| dc.contributor.author | AlRashidi, Mohammed. | en_US |
| dc.date.accessioned | 2014-10-21T12:35:35Z | |
| dc.date.available | 2007 | |
| dc.date.issued | 2007 | en_US |
| dc.identifier.other | AAINR35790 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10222/54978 | |
| dc.description | Power utilities have been facing new challenges in the past few decades that require changes to their traditional operational practices. Two of the main challenges are the rising concerns about the harmful impacts of electric power production on the environment and the deregulation of the electric power industry. In the past few decades, environmental awareness led to the adoption of rigid environmental policies on power utilities to regulate their emissions. One way to cope with this problem is to dispatch power with environmental considerations. The emission-economic dispatch is an extension of the traditional economic cost dispatch where the ultimate goal is not only to minimize the total production cost but rather to minimize both the production cost and emission of the generating units. Deregulating the power industry also created a highly vibrant and competitive market in which major market players strive to maximize their profits while meeting their other system-wide obligations. One way to do this is to develop a more precise system modeling that eliminates oversimplified assumptions included in representing the original system. | en_US |
| dc.description | This thesis addresses two main problems commonly encountered in studies related to power system analysis, namely the emission-economic dispatch and optimal power flow. The former is formulated as a nonlinear multi-objective optimization problem with conflicting objectives and subjected to both equality and inequality constraints. The latter problem is formulated as a mixed integer optimization problem with various objectives, i.e. emission, fuel cost, and real power losses, in which some are of non-convex and non-differentiable nature. In both studies, special attention is paid to the environmental and economical aspects of electric power generation. | en_US |
| dc.description | Numerical solutions to the two problems are investigated via particle swarm optimization (PSO) based algorithms. PSO is a new metaheuristic optimization method which is receiving additional attention recently for several reasons. Modifications and enhancements of the PSO are presented to improve its performance and to make it more suitable to some specific power system problems. Special treatments of control variables and improved constraints handling mechanisms are proposed to tailor the PSO to the aforementioned problems. In the emission-economic dispatch problem, a PSO approach is developed to capture the shape of the Pareto optimal solution set that shows the trade-off relationship between competing objectives. Two aggregation methods are used and analyzed to combine the conflicting objectives. The nature of the control variables and the objectives considered in the optimal power flow study are troublesome to most derivative-based optimization algorithms. Therefore, a hybrid PSO algorithm is developed to overcome such difficulty with promising results. The proposed algorithms are tested on various testing systems and their performances are compared to other optimization techniques. Results indicate the promising potential pertaining to PSO applicability to some of the commonly formulated optimization problems in power systems. | en_US |
| dc.description | Thesis (Ph.D.)--Dalhousie University (Canada), 2007. | en_US |
| dc.language | eng | en_US |
| dc.publisher | Dalhousie University | en_US |
| dc.publisher | | en_US |
| dc.subject | Engineering, Electronics and Electrical. | en_US |
| dc.title | Improved optimal economic and environmental operations of power systems using particle swarm optimization. | en_US |
| dc.type | text | en_US |
| dc.contributor.degree | Ph.D. | en_US |
