| Author | Saksorn Chalermchaiarbha |
| Note | A dissertation submitted in partial fulfillment of the requirements for the
degree of Doctor of Engineering in
Energy, School of Environment, Resources and Development |
| Publisher | Asian Institute of Technology |
| Abstract | This dissertation proposes particle swarm optimization for solving single- and multiobjective
economic dispatch problems. For a single-objective economic problem, a
stochastic weight trade-off particle swarm optimization (SWT_PSO) is proposed for
solving non-convex economic dispatch. The main concept of the proposed SWT_PSO is to
preserve the balance between global exploration and local exploitation along the
optimization process to improve the algorithm’s search capabilities. The balance is retained
through trading off stochastic weights amongst previous velocity momentum, cognitive
and social components together with using dynamic acceleration coefficients trade-off.
Moreover, mechanisms for increasing diversity of swarm members are also incorporated to
avoid premature convergence. In addition, a novel stochastic trade-off momentum control
factor is exploited to enhance the capability of refining quality of a candidate solution
during the late search process. The proposed SWT_PSO is tested on four economic
dispatch test systems. Test results demonstrate that the proposed approach yielding better
solution quality than the best reported results in the literature for all test systems.
For a multi-objective economic dispatch problem, an elitist multi-objective particle swarm
optimization (EMPSO) is proposed. The EMPSO utilizes the SWT_PSO to generate the
possible Pareto-optimal solutions and fuzzy multi-attribute decision making (FMADM) to
handle three main tasks including maximizing the diversity of Pareto-optimal solutions,
limiting the number of Pareto-optimal solutions to predetermined size as well as extracting
the best compromise solution. Capabilities of diversifying the Pareto-optimal solutions
through the FMADM mechanism with three other widely used mechanisms including
random, fitness sharing-cum-niching and strength Pareto dominance-based mechanisms
are compared on both bi- and tri- objective optimization problems. All three mechanisms
have used the same proposed EMPSO for a fair comparison. The simulation results of
several optimization runs indicate that the FMADM mechanism could yield a better
distributed Pareto front, wider extension range, and faster computing time than those
obtained from its three counterparts. Moreover, the best compromise solution obtained
from the proposed approach yields a good trade-off characteristic.
In summary, the proposed SWT_PSO could effectively provide a high solution quality of
non-convex economic dispatch in a fast computing manner. Consequently, SWT_PSO is
potentially viable for online implementation. As for the proposed EMPSO, it is a robust
algorithm yielding the Pareto fronts with good uniformity as well as high diversity
characteristics. In addition, the best compromise solution obtained from the FMADM
possesses the good trade-off characteristic. As a result, the proposed EMPSO is a
promising approach to the multi-objective economic dispatch problem. |
| Year | 2014 |
| Type | Dissertation |
| School | School of Environment, Resources, and Development (SERD) |
| Department | Department of Energy and Climate Change (Former title: Department of Energy, Environment, and Climate Change (DEECC)) |
| Academic Program/FoS | Energy and Environment (EE) |
| Chairperson(s) | Weerakorn Ongsakul; |
| Examination Committee(s) | Manukid Parnichkun;Singh, Jai Govind;Bansal, Ramesh ; |
| Scholarship Donor(s) | HM Queen Sirikit Scholarship – AIT Fellowship; |