Optimal power flow using improved evolutionary programming | |
| Author | Thawatchai Tantimaporn |
| Call Number | AIT Thesis no.ET-03-4 |
| Subject(s) | Mathematical optimization Electric power |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering. |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. ET-03-4 |
| Abstract | The optimal power flow (OPF) is a large scale, highly constrained, nonlinear, and nonconvex optimization problem. In general, the objective function is nonconvex, nonsmooth, and nondifferentiable. With the complexity in nature, the OPF is a multimodal optimization, that is, there is more than one local optimum. Local or traditional optimization techniques employing derivatives are unable to search or locate the global optimum of the problem. Therefore, many mathematical assumptions such as linearity, convexity, and differentiability of the objective function are required to simplify the problem. To cope with such difficulties, the emergence of artificial intelligence (AI) makes future of global optimization more brighten. Many of AI techniques, such as genetic algorithm (GA), evolutionary programming (EP), tabu search (TS), simulated annealing (SA), particle swarm optimization (PSO), have been proposed to solve power system optimization problems. In addition, a hybrid tabu search and simulated annealing (TS/SA) and a hybrid tabu search and evolutiona1y programming or improved tabu search (ITS) are introduced to enhance the performance of algorithms. In this thesis, an improved evolutionary programming (IEP) is proposed to solve the OPF problem with the objective to minimize the generator fuel cost. The standard 30- bus IEEE system with three different types of generator fuel cost curves is used to investigate the effectiveness of the proposed IEP. Test results indicate that the proposed IEP algorithm can provide the better solutions than EP, TS, hybrid TS/SA, and ITS. It reliably converges to the global or near global optimum solution. Furthermore, the inherent parallel mechanism of IEP provides a great opportunity for the application on parallel computers. |
| Year | 2003 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. ET-03-4 |
| Type | Thesis |
| 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 Technology (ET) |
| Chairperson(s) | Weerakom Ongsakul |
| Examination Committee(s) | Surapong Chairarattananon ;Nadarajah, Mithulananthan |
| Scholarship Donor(s) | H. M. Queen of Thailand |