scholarly journals Robust Energy Management in Active Distribution Systems Considering Temporal and Spatial Correlation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 153635-153649 ◽  
Author(s):  
Suyang Zhou ◽  
Yi Zhao ◽  
Wei Gu ◽  
Zhi Wu ◽  
Yunpeng Li ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Energy Management ◽  
Distribution Systems ◽  
Temporal And Spatial

Deterministic and Probabilistic Models for Energy Management in Distribution Systems

2020 ◽  
pp. 343-382
Author(s):  
Milad Kabirifar ◽  
Niloofar Pourghaderi ◽  
Ali Rajaei ◽  
Moein Moeini-Aghtaie ◽  
Amir Safdarian
Keyword(s):  
Energy Management ◽  
Distribution Systems ◽  
Probabilistic Models

CVaR risk-based optimization framework for renewable energy management in distribution systems with DGs and EVs

Energy ◽  
2018 ◽  
Vol 143 ◽  
pp. 323-336 ◽  
Author(s):  
Jiekang Wu ◽  
Zhijiang Wu ◽  
Fan Wu ◽  
Huiling Tang ◽  
Xiaoming Mao
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Distribution Systems ◽  
Optimization Framework

scholarly journals Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 75666-75681
Author(s):  
Abbas Mehrabi ◽  
H. S. V. S. Kumar Nunna ◽  
Aresh Dadlani ◽  
Seungpil Moon ◽  
Kiseon Kim
Keyword(s):  
Energy Distribution ◽  
Electric Vehicle ◽  
Energy Management ◽  
Distribution Systems ◽  
Smart Energy Management

Probit-Based Time-Dependent Stochastic User Equilibrium Traffic Assignment Model

2008 ◽  
Vol 2085 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Kuilin Zhang ◽  
Hani S. Mahmassani ◽  
Chung-Cheng Lu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Spatial Correlation ◽  
Traffic Assignment ◽  
User Equilibrium ◽  
The Other ◽  
Time Dependent ◽  
Stochastic User Equilibrium ◽  
Assignment Model ◽  
Temporal And Spatial

This study presents a time-dependent stochastic user equilibrium (TDSUE) traffic assignment model within a probit-based path choice decision framework that explicitly takes into account temporal and spatial correlation (traveler interactions) in travel disutilities across a set of paths. The TDSUE problem, which aims to find time-dependent SUE path flows, is formulated as a fixed-point problem and solved by a simulation-based method of successive averages algorithm. A mesoscopic traffic simulator is employed to determine (experienced) time-dependent travel disutilities. A time-dependent shortest-path algorithm is applied to generate new paths and augment a grand path set. Two vehicle-based implementation techniques are proposed and compared in order to show their impact on solution quality and computational efficiency. One uses the classical Monte Carlo simulation approach to explicitly compute path choice probabilities, and the other determines probabilities by sampling vehicles’ path travel costs from an assumed perception error distribution (also using a Monte Carlo simulation process). Moreover, two types of variance-covariance error structures are discussed: one considers temporal and spatial path choice correlation (due to path overlapping) in terms of aggregated path travel times, and the other uses experienced (or empirical) path travel times from a sample of individual vehicle trajectories. A set of numerical experiments are conducted to investigate the convergence pattern of the solution algorithms and to examine the impact of temporal and spatial correlation on path choice behavior.

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