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123 results on '"Saber, Ahmed Yousuf"'

11. Memory-bounded ant colony optimization with dynamic programming and [A.sup.*] local search for generator planning

Author

Saber, Ahmed Yousuf and Senjyu, Tomonobu

Subjects
Electric power systems -- United States, Electric power systems -- Planning, Mathematical optimization -- Methods, Company business planning, Business, Electronics, Electronics and electrical industries
Abstract

Swarm-inspired optimization has become very popular in recent years. Ant colony optimization (ACO) is successfully applied in the traveling salesman problem. Performance of the basic ACO for small problems with moderate dimensions and searching space is satisfactory. As the searching space grows exponentially in large-scale power systems generator planning, the basic ACO is not applicable for the vast size of the pheromone matrix of the ACO in practical-time and physical computer-memory limits. However, memory-bounded methods prune the least-promising nodes to fit the system in the computer memory. Therefore, the authors propose a memory-bounded version of ant colony optimization (MACO) with selected dynamic programming search in this paper for the scalable generator planning problem. This MACO solves the limitation of computer memory, and does not permit the system to grow beyond a bound on memory. In the memory-bounded ACO implementation, A * heuristic is introduced to increase local searching ability and the authors propose probabilistic nearest neighbor approach to estimate pheromone intensity for the forgotten value. Finally, the benchmark data and methods are used to show the effectiveness of the proposed method. Index Terms--[A.sup.*] heuristic, memory-bounded ant colony optimization (MACO), pheromone matrix, probabilistic nearest neighbor.

Published
2007

12. Fuzzy unit commitment scheduling using absolutely stochastic simulated annealing

Author

Saber, Ahmed Yousuf, Senjyu, Tomonobu, Miyagi, Tsukasa, Urasaki, Naomitsu, and Funabashi, Toshihisa

Subjects
Simulated annealing (Mathematics) -- Usage, Fuzzy algorithms -- Analysis, Fuzzy logic -- Analysis, Fuzzy systems -- Analysis, Fuzzy logic, Business, Electronics, Electronics and electrical industries
Abstract

This paper presents a new approach to the fuzzy unit commitment problem using the absolutely stochastic simulated annealing method. In every iteration, a solution is taken with a certain probability. Typically in the simulated annealing minimization method, a higher cost feasible solution is accepted with temperature-dependent probability, but other solutions are accepted deterministically. However, in this paper, all the solutions, both higher and lower cost, are associated with acceptance probabilities, e.g., the minimum membership degree of all the fuzzy variables. Besides, the number of bits flipping is decided by the linguistic fuzzy control. Excess units with system-dependent distribution handle constraints efficiently and reduce overlooking the optimal solution. To reduce the economic load dispatch calculations, a sign bit vector is introduced with imprecise calculation of the fuzzy model as well. The proposed method is tested using the reported problem data sets. Simulation results are compared to previous reported results. Numerical results show an improvement in solution cost and time compared to the results obtained from powerful algorithms. Index Terms--Best heat rate, fuzzy logic, linguistic fuzzy control, sign vector, simulated annealing (SA), unit commitment (UC).

Published
2006

21. A technique for unit commitment with energy storage system

Author

Tsukasa Miyagi, Toshihisa Funabashi, Naomitsu Urasaki, Saber Ahmed Yousuf, and Tomonobu Senjyu

Subjects
Mathematical optimization, Schedule, Engineering, Computer simulation, Total cost, Priority list, business.industry, Energy Engineering and Power Technology, Energy storage, Scheduling (computing), Power system simulation, Electrical and Electronic Engineering, Heuristics, business, Simulation
Abstract

This paper introduces the scheduling method for thermal and energy storage system (ESS) unit commitment. The ESS is incorporated to achieve peak load-levelling and reduce the total cost. The thermal scheduling is implemented by the extended priority list (EPL) method. The EPL method consists of two steps, in the first step we get rapidly some initial unit commitment schedules by the priority list (PL) method. In this step, operational constraints are disregarded. In the second step, unit schedules are modified using the problem specific heuristics to fulfill operational constraints and to reduce the total cost. To calculate efficiently, heuristics are only applied to solutions which can be expected improvement. The ramp constraints for thermal unit are considered at the last of EPL method. The ESS scheduling is carried out based on the thermal unit schedule obtained by the EPL method. Several numerical examples demonstrate the effectiveness of the proposed method.

Published
2007

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