A New Technique for Simulating the Operation of Multiple Assigned-Energy Generating Units Suitable for Use in Generation System Expansion Planning Models

1982 ◽  
Vol PAS-101 (10) ◽  
pp. 3861-3869 ◽  
Author(s):  
Brian Manhire ◽  
R. Jenkins
Keyword(s):  
New Technique ◽  
System Expansion ◽  
Generation System ◽  
Expansion Planning ◽  
Planning Models ◽  
A New Technique

scholarly journals Flexible Distributed Multienergy Generation System Expansion Planning Under Uncertainty

2016 ◽  
Vol 7 (1) ◽  
pp. 348-357 ◽  
Author(s):  
Eduardo A. Martinez Cesena ◽  
Tomislav Capuder ◽  
Pierluigi Mancarella
Keyword(s):  
System Expansion ◽  
Planning Under Uncertainty ◽  
Generation System ◽  
Expansion Planning

New Developments in Modeling Network Constraints in Techno-economic Energy System Expansion Planning Models

2011 ◽  
Vol 36 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Martin Schönfelder ◽  
Anke Eßer-Frey ◽  
Michael Schick ◽  
Wolf Fichtner ◽  
Vincent Heuveline ◽  
...  
Keyword(s):  
Energy System ◽  
System Expansion ◽  
New Developments ◽  
Expansion Planning ◽  
Network Constraints ◽  
Planning Models

scholarly journals Solving financial allocation problem in distribution system expansion planning

2019 ◽  
Vol 8 (1) ◽  
pp. 320-327
Author(s):  
Siti Hajar Mohd Tahar ◽  
Shamshul Bahar Yaakob ◽  
Ahmad Shukri Fazil Rahman ◽  
Amran Ahmed
Keyword(s):  
Distribution System ◽  
Hopfield Neural Network ◽  
Mixed Integer ◽  
Second Phase ◽  
System Expansion ◽  
Expansion Planning ◽  
Proposed Model ◽  
A New Technique ◽  
Financial Allocation ◽  
Mean Variance

This paper introduces a new technique to solve financial allocation in Distribution System Expansion Planning (DSEP) problem. The proposed technique will be formulated by using mean-variance analysis (MVA) approach in the form of mixed-integer programming (MIP) problem. It consist the hybridization of Hopfield Neural Network (HNN) and Boltzmann Machine (BM) in first and second phase respectively. During the execution at the first phase, this model will select the feasible units meanwhile the second phase will restructured until it finds the best solution from all the feasible solution. Due to this feature, the proposed model has a fast convergence and the accuracy of the obtained solution. This model can help planners in decision-making process since the solutions provide a better allocation of limited financial resources and offer the planners with the flexibility to apply different options to increase the profit.

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