Application of Stochastic Optimization Techniques in the Smart Grid

2014 ◽  
pp. 151-174
Keyword(s):  
Smart Grid ◽  
Stochastic Optimization ◽  
Optimization Techniques

Application of Stochastic Optimization Techniques in the Smart Grid

2018 ◽  
pp. 151-174
Author(s):  
Sawan Sen ◽  
Samarjit Sengupta ◽  
Abhijit Chakrabarti
Keyword(s):  
Smart Grid ◽  
Stochastic Optimization ◽  
Optimization Techniques

scholarly journals STOCHASTIC OPTIMIZATION OF HEURISTIC METHOD RULE TO DETERMINE ASSET ALLOCATION TO RETIREMENT PORTFOLIO / STOCHASTINIS EURISTINIO METODO TAISYKLĖS PENSIJOS PORTFELIO SUDĖČIAI NUSTATYTI OPTIMIZAVIMAS

2011 ◽  
Vol 12 (1) ◽  
pp. 92-98
Author(s):  
Aušra Klimavičienė
Keyword(s):  
Stochastic Optimization ◽  
Stochastic Simulation ◽  
Asset Allocation ◽  
Heuristic Method ◽  
Simulation Models ◽  
Optimization Techniques ◽  
Human Lifespan ◽  
Stochastic Returns ◽  
Dynamic Stochastic ◽  
Population Mortality

The article examines the problem of determining asset allocation to sustainable retirement portfolio. The article attempts to apply heuristic method – 100 minus age in stocks rule – to determine asset allocation to sustainable retirement portfolio. Using dynamic stochastic simulation and stochastic optimization techniques the optimization of heuristic method rule is presented and the optimal alternative to „100“ is found. Seeking to reflect the stochastic nature of stock and bond returns and the human lifespan, the dynamic stochastic simulation models incorporate both the stochastic returns and the probability of living another year based on Lithuania‘s population mortality tables. The article presents the new method – adjusted heuristic method – to be used to determine asset allocation to retirement portfolio and highlights its advantages.

DSM for Energy Optimization and Communications Within Smart Grid CPSs

2022 ◽  
pp. 506-528
Author(s):  
Sa'ed Abed ◽  
Areej Abdelaal ◽  
Amjad Gawanmeh
Keyword(s):  
Developing Countries ◽  
Smart Grid ◽  
Energy Demand ◽  
New Technologies ◽  
Power Grid ◽  
Energy Optimization ◽  
Cyber Physical Systems ◽  
Optimization Techniques ◽  
Grid System ◽  
Potential Solution

Energy demand has increased significantly in the recent years due to the emerging of new technologies and industries, in particular in the developing countries. This increase requires much more developed power grid system than the existing traditional ones. Smart grid (SG) offers a potential solution to this problem. Being one of the most needed and complex cyber-physical systems (CPS), SG has been addressed exhaustively by researchers, from different views and aspects. However, energy optimization yet needs much more studying and examination. Therefore, this chapter presents a comprehensive investigation and analysis of the state-of-the-art developments in SG as a CPS with emphasis on energy optimization techniques and challenges. It also surveys the main challenges facing the SG considering CPS factors and the remarkable accomplishments and techniques in addressing these challenges. In addition, the document contrasts between different techniques according to their efficiency, usage, and feasibility. Moreover, this work explores the most effective applications of the SG as a CPS.

An Agent Based Optimization Approach to Manufacturing Process Planning

2001 ◽  
Author(s):  
Saurabh Deshpande ◽  
Jonathan Cagan
Keyword(s):  
Stochastic Optimization ◽  
Process Planning ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Search Space ◽  
Optimization Techniques ◽  
Process Models ◽  
Optimization Approach ◽  
Agent Based ◽  
Manufacturing Process Planning

Abstract Many optimization problems, such as manufacturing process planning optimization, are difficult problems due to the large number of potential configurations (process sequences) and associated (process) parameters. In addition, the search space is highly discontinuous and multi-modal. This paper introduces an agent based optimization algorithm that combines stochastic optimization techniques with knowledge based search. The motivation is that such a merging takes advantage of the benefits of stochastic optimization and accelerates the search process using domain knowledge. The result of applying this algorithm to computerized manufacturing process models is presented.

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