scholarly journals Life-Cycle Asset Allocation and Optimal Consumption Using Stochastic Linear Programming

2007 ◽  
Author(s):  
Alois Geyer ◽  
Michael Hanke ◽  
Alex Weissensteiner
Keyword(s):  
Linear Programming ◽  
Life Cycle ◽  
Asset Allocation ◽  
Optimal Consumption ◽  
Stochastic Linear Programming

scholarly journals Life-cycle welfare losses from rules-of-thumb asset allocation

2021 ◽  
Vol 198 ◽  
pp. 109655
Author(s):  
Fabio C. Bagliano ◽  
Carolina Fugazza ◽  
Giovanna Nicodano
Keyword(s):  
Life Cycle ◽  
Asset Allocation ◽  
Welfare Losses ◽  
Rules Of Thumb

Life-Cycle Asset Allocation and Unemployment Risk

2015 ◽  
Author(s):  
Fabio C. Bagliano ◽  
Carolina Fugazza ◽  
Giovanna Nicodano
Keyword(s):  
Life Cycle ◽  
Asset Allocation ◽  
Unemployment Risk

scholarly journals Planning of Electric Power Systems Considering Virtual Power Plants with Dispatchable Loads Included: An Inexact Two-Stage Stochastic Linear Programming Model

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Changyu Zhou ◽  
Guohe Huang ◽  
Jiapei Chen
Keyword(s):  
Linear Programming ◽  
Power System ◽  
Electric Power ◽  
Power Supply ◽  
Power Plants ◽  
Electricity Demand ◽  
Virtual Power ◽  
Stochastic Linear Programming ◽  
Two Stage ◽  
Virtual Power Plants

In this study, an inexact two-stage stochastic linear programming (ITSLP) method is proposed for supporting sustainable management of electric power system under uncertainties. Methods of interval-parameter programming and two-stage stochastic programming were incorporated to tackle uncertainties expressed as interval values and probability distributions. The dispatchable loads are integrated into the framework of the virtual power plants, and the support vector regression technique is applied to the prediction of electricity demand. For demonstrating the effectiveness of the developed approach, ITSLP is applied to a case study of a typical planning problem of power system considering virtual power plants. The results indicate that reasonable solutions for virtual power plant management practice have been generated, which can provide strategies in mitigating pollutant emissions, reducing system costs, and improving the reliability of power supply. ITSLP is more reliable for the risk-aversive planners in handling high-variability conditions by considering peak-electricity demand and the associated recourse costs attributed to the stochastic event. The solutions will help decision makers generate alternatives in the event of the insufficient power supply and offer insight into the tradeoffs between economic and environmental objectives.

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