scholarly journals An Efficient Simulated Annealing Algorithm for Network Reconfiguration in Large-Scale Distribution Systems

2002 ◽  
Vol 22 (7) ◽  
pp. 61-62 ◽  
Author(s):  
Y. J. Jeon ◽  
J. C. Kim ◽  
J. O. Kim ◽  
K. Y. Lee ◽  
J. R. Shin
Keyword(s):  
Simulated Annealing ◽  
Distribution Systems ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Network Reconfiguration ◽  
Annealing Algorithm

A New Hybrid Simulated Annealing Algorithm for Large Scale Global Optimization

2015 ◽  
Vol 5 (3) ◽  
pp. 24-36
Author(s):  
Seifedine N. Kadry ◽  
Abdelkhalak El Hami
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Structural Optimization ◽  
Stochastic Approximation ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Simultaneous Perturbation Stochastic Approximation ◽  
Hybrid Simulated Annealing ◽  
Annealing Algorithm

The present paper focus on the improvement of the efficiency of structural optimization, in typical structural optimization problems there may be many locally minimum configurations. For that reason, the application of a global method, which may escape from the locally minimum points, remain essential. In this paper, a new hybrid simulated annealing algorithm for large scale global optimization problems with constraints is proposed. The authors have developed a stochastic algorithm called SAPSPSA that uses Simulated Annealing algorithm (SA). In addition, the Simultaneous Perturbation Stochastic Approximation method (SPSA) is used to refine the solution. Commonly, structural analysis problems are constrained. For the reason that SPSA method involves penalizing constraints a penalty method is used to design a new method, called Penalty SPSA (PSPSA) method. The combination of both methods (Simulated Annealing algorithm and Penalty Simultaneous Perturbation Stochastic Approximation algorithm) provides a powerful hybrid stochastic optimization method (SAPSPSA), the proposed method is applicable for any problem where the topology of the structure is not fixed. It is simple and capable of handling problems subject to any number of constraints which may not be necessarily linear. Numerical results demonstrate the applicability, accuracy and efficiency of the suggested method for structural optimization. It is found that the best results are obtained by SAPSPSA compared to the results provided by the commercial software ANSYS.

scholarly journals Solving Bid-Based Dynamic Economic Dispatch in Competitive Electricity Market Using Improved Simulated Annealing Algorithm

2020 ◽  
Vol 7 (4) ◽  
pp. 621-630
Author(s):  
Riyadh Bouddou ◽  
Farid Benhamida ◽  
Ismail Ziane ◽  
Amine Zeggai ◽  
Moussa Belgacem
Keyword(s):  
Simulated Annealing ◽  
Power Systems ◽  
Electricity Markets ◽  
Electricity Market ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Economic Dispatch ◽  
Unit System ◽  
Annealing Algorithm ◽  
Dynamic Economic Dispatch

Electricity markets are open after the deregulation of power systems due to competition. An optimization problem based on dynamic economic dispatch has recently come up in the new context of deregulated power systems known as bid-based dynamic economic dispatch (BBDED). It is one of the major operations and control functions in the electricity markets used to determine the optimal operations of market participants with scheduled load demands during a specified period. BBDED involves power generation companies (GENCOs) and customers to submit energy and price bids to the independent system operator (ISO) in a day-ahead market. The ISO clears the market with the objective of social profit maximization. In this paper, a BBDED problem is solved using an improved simulated annealing algorithm (ISA), including system constraints with different periods under bidding strategies. The proposed ISA technique is implemented in MATLAB and applied on a 3-unit system, a 6-unit system, and a 40-unit large-scale system. The proposed ISA is evaluated by comparison with relevant methods available in the literature, to demonstrate and confirm its potential in terms of convergence, robustness, and effectiveness for solving the BBDED problem.

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