1. Title
Paper # 1:
Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem
Paper # 2:
Multiobjective Optimal VAR Dispatch Using Strength Pareto Evolutionary Algorithm
Paper # 3:
Two-Level of Nondominated Solutions Approach To Multiobjective Particle Swarm Optimization
2. Author
Dr Mohammad A Abido, Associate Professor
Electrical Engineering Department
King Fahd University of Petroleum & Minerals
KFUPM Box 183
Dhahran 31261, Saudi Arabia
Tel: +966 3 860 4379 (Office)
Fax: +966 3 860 3535
E-mail: mabido@kfupm.edu.sa
Homepage: http://faculty.kfupm.edu.sa/ee/mabido/
3. Corresponding Author
Dr Mohammad A Abido
4. Abstracts
Paper # 1:
The potential and effectiveness of the newly developed Pareto-based Multiobjective Evolutionary Algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem is comprehensively discussed and evaluated in this paper. Specifically, Nondominated Sorting Genetic Algorithm (NSGA), Niched Pareto Genetic Algorithm (NPGA), and Strength Pareto Evolutionary Algorithm (SPEA) have been developed and successfully applied to Environmental/Economic electric power Dispatch (EED) problem. A new procedure for quality measure is proposed in this study in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, a fuzzy set theory based approach is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus 6-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems.
Paper # 2:
In this paper, Strength Pareto Evolutionary Algorithm (SPEA) for optimal reactive power (VAR) dispatch problem is presented. The optimal VAR dispatch problem is formulated as a nonlinear constrained multiobjective optimization problem where the real power loss and the voltage stability are to be optimized simultaneously. The proposed approach handles the problem as a true multiobjective optimization problem. A hierarchical clustering algorithm is imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Moreover, fuzzy set theory is employed to extract the best compromise solution over the trade-off curve. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal solutions of the multiobjective VAR dispatch problem in one single run. In addition, the effectiveness of the proposed approach and its potential to solve the multiobjective VAR dispatch problem are confirmed.
Paper # 3:
In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle of the population has a great impact on the convergence and diversity of solutions, especially when optimizing problems with high number of objectives. This paper presents a two-level of nondominated solutions approach to MOPSO. The ability of the proposed approach to detect the true Pareto optimal solutions and capture the shape of the Pareto front is evaluated through experiments on well-known non-trivial test problems. The diversity of the nondominated solutions obtained is demonstrated through different measures. The proposed approach has been assessed through a comparative study with the reported results in the literature.
5. Criteria Satisfied
B, D, E, F, and G
6. Reasons for Criteria Satisfaction
The basic objective of economic dispatch (ED) problem of electric power generation, addressed in Paper # 1, is to schedule the committed generating unit outputs so as to meet the load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. In addition, the increasing public awareness of the environmental protection and the passage of the Clean Air Act Amendments of 1990 have forced the utilities to modify their design or operational strategies to reduce pollution and atmospheric emissions of the thermal power plants. Since then different techniques have been reported in the literature pertaining to environmental/economic dispatch (EED) problem. In 1992, G. P. Granelli, M. Montagna, G. L. Pasini, and P. Marannino, has reduced the problem to a single objective problem by treating the emission as a constraint with a permissible limit. In 1995, A. Farag, S. Al-Baiyat, and T. C. Cheng, handled the emission as another objective in addition to usual cost objective where linear programming based optimization procedures in which the objectives are considered one at a time. The multiobjective evolutionary evolved solution methodology is an improvement over the reported approaches so far.
Paper # 2 presents the implementation of the multiobjective evolutionary algorithms to more complex reactive power dispatch problem where the results are much better then the reported ones.
A novel approach to multiobjective particle swarm optimization technique is presented in Paper # 3 where the results on standard test problems show the superiority of the proposed approach. The proposed approach in Paper # 3 has been implemented to real-life EED problem. The results were submitted and currently under review by IEEE Trans. on Power Systems.
Referring to the eight criteria for establishing that an automatically created result is competitive with a human-produced result, the creation by multiobjective evolutionary algorithms of improved solution methodology of power system optimization problems satisfies the following five of the eight criteria:
(B) The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal.
(D) The result is publishable in its own right as a new scientific result--independent of the fact that the result was mechanically created.
(E) The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions.
(F) The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered.
(G) The result solves a problem of indisputable difficulty in its field.
7. Full Citation
Paper # 1:
M. A. Abido, “Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem” IEEE Trans. on Evolutionary Computations, Vol. 10, No. 3, June 2006, pp. 315-329.
Paper # 2:
M. A. Abido, “Multiobjective Optimal VAR Dispatch Using Strength Pareto Evolutionary Algorithm,” Proceedings of The IEEE World Congress on Computational Intelligence 2006, July 16-21, Vancouver, Canada, 2006, pp. 730 - 736.
Paper # 3:
M. A. Abido, “Two-Level of Nondominated Solutions Approach To Multiobjective Particle Swarm Optimization,” To be presented in the 2007 Genetic and Evolutionary Computation Conference, GECCO’2007, 7 - 11 July 2007, London, UK.
8. Prize Money
Any prize money, if any, is to be directed to Dr. M. Abido
9. Reasons for Nomination
The novelty of developing and applying multiobjective evolutionary algorithms to power system problems is addressed in the first two papers where implementing the newly developed multiobjective evolutionary algorithms to two real-life highly nonlinear multiobjective optimization problems in the field of power systems operation have been successfully carried out. The work done in these two papers is a continuation to the novel work started earlier by the author himself. All the multiobjective evolutionary algorithms codes have been developed and written by the author using FORTRAN. The results in these two papers outperform those reported in the literature. The results also are highly impressive and widely accepted by the researchers in this area. As a measure of the originality of this pioneering work is the number of citations since the author presented the multiobjective principles to power system optimization problems. To the best of the author’s knowledge, 60 citations in top quality journals for the work done by the author in this field have been recorded so far.