Multistage Stochastic Programming (MSP) Model for Carbon Nanotube Production Capacity Expansion Planning

2014 ◽  
Vol 2 (7) ◽  
pp. 1633-1641 ◽  
Author(s):  
Serkan Erbis ◽  
Sagar Kamarthi ◽  
Thomas P. Cullinane ◽  
Jacqueline A. Isaacs
Keyword(s):  
Carbon Nanotube ◽  
Stochastic Programming ◽  
Production Capacity ◽  
Capacity Expansion ◽  
Multistage Stochastic Programming ◽  
Expansion Planning

scholarly journals A Long-Term Evaluation on Transmission Line Expansion Planning with Multistage Stochastic Programming

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1899 ◽  
Author(s):  
Sini Han ◽  
Hyeon-Jin Kim ◽  
Duehee Lee
Keyword(s):  
Stochastic Programming ◽  
Transmission Line ◽  
Transmission Lines ◽  
Mixed Integer ◽  
Planning Problem ◽  
Multistage Stochastic Programming ◽  
Dual Decomposition ◽  
Expansion Planning ◽  
Integer Variables ◽  
Progressive Hedging

The purpose of this paper is to apply multistage stochastic programming to the transmission line expansion planning problem, especially when uncertain demand scenarios exist. Since the problem of transmission line expansion planning requires an intensive computational load, dual decomposition is used to decompose the problem into smaller problems. Following this, progressive hedging and proximal bundle methods are used to restore the decomposed solutions to the original problems. Mixed-integer linear programming is involved in the problem to decide where new transmission lines should be constructed or reinforced. However, integer variables in multistage stochastic programming (MSSP) are intractable since integer variables are not restored. Therefore, the branch-and-bound algorithm is applied to multistage stochastic programming methods to force convergence of integer variables.In addition, this paper suggests combining progressive hedging and dual decomposition in stochastic integer programming by sharing penalty parameters. The simulation results tested on the IEEE 30-bus system verify that our combined model sped up the computation and achieved higher accuracy by achieving the minimised cost.

scholarly journals An Inventory-Theory-Based Inexact Multistage Stochastic Programming Model for Water Resources Management

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
M. Q. Suo ◽  
Y. P. Li ◽  
G. H. Huang ◽  
Y. R. Fan ◽  
Z. Li
Keyword(s):  
Water Resources ◽  
Stochastic Programming ◽  
Water Resources Management ◽  
Programming Model ◽  
Water Shortage ◽  
Multistage Stochastic Programming ◽  
Dynamic Features ◽  
Inventory Theory ◽  
Resources Management ◽  
Water Resources Systems

An inventory-theory-based inexact multistage stochastic programming (IB-IMSP) method is developed for planning water resources systems under uncertainty. The IB-IMSP is based on inexact multistage stochastic programming and inventory theory. The IB-IMSP cannot only effectively handle system uncertainties represented as probability density functions and discrete intervals but also efficiently reflect dynamic features of system conditions under different flow levels within a multistage context. Moreover, it can provide reasonable transferring schemes (i.e., the amount and batch of transferring as well as the corresponding transferring period) associated with various flow scenarios for solving water shortage problems. The applicability of the proposed IB-IMSP is demonstrated by a case study of planning water resources management. The solutions obtained are helpful for decision makers in not only identifying different transferring schemes when the promised water is not met, but also making decisions of water allocation associated with different economic objectives.

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