive decision logic. The framework coordinates power, DC‐link voltage, and grid currents under fast load and generation changes, enabling safe exploration of extreme
Wulfran Fendzi Mbasso   +5 more
wiley   +1 more source

A formula for the value of a stochastic game. [PDF]

open access: yesProc Natl Acad Sci U S A, 2019
Attia L, Oliu-Barton M.
europepmc   +1 more source

Bi‐Level Coordination of Demand Response and Multiple Virtual Power Plants in a Distribution Network for Flexibility Assessment

open access: yesEnergy Science &Engineering, EarlyView.
This study presents a hierarchical coordination framework where multiple VPPs interact with the DSO to optimize energy trades and flexibility offers. Each VPP aggregates DERs and DR, performing internal optimization, day‐ahead bidding, and assessing flexibility to reduce excess renewable generation and pollution.
Alireza Zare   +4 more
wiley   +1 more source

Machine Learning and Artificial Intelligence Techniques for Intelligent Control and Forecasting in Energy Storage‐Based Power Systems

open access: yesEnergy Science &Engineering, EarlyView.
A new energy paradigm assisted by AI. ABSTRACT The tremendous penetration of renewable energy sources and the integration of power electronics components increase the complexity of the operation and power system control. The advancements in Artificial Intelligence and machine learning have demonstrated proficiency in processing tasks requiring ...
Balasundaram Bharaneedharan   +4 more
wiley   +1 more source

Using Deep Learning Conditional Value‐at‐Risk Based Utility Function in Cryptocurrency Portfolio Optimisation

open access: yesInternational Journal of Finance &Economics, EarlyView.
ABSTRACT One of the critical risks associated with cryptocurrency assets is the so‐called downside risk, or tail risk. Conditional Value‐at‐Risk (CVaR) is a measure of tail risks that is not normally considered in the construction of a cryptocurrency portfolio.
Xinran Huang   +3 more
wiley   +1 more source

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