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A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement
The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging
technologies, emphasizes the critical necessity for expanding EV recharging infrastructure …
technologies, emphasizes the critical necessity for expanding EV recharging infrastructure …
A quantitative risk assessment model for distribution cyber-physical system under cyberattack
An accurate and comprehensive risk assessment in a distribution cyber-physical system
(DCPS) is essential to ensure its smooth operation, effective control, and exposure of hidden …
(DCPS) is essential to ensure its smooth operation, effective control, and exposure of hidden …
A Bayesian deep learning-based probabilistic risk assessment and early-warning model for power systems considering meteorological conditions
The ongoing process of decarbonizing the power system and the frequent occurrence of
extreme weather have led to a significant increase in operating uncertainty and greatly …
extreme weather have led to a significant increase in operating uncertainty and greatly …
[HTML][HTML] Resilience quantification of smart distribution networks—A bird's eye view perspective
The introduction of pervasive telecommunication devices, in the scope of smart grids (SGs),
has accentuated interest in the distribution network, which integrates a huge portion of new …
has accentuated interest in the distribution network, which integrates a huge portion of new …
A Bayesian approach for estimating uncertainty in stochastic economic dispatch considering wind power penetration
The increasing penetration of renewable energy resources in power systems, represented
as random processes, converts the traditional deterministic economic dispatch problem into …
as random processes, converts the traditional deterministic economic dispatch problem into …
A data-driven nonparametric approach for probabilistic load-margin assessment considering wind power penetration
A modern power system is characterized by an increasing penetration of wind power, which
results in large uncertainties in its states. These uncertainties must be quantified properly; …
results in large uncertainties in its states. These uncertainties must be quantified properly; …
A data-driven mixed integer programming approach for joint chance-constrained optimal power flow under uncertainty
This paper introduces a novel mixed integer programming (MIP) reformulation for the joint
chance-constrained optimal power flow problem under uncertain load and renewable …
chance-constrained optimal power flow problem under uncertain load and renewable …
Data-driven and privacy-preserving risk assessment method based on federated learning for smart grids
Timely and precise security risk evaluation is essential for optimal operational planning,
threat detection, and the reliable operation of smart grid. The smart grid can integrate …
threat detection, and the reliable operation of smart grid. The smart grid can integrate …
An iterative response-surface-based approach for chance-constrained ac optimal power flow considering dependent uncertainty
A modern power system is characterized by a stochastic variation of the loads and an
increasing penetration of renewable energy generation, which results in large uncertainties …
increasing penetration of renewable energy generation, which results in large uncertainties …
Physics-guided residual learning for probabilistic power flow analysis
Probabilistic power flow (PPF) analysis is critical to power system operation and planning.
PPF aims at obtaining probabilistic descriptions of the state of the system with stochastic …
PPF aims at obtaining probabilistic descriptions of the state of the system with stochastic …