Simulated Annealing Technique for Dynamic Economic Dispatch
By Chinmoy Kumar Panigrahi, Pranab Kumar Chattopadhyay, R. Chakrabarti, Malabika Basu. 2006
254 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/b47ac60dea685e752ab344ca29c9ba9366671f7a
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Citations in the corpus
- Hybrid SOA–SQP algorithm for dynamic economic dispatch with valve-point effects (2010, methodology): “The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient (EG) by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule.”
- Optimal dynamic economic dispatch of generation: A review (2010, methodology): “The second category is the methods based on artificial intelligence, such as artificial neural networks [28,40] and stochastic optimization methods such as genetic algorithm (GA) [33], simulated annealing (SA) [41], evolutionary programming (EP) [42], differential evolution (DE) [43,44], particle sw”
- Differential Evolution-based Dynamic Economic Dispatch of Generating Units with Valve-point Effects (2008, methodology): “Example 1: Five-unit system The cost coefficients, generation limits, load demand in each interval, and ramp-rate limits of a five-unit sample system with valve-point loading are given in the appendix, which is taken from [16].”
- Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch (2012, methodology): “[22] Deterministically guided PSO No Yes [23] Multiple tabu search Yes No [24] Enhanced cross-entropy method No Yes [25] Simulated annealing No Yes [26] Hybrid AIS and SQP No Yes [27] Hybrid SOA and SQP No Yes [28] Hybrid HNN and QP No No [29] Hybrid HNN and QP No No”
- A hybrid MILP and IPM approach for dynamic economic dispatch with valve-point effects (2017, methodology): “Due to the intractability of the problem, most of the currently available approaches for DED-VPE are heuristic optimization techniques[2–26], such as genetic algorithm (GA) [2], evolutionary programming (EP) [3], simulated annealing (SA) [5], particle swarm optimization (PSO) [6], differential evolu”
- A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads (2014, methodology): “The 5-unit economic dispatch benchmark data is taken from [28].”